Template-Type: ReDIF-Article 1.0
Author-Name: Luis Pedro Román Palma
Author-Name-First: Luis Pedro
Author-Name-Last: Román Palma
Author-Name: Teodoro Reyes Fong
Author-Name-First: Teodoro
Author-Name-Last: Reyes Fong
Author-Name: Walter Danilo Leiva Mardones
Author-Name-First: Walter Danilo
Author-Name-Last: Leiva Mardones
Author-Name: Robinson Dueñas Casallas
Author-Name-First: Robinson
Author-Name-Last: Dueñas Casallas
Title: Ratios determining of microenterprise profitability: an analysis based on multilinear regression models
Abstract: Introduction: to determine the best multilinear regression model(s) of the Return on Assets and the Return on Equity of microenterprises in the commune of San Bernardo de Santiago de Chile; to strengthen their competitiveness in the industry, contributing to the strengthening of public policy in the sector, the source of information was the Mayor's Office, Directorate of Productive Development; Local Economic Development and College of Accountants Method: an analysis of financial ratios was developed, using predictor variables according to economic sector and size, consolidating 29 financial ratios derived from the balance sheets and income statements of the last 5 business years, discriminating collinearity by Pearson correlation and variance inflation factor (VIF> 10), then a normality analysis was developed through the Kolmogorov-Smirnoff and Shapiro - Wilk tests. generating 8 multiple linear regression models, evaluating the metrics R2, adjusted R2, F and P-Value Statistics, MAE (mean absolute error) RMSE (mean square error), to relate the measures of profitability. Results: 6 of the 8 models present high degrees of prediction, performance and level of adjustment, demonstrating statistical soundness of the ROA and ROE indices for the financial performance of microenterprises, segmented by sector and size, defining the impact of long-term debt, working capital, current amount, DuPont and acid index on profitability. Conclusions: The study constructed 6 statistically robust and highly predictive regression models to define the financial variables with the highest degree of impact on microenterprise profitability.
Journal: Data and Metadata
Pages: 1195
Volume: 4
Year: 2025
DOI: 10.56294/dm20251195
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1195:id:1056294dm20251195

Template-Type: ReDIF-Article 1.0
Author-Name: Saad Saadouni
Author-Name-First: Saad
Author-Name-Last: Saadouni
Author-Name: Siham Ammari
Author-Name-First: Siham
Author-Name-Last: Ammari
Author-Name: Souad Habbani
Author-Name-First: Souad
Author-Name-Last: Habbani
Title: Unveiling Global Economic Stratification: A Machine Learning Framework for Multi-Dimensional Macroeconomic Analysis
Abstract: Introduction: Traditional econometric approaches to multi-country macroeconomic analysis face critical limitations in capturing complex, non-linear relationships across diverse economic systems. Objective: This study aims to introduce a comprehensive machine learning framework, implemented in Python, that transcends conventional VAR model constraints by analyzing 13 key macroeconomic indicators across 217 countries (2010–2025). Method: Advanced clustering techniques (K-means) and ensemble learning (Random Forest), along with Principal Component Analysis (PCA), were applied to reveal hidden economic stratification patterns previously undetectable through traditional methods. Result: The analysis uncovers four distinct global economic clusters representing differentiated development trajectories, with middle-income economies comprising the majority of observations (57.4%). Fiscal indicators demonstrate exceptional forecasting accuracy through Random Forest algorithms, while growth dynamics remain inherently unpredictable, revealing fundamental asymmetries in economic system behaviors. Conclusions: This study demonstrates that machine learning techniques, implemented in Python, can systematically identify which macroeconomic relationships are structurally determined versus stochastically driven. This differential predictability framework provides immediate policy implications for targeted intervention strategies, enabling policymakers to focus resources on controllable fiscal mechanisms rather than pursuing futile attempts to predict volatile growth patterns.
Journal: Data and Metadata
Pages: 1180
Volume: 4
Year: 2025
DOI: 10.56294/dm20251180
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1180:id:1056294dm20251180

Template-Type: ReDIF-Article 1.0
Author-Name: Arizona Firdonsyah
Author-Name-First: Arizona
Author-Name-Last: Firdonsyah
Author-Name: Purwanto
Author-Name-Last: Purwanto
Author-Name: Imam Riadi
Author-Name-Last: Imam Riadi
Author-Name: Mahrus Ali
Author-Name-Last: Mahrus Ali
Author-Name: Ammar Fauzan
Author-Name-Last: Ammar Fauzan
Title: A Supervisory Approach to Building Ethical Digital Forensic Frameworks through Participatory Action Research
Abstract: Introduction: the integrity of digital forensic case handling plays a crucial role in safeguarding the public interest. Breaches in ethical compliance within the forensic process can undermine the credibility of investigations and erode public trust in their outcomes. Methods: the Participatory Action Research (PAR) approach. The research engaged stakeholders from both academic and professional sectors. Data collection was conducted through comprehensive literature reviews and structured stakeholder discussions to ensure the resulting framework reflects both theoretical and practical considerations. Results: the study introduced the Supervisory Framework to Respect Ethics or we call it SUFREE, a model specifically designed to address ethical oversight in the digital forensic process, specific to conditions in Indonesia. The framework was developed through iterative consultation and validation involving relevant experts, aiming to ensure methodological robustness and applicability within the Indonesian setting. Conclusion: the SUFREE framework offers a structured, ethics-focused supervisory model expected to enhance the quality, integrity, and professionalism of digital forensic practices in Indonesia, thereby contributing to improved public trust in forensic investigations.
Journal: Data and Metadata
Pages: 1179
Volume: 4
Year: 2025
DOI: 10.56294/dm20251179
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1179:id:1056294dm20251179

Template-Type: ReDIF-Article 1.0
Author-Name: Budi Warsito
Author-Name-First: Budi
Author-Name-Last: Warsito
Author-Name: Jatmiko Endro Suseno
Author-Name-First: Jatmiko
Author-Name-Last: Endro Suseno
Author-Name: Asa Arifudin
Author-Name-First: Asa
Author-Name-Last: Arifudin
Title: Embedding and Topic Modeling Techniques for Short Text Analysis on Social Media: A Systematic Literature Review
Abstract: Introduction: The analysis of short texts from social media is critical for gaining insights but is challenged by data sparsity and noise. Integrating embedding and topic modeling techniques has emerged as a key solution. Methods: This study conducted a Systematic Literature Review (SLR) following PRISMA guidelines. A systematic search across IEEE, ScienceDirect, and Scopus databases was performed to identify relevant studies, which were then screened and selected based on predefined inclusion and exclusion criteria. Results: The analysis of 22 included studies revealed a clear methodological trend toward hybrid models that integrate transformer-based embeddings, such as BERT, with topic modeling frameworks. These integrated approaches consistently demonstrated superior performance in generating coherent topics and improving downstream task accuracy compared to standalone or traditional methods. However, limitations related to model generalizability, computational cost, and domain adaptation were identified. Conclusions: The integration of contextual embeddings with topic models is the most effective approach for short-text analysis on social media. Future research should focus on developing more adaptive and efficient models, including fine-tuning language models on domain-specific corpora and exploring the integration of Large Language Models (LLMs) to enhance automation and accuracy.
Journal: Data and Metadata
Pages: 1168
Volume: 4
Year: 2025
DOI: 10.56294/dm20251168
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1168:id:1056294dm20251168

Template-Type: ReDIF-Article 1.0
Author-Name: Mohammad Haroun Haroun Sharairi
Author-Name-First: Mohammad Haroun
Author-Name-Last: Haroun Sharairi
Title: Detection and Prediction of Financial Fraud Using Deep Learning Methods: A case of the Companies Listed in the Amman Stock Exchange
Abstract: Introduction: The study examined the ongoing issue of identifying financial fraud in emerging economies, concentrating on companies listed on the Amman Stock Exchange (ASE). Methods: A panel of 176 ASE-listed enterprises was studied from 2011 to 2021. Starting with a preliminary analysis of Beneish M-Score constituents and associated metrics, a supervised neural network (FNN) had been trained, and an ordinary least-squares (OLS) analysis was computed. The performance study was executed using reliability, recall, reliability, F1-score, and ROC-AUC. Results: The FNN achieved an accurate identification rate of 0.9844 with a recall of 1.0, indicating it accurately identified all fraudulent transactions in the experimental dataset. The ROC-AUC was 0.97. The OLS model, albeit less precise, demonstrated statistically significant correlations—particularly for GMI, SGAI, and LVGI—with the Beneish M-Score, thereby providing interpretable risk indicators. Conclusions: The study revealed that deep learning, namely a feedforward neural network (FNN), surpassed a traditional ordinary least squares (OLS) method in detecting fraud among ASE enterprises, whereas OLS offered contextual information about the factors associated with fraud. An integrated analytical framework was proposed to assist regulators and investors in achieving improved transparency and early warning in the Jordanian market.
Journal: Data and Metadata
Pages: 1163
Volume: 4
Year: 2025
DOI: 10.56294/dm20251163
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1163:id:1056294dm20251163

Template-Type: ReDIF-Article 1.0
Author-Name: Hade Afriansyah
Author-Name-First: Hade
Author-Name-Last: Afriansyah
Author-Name: Nurhizrah Gistituati
Author-Name-First: Nurhizrah
Author-Name-Last: Gistituati
Author-Name: Irsyad
Author-Name-First: Irsyad
Author-Name-Last: Irsyad
Author-Name: Sufyarma Marsidin
Author-Name-First: Sufyarma
Author-Name-Last: Marsidin
Author-Name: Rusdinal
Author-Name-First: Rusdinal
Author-Name-Last: Rusdinal
Author-Name: Ahmad Sabandi
Author-Name-First: Ahmad
Author-Name-Last: Sabandi
Author-Name: Nurul Widya
Author-Name-First: Nurul
Author-Name-Last: Widya
Title: Evaluating the Effectiveness of the All-in-One Dashboard System (AIODS) in Enhancing Vocational High School Management
Abstract: This study evaluates the effectiveness of the All-in-One Dashboard System (AIODS) in improving school management, particularly in vocational high schools (SMKs). The objective was to assess the AIODS’s potential in enhancing decision-making processes by providing real-time data on academics, finances, human resources, and infrastructure. The study used a mixed-methods approach, combining quantitative surveys to evaluate the practicality of the AIODS and qualitative interviews with school principals to gain deeper insights into its functionality. The data collected were analyzed using an effectiveness index based on six key indicators: usability, technical performance, security, content and information effectiveness, usage efficiency, and user satisfaction. Results showed that the system achieved an overall effectiveness score of 96 %, with user satisfaction and usability being the highest-rated aspects, both scoring above 97 %. The findings indicate that AIODS is highly effective in supporting data-driven decision-making, streamlining school management, and improving administrative efficiency. However, the study also highlighted areas for further development, including the addition of features like alumni data and news channels. In conclusion, the AIODS provides a promising model for enhancing school leadership and management, offering a comprehensive, user-friendly platform that aligns with the evolving needs of vocational education.
Journal: Data and Metadata
Pages: 1159
Volume: 4
Year: 2025
DOI: 10.56294/dm20251159
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1159:id:1056294dm20251159

Template-Type: ReDIF-Article 1.0
Author-Name: Firman Syah Putra
Author-Name-First: Firman
Author-Name-Last: Syah Putra
Author-Name: Muhammad Giatman
Author-Name-First: Muhammad
Author-Name-Last: Giatman
Author-Name: Resmi Darni
Author-Name-First: Resmi
Author-Name-Last: Darni
Author-Name: Yudha Aditya Fiandra
Author-Name-First: Yudha
Author-Name-Last: Aditya Fiandra
Author-Name: Deval Gusrion
Author-Name-First: Deval
Author-Name-Last: Gusrion
Author-Name: Herio Rizki Dewinda
Author-Name-First: Herio
Author-Name-Last: Rizki Dewinda
Title: Metadata-Driven Competency Modeling for Civil Servant Placement: A Structural Equation Approach
Abstract: Introduction: The objective of this study is to examine both the direct and indirect effects of data communication competence, data-driven critical thinking, and data-informed leadership on the perceived suitability of job placement decisions based on data assessments within Indonesia’s civil service system, while specifically assessing the mediating role of data-driven critical thinking in these relationships. Methods: A quantitative, cross-sectional design was applied using Partial Least Squares Structural Equation Modeling (PLS-SEM). A total of 256 civil servants from various Indonesian public institutions participated. Five hypotheses were tested to examine both direct and mediated relationships. Results: The findings revealed that both data communication competence (β = 0.123; p = 0.044) and data-driven critical thinking (β = 0.156; p = 0.006) significantly influenced perceived suitability. However, data-informed leadership did not have a significant direct effect (β = -0.061; p = 0.159). Mediation analysis showed that data-driven critical thinking significantly mediated the relationship between both communication competence and leadership with perceived suitability. Conclusions: The study highlights the pivotal role of cognitive competencies, particularly critical thinking, in influencing perceptions of data-based placement decisions. While leadership alone did not directly impact perceptions, its indirect role through critical thinking was substantial. These findings offer insights for refining leadership development and assessment practices within bureaucratic systems.
Journal: Data and Metadata
Pages: 1152
Volume: 4
Year: 2025
DOI: 10.56294/dm20251152
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1152:id:1056294dm20251152

Template-Type: ReDIF-Article 1.0
Author-Name: Mohammed Aftatah
Author-Name-First: Mohammed
Author-Name-Last: Aftatah
Author-Name: Abdelhak Khalil
Author-Name-First: Abdelhak
Author-Name-Last: Khalil
Author-Name: Khalil ZEBBARA
Author-Name-First: Khalil
Author-Name-Last: ZEBBARA
Title: AfKh-OpenIMU Generator: An open-source platform for simulating INS grades and generating datasets for machine learning applications
Abstract: This paper presents AfKh-OpenIMU Generator, an open-source platform developed in Java and deployed via Docker, aimed at supporting machine learning research in inertial navigation. The objective is to address two major challenges: the high cost of inertial navigation systems (INS) and the lack of large, labeled datasets required for training neural networks. Our platform simulates all six inertial sensors, including three accelerometers and three gyroscopes, using configurable error models that incorporate bias, scale factor, and stochastic noise. From user-defined reference trajectories, it generates raw inertial data and supports large-scale data augmentation by varying noise profiles, enabling the creation of diverse datasets without requiring physical hardware. Simulation results demonstrate high fidelity with real-world INS performance. The generated data yielded root mean square error (RMSE) values of 32.98 meters for low-cost INS, 8.99 meters for industrial-grade INS, and 1.07 meters for tactical-grade INS. In addition, the data augmentation mechanism allows dataset expansion by up to 10,000 times, significantly enhancing training robustness and helping to prevent overfitting in deep learning models. Our platform provides a flexible, low-cost, and reproducible solution for generating realistic inertial data. It facilitates the development and evaluation of machine learning algorithms for sensor fusion and secure navigation, making it particularly valuable for research in GPS-denied environments.
Journal: Data and Metadata
Pages: 1150
Volume: 4
Year: 2025
DOI: 10.56294/dm20251150
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1150:id:1056294dm20251150

Template-Type: ReDIF-Article 1.0
Author-Name: Afnan Ali Hasan Al-rabiaa
Author-Name-First: Afnan Ali Hasan
Author-Name-Last: Al-rabiaa
Title: Convolution maximization sequence in L <sub>1+E</sub>(R<sup>n</sup>) for Kernels of Lorentz space
Abstract: Introduction: This paper addresses the existence of amplifying convolution operators on Lebesgue spaces with kernels contained in Lorentz spaces. The analysis is rooted in the framework established by Reeve in his 1983 treatment of the Hardy-Littlewood-Sobolev inequality and is driven by the problem of determining whether convolution maximizers can be characterized when the convolution kernels lie in Lorentz spaces situated between the strong and the weak classes. Methods: The investigation capitalizes on the prior results of G.V. Kalachev and S.Yu. Sadov by using functional analytic techniques and operator-theoretic tools. Methodological steps include the systematic examination of necessary and sufficient criteria for the existence of maximizers, the application of compactness arguments in the dual space framework, and the refinement of kernel properties through Lorentz space inequalities. Results: The analysis establishes the existence of maximizers for convolution operators when the kernel class is contained in a slightly smaller set than weak , yet encompasses the entirety of the relevant Lorentz spaces. The abstract analytic assumptions of Theorem 2.3 are converted into explicit measurable criteria in Theorem 2.4, demonstrating that kernels selected from the identified Lorentz spaces fulfill all requisite properties for the existence of convolution maximizers. Conclusions: The exposition achieves a systematic enlargement of convolution operator theory by admitting kernels that reside within Lorentz spaces, affording explicit existence theorems for maximizers. As a consequence, the work deepens the structural analysis of extremal functions within the harmonic analysis canon and simultaneously furnishes a robust framework for prospective inquiries regarding the deployment of Lorentz-space convolution in both pure and applied mathematics.
Journal: Data and Metadata
Pages: 1149
Volume: 4
Year: 2025
DOI: 10.56294/dm20251149
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1149:id:1056294dm20251149

Template-Type: ReDIF-Article 1.0
Author-Name: Supavit Phuvarit
Author-Name-First: Supavit
Author-Name-Last: Phuvarit
Author-Name: Pongsathon Pookduang
Author-Name-First: Pongsathon
Author-Name-Last: Pookduang
Author-Name: Rapeepat Klangbunrueang
Author-Name-First: Rapeepat
Author-Name-Last: Klangbunrueang
Author-Name: Sumana Chiangnangam
Author-Name-First: Sumana
Author-Name-Last: Chiangnangam
Author-Name: Wirapong Chansanam
Author-Name-First: Wirapong
Author-Name-Last: Chansanam
Author-Name: Kulthida Tuamsuk
Author-Name-First: Kulthida
Author-Name-Last: Tuamsuk
Author-Name: Tassanee Lunrasri
Author-Name-First: Tassanee
Author-Name-Last: Lunrasri
Title: Ontology-Based Semantic Retrieval for Museum News Systems
Abstract: Introduction: Museums face challenges in managing and retrieving timely news content due to fragmented information systems. This study investigates how semantic web technologies can enhance contextual accuracy and accessibility in museum information retrieval. Methods: We created a domain-specific ontology integrated with relational databases via Ontology-Based Data Access (OBDA). A semantic search system accepting natural language queries was implemented and evaluated by experts using standard information retrieval metrics. Results: The system achieved strong performance with precision of 0,85, recall of 0,96, and F1-score of 0,88, demonstrating effective semantic retrieval of museum news. Conclusions: The findings demonstrate that semantic web technologies improve the accessibility and contextual relevance of museum news, contributing to digital heritage information management.
Journal: Data and Metadata
Pages: 1147
Volume: 4
Year: 2025
DOI: 10.56294/dm20251147
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1147:id:1056294dm20251147

Template-Type: ReDIF-Article 1.0
Author-Name: Fatima-Zahrae LAKHLIFI
Author-Name-First: Fatima-Zahrae
Author-Name-Last: LAKHLIFI
Author-Name: Mohammed ABDELLAOUI
Author-Name-First: Mohammed
Author-Name-Last: ABDELLAOUI
Title: The Concentration of AI Talent as an Industrial Strategy: A Cross-Country Panel Data Analysis Applied to Financial Services as an Industry
Abstract: Artificial intelligence is redrawing comparative advantages in financial services, while many studies remain descriptive or focused on a single country, without testing whether the observed gaps reflect distinct industrial strategies. In this context, our objective is to establish whether the concentration of AI talent in finance is due to a simple global trend or to differentiated national choices. Empirically, we conduct an observational, comparative, and longitudinal study on 10 OECD countries monitored annually between 2016 and 2025 (N = 100 country-years). The dependent variable is the share of professionals trained in AI in finance (AI_pct, harmonized definition). The dynamics is captured by a linear time trend, supplemented for robustness by annual dummies; heterogeneity is modeled via a random effects GLS with country intercepts, clustered standard errors, and the Hausman test does not reject the RE option. On the data side, we mobilize a single, harmonized public source (OECD.AI) and anchor the analysis in 18 scientific references. The results indicate an average increase of approximately +0.2263 percentage points per year (significant), an overall average of 2.86%, and persistent gaps between countries (e.g., Israel ≈ 4.08% vs. the United States ≈ 2.27%), stable when the trend is replaced by time fixed effects. In sum, the rise in AI skills is general, but is part of national trajectories consistent with industrial strategy; hence implications for upskilling, data and model governance, and state-market coordination, subject to a limited scope and the absence of causal identification.
Journal: Data and Metadata
Pages: 1144
Volume: 4
Year: 2025
DOI: 10.56294/dm20251144
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1144:id:1056294dm20251144

Template-Type: ReDIF-Article 1.0
Author-Name: Aries Dwi Indriyanti
Author-Name-First: Aries Dwi
Author-Name-Last: Indriyanti
Author-Name: Rahmat Gernowo
Author-Name-First: Rahmat
Author-Name-Last: Gernowo
Author-Name: Eko Sediyono
Author-Name-First: Eko
Author-Name-Last: Sediyono
Author-Name: Mahrus Ali
Author-Name-First: Mahrus
Author-Name-Last: Ali
Title: Detecting Public Issues in the 2024 East Java Gubernatorial Election through LDA Topic Modeling on Social Media X
Abstract: General elections are one of the pillars of democracy, so understanding public issues developing in the digital space is crucial to strengthening the legitimacy and quality of their implementation. The 2024 East Java gubernatorial election presents a strategic opportunity given the high level of regional political dynamics and the use of social media as a platform for political discourse. This study aims to identify key issues in public conversations on social media platforms related to the election, map their thematic distribution, and illustrate the distribution of public opinion across each topic. The research method used topic modeling analysis based on Latent Dirichlet Allocation. Data was collected from 3,500 public posts relevant to the 2024 East Java gubernatorial election. The analysis process included text data cleaning, tokenization, removal of common words, and model development to obtain optimal topics. The results showed a model coherence value of 0.51, with three main topics: First, the implementation of regional head elections, including technical aspects, regional context, and the role of election organizers. Second, the security and smoothness of the election process, including voter management and trust in the results. Third, voter participation and the role of election organizers in ensuring legitimacy. The distribution of topics varies by region, influenced by local political backgrounds and previous election experiences. In conclusion, social media is a crucial arena for shaping public opinion and disseminating political issues. Digital data-based thematic analysis can help election organizers and policymakers design more effective public communications, increase participation, and strengthen public trust in elections at the regional level.
Journal: Data and Metadata
Pages: 1140
Volume: 4
Year: 2025
DOI: 10.56294/dm20251140
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1140:id:1056294dm20251140

Template-Type: ReDIF-Article 1.0
Author-Name: Ahmed A.F Osman
Author-Name-First: Ahmed A.F
Author-Name-Last: Osman
Author-Name: Rajit Nair
Author-Name-First: Rajit
Author-Name-Last: Nair
Author-Name: Sultan Ahmad
Author-Name-First: Sultan
Author-Name-Last: Ahmad
Author-Name: Mosleh Hmoud Al-Adhaileh
Author-Name-First: Mosleh Hmoud
Author-Name-Last: Al-Adhaileh
Author-Name: Ramgopal Kashyap
Author-Name-First: Ramgopal
Author-Name-Last: Kashyap
Author-Name: Hikmat A. M. Abdeljaber
Author-Name-First: Hikmat A. M.
Author-Name-Last: Abdeljaber
Author-Name: Sami A. Morsi
Author-Name-First: Sami A.
Author-Name-Last: Morsi
Author-Name: Rami Taha Shehab
Author-Name-First: Rami Taha
Author-Name-Last: Shehab
Title: Exploring Deep Learning Approaches for Multimodal Breast Cancer Dataset Classification and Detection
Abstract: Introduction; Globally, we need advanced testing to detect breast cancer early. New breast cancer diagnosis methods using mixed datasets and deep learning promise improved accuracy. Objective; These sets, which comprise several imaging modalities, show tumor characteristics well. VGG16, AlexNet, and ResNet50 are effective deep learning models in many domains, yet their breast cancer diagnosis performance is unclear. Method; This paper examines these patterns' benefits, downsides, and research gaps. We also provide two novel approaches, Attention-based Multimodal Fusion (AMF) and Improved Generative Adversarial Augmentation (GAA), to improve deep learning models on breast cancer datasets. Result; The findings highlight the potential of machine learning to show tumor characteristics well. Conclusion; We prove that our breast cancer screening technologies are the most accurate and dependable via extensive testing.
Journal: Data and Metadata
Pages: 1136
Volume: 4
Year: 2025
DOI: 10.56294/dm20251136
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1136:id:1056294dm20251136

Template-Type: ReDIF-Article 1.0
Author-Name: Carina Del Rocío Cevallos Ramos
Author-Name-First: Carina Del Rocío
Author-Name-Last: Cevallos Ramos
Author-Name: Gabriela Natali Fonseca Romero
Author-Name-First: Gabriela Natali
Author-Name-Last: Fonseca Romero
Author-Name: Katherine Elizabeth Sandoval Escobar
Author-Name-First: Katherine Elizabeth
Author-Name-Last: Sandoval Escobar
Author-Name: Myriam Johanna Naranjo Vaca
Author-Name-First: Myriam Johanna
Author-Name-Last: Naranjo Vaca
Title: Impact of data protection legislation on the digitalization of small and medium-sized enterprise
Abstract: Introduction: Decision-making in small and medium-sized enterprises (SMEs) relies heavily on the proper management of data. Objective: The objective of this research was to describe the scientific output on data protection legislation in the digitization of small and medium-sized enterprises, which is required by these enterprises for decision-making. Methods: To this end, the Scimago portal was used as a source for analyzing scientific output, and a review of the distribution by quartiles was carried out based on bibliometric indicators such as the H index, the impact factor of journals, the number of documents published, the average number of citations per document, international scientific collaboration, and citations from public funding agencies. Likewise, the number of articles cited in the most relevant journals in each quartile was analyzed in order to assess the relevance of scientific innovation in the formulation of adequate data protection legislation in the process of digitizing SMEs. Results: The findings reveal that most scientific output is concentrated in journals belonging to countries with a high level of technological development. The areas with the highest representation are threat detection and data protection. Conclusions: A low level of dissemination of research related to the development of specific legislative frameworks for data protection in the context of the digitization of small and medium-sized enterprises was identified.
Journal: Data and Metadata
Pages: 1135
Volume: 4
Year: 2025
DOI: 10.56294/dm20251135
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1135:id:1056294dm20251135

Template-Type: ReDIF-Article 1.0
Author-Name: Laely Indah Lestari
Author-Name-First: Laely
Author-Name-Last: Indah Lestari
Author-Name: Evi Novianti
Author-Name-First: Evi
Author-Name-Last: Novianti
Author-Name: Dadang Sugiana
Author-Name-First: Dadang
Author-Name-Last: Sugiana
Author-Name: Ute Lies Siti Khadijah
Author-Name-First: Ute Lies
Author-Name-Last: Siti Khadijah
Title: From Weaving Patterns to Data Patterns: AI-Driven Cultural Documentation of East Sumba’s Ikat Traditions
Abstract: Introduction: This study aims to develop and test a metadata-driven, AI-assisted framework for documenting the visual, oral, and symbolic elements of ikat weaving in East Sumba. It seeks to explore how artificial intelligence can transform traditional knowledge into machine-readable cultural data structures while maintaining epistemological integrity and community participation. Methods: This research employed a hybrid qualitative-technical methodology. Ethnographic fieldwork was conducted with ikat artisans in East Sumba to gather narrative and visual data, including interviews, ritual transcripts, and photographs of woven fabrics. These data were analyzed using a combination of natural language processing (NLP) and computer vision algorithms. NLP was used to extract recurring linguistic patterns and cosmological themes, while computer vision categorized visual motifs by type, symmetry, and symbolic meaning. Results:The AI-driven approach effectively captured the symbolic and narrative complexity of the ikat tradition. Computer vision techniques successfully identified and classified motif types and regional styles, linking them to spiritual meanings conveyed by the artisans. NLP analysis of transcribed interviews revealed consistent narrative patterns related to ancestral cosmology, customary law, and motif symbolism. Conclusion: This research demonstrates the viability of combining AI technologies with ethnographic fieldwork to create a robust, ethical, and culturally sensitive system for documenting living traditions. By translating the complexity of ikat knowledge into semantic data patterns, the study provides a model for intelligent cultural heritage documentation. The key contribution lies in bridging indigenous epistemologies with digital infrastructures, enabling scalable cultural preservation without compromising authenticity.
Journal: Data and Metadata
Pages: 1129
Volume: 4
Year: 2025
DOI: 10.56294/dm20251129
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1129:id:1056294dm20251129

Template-Type: ReDIF-Article 1.0
Author-Name: Budi Syahri
Author-Name-First: Budi
Author-Name-Last: Syahri
Author-Name: Syahril
Author-Name-First: Syahril
Author-Name-Last: Syahril
Author-Name: Refdinal
Author-Name-First: Refdinal
Author-Name-Last: Refdinal
Author-Name: Eko Indrawan
Author-Name-First: Eko
Author-Name-Last: Indrawan
Author-Name: Afriza Media
Author-Name-First: Afriza
Author-Name-Last: Media
Author-Name: Rifelino
Author-Name-First: Rifelino
Author-Name-Last: Rifelino
Title: AI-PBL Framework: Innovative Problem Based Learning Model Supported by Artificial Intelligence Technology
Abstract: Introduction: This study aims to develop a Problem-Based Learning (PBL) framework integrated with Artificial Intelligence (AI) technology to enhance the critical thinking skills of students in the Mechanical Engineering Study Program at Padang State University (UNP). Methods: A developmental research methodology based on the ADDIE framework was implemented in this study. The subjects involved were students enrolled in the Mechanical Engineering Department at UNP. Results: Validation results from seven experts indicated that the developed product falls into the valid category. In addition, the practicality test involving two lecturers and ten students yielded a score of 80.99%, placing it in the "highly practical" category. Regarding effectiveness, the t-test produced a value of 0.000, which is less than 0.05, indicating a statistically significant difference between the experimental and control groups. Conclusion: Based on the findings from the validation, practicality, and effectiveness assessments, the AI-supported PBL model is considered valid, highly practical, and effective in enhancing the critical thinking abilities of Mechanical Engineering students at UNP.
Journal: Data and Metadata
Pages: 1116
Volume: 4
Year: 2025
DOI: 10.56294/dm20251116
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1116:id:1056294dm20251116

Template-Type: ReDIF-Article 1.0
Author-Name: Carina Del Rocio Cevallos Ramos
Author-Name-First: Carina Del Rocio
Author-Name-Last: Cevallos Ramos
Author-Name: Fausto Francisco Navarrete Chávez
Author-Name-First: Fausto Francisco
Author-Name-Last: Navarrete Chávez
Author-Name: Fernando Ricardo Márquez Sañay
Author-Name-First: Fernando Ricardo
Author-Name-Last: Márquez Sañay
Author-Name: Mauro Patricio Andrade Romero
Author-Name-First: Mauro Patricio
Author-Name-Last: Andrade Romero
Title: Strengthening cyber resilience in universities using artificial intelligence for proactive threat detectio
Abstract: The data used by companies for decision making are exposed to various risks that can compromise their security. In this context, it is essential to identify tools to detect and manage these risks. Therefore, the main objective of this research was to conduct a bibliometric analysis aimed at assessing the development of scientific literature on the application of cyber resilience in universities using artificial intelligence for proactive threat detection. To achieve this purpose, the Scimago portal, specialized in the analysis of scientific production, was used. The study reviewed the distribution of quartiles using bibliometric indicators such as the H-index, journal impact factor, number of published documents, average citations per document, international scientific collaboration, and citations from public funding entities. Likewise, the number of articles cited in the most relevant journals of each quartile was evaluated in order to assess the importance of scientific innovation in improving proactive threat detection through cyber resilience. The results show that most of the scientific production is concentrated in journals belonging to countries with high technological development, especially in the areas of threat detection and application of artificial intelligence. However, there is a low diffusion of research related to cyber resilience in universities through artificial intelligence. which suggests the need to increase investment in science and technology, considering the high risk of cybersecurity attacks to which these institutions are exposed
Journal: Data and Metadata
Pages: 1109
Volume: 4
Year: 2025
DOI: 10.56294/dm20251109
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1109:id:1056294dm20251109

Template-Type: ReDIF-Article 1.0
Author-Name: Hamdi Alaoui Abdelhafid
Author-Name-First: Hamdi
Author-Name-Last: Alaoui Abdelhafid
Author-Name: Anwar Meddaoui
Author-Name-First: Anwar
Author-Name-Last: Meddaoui
Author-Name: Ahmed En-nhaili
Author-Name-First: Ahmed
Author-Name-Last: En-nhaili
Title: Reliable prediction of industrial components Remaining Useful Life using Cox and Weibull models: A Comparative Study
Abstract: Predicting the Remaining Useful Life (RUL) of industrial equipment is a cornerstone of predictive maintenance strategies aimed at minimizing downtime and optimizing maintenance costs. This study presents a comparative evaluation of two prominent survival analysis techniques Cox Proportional Hazards (Cox PH) and the Weibull model for RUL prediction using the AI4I 2020 Predictive Maintenance Dataset. We implement a robust analytical framework incorporating Kaplan-Meier survival curves, log-rank tests, and multivariate survival modeling. Our methodology includes detailed data preprocessing, model validation using the C-index and Akaike Information Criterion (AIC), and the identification of significant predictors of failure. The results reveal that the Cox PH model outperforms the Weibull model in terms of flexibility, predictive accuracy, and capacity to handle multiple covariates. This work highlights the strengths and limitations of both models and emphasizes the superior applicability of the Cox PH model for complex industrial datasets. These findings offer actionable insights for developing more reliable, data-driven maintenance strategies in Industry 4.0 environments.
Journal: Data and Metadata
Pages: 1102
Volume: 4
Year: 2025
DOI: 10.56294/dm20251102
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1102:id:1056294dm20251102

Template-Type: ReDIF-Article 1.0
Author-Name: You He
Author-Name-First: You
Author-Name-Last: He
Author-Name: Mohd Jaki Bin Mamat
Author-Name-First: Mohd Jaki
Author-Name-Last: Bin Mamat
Author-Name: Li Sun
Author-Name-First: Li
Author-Name-Last: Sun
Title: Adaptation Potential Evaluation of Ornamental Motifs in Huizhou Heritage Buildings under Contemporary Context: An AHP-Fuzzy Comprehensive Evaluation Model Approach
Abstract: The ornamental motifs in the structural elements of traditional Huizhou dwellings, listed as a World Heritage Site, embody the convergence of Confucian philosophy and geomantic principles. They integrated into wooden, stone, and brick carvings, reflect the local populace’s aspirations for prosperity, longevity, and familial harmony, possessing important inheritance significance. This study aims to establish a model to evaluate the adaptation potential of ornamental motifs under contemporary context in traditional Huizhou dwellings by investigating their development history, categories, and cultural meanings. This model quantifies the factors influencing the adaptation potential of these ornamental motifs, calculates the weight of each factor. The analysis results indicate that, among all the factors, the most important primary indicator is cultural connotation (0,3118). The secondary indicators are visual appeal (0.2300), compatibility with modern aesthetic needs (0.0988) and noble, elegant (0.0836). Additionally, the overall grade of adaptation potential was determined by scoring and ranking motif samples based on the fuzzy comprehensive evaluation method. This approach enhances the objectivity, scientific rigor, and accuracy of the selection of ornamental motifs, which not only provides theoretical support and practical guidance for the sustainable development of Huizhou cultural symbols, but also serves methodological reference for related fields in other countries and regions.
Journal: Data and Metadata
Pages: 1104
Volume: 4
Year: 2025
DOI: 10.56294/dm20251104
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1104:id:1056294dm20251104

Template-Type: ReDIF-Article 1.0
Author-Name: Richar Jacobo Posso Pacheco
Author-Name-First: Richar Jacobo
Author-Name-Last: Posso Pacheco
Author-Name: Rosangela Caicedo-Quiroz
Author-Name-First: Rosangela
Author-Name-Last: Caicedo-Quiroz
Author-Name: Giceya Maqueira-Caraballo
Author-Name-First: Giceya
Author-Name-Last: Maqueira-Caraballo
Author-Name: Julio Barzola-Monteses
Author-Name-First: Julio
Author-Name-Last: Barzola-Monteses
Author-Name: Laura Cristina Barba Miranda
Author-Name-First: Laura Cristina
Author-Name-Last: Barba Miranda
Author-Name: Jorge Ricardo Amancha Gabela
Author-Name-First: Jorge Ricardo
Author-Name-Last: Amancha Gabela
Title: Methodological Proposal for the Design and Validation of Research Instruments Supported by Artificial Intelligence
Abstract: Introduction: the validity of data collection instruments is essential to ensure the quality and replicability of scientific studies; traditional methods require time, resources, and expert participation, making validation difficult. Objective: To develop a procedure for the design and validation of research instruments using Artificial Intelligence as a methodological support tool. Methods: an eight-phase model was designed, ranging from conceptual review and item formulation to linguistic evaluation, simulated rational validation, comprehension verification, internal consistency analysis, and structural optimization. Results: the process demonstrated applicability, technical coherence, and practical utility. ChatGPT 4.5 enabled the automation of analyses and the generation of content aligned with theoretical constructs, optimizing the preliminary validation phases. Conclusions: AI represents a viable alternative in resource-limited settings. While it does not replace classic empirical methods, it complements methodological rigor in key stages. Ethical and technical protocols must be established for its responsible use in scientific research.
Journal: Data and Metadata
Pages: 1103
Volume: 4
Year: 2025
DOI: 10.56294/dm20251103
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1103:id:1056294dm20251103

Template-Type: ReDIF-Article 1.0
Author-Name: Judith Josefina Hernandez Garcia de Velazco
Author-Name-First: Judith Josefina
Author-Name-Last: Hernandez Garcia de Velazco
Author-Name: John Eric Rhenals Turriago
Author-Name-First: John Eric
Author-Name-Last: Rhenals Turriago
Title: Artificial Intelligence and Human Rights: Protection of privacy and personal data
Abstract: Trends in digital development and updating issues have been characterized by a permanent asymmetry between their characterization and application, varying according to the available resources and social environments. In this sense, new technological trends such as robotics, augmented reality, blockchains, sustainable technology, and artificial intelligence are directed. Artificial intelligence, with its impact on personal rights such as privacy, freedom, intimacy, and human dignity itself, requires protection and recognition of fundamental rights both at the level of the domestic systems of each nation, as well as international justice, especially by the Human Rights Courts. From the bibliographic review, the purpose is to analyze the evolution of the law in protecting privacy and personal data from a global perspective, with the effective recognition of human rights contributing to the discussion of ethical restrictions and transparency as a guarantee. Ethics, morality, and transcendental human values must permeate the use of artificial intelligence, as well as national and transnational legal safeguards to protect privacy rights and personal data.
Journal: Data and Metadata
Pages: 1095
Volume: 4
Year: 2025
DOI: 10.56294/dm20251095
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1095:id:1056294dm20251095

Template-Type: ReDIF-Article 1.0
Author-Name: Carlos Murillo Barrera
Author-Name-First: Carlos
Author-Name-Last: Murillo Barrera
Author-Name: Deysi Medina Hinojosa
Author-Name-First: Deysi
Author-Name-Last: Medina Hinojosa
Author-Name: Juan Calderón Cisneros
Author-Name-First: Juan
Author-Name-Last: Calderón Cisneros
Title: Quality of Work Life and CSR: A Multi-occupational Approach with Biplot Methods inEcuadorian Companies
Abstract: Introduction: Quality of work life (QWL) has gained relevance as a strategic indicator in organizational sustainability. This study analyzed the relationship between LQOL and corporate social responsibility (CSR) in Ecuadorian workers from different sectors, focusing on psychosocial factors such as burnout and secondary traumatic stress. Methods: A multivariate quantitative approach was applied to a sample of 1236 public and private sector workers. A Likert-type questionnaire with 17 items was used and principal component analysis (PCA), Biplot techniques and hierarchical clustering and K-means algorithms were applied to identify patterns of perception and occupational profiles. Results: The results revealed a strong association between emotional exhaustion and secondary traumatic stress, especially in occupations with high human exposure. Biplot analysis allowed us to identify nonlinear relationships and to group occupational profiles according to their perception of occupational well-being. The cumulative variance explained by the first two components was 79%. Conclusions: CVL was consolidated as a critical dimension within CSR in the Ecuadorian context. The study showed the need for differentiated strategies according to occupational profile, promoting mental health and organizational sustainability. It was recommended that LVC metrics be incorporated into sustainability reports and human talent management policies.
Journal: Data and Metadata
Pages: 1085
Volume: 4
Year: 2025
DOI: 10.56294/dm20251085
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1085:id:1056294dm20251085

Template-Type: ReDIF-Article 1.0
Author-Name: Asma S. Alzwi
Author-Name-First: Asma S.
Author-Name-Last: Alzwi
Author-Name: Hani Nuri Rohuma
Author-Name-First: Hani Nuri
Author-Name-Last: Rohuma
Author-Name: Esraa Esam Alharasis
Author-Name-First: Esraa Esam
Author-Name-Last: Alharasis
Author-Name: Sajead Mowafaq Alshdaift
Author-Name-First: Sajead Mowafaq
Author-Name-Last: Alshdaift
Author-Name: Jamil J. Jaber
Author-Name-First: Jamil J.
Author-Name-Last: Jaber
Title: Determinants of Bank Profitability in Developed and Emerging Countries
Abstract: Aim: This study examines Arab and international bank profitability—Return on Assets (ROA) and Return on Equity (ROE)—determinants. It highlights regional performance differences and the main financial drivers of profitability, focusing on macroeconomic shocks. The paper compares Arab and international bank performance and provides new profitability driver insights. It also shows that linear modelling is insufficient to describe ROE in Arab countries and that nonlinear modelling is needed. The discussion suggests ways policymakers and banks can boost profitability and financial resilience. Method: The paper uses “multiple linear regression (MLR)” with panel data from heterogeneous countries over several years. Interest income, capital adequacy, non-interest income, cost-to-income ratio, loan-to-deposit ratio, and non-performing loans are tested for their effect on ROA and ROE in the MLR specifications. Time and regional trends are tested to account for economic crises like the 2008 global financial crisis (GFC) and the 2020 COVID-19 pandemic. Results: Findings exhibit very high regional variation in bank profitability. African countries—e.g., Botswana, Ethiopia, and Malawi—ranked better than European and Asian counterparts on both ROA and ROE. Arab countries such as Iraq, Syria, and Saudi Arabia exhibited high ROA, while Yemen, Egypt, and Djibouti showed high ROE. The research could not find a valid linear regression model for the ROE in the Arab nations, implying complex, non-linear dynamics. The major determinants of profitability are interest income, capital adequacy, non-interest income, and cost-to-income ratio. The macroeconomic shocks also tended to decrease profitability significantly by region.
Journal: Data and Metadata
Pages: 1084
Volume: 4
Year: 2025
DOI: 10.56294/dm20251084
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1084:id:1056294dm20251084

Template-Type: ReDIF-Article 1.0
Author-Name: Yulia Aryati
Author-Name-First: Yulia
Author-Name-Last: Aryati
Author-Name: Agusti Efi
Author-Name-First: Agusti
Author-Name-Last: Efi
Author-Name: Mukhlidi Muskhir
Author-Name-First: Mukhlidi
Author-Name-Last: Muskhir
Author-Name: Ernawati
Author-Name-First: Ernawati
Author-Name-Last: Ernawati
Author-Name: M Giatman
Author-Name-First: M
Author-Name-Last: Giatman
Author-Name: Yusmerita
Author-Name-First: Yusmerita
Author-Name-Last: Yusmerita
Title: Validation of the Syntax of a Local Wisdom-Based Learning Model for Fashion Design in Apparel Creation
Abstract: Introduction: This study validates the development of the syntax of a Local Wisdom-Based Fashion Design Learning Model, aimed at supporting students in creating modern fashion designs that align with global trends while remaining rooted in local cultural heritage. The model is designed to expand the theoretical framework of creative learning by integrating cultural exploration, identity reflection, and design idea development into a coherent and structured learning system. Methods: The research used by Plomp (2013) R&D Model, consisting of three phases: Preliminary Research, Prototyping, and Assessment. The validation process involved seven experts through Focus Group Discussions (FGDs), and data analysis was conducted using Confirmatory Factor Analysis (CFA) based on Covariance-Based Structural Equation Modeling (CB-SEM). Results: The analysis results show that the model demonstrates a high level of validity and reliability, with R-Square values ranging from 0.724 to 0.914. These findings support the internal consistency and structural strength of the proposed learning model. Conclusions: The Local Wisdom-Based Fashion Design Learning Model is proven to be valid and reliable. It holds significant potential to support the development of fashion design learning that harmonizes global trends with local cultural values, thereby fostering creativity grounded in cultural identity.
Journal: Data and Metadata
Pages: 1082
Volume: 4
Year: 2025
DOI: 10.56294/dm20251082
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1082:id:1056294dm20251082

Template-Type: ReDIF-Article 1.0
Author-Name: Javier Guaña Moya
Author-Name-First: Javier
Author-Name-Last: Guaña Moya
Author-Name: Yamileth Arteaga Alcívar
Author-Name-First: Yamileth
Author-Name-Last: Arteaga Alcívar
Author-Name: Armando Tarco Sánchez
Author-Name-First: Armando
Author-Name-Last: Tarco Sánchez
Author-Name: Mérida Marlleny Alatrista Gironzini
Author-Name-First: Mérida Marlleny
Author-Name-Last: Alatrista Gironzini
Title: Artificial intelligence applications to improve accessibility in education for students with disabilities
Abstract: Artificial intelligence is revolutionizing education, offering a range of possibilities for creating more accessible, inclusive and effective learning experiences for students with disabilities. In this systematic review, we were able to find several references to personalization of learning and assistive technologies for learning. Similarly, artificial intelligence has the potential to be a powerful tool for inclusive education, providing all students with the opportunities and support they need to reach their full potential, as well as the continued research on them serves to develop new tools and technologies that respond to the specific needs of each student. This research is articulated based on Kitchenham's systematic review, considering various research sources, such as academic journal articles and research papers with similar themes to the topic of this paper. Also, this review aims to identify the various tools with artificial intelligence that are found today to improve access to education for people with disabilities, concluding that there are multiple tools to facilitate access to people with disabilities, and that they are also used to improve the learning process of these people.
Journal: Data and Metadata
Pages: 1081
Volume: 4
Year: 2025
DOI: 10.56294/dm20251081
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1081:id:1056294dm20251081

Template-Type: ReDIF-Article 1.0
Author-Name: Chrismondari
Author-Name-Last: Chrismondari
Author-Name: Wakhinuddin Simatupang
Author-Name-First: Wakhinuddin
Author-Name-Last: Simatupang
Author-Name: Waskito
Author-Name-Last: Waskito
Author-Name: Rahmat Fadillah
Author-Name-Last: Rahmat Fadillah
Author-Name: Bayu Rianto
Author-Name-Last: Bayu Rianto
Title: Implementation of a Project-Based Inquiry Learning Model for Electrical Motor Installation: Evaluating Its Effectiveness in Vocational Education
Abstract: Vocational education plays a key role in equipping students with the practical skills needed for workforce participation, particularly in technical fields like electrical motor installation. This study investigates the effectiveness of the Project-Based Inquiry Learning (PBIL) model in enhancing students’ learning outcomes in electrical motor installation within vocational education. The primary objective is to evaluate whether PBIL can improve students' technical competencies, critical thinking, problem-solving abilities, and collaborative skills, which are crucial for success in the electrical and industrial sectors. The research was conducted using a quasi-experimental design with a Nonequivalent Control Group. The experimental group (37 students) received PBIL instruction, while the control group (35 students) followed traditional teacher-centered methods. Data were collected through pre- and post-tests, practical performance assessments, surveys, and semi-structured interviews, analyzed using both quantitative and qualitative methods. The results indicate that the experimental group showed significant improvements in technical skills, problem-solving, and teamwork compared to the control group. PBIL also enhanced students’ motivation, engagement, and adaptability to technological demands. The study concludes that PBIL is an effective instructional approach for bridging the gap between theoretical knowledge and real-world application in vocational education. It is recommended that further research explore PBIL’s long-term impact and its scalability across different vocational fields. Overall, PBIL supports the development of a skilled workforce, better aligning education with industry needs.
Journal: Data and Metadata
Pages: 1079
Volume: 4
Year: 2025
DOI: 10.56294/dm20251079
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1079:id:1056294dm20251079

Template-Type: ReDIF-Article 1.0
Author-Name: Arip Ramadan
Author-Name-First: Arip
Author-Name-Last: Ramadan
Author-Name: Muhammad Axel Syahputra
Author-Name-First: Muhammad Axel
Author-Name-Last: Syahputra
Author-Name: Dwi Rantini
Author-Name-First: Dwi
Author-Name-Last: Rantini
Author-Name: Ratih Ardiati Ningrum
Author-Name-First: Ratih Ardiati
Author-Name-Last: Ningrum
Author-Name: Muhammad Noor Fakhruzzaman
Author-Name-First: Muhammad Noor
Author-Name-Last: Fakhruzzaman
Author-Name: Aziz Fajar
Author-Name-First: Aziz
Author-Name-Last: Fajar
Author-Name: Maryamah
Author-Name-Last: Maryamah
Author-Name: Muhammad Mahdy Yandra
Author-Name-First: Muhammad Mahdy
Author-Name-Last: Yandra
Author-Name: Najma Attaqiya Alya
Author-Name-First: Najma Attaqiya
Author-Name-Last: Alya
Author-Name: Mochammad Fahd Ali Hillaby
Author-Name-First: Mochammad Fahd Ali
Author-Name-Last: Hillaby
Author-Name: Alhassan Sesay
Author-Name-First: Alhassan
Author-Name-Last: Sesay
Title: Predicting Surabaya's Rainfall: A Comparative Study of Naïve Bayes, K-Nearest Neighbor, and Random Forest
Abstract: Introduction: Accurate rainfall prediction plays a critical role in climate change adaptation, particularly in mitigating the risks of extreme droughts and floods. Reliable forecasts support sustainable water resource and agricultural management, contributing to reduced socio-economic vulnerability. This study aims to analyze rainfall conditions in Surabaya City and evaluate the performance of three classification methods to determine the most effective model for rainfall classification. Methods: This is a descriptive observational study using secondary data from the Meteorology, Climatology, and Geophysics Agency Maritime Station in Surabaya, covering the period from January 2019 to December 2023. The dataset consists of 1,822 daily weather observations, including rainfall, sunshine duration, temperature, wind speed, and humidity. After preprocessing, the rainfall variable was categorized into multiple classes. Three classification methods—Naïve Bayes, K-Nearest Neighbor, and Random Forest—were applied. Model performance was evaluated using accuracy, precision, recall, AUC-ROC, and loss function values. Results: All models achieved high accuracy, exceeding 0.93. Although Naïve Bayes showed slightly lower accuracy than the other two methods, it had the highest AUC-ROC and the lowest loss function value, indicating better class discrimination and generalization. Conclusions: The Naïve Bayes classifier is the most effective method for rainfall classification in Surabaya City. Among the predictor variables, sunshine duration is identified as the most influential factor in rainfall classification, followed by humidity, temperature, and wind speed
Journal: Data and Metadata
Pages: 1075
Volume: 4
Year: 2025
DOI: 10.56294/dm20251075
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1075:id:1056294dm20251075

Template-Type: ReDIF-Article 1.0
Author-Name: Hamzeh yuosef Alsha’ar
Author-Name-First: Hamzeh
Author-Name-Last: yuosef Alsha’ar
Author-Name: Ehsan Ali Alqararah
Author-Name-First: Ehsan
Author-Name-Last: Ali Alqararah
Author-Name: Samah Shalluf
Author-Name-First: Samah
Author-Name-Last: Shalluf
Author-Name: Saleh Yahya AL Freijat
Author-Name-First: Saleh Yahya
Author-Name-Last: AL Freijat
Author-Name: Ahmad Hanandeh
Author-Name-First: Ahmad
Author-Name-Last: Hanandeh
Title: Optimizing HR Performance and Strategy through Business Intelligence Talent Systems: A Focus on Workforce Analytics and Project Decision-Making
Abstract: Introduction: The main study objective is to study the effects of applying business intelligence talent systems human resource performance through taking the mediating role workforce analytics and project decision making in SAP firm in German. The research conducted in SAP firm in is an IT consulting firm specialized in giving solutions based on BI and IT, this research designed and distributed a survey on biotech managerial and technical staffs and collected 219 valid questionnaires for data analysis process through the usage of structural equation modeling program method SEM. Methods: The research chose specific indicators for the independent and dependent variables as follows, for business intelligence talent system this research chose AI-driven HR tools adoption, HR data integration capabilities, Predictive analytics utilization, for HR performance the following indicators were chosen employee productivity levels, talent retention rate, and training effectiveness, for workforce variable this research chose real-time workforce monitoring, turnover prediction accuracy, and employee engagement analytics. And for the decision-making variable the indicators which have been chose are the speed of Project decision making, data-driven decision implementation rate, and accuracy of workforce forecasting. Results: The research arrived to the result that applying business intelligence talent system in human resource department has a positive and effective impact on enhancing human resource department and this effect enhanced through illustrate the role of workforce and project decision making effectiveness. Conclusions: This study has relied on previous research in the same field, which focused on understanding the importance of improving advanced human resources practices and enhancing employee satisfaction and the importance of the results of these studies in continuing to work in a complex communications sector while maintaining employee satisfaction and high performance that always leads to providing high-quality and efficient products and services to customers.
Journal: Data and Metadata
Pages: 1072
Volume: 4
Year: 2025
DOI: 10.56294/dm20251072
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1072:id:1056294dm20251072

Template-Type: ReDIF-Article 1.0
Author-Name: Myriam Johanna Naranjo Vaca
Author-Name-First: Myriam Johanna
Author-Name-Last: Naranjo Vaca
Author-Name: Eduardo Xavier Macías Collahuazo
Author-Name-First: Eduardo Xavier
Author-Name-Last: Macías Collahuazo
Author-Name: Paulina Fernanda Bolaños-Logroño
Author-Name-First: Paulina Fernanda
Author-Name-Last: Bolaños-Logroño
Author-Name: Henry David Vásconez Vásconez
Author-Name-First: Henry David
Author-Name-Last: Vásconez Vásconez
Title: Adoption of generative artificial intelligence to improve business management innovation in Ecuador
Abstract: Generative artificial intelligence (GAI) has emerged as a disruptive technology with the potential to transform administrative processes in organisations. However, its adoption in business contexts in emerging economies, such as Ecuador, requires an understanding of the factors that influence organisational readiness for its implementation. The objective of the research was to analyse the intention to adopt the use of generative artificial intelligence to improve innovation in the administration of Ecuadorian companies, which has been empirically validated by a theoretical model based on technological and organisational dimensions. A quantitative, cross-sectional, non-experimental research design was developed. The population consisted of small and medium-sized enterprises (SMEs) in the province of Tungurahua in Ecuador, selected by random convenience sampling. A structured questionnaire with 20 items distributed across six dimensions was applied. A correlational and exploratory factor analysis (EFA) was performed to validate the structure of the instrument and examine the relationships between variables. The EFA confirmed a one-dimensional model that explained 79.24% of the total variance, with factor loadings above 0.83. The adoption of IAG showed a high correlation with expected innovation r= 0.944, technological complexity and compatibility. The participating companies show a consistent perception of IAG adoption, based on technological, organisational and strategic factors.
Journal: Data and Metadata
Pages: 1070
Volume: 4
Year: 2025
DOI: 10.56294/dm20251070
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1070:id:1056294dm20251070

Template-Type: ReDIF-Article 1.0
Author-Name: Ilham Essaket
Author-Name-First: Ilham
Author-Name-Last: Essaket
Author-Name: Mhammed El Bakkali
Author-Name-First: Mhammed
Author-Name-Last: El Bakkali
Author-Name: Anas El Maliki
Author-Name-First: Anas
Author-Name-Last: El Maliki
Author-Name: Omar Cherkaoui
Author-Name-First: Omar
Author-Name-Last: Cherkaoui
Title: Fuzzy Logic Prediction Model for Mechanical and Absorption Behavior of Treated Woven Sisal Composites
Abstract: The transition towards environmentally friendly and sustainable materials has intensified interest in natural fiber-reinforced composites, with sisal fibers standing out due to their biodegradability and mechanical performance. Despite these advantages, their practical use remained hindered by poor interfacial adhesion and high moisture uptake, largely attributed to their hydrophilic nature and surface impurities. In this study, a dual chemical treatment using sodium hydroxide (NaOH) followed by potassium permanganate (KMnO₄) was applied to three types of woven sisal fabrics (plain, twill, and satin) to enhance fiber–matrix interaction and overall composite properties. Twenty-seven composite variants were produced and evaluated to investigate the influence of weave structure, treatment concentration, and immersion time on tensile strength and water absorption. To capture the intricate relationships between these variables, a fuzzy logic-based predictive model was developed. This model effectively forecasted material behavior, achieving low average absolute errors of 1.77% for tensile strength and 3.46% for water absorption, demonstrating its robustness and value as a tool for process optimization. This study contributed not only to the development of high-performance, bio-based textile composites, but also introduced an intelligent, cost-effective predictive framework capable of reducing experimental demands while guiding sustainable material development.
Journal: Data and Metadata
Pages: 1068
Volume: 4
Year: 2025
DOI: 10.56294/dm20251068
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1068:id:1056294dm20251068

Template-Type: ReDIF-Article 1.0
Author-Name: Juan Bladimir Aguilar-Poaquiza
Author-Name-First: Juan Bladimir
Author-Name-Last: Aguilar-Poaquiza
Author-Name: Carla Sofía Arguello Guadalupe
Author-Name-First: Carla Sofía
Author-Name-Last: Arguello Guadalupe
Author-Name: William Patricio Cevallos-Silva
Author-Name-First: William Patricio
Author-Name-Last: Cevallos-Silva
Author-Name: Jorge Daniel Córdova Lliquin
Author-Name-First: Jorge Daniel
Author-Name-Last: Córdova Lliquin
Author-Name: Carlos Eduardo Cevallos Hermida
Author-Name-First: Carlos Eduardo
Author-Name-Last: Cevallos Hermida
Author-Name: Oscar Danilo Gavilánez Álvarez
Author-Name-First: Oscar Danilo
Author-Name-Last: Gavilánez Álvarez
Author-Name: Diego Cajamarca-Carrazco
Author-Name-First: Diego
Author-Name-Last: Cajamarca-Carrazco
Title: Management conservation of ecosystem services based on artificial intelligence: a analysis of citations in Scopus
Abstract: Technological development has allowed a global advance of artificial intelligence (AI), which is used as an advanced tool for the conservation of ecosystem services in order to achieve planetary sustainability. The objective of the article was to analyze the conservation of ecosystem services focused on the application of artificial intelligence based on bibliometric research. The Scopus database was used as a direct source of research. Using the 2020 prism methodology, 69 articles were quantified, considered from the year 2020 to 2025, with a notable growth of study from the year 2022 to 2024, obtaining in 2024 the maximum point with a total of 31 publications, of which environmental science is the most studied with 24 % and the country in which more research is generated is the United States with 15,84 %, followed by Italy with 14,85 %. Most of the studies involving ecosystem services seek to generate a green solution for the preservation of natural resources that provide great benefits and contribute to human wellbeing.
Journal: Data and Metadata
Pages: 1066
Volume: 4
Year: 2025
DOI: 10.56294/dm20251066
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1066:id:1056294dm20251066

Template-Type: ReDIF-Article 1.0
Author-Name: Arip Ramadan
Author-Name-First: Arip
Author-Name-Last: Ramadan
Author-Name: Mutiara Afifah
Author-Name-First: Mutiara
Author-Name-Last: Afifah
Author-Name: Dwi Rantini
Author-Name-First: Dwi
Author-Name-Last: Rantini
Author-Name: Indah Fahmiyah
Author-Name-First: Indah
Author-Name-Last: Fahmiyah
Author-Name: Ratih Ardiati Ningrum
Author-Name-First: Ratih Ardiati
Author-Name-Last: Ningrum
Author-Name: Mohammad Ghani
Author-Name-First: Mohammad
Author-Name-Last: Ghani
Author-Name: Septia Devi Prihastuti Yasmirullah
Author-Name-First: Septia Devi Prihastuti
Author-Name-Last: Yasmirullah
Author-Name: Najma Attaqiya Alya
Author-Name-First: Najma Attaqiya
Author-Name-Last: Alya
Author-Name: Muhammad Mahdy Yandra
Author-Name-First: Muhammad Mahdy
Author-Name-Last: Yandra
Author-Name: Vidyana Yulianingrum
Author-Name-First: Vidyana
Author-Name-Last: Yulianingrum
Author-Name: Fazidah Othman
Author-Name-First: Fazidah
Author-Name-Last: Othman
Title: Implementation of GWR and MGWR in Modelling Gross Regional Domestic Product (GRDP) in East Java
Abstract: Introduction: One important indicator of national development success is the increase in real Gross Regional Domestic Product (GRDP), which reflects regional economic performance. The GRDP growth rate, calculated as the percentage increase from the previous year, serves as a critical measure for evaluating economic progress. In the case of East Java, identifying the factors influencing GRDP growth is essential to support more effective and region-specific policy-making. This research aims to analyze those influencing factors using spatial regression methods. Methods: In this research, Geographically Weighted Regression and Mixed Geographically Weighted Regression methods are used to model the factors that influence the growth rate of GRDP in East Java. Results: Based on the results of the research that has been analyzed, it is known that the GWR model has an AICc score of 136,646, while the MGWR model has an AICc score of 134,3184, so it can be concluded that the MGWR method with a fixed gaussian kernel has better performance in modeling the factors that influence the GRDP growth rate in East Java. Conclusions: Globally, the General Allocation Fund and the Open Unemployment Rate significantly affect GRDP growth. Locally, the Percentage of Poor Population, Average Minimum Wage, Local Original Income, and Production Agglomeration show significant effects in specific areas. On the other hand, the Human Development Index and Population Density do not exhibit significant influence on GRDP growth, either globally or locally.
Journal: Data and Metadata
Pages: 1065
Volume: 4
Year: 2025
DOI: 10.56294/dm20251065
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1065:id:1056294dm20251065

Template-Type: ReDIF-Article 1.0
Author-Name: Ana Lucía Rivera-Abarca
Author-Name-First: Ana Lucía
Author-Name-Last: Rivera-Abarca
Author-Name: Jazmín Isabel García-Guerra
Author-Name-First: Jazmín Isabel
Author-Name-Last: García-Guerra
Author-Name: Héctor Oswaldo Aguilar-Cajas
Author-Name-First: Héctor Oswaldo
Author-Name-Last: Aguilar-Cajas
Author-Name: Heidy Elizabeth Vergara-Zurita
Author-Name-First: Heidy Elizabeth
Author-Name-Last: Vergara-Zurita
Author-Name: José Israel López-Pumalema
Author-Name-First: José Israel
Author-Name-Last: López-Pumalema
Author-Name: Freddy Armijos-Arcos
Author-Name-First: Freddy
Author-Name-Last: Armijos-Arcos
Title: Predictive Models of Typographic Preference in Digital Media
Abstract: Introduction: This article explores how typography influences user experience in digital environments, highlighting its evolution from the 11th century to the Internet era. Objective: The aim of this research was to examine the psychological impact of fonts, which evoke emotional responses and affect readability, design and user behavior. Methodology: Predictive models, such as regression, classification and time series, are used to analyze typographic preferences, helping designers to optimize digital interfaces. Results: The study simulated data from 1,000 participants, considering variables such as age, gender, educational level and context of use, revealing a predominant preference for Sans Serif typefaces (63.3%), especially in academic reading. The Logistic Regression and SVM models showed a moderate performance (accuracy of 0.627 and 0.634), with better ability to identify preferences for Sans Serif, although with limitations for the minority class (Serif). Conclusion: It was concluded that psychological, cultural and contextual factors significantly influence preferences, highlighting the need to integrate these variables in future models to improve accuracy and personalization in digital design.
Journal: Data and Metadata
Pages: 1062
Volume: 4
Year: 2025
DOI: 10.56294/dm20251062
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1062:id:1056294dm20251062

Template-Type: ReDIF-Article 1.0
Author-Name: Jazmín Isabel García-Guerra
Author-Name-First: Jazmín Isabel
Author-Name-Last: García-Guerra
Author-Name: Héctor Oswaldo Aguilar-Cajas
Author-Name-First: Héctor Oswaldo
Author-Name-Last: Aguilar-Cajas
Author-Name: Heidy Elizabeth Vergara-Zurita
Author-Name-First: Heidy Elizabeth
Author-Name-Last: Vergara-Zurita
Author-Name: Ana Lucía Rivera-Abarca
Author-Name-First: Ana Lucía
Author-Name-Last: Rivera-Abarca
Author-Name: Freddy Armijos-Arcos
Author-Name-First: Freddy
Author-Name-Last: Armijos-Arcos
Author-Name: José Israel López-Pumalema
Author-Name-First: José Israel
Author-Name-Last: López-Pumalema
Title: Predictive Analytics in Digital Marketing: A Statistical Modeling Approach for Predicting Consumer Behavior
Abstract: Introduction: The evolution of predictive analytics in digital marketing is deeply rooted in the development of statistical modeling and data analytics. Aim: The aim of the present research was to analyze the use of advanced statistical models for predicting consumer behavior in digital marketing environments, highlighting the relevance of predictive analytics in data- driven strategic decision making. Methodology: five machine learning, logistic regression, decision tree, random forest, support vector machines (SVM) and neural networks models were evaluated on a synthetic dataset representative of digital consumers belonging to Generation Z. The analysis considered key metrics such as overall accuracy, cross-validation mean and standard deviation, in order to measure both the effectiveness and stability of each model. Results: The results showed that the logistic regression, Random Forest, SVM and neural network models achieved an accuracy of 97% with overall consistency (standard deviation of 0.0), positioning them as reliable tools for predicting consumption trends. In contrast, the decision tree showed lower accuracy (92%) and higher variability, which limits its applicability in complex scenarios. Conclusion: The study concludes that the combination of accuracy and stability is essential for the implementation of effective predictive models in digital marketing and also highlights the importance of integrating these models into campaign automation and personalization systems to anticipate preferences, improve customer experience and optimize resources.
Journal: Data and Metadata
Pages: 1061
Volume: 4
Year: 2025
DOI: 10.56294/dm20251061
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1061:id:1056294dm20251061

Template-Type: ReDIF-Article 1.0
Author-Name: Mahmoud Al Qerom
Author-Name-First: Mahmoud
Author-Name-Last: Al Qerom
Title: RIFD-LZW: A Hybrid Approach for Lossy Image Compression Using Intensity Rounding, Division, and Lempel-Ziv-Welch Encoding
Abstract: This research presents RIFD-LZW, a new hybrid lossy image compression algorithm designed for both color and grayscale images across varying resolutions. The method integrates the Rounding the Intensity and Dividing (RIFD) technique with Lempel-Ziv-Welch (LZW) encoding to enhance compression efficiency while preserving high image quality. The RIFD stage reduces data redundancy through intensity quantization and scaling, while LZW applies efficient lossless dictionary-based encoding to the transformed data. Comprehensive experiments were conducted on four benchmark datasets EPFL, Kodak, Waterloo, and HQ-50K to evaluate the performance of the proposed method. The results demonstrate that RIFD-LZW consistently outperforms traditional RIFD, LZW, and standard compression algorithms including JPEG2000, JPEG-LS, and RIFD-Huffman. On average, RIFD-LZW achieved a compression efficiency of 7,51 Bits Per Pixel (BPP) for color datasets, representing a 49,93% improvement over RIFD and 62,49% over LZW. For grayscale images, RIFD-LZW attained an average BPP of 1,92, significantly outperforming RIFD (5,00) and LZW (4,74), with an improvement exceeding 59%. The RIFD-LZW algorithm delivers high visual quality despite being lossy, achieving average PSNR values 38,36 dB with minimal visible distortion. It effectively reduces file sizes while preserving acceptable image quality, making it well-suited for applications that require efficient compression with good visual retention.
Journal: Data and Metadata
Pages: 1055
Volume: 4
Year: 2025
DOI: 10.56294/dm20251055
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1055:id:1056294dm20251055

Template-Type: ReDIF-Article 1.0
Author-Name: Stalin Javier Caiza Lema
Author-Name-First: Stalin Javier
Author-Name-Last: Caiza Lema
Author-Name: Paúl Adrián Arias Córdova
Author-Name-First: Paúl Adrián
Author-Name-Last: Arias Córdova
Author-Name: Angela Priscila Campos Moposita
Author-Name-First: Angela Priscila
Author-Name-Last: Campos Moposita
Author-Name: Josselyn Gabriela Bonilla Ayala
Author-Name-First: Josselyn Gabriela
Author-Name-Last: Bonilla Ayala
Author-Name: Andrea Carolina Peñafiel Luna
Author-Name-First: Andrea Carolina
Author-Name-Last: Peñafiel Luna
Title: Use of machine learning and deep learning for exercise prescription
Abstract: Introduction: artificial intelligence is revolutionizing exercise prescription in sports physiotherapy by offering more personalized and data-driven approaches. Through machine learning and deep learning algorithms, AI enables the analysis of complex variables such as biomechanics, physiological responses, and the patient's clinical history, dynamically adjusting exercise programs. This optimizes performance, prevents injuries, and enhances rehabilitation. Methodology: a systematic review of studies on the use of AI in sports physiotherapy, based on articles published between 2015 and 2024, using specific inclusion criteria. The findings highlight the benefits of AI in personalizing exercise programs, emphasizing its capacity to improve adherence, load dosing, and injury prevention. However, clinical implementation of AI faces challenges such as external model validation, result interpretability, and the ethical management of sensitive data. Discussion: the review results show that AI is transforming exercise prescription in sports physiotherapy through a personalized and data-driven approach. AI algorithms, such as machine learning and deep learning, allow for the analysis of complex variables like biomechanics, physiological responses, and clinical history, dynamically adjusting exercise programs. Nevertheless, significant challenges remain for its clinical implementation, including external validation of models, interpretability of outcomes, and ethical concerns in handling sensitive data. Conclusion: AI holds tremendous potential to transform sports physiotherapy, but its integration into clinical practice requires overcoming technical and ethical challenges. Model validation, healthcare professional training, and equitable access to these technologies are essential aspects to ensure effective and safe implementation. Future research should address these challenges to maximize the benefits of AI in the field of exercise.
Journal: Data and Metadata
Pages: 1054
Volume: 4
Year: 2025
DOI: 10.56294/dm20251054
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1054:id:1056294dm20251054

Template-Type: ReDIF-Article 1.0
Author-Name: Mayerly Llanos-Redondo
Author-Name-First: Mayerly
Author-Name-Last: Llanos-Redondo
Author-Name: Sonia Maritza Mendoza Lizcano
Author-Name-First: Sonia Maritza
Author-Name-Last: Mendoza Lizcano
Author-Name: Pastor Ramírez Leal
Author-Name-First: Pastor
Author-Name-Last: Ramírez Leal
Author-Name: Andrés Llanos-Redondo
Author-Name-First: Andrés
Author-Name-Last: Llanos-Redondo
Title: Educational Data Analysis for Decision-Making: Performance in the Saber Pro Test among Early Childhood Education Programs in Colombia
Abstract: This study applies descriptive and exploratory data analysis techniques to examine the performance of students from 59 undergraduate programs in Early Childhood Education and Pedagogy in Colombia on the 2022 Saber Pro standardized test. Official datasets from ICFES were processed and analyzed using SPAD 5.6 and SPSS 28, focusing on five generic competencies: critical reading, quantitative reasoning, written communication, English, and citizenship competencies. Performance levels were examined in relation to institutional, sectoral, and regional variables. The findings reveal consistently low performance in quantitative reasoning, advantages associated with accredited institutions, and regional disparities. This research highlights the potential of data science tools—such as data mining and statistical visualization—for guiding evidence-based educational strategies and policy-making. It underscores the value of educational data systems as foundations for improving academic outcomes and reducing inequality through informed decision-making.
Journal: Data and Metadata
Pages: 1034
Volume: 4
Year: 2025
DOI: 10.56294/dm20251034
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1034:id:1056294dm20251034

Template-Type: ReDIF-Article 1.0
Author-Name: Jesús Alexander Pinillos Villamizar
Author-Name-First: Jesús Alexander
Author-Name-Last: Pinillos Villamizar
Author-Name: Hugo Macias
Author-Name-First: Hugo
Author-Name-Last: Macias
Author-Name: Luis Castrillon
Author-Name-First: Luis
Author-Name-Last: Castrillon
Title: Capital structure and debt tax shield: literature review and bibliometric analysis
Abstract: Introduction: this study presents a bibliometric analysis and literature review focused on identifying publications, key themes, and recent trends in frontier research related to capital structure and the debt tax shield. The aim is to explore how the academic field has evolved over time and highlight the most influential works and recurring topics. Methods: the study analyzed 33 documents indexed in Scopus, published between 1978 and 2023. A bibliometric approach was used to determine publication patterns, countries of origin, journal prominence, and citation metrics. Results: the bibliometric analysis revealed that the United States accounts for the highest number of studies. Journals such as Applied Financial Economics and Investment Management and Financial Innovations lead in publication volume but not in citation count. The main themes explored by the authors include corporate debt policies, optimal capital structure, valuation of tax shields, trade-off and pecking order theories, corporate social responsibility, and profitability. Key research trends focus on evaluating factors such as debt levels, tax rates, credit risk, and future fiscal policies and their impact on the value of the tax shield. Additionally, recent works analyze the effects of events such as the COVID-19 crisis on leverage strategies and capital structure, as well as the integration of modern models—such as compensation and information asymmetry-based pecking order—with traditional theories. Conclusions: The literature demonstrates a growing interest in understanding the interplay between tax factors and corporate financing decisions, especially in light of evolving economic contexts and theoretical frameworks. The field continues to expand through the incorporation of new models and empirical evidence, signaling opportunities for future research in this area.
Journal: Data and Metadata
Pages: 1031
Volume: 4
Year: 2025
DOI: 10.56294/dm20251031
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1031:id:1056294dm20251031

Template-Type: ReDIF-Article 1.0
Author-Name: Darwin Marcelo Varela Lascano
Author-Name-First: Darwin Marcelo
Author-Name-Last: Varela Lascano
Author-Name: Tania Lisbeth Chicaiza Zambrano
Author-Name-First: Tania Lisbeth
Author-Name-Last: Chicaiza Zambrano
Author-Name: Eduardo Xavier Macías Collahuazo
Author-Name-First: Eduardo Xavier
Author-Name-Last: Macías Collahuazo
Author-Name: Yordan Ernesto Calero Ocaña
Author-Name-First: Yordan Ernesto
Author-Name-Last: Calero Ocaña
Title: Impact of generative artificial intelligence on the decision-making of university students in the health sciences: A transversal study
Abstract: Advanced AI systems, such as those in their generative phase, cause uncertainty among higher education students about their functionality and the academic level they may have when interacting with IAGs such as ChatGPT. The study aimed to examine how interaction with AI tools, such as generative language models, influences students' ability to select learning strategies, manage academic resources, and make informed decisions during their professional training. A quantitative, descriptive, non-experimental approach was used. The initial population was 500 students from the Faculty of Medicine of two recognized higher education institutions in Ecuador, after applying certain inclusion criteria through random convenience sampling. The results showed that generative artificial intelligence significantly influences the academic decision-making of medical students, with scalability and efficiency standing out as key factors. In contrast, user satisfaction showed an inverse relationship, and institutional integration was not a determining factor. It is concluded that the impact of these tools depends on their strategic functionality rather than their superficial perception.
Journal: Data and Metadata
Pages: 1017
Volume: 4
Year: 2025
DOI: 10.56294/dm20251017
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1017:id:1056294dm20251017

Template-Type: ReDIF-Article 1.0
Author-Name: Nora Abia
Author-Name-First: Nora
Author-Name-Last: Abia
Author-Name: Hanane Sadeq
Author-Name-First: Hanane
Author-Name-Last: Sadeq
Author-Name: Ibtissam Medarhri
Author-Name-First: Ibtissam
Author-Name-Last: Medarhri
Author-Name: Aziz Soulhi
Author-Name-First: Aziz
Author-Name-Last: Soulhi
Title: A Fuzzy Logic-Based Decision Support System for Predicting Entrepreneurial Intention Among Textile Students
Abstract: This article presents a fuzzy logic-based decision support system, which functions as a predictive model for assessing and guiding entrepreneurial intention among Moroccan university students in the textile sector. The model used four key variables, namely Desirability, Self-Concept, University Context, and Feasibility. These latter and their influence were identified through a previous ANN model. Fuzzy memberships functions were designed, and 81 expert validated rules were constructed for the fuzzy model. The model provided entrepreneurial scores based on the students’ inputs and was tested on a new 40 student survey responses. Key findings highlighted that desirability and self- concept are critical drivers of entrepreneurial intention, and the results showed an alignment with expert judgments and theoretical models. Furthermore, the model provided personalised recommendations for both students and university and makes clear contributions to both theoretical and practical advancements in the entrepreneurship studies.
Journal: Data and Metadata
Pages: 1010
Volume: 4
Year: 2025
DOI: 10.56294/dm20251010
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1010:id:1056294dm20251010

Template-Type: ReDIF-Article 1.0
Author-Name: Patricio Moreno-Vallejo
Author-Name-First: Patricio
Author-Name-Last: Moreno-Vallejo
Author-Name: PATRICIO MORENO-COSTALES
Author-Name-First: PATRICIO
Author-Name-Last: MORENO-COSTALES
Author-Name: GISEL BASTIDAS-GUACHO
Author-Name-First: GISEL
Author-Name-Last: BASTIDAS-GUACHO
Author-Name: MARIA VALLEJO-SANAGUANO
Author-Name-First: MARIA
Author-Name-Last: VALLEJO-SANAGUANO
Title: Prediction of ICT Usage in Ecuador Through Machine Learning: impact of Education Level, Age, and Income on Digital Inclusion
Abstract: The progress of digital technologies in Ecuador during 2023–2024 was analyzed using data from the ENEMDU and machine learning models, processing 56 941 records that were carefully cleaned, normalized, and organized. Most notably, there was an increase in ICT usage across all age groups: usage among young people aged 18–29 rose by 1,7 %, among adults aged 30–49 by 1 %, and among those over 50 by 1,2 %. Education level emerged as the most decisive factor, showing a strong correlation of 0,69, although improvements were observed across all income levels. However, the gap between urban and rural areas remains significant, highlighting the need for more inclusive policies. The results suggest that this growth is expected to continue through 2025 and begin to stabilize between 2026 and 2027.
Journal: Data and Metadata
Pages: 1006
Volume: 4
Year: 2025
DOI: 10.56294/dm20251006
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1006:id:1056294dm20251006

Template-Type: ReDIF-Article 1.0
Author-Name: Saif Hameed
Author-Name-First: Saif
Author-Name-Last: Hameed
Author-Name: Hend Marouane
Author-Name-First: Hend
Author-Name-Last: Marouane
Author-Name: Ahmed Fakhfakh
Author-Name-First: Ahmed
Author-Name-Last: Fakhfakh
Author-Name: Sinan Salih
Author-Name-First: Sinan
Author-Name-Last: Salih
Title: Improved Sine-Cosine Nomadic People Optimizer (NPO) for Large and Synthetic Extra-large Scientific Workflow Task Scheduling Optimization in Cloud Environment
Abstract: Cloud computing has become an increasingly fundamental technology in recent years, influencing many different areas of the economy. It offers significant features such as greater scalability, on-demand resource allocation for varied workflows, and a pay-as-you-go pricing system. For cloud service providers, efficient and optimized scheduling is essential since it lowers resources consumption, operation expenses, and guarantees users' service level agreements. However, scheduling optimization becomes increasingly challenging due to the inherent heterogeneity of cloud resources and the growing scale of workflows. To tackle these issues, this study presents hybrid Sine-Cosine Nomadic People Optimizer (called QNPO) aimed at optimization of multi-objective cloud task scheduling with a special emphasis on large and extra-large scientific workflow. Sixteen synthetic extra-large heterogeneous workflows datasets were composed in this study and used to evaluate the proposed approach on a heterogeneous cloud infrastructure configure in Workflow Sim. The results indicated that the QNPO consistently outperformed traditional optimization algorithms in all proposed evaluation scenarios, achieving a significant improvement in scheduling efficiency between 30 and 60 %.
Journal: Data and Metadata
Pages: 1000
Volume: 4
Year: 2025
DOI: 10.56294/dm20251000
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1000:id:1056294dm20251000

Template-Type: ReDIF-Article 1.0
Author-Name: Jennifer Lorena Sánchez Cruz
Author-Name-First: Jennifer Lorena
Author-Name-Last: Sánchez Cruz
Title: Machine learning-based predictive models for digital behavioral analysis
Abstract: Introduction: The rise of digital technology use in Ecuador has produced large volumes of data on user behavior. In this context, machine learning models provide an effective way to analyze and predict digital behavior patterns, supporting informed decision-making in fields such as marketing, education, and public policy. Methods: A quantitative, non-experimental, cross-sectional methodology was used. A Random Forest model was applied to a simulated dataset based on parameters from the National Institute of Statistics and Censuses (INEC). The analysis focused on variables such as age, internet connection frequency, device type, and type of content consumed. Data were processed using Python and specialized machine learning libraries. Results: The model achieved 91,3 % accuracy in classifying digital user profiles. The most predictive variables were weekly connection frequency, type of digital content, and age. Distinct behavioral patterns were identified among age groups, allowing for relevant personalized strategies. Conclusions: The results demonstrated the effectiveness of machine learning in classifying and understanding digital behavior in Ecuador. This approach proves useful for designing more effective and ethically responsible digital interventions, as long as data privacy and protection principles are upheld.
Journal: Data and Metadata
Pages: 994
Volume: 4
Year: 2025
DOI: 10.56294/dm2025994
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:994:id:1056294dm2025994

Template-Type: ReDIF-Article 1.0
Author-Name: Nuohan Li
Author-Name-First: Nuohan
Author-Name-Last: Li
Author-Name: Nadzirah Binti Rosli
Author-Name-First: Nadzirah
Author-Name-Last: Binti Rosli
Title: Data Analysis for Live Streaming Behavior: Insights from a Bibliometric Analysis (2010–2024)
Abstract: To elucidate the developmental trajectory of live streaming marketing re-search, this study conducts a quantitative bibliometric analysis and visualiza-tion of 216 peer-reviewed articles indexed in the Web of Science (WoS) core collection between 2010 and 2024, utilizing VOSviewer software. Through co-occurrence analysis, cluster analysis, and co-citation mapping, this re-search identifies evolving research hotspots, collaborative networks, and dis-ciplinary trends. Key findings include: (1) The establishment of a nascent yet fragmented author collaboration network, with core authors contributing 59,26 % of publications; (2) A dominance of retail and e-commerce-focused journals, notably “Journal of Retailing and Consumer Services” and “Frontiers in Psychology”, with open-access journals significantly advancing the field; (3) Rapid expansion in research breadth and depth over the past decade, driven by interdisciplinary explorations of consumer behavior, technological affordances and trust dynamics. This study synthesizes foundational literature, highlights methodological contribu-tions, and outlines future research directions to guide scholars and practition-ers in navigating this dynamic domain.
Journal: Data and Metadata
Pages: 987
Volume: 4
Year: 2025
DOI: 10.56294/dm2025987
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:987:id:1056294dm2025987

Template-Type: ReDIF-Article 1.0
Author-Name: Lidia Castro-Cepeda
Author-Name-First: Lidia
Author-Name-Last: Castro-Cepeda
Author-Name: Fernando Molina-Granja
Author-Name-First: Fernando
Author-Name-Last: Molina-Granja
Author-Name: Ana Elizabeth Congacha
Author-Name-First: Ana Elizabeth
Author-Name-Last: Congacha
Author-Name: Ximena Quintana-Lopez
Author-Name-First: Ximena
Author-Name-Last: Quintana-Lopez
Author-Name: Miryan Estela Narváez
Author-Name-First: Miryan Estela
Author-Name-Last: Narváez
Title: Postgraduate degree in Applied Information Technologies, with a major in data analysis and artificial intelligence: a current trend
Abstract: Introduction: The growing need for experts in Information Technology, specifically in Data Analysis and Artificial Intelligence, posed a challenge for educational institutions in Ecuador. This article analyzed the feasibility of offering a master’s program aligned with the demands of the labor market at that time. Methods: A mixed approach was applied, combining surveys of professionals in the sector with interviews conducted with academics, to evaluate the relevance of the proposed program. The regulatory framework was examined under the Organic Law of Higher Education. Results: The findings indicated a high demand for specialization in data analysis and artificial intelligence, with notable job placement opportunities. Furthermore, shortcomings were identified in the existing postgraduate program offerings in these fields within the national context. Conclusions: The creation of a master’s program in this field proved to be both feasible and relevant, aligning with labor market needs and complying with the regulatory requirements of the Ecuadorian educational system.
Journal: Data and Metadata
Pages: 980
Volume: 4
Year: 2025
DOI: 10.56294/dm2025980
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:980:id:1056294dm2025980

Template-Type: ReDIF-Article 1.0
Author-Name: Ferial Fahmi
Author-Name-First: Ferial
Author-Name-Last: Fahmi
Author-Name: Aripin
Author-Name-First: Aripin
Author-Name-Last: Aripin
Title: The benefits of Artificial Intelligence in the process of educating Students on ethics and abilities in higher education
Abstract: Introduction: The development of Artificial Intelligence technology is increasing in the world of higher education, including ethical practices and the formation of student abilities. Objectives: This research aims to assess the implementation of AI in ethics education and student empowerment in higher education. Method: Using quantitative methods, this research conducted a survey with a questionnaire given to students at several universities that have used AI as a learning support. The collected data was then analyzed using linear regression with statistical tests to assess the differences and correlations between AI implementation variables and the level of ethical understanding and improvement in student abilities. Results: From the results obtained, the implementation of AI has a positive influence on students' ethical practices with an X correlation (p < 0.05) found, with a significant increase in preventing plagiarism, ethics-based simulations, and increasing awareness of academic values. Apart from that, AI also has a positive influence on improving students' skills. It was also found that AI facilitates increasing skills for critical thinking, problem solving, and independence and X (p < 0.05). Conclusion: This study found that reliance on AI in ethics and skills education practices resulted in reduced interaction and thinking in student learning. Based on this conclusion, this study states that the application of Artificial Intelligence in higher education provides high benefits in ethics and skills learning.
Journal: Data and Metadata
Pages: 976
Volume: 4
Year: 2025
DOI: 10.56294/dm2025976
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:976:id:1056294dm2025976

Template-Type: ReDIF-Article 1.0
Author-Name: Nuzul Hidayat
Author-Name-First: Nuzul
Author-Name-Last: Hidayat
Author-Name: Wakhinuddin
Author-Name-First: Wakhinuddin
Author-Name-Last: Wakhinuddin
Author-Name: Remon Lapisa
Author-Name-First: Remon
Author-Name-Last: Lapisa
Author-Name: M. Giatman
Author-Name-First: M.
Author-Name-Last: Giatman
Author-Name: Ika Parma Dewi
Author-Name-First: Ika Parma
Author-Name-Last: Dewi
Author-Name: Juli Sardi
Author-Name-First: Juli
Author-Name-Last: Sardi
Author-Name: Jackly Muriban
Author-Name-First: Jackly
Author-Name-Last: Muriban
Title: Effectiveness of the PBLMAR Model in Improving Student Learning Outcomes: An N-Gain Analysis in Air Conditioning Technology Course
Abstract: Introduction: this study aims to evaluate the effectiveness of the Problem-Based Learning model assisted by Mobile Augmented Reality (PBLMAR) in improving student learning outcomes in the Air Conditioning Technology course. The integration of MAR technology is expected to support student-centered learning and enhance conceptual understanding in vocational education settings. Methods: a quasi-experimental method was applied using a one-group pretest-posttest design. Data collection involved pretest and posttest assessments administered to 30 vocational students. The normalized gain (N-Gain) was calculated using Microsoft Excel to measure the increase in students’ cognitive achievement after implementing the PBLMAR model. N-Gain results were interpreted using standard criteria (high, medium, low). Results: the analysis showed that the average N-Gain score was 0.7, which falls into the category, high category. This indicates a significant improvement in student learning outcomes. Additionally, student responses suggested positive engagement and interest in using MAR-based learning media. Conclusions: the PBLMAR model is effective in improving students’ conceptual understanding and engagement in the Air Conditioning Technology course. The use of N-Gain analysis provides clear evidence of the model's impact, supporting its further application in vocational education.
Journal: Data and Metadata
Pages: 958
Volume: 4
Year: 2025
DOI: 10.56294/dm2025958
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:958:id:1056294dm2025958

Template-Type: ReDIF-Article 1.0
Author-Name: Luis Leonardo Camargo Ariza
Author-Name-First: Luis Leonardo
Author-Name-Last: Camargo Ariza
Author-Name: Maira Cecilia Gasca Mantilla
Author-Name-First: Maira Cecilia
Author-Name-Last: Gasca Mantilla
Author-Name: Byron Medina Delgado
Author-Name-First: Byron
Author-Name-Last: Medina Delgado
Title: IT Management Framework for Municipalities
Abstract: The integration of information technology (IT) into urban processes can significantly enhance public management by providing objective and timely information for decision-making at all levels of governance. It facilitates the administration and control of city resources, strengthens processes, and fosters greater citizen participation, efficiency, and transparency in their execution. This article proposes an IT management framework aligned with the smart city concept. The framework was organized into three levels of IT management: strategic, tactical, and operational, which corresponded to the city's maturity, intelligence, and technology, respectively. The model was based on best practices, international IT standards, and recognized theories. It was developed from the results of an instrument designed to quantify the indicators of two research variables: IT management and smart city, applied to the governing bodies of the city of Santa Marta, Colombia. The analysis of the responses allowed us to quantitatively and qualitatively describe the research variables and their interrelations. It also enabled us to identify strengths and weaknesses in order to make recommendations to improve the technological state and the development of services that increase the level of intelligence from the city of Santa Marta.
Journal: Data and Metadata
Pages: 950
Volume: 4
Year: 2025
DOI: 10.56294/dm2025950
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:950:id:1056294dm2025950

Template-Type: ReDIF-Article 1.0
Author-Name: Verenice Sánchez Castillo
Author-Name-First: Verenice
Author-Name-Last: Sánchez Castillo
Author-Name: Carlos Alberto Gómez Cano
Author-Name-First: Carlos Alberto
Author-Name-Last: Gómez Cano
Title: Analysis of scientific production on women entrepreneurs in conflict zones
Abstract: This article presents a bibliometric analysis of the scientific production about women entrepreneurs in conflict zones, to identify trends, gaps, and areas of opportunity in research on this topic. Through a methodological approach based on seven dimensions and a continuous disaggregation process, the evolution of the field and its impact in contexts affected by violence were examined. The results showed a significant growth in academic production during the last decade, with an interdisciplinary approach that integrates gender, economic, and human rights perspectives. International collaboration networks were identified, as well as the predominance of topics such as economic empowerment, gender violence, and resilience in post-conflict contexts. However, gaps were observed in the representation and practical application of academic findings in public policies. The study presents lines to strengthen research, foster global collaboration, generate new knowledge, and base concrete actions that promote female entrepreneurship as a tool for social transformation in conflict zones.
Journal: Data and Metadata
Pages: 949
Volume: 4
Year: 2025
DOI: 10.56294/dm2025949
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:949:id:1056294dm2025949

Template-Type: ReDIF-Article 1.0
Author-Name: Ronald Fransyaigu
Author-Name-First: Ronald
Author-Name-Last: Fransyaigu
Author-Name: Darmansyah
Author-Name-First: Darmansyah
Author-Name-Last: Darmansyah
Author-Name: Desyandri
Author-Name-First: Desyandri
Author-Name-Last: Desyandri
Author-Name: Syafri Ahmad
Author-Name-First: Syafri
Author-Name-Last: Ahmad
Author-Name: Nurhizrah Gistituati
Author-Name-First: Nurhizrah
Author-Name-Last: Gistituati
Title: The Effect of the Project Citizen Augmented Reality (PjCAR) Learning Model on Elementary School
Abstract: Introduction: The study examines the effect of the Project Citizen Augmented Reality (PjCAR) learning model on students' national awareness competence. The research was conducted in Langsa, Aceh, with fifth-grade elementary school students as the sample. Methods: A quasi-experimental research design was employed, and data were collected using tests, questionnaires, observations, and documentation. The validity and reliability of the data were tested, followed by prerequisite tests (homogeneity and normality) and hypothesis testing (t-test and n-gain test). Results: The results showed a significance value (2-tailed) of 0.001, smaller than 0.05. The average national awareness competence, comprising national sentiment and national spirit indicators, increased from 148.11 to 164.52 after implementing the PjCAR model. Meanwhile, cognitive understanding of national awareness improved from 66.30 to 80.43. Conclusions: These findings indicate a significant difference between pretest scores (before treatment) and posttest scores (after implementing the PjCAR model). This also suggests that students' national awareness competence improved after applying the PjCAR model in Pancasila Education for fifth-grade students.
Journal: Data and Metadata
Pages: 944
Volume: 4
Year: 2025
DOI: 10.56294/dm2025944
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:944:id:1056294dm2025944

Template-Type: ReDIF-Article 1.0
Author-Name: Ika Berty Apriliyani
Author-Name-First: Ika
Author-Name-Last: Berty Apriliyani
Author-Name: Rudi Zulfikar
Author-Name-First: Rudi
Author-Name-Last: Zulfikar
Author-Name: Elvin Bastian
Author-Name-First: Elvin
Author-Name-Last: Bastian
Author-Name: Helmi Yazid
Author-Name-First: Helmi
Author-Name-Last: Yazid
Title: Audit Quality in the Modern Era: The Significance of Auditor Personality and Features
Abstract: Audit quality is essential for ensuring the reliability and credibility of financial reports. This study examines the influence of auditor personality traits and professional attributes on audit quality using a quantitative approach, where data were collected through surveys and analyzed with multiple regression analysis. The findings indicate that while auditor personality—encompassing traits such as prudence, integrity, and professional skepticism—positively influences audit quality, auditor features, including experience, educational background, certification, and industry specialization, have a stronger and more significant impact. Technical competence and professional credentials play a dominant role in ensuring compliance with audit standards and enhancing financial reporting accuracy. These results highlight that professional qualifications and experience are more critical than personality traits in achieving high-quality audits. This study contributes to the discourse on audit quality by emphasizing the greater role of professional attributes. It suggests that future research should explore additional contextual factors, such as regulatory frameworks and corporate governance mechanisms, to further understand audit quality determinant.
Journal: Data and Metadata
Pages: 882
Volume: 4
Year: 2025
DOI: 10.56294/dm2025882
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:882:id:1056294dm2025882

Template-Type: ReDIF-Article 1.0
Author-Name: Javier Guaña-Moya
Author-Name-First: Javier
Author-Name-Last: Guaña-Moya
Author-Name: Sofía Villacís
Author-Name-First: Sofía
Author-Name-Last: Villacís
Author-Name: Danilo Miniguano Miniguano
Author-Name-First: Danilo
Author-Name-Last: Miniguano Miniguano
Title: Vulnerability analysis in the university community using social engineering and phishing applications
Abstract: The project focused on analyzing the impact of social engineering on the security of confidential information in a university community, highlighting the risks which individuals are exposed to when falling victim to such an attack. To this end, a controlled phishing attack was implemented to identify the main vulnerabilities that allow unauthorized access to personal data. The methodology used was descriptive, allowing for the analysis of factors such as the type of passwords used and the level of prior knowledge of social engineering. The results revealed that the group most affected by the attack was people between 23 and 27 years of age, representing 27,5 % of the total, followed by older adults between 58 and 63 years of age at 19,6 %, demonstrating that both young and older adults are the most susceptible. Furthermore, it was found that 43,1 % of users used passwords composed of names and numbers, reflecting a low complexity in their construction. Only 5,9 % used password managers, and only 11,8% incorporated special characters, indicating a low adoption of secure practices. The first phase of the attack, investigative in nature, was key to identifying exploitable personal patterns. Finally, after an awareness campaign was launched, it became clear that the main cause of vulnerability is a lack of knowledge about social engineering, highlighting the importance of strengthening cybersecurity education within the academic environment.
Journal: Data and Metadata
Pages: 930
Volume: 4
Year: 2025
DOI: 10.56294/dm2025930
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:930:id:1056294dm2025930

Template-Type: ReDIF-Article 1.0
Author-Name: Diana binti Mohamad
Author-Name-First: Diana binti
Author-Name-Last: Mohamad
Author-Name: Qiong Wu
Author-Name-First: Qiong
Author-Name-Last: Wu
Title: Sustainable development of road tourism: Model-based forecast of future trends
Abstract: Objective: Road tourism plays a crucial role in sustainable regional development, involving intelligent route planning to balance tourist demand, environmental sustainability, and infrastructure capacity. Traditional methods often fail to capture the dynamic nature of visitor preferences, which are influenced by the previous behaviors, traffic congestion, and environmental factors. The research aims to address these limits by forecasting future trends in sustainable road tourism using a superior predictive model. Method: To tackle the challenges, this research proposes a Bidirectional Gated Recurrent Unit fused Dynamic Random Forest (Bi-GRUForest) model. The Bi- model integrates a Bi-GRU for tourist route recommendation and a Dynamic Random Forest (DRF) network to capture travel patterns, with a temporal attention mechanism incorporated to prioritize key travel intentions. Multi-source data, comprising environmental data, road infrastructure, and tourist movement patterns, are gathered and preprocessed utilizing techniques like outlier removal, missing value handling, and normalization to ensure consistency, reliability, and accuracy. Real-time data on road conditions, weather updates are integrated to promote eco-friendly travel choices. Result: Experimental results demonstrate that Bi-GRUForest outperforms existing models in forecasting travel trends, optimizing road network efficiency, and supporting environmentally responsible tourism development. The model achieves a recall of 90.2%, precision of 89.5%, F1-score of 87.9%, and lower error rates with MAE of 600.01, MAPE of 15.10, and RMSE of 700.01. Conclusion: The research provides valuable insights for policymakers, transportation planners, and tourism stakeholders, improving route prediction accuracy, reducing carbon emissions, and alleviating traffic congestion, contributing to the development of more sustainable road tourism and practices.
Journal: Data and Metadata
Pages: 928
Volume: 4
Year: 2025
DOI: 10.56294/dm2025928
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:928:id:1056294dm2025928

Template-Type: ReDIF-Article 1.0
Author-Name: Zainab Ahmed Attiyah
Author-Name-First: Zainab
Author-Name-Last: Ahmed Attiyah
Author-Name: SHATHA H. ALI
Author-Name-First: SHATHA
Author-Name-Last: H. ALI
Author-Name: Anwar Tuama Obaid
Author-Name-First: Anwar
Author-Name-Last: Tuama Obaid
Title: The Effects of (rs3765467) polymorphism in the gene encoding <i>GLPR1</i> on Serum GLP1 Level and Response to Sitagliptin in Combination with Metformin Therapy in Iraqi Type 2 Diabetics Patients
Abstract: The dipepdityl peptidase-4 (DPP-4) inhibitors, which prevent incretin degradation, have become popular oral hypoglycemic agents for type 2 diabetes. Despite the wide use of DPP-4 inhibitors, little is known of clinical and pharmacogenomics factors that specifically associated with DPP-4 inhibitor treatment response. Meanwhile, a genetics studies identify important factors involved in the progression of diabetes disease, and identify individuals at risk of developing T2DM. Purpose of present study is to assess the possible association of (rs3765467) polymorphism in the gene encoding GLP1R with serum level of GLP1 and glycemic response for the treatment with sitagliptin in combination with metformin in Iraqi diabetic patients. The results indicated that SNP (rs3765467) was not detected in our study population of 90 individuals. However, Sanger sequencing had successfully identified three SNPs for the study population, including rs3765466, rs910163& (rs910162), located within the same region of the target SNP, rs3765467, in the gene encoding GLP1R. Furthermore, these SNPs (rs3765466), (rs910163) & (rs910162) show no significant effect on the response to the treatment based on HbAIc level (patients with HbA1c of less than or equal to 7.0% are classified as clinical responders, while those with HbA1c greater than 7.0% are classified as non-responders), but these SNPs significantly affect the serum GLP1 level. Additionally, (rs910163) & (rs910162) genotypes were significantly associated with serum creatinine levels, suggesting a potential role of the (rs910163) & (rs910162) variant in renal function regulation.
Journal: Data and Metadata
Pages: 927
Volume: 4
Year: 2025
DOI: 10.56294/dm2025927
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:927:id:1056294dm2025927

Template-Type: ReDIF-Article 1.0
Author-Name: Tariq Abdelhamid Ali Mussalam
Author-Name-First: Tariq Abdelhamid
Author-Name-Last: Ali Mussalam
Author-Name: Amged Saleh Shkeer
Author-Name-First: Amged
Author-Name-Last: Saleh Shkeer
Author-Name: Khalid Faris Alomari
Author-Name-First: Khalid
Author-Name-Last: Faris Alomari
Author-Name: Hazem Mohammad Al-Kaseasbeh
Author-Name-First: Hazem Mohammad
Author-Name-Last: Al-Kaseasbeh
Author-Name: Omar Mohammad Ali Alqudah
Author-Name-First: Omar Mohammad
Author-Name-Last: Ali Alqudah
Title: The Impact of Brand Awareness, Digital Influencers, and Word of Mouth on Purchase Intentions: Evidence from Jordanian SMEs
Abstract: Introduction: This study examines the definitional linkages between brand awareness, digital influencer marketing, word-of-mouth (WOM) communication, and purchase intentions within digital marketing contexts. The primary goal is to explore how these elements influence consumer purchasing decisions in the era of digital marketing. As the digital landscape continues to evolve, understanding the interplay between these factors becomes increasingly critical for marketers aiming to enhance customer engagement and drive sales. Methods: An online survey was distributed to 344 MSME (Micro, Small, and Medium Enterprises) owners, collecting data relevant to their perceptions of digital marketing, brand awareness, and their interactions with digital influencers and WOM communication. The research employed Partial Least Squares Structural Equation Modeling (PLS-SEM) for statistical analysis, using SmartPLS software to evaluate the relationships among the studied variables. Results: The findings indicate that digital influencer promotions, brand awareness, and WOM communication all exert strong positive effects on consumer purchase intentions. Specifically, digital influencer marketing and WOM interactions are significantly correlated with heightened brand awareness, which in turn positively influences consumer decisions to purchase. These results highlight the importance of leveraging digital marketing strategies that incorporate these elements to drive consumer buying intentions. Conclusions: The study demonstrates that digital marketing strategies based on brand awareness, influencer promotions, and WOM interactions can significantly enhance consumer purchase intentions. The findings offer new insights into how digital marketing approaches shape user behavior, providing valuable guidance for marketers to optimize their online marketing efforts. Further research should explore how these factors interact across different cultures and geographic regions, offering a broader understanding of their impact on global consumer behavior.
Journal: Data and Metadata
Pages: 926
Volume: 4
Year: 2025
DOI: 10.56294/dm2025926
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:926:id:1056294dm2025926

Template-Type: ReDIF-Article 1.0
Author-Name: Haya Awawdeh
Author-Name-First: Haya
Author-Name-Last: Awawdeh
Author-Name: Najwa Alsuwais
Author-Name-First: Najwa
Author-Name-Last: Alsuwais
Author-Name: Shatha Alrawabdeh
Author-Name-First: Shatha
Author-Name-Last: Alrawabdeh
Author-Name: Alaa Albusaili
Author-Name-First: Alaa
Author-Name-Last: Albusaili
Author-Name: Bayan Rabi
Author-Name-First: Bayan
Author-Name-Last: Rabi
Author-Name: Hazem Mohammad Al-Kaseasbeh
Author-Name-First: Hazem Mohammad
Author-Name-Last: Al-Kaseasbeh
Author-Name: Ibrahim Siam
Author-Name-First: Ibrahim
Author-Name-Last: Siam
Title: The Role of Digital Marketing in Achieving Sustainable Financial Growth in Jordanian Banks: An Empirical Study Using Digital Entrepreneurship as a Mediating Variable
Abstract: Introduction: The study aimed to investigate the role of digital marketing in achieving sustainable financial growth in Jordanian banks, with a specific focus on entrepreneurship as a mediating variable. In the context of rapid technological advancements and heightened competition in the banking sector, this study explores how digital marketing and digital entrepreneurship contribute to long-term financial success in Jordanian banks. Methods: The study was conducted using a sample of 270 senior management individuals from four Jordanian banks. A total of 170 questionnaires were distributed, and 165 were returned. After screening for completeness and suitability, 159 questionnaires were deemed appropriate for analysis. The data collected was then analyzed using empirical methods to evaluate the relationship between digital marketing, entrepreneurship, and financial growth. Results: The findings revealed that the integration of digital marketing strategies with digital entrepreneurship plays a pivotal role in ensuring sustainable financial growth in Jordanian banks. This integration is increasingly important in an era marked by rapid technological advancements and intensifying competition within the banking sector. Conclusions: The study concluded that digital entrepreneurship, supported by digital marketing, is essential for the long-term growth of Jordanian banks. The research highlighted the importance of fostering a culture of innovation, collaborating with fintech startups, and investing in cutting-edge digital solutions to enhance banking services.
Journal: Data and Metadata
Pages: 925
Volume: 4
Year: 2025
DOI: 10.56294/dm2025925
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:925:id:1056294dm2025925

Template-Type: ReDIF-Article 1.0
Author-Name: Ahmad Mohammad Ali AlJabali
Author-Name-First: Ahmad Mohammad Ali
Author-Name-Last: AlJabali
Title: The Effect of Financial Performance Indicators on the Degree of Credit Risk in Jordanian Islamic Banks
Abstract: Background: Credit risk management has become a critical concept that defines the survival, growth, and profitability of banks, and more recently, financial institutions and banks have played an essential role in the economic growth and development of any country. Objective: This study examines the impact of financial performance indicators on the degree of credit risk in Jordanian Islamic banks. Utilizing a dataset spanning from 2018 to 2022, the research focuses on key independent variables: liquidity risk, pricing risk, collateral erosion risk, and the non-performing loan (NPL) ratio relative to total loans. The dependent variable is the degree of credit risk, measured through a composite risk score. Method: The study employs a quantitative approach, collecting data from annual reports of Jordanian Islamic banks and publications by the Central Bank of Jordan. Descriptive statistics, correlation analysis, and multiple regression models are utilized to analyze the relationships between the independent variables and credit risk. Results: The findings reveal that liquidity risk and NPL ratio have a significant positive impact on credit risk, indicating that higher liquidity issues and a greater proportion of non-performing loans elevate credit risk levels. Conclusion: Effective pricing strategies and robust collateral management are associated with reduced credit risk. These results underscore the importance of comprehensive risk management practices in enhancing the financial stability of Jordanian Islamic banks.
Journal: Data and Metadata
Pages: 923
Volume: 4
Year: 2025
DOI: 10.56294/dm2025923
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:923:id:1056294dm2025923

Template-Type: ReDIF-Article 1.0
Author-Name: Anber AbraheemShlash Mohammad
Author-Name-First: Anber AbraheemShlash
Author-Name-Last: Mohammad
Author-Name: Ammar Mohammad Al-Ramadan
Author-Name-First: Ammar Mohammad
Author-Name-Last: Al-Ramadan
Author-Name: Suleiman Ibrahim Mohammad
Author-Name-First: Suleiman Ibrahim
Author-Name-Last: Mohammad
Author-Name: Badrea Al Oraini
Author-Name-First: Badrea
Author-Name-Last: Al Oraini
Author-Name: Asokan Vasudevan
Author-Name-First: Asokan
Author-Name-Last: Vasudevan
Author-Name: Nawaf Alshdaifat
Author-Name-First: Nawaf
Author-Name-Last: Alshdaifat
Author-Name: Mohammad Faleh Ahmmad Hunitie
Author-Name-First: Mohammad Faleh
Author-Name-Last: Ahmmad Hunitie
Title: Customer Sentiment Analysis for Food and Beverage Development in Restaurants using AI in Jordan
Abstract: Introduction: customer sentiment analysis is a vital tool for understanding consumer preferences and enhancing service quality in the food and beverage industry. Online reviews significantly influence customer decisions, making it essential for businesses to analyze sentiment trends and manage their digital reputation effectively. This study examines customer sentiment across different establishment types and digital platforms in Jordan, providing insights into sentiment patterns and their strategic implications. Method: a dataset of 384 customer reviews from various restaurants and hotels was analyzed using a rule-based sentiment classification approach. Sentiments were categorized as positive, neutral, or negative. To assess sentiment variations, an ANOVA test was conducted to compare establishment types, and a Chi-Square test was performed to examine differences across digital platforms. Results: findings indicate that luxury hotels and fine dining establishments receive more positive sentiment, while budget hotels and fast food chains experience higher negative sentiment. However, the ANOVA test showed no statistically significant sentiment differences across establishment types, suggesting that all businesses receive a mix of sentiment categories. The Chi-Square test confirmed significant sentiment differences across platforms, with TripAdvisor attracting the most positive reviews, Facebook and Google Reviews showing balanced sentiment, and Twitter experiencing the highest negative sentiment. Conclusion: these findings emphasize the importance of platform-specific digital reputation management. Businesses should strategically engage with customers on different platforms, address complaints proactively, and utilize AI-driven sentiment analysis tools to improve customer satisfaction. Future research should explore AI-based predictive analytics and sentiment monitoring for enhancing service quality in the hospitality industry.
Journal: Data and Metadata
Pages: 922
Volume: 4
Year: 2025
DOI: 10.56294/dm2025922
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:922:id:1056294dm2025922

Template-Type: ReDIF-Article 1.0
Author-Name: Derar Alqudah
Author-Name-First: Derar
Author-Name-Last: Alqudah
Author-Name: Basim Abbas Ali AlObaydi
Author-Name-First: Basim Abbas
Author-Name-Last: Ali AlObaydi
Author-Name: Ahmed Alsswey
Author-Name-First: Ahmed
Author-Name-Last: Alsswey
Author-Name: Ali Mohammad Ali Alqudah
Author-Name-First: Ali Mohammad
Author-Name-Last: Ali Alqudah
Title: The Role of Digital Design through Multimedia Technology to Achieve Sustainability Technology
Abstract: Introduction: The potential of digital design and multimedia technologies and their effects on sustainability technology are the two primary topics that frequently define discussions about the digital economy. Accordingly, most companies find it difficult to strike a balance between taking advantage of the opportunities offered by the digital media to promote sustainability technology and avoiding any obstacles that could jeopardize initiatives. Objective: The main aim of this paper is to explore the role of digital design through multimedia technology to achieve sustainability technology. Methods: A descriptive analytical approach was adopted, and a questionnaire was designed and distributed to the study sample, which consisted of 100 digital media specialists. Then, the questionnaire was analyzed through the SPSS Program. Results: The results show that there is a significant role of digital design through multimedia technology to achieve sustainability technology in all its dimensions, including environmental, social and economic. This reflects the role of digital design and multimedia technologies in enhancing the competitiveness of companies to achieve sustainability technology in the coming years. Conclusions: Digital design and multimedia technology provide a modern technological infrastructure that contributes to saving time and effort during the design process, as well as energy and money during implementation, and has made the reasons and tools of luxury more diverse in line with budgets and quality and safety requirements.
Journal: Data and Metadata
Pages: 920
Volume: 4
Year: 2025
DOI: 10.56294/dm2025920
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:920:id:1056294dm2025920

Template-Type: ReDIF-Article 1.0
Author-Name: Qais Hammouri
Author-Name-First: Qais
Author-Name-Last: Hammouri
Author-Name: Nawras M. Nusairat
Author-Name-First: Nawras
Author-Name-Last: M. Nusairat
Author-Name: Abdullah A.M AlSokkar
Author-Name-First: Abdullah
Author-Name-Last: A.M AlSokkar
Author-Name: Asma Mohammad Jdaitawi
Author-Name-First: Asma
Author-Name-Last: Mohammad Jdaitawi
Author-Name: Ali M. Mistarihi
Author-Name-First: Ali
Author-Name-Last: M. Mistarihi
Author-Name: Dana Abed Alhakim Akhuirshaideh
Author-Name-First: Dana Abed
Author-Name-Last: Alhakim Akhuirshaideh
Author-Name: Sakher Faisal AlFraihat
Author-Name-First: Sakher
Author-Name-Last: Faisal AlFraihat
Title: Engaging Gen Z through Personalized Social Media Content: The Mediating Role of Perceived Relevance on Platform Engagement
Abstract: The raising usage of social media lead to an increase in the importance of platform engagement by Gen Z through personalized content provided by these platforms. However, the aim of this study is to understand the role of both personalization and interactivity in supporting the experience of Gen Z through investigating the mediating role of perceived relevance on platform engagement. This study utilized a sample of 412 from Gen Z to explore the proposed hypotheses. The data was collected using a survey containing 17 items to measure the proposed constructs and the collected data analyzed using SEM-PLS. The findings revealed that all the proposed hypotheses are supported. Specififically, the study found that content personalization and content interactivity lead to support the perception of relevance. Moreover, the findings also revealed that perception of relevance could be enhance the platform engagement through their mediating role in the relationship between content personalization and platform engagement, and between content interactivity and platform engagement. Such findings contribute to implementing content strategies by marketers to support the engagement of Gen Z in social media.
Journal: Data and Metadata
Pages: 918
Volume: 4
Year: 2025
DOI: 10.56294/dm2025918
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:918:id:1056294dm2025918

Template-Type: ReDIF-Article 1.0
Author-Name: Akmar Efendi
Author-Name-First: Akmar
Author-Name-Last: Efendi
Author-Name: Iskandar Fitri
Author-Name-First: Iskandar
Author-Name-Last: Fitri
Author-Name: Gunadi Widi Nurcahyo
Author-Name-First: Gunadi Widi
Author-Name-Last: Nurcahyo
Title: Improving Student Graduation Timeliness Prediction Using SMOTE and Ensemble Learning with Stacking and GridSearchCV Optimization
Abstract: Introduction: Timely graduation is a key performance indicator in higher education. This study aims to improve the accuracy of predicting student graduation timeliness using ensemble machine learning techniques combined with SMOTE and hyperparameter optimization. Methods: This is a quantitative predictive study. The population includes students and alumni of Universitas Islam Riau. A sample of 160 respondents was obtained via purposive sampling. Data were collected using structured questionnaires encompassing academic variables (e.g., GPA, credits, attendance) and non-academic variables (e.g., stress, social support, extracurricular activity). After preprocessing and label encoding, SMOTE was applied to balance class distribution. Several classifiers (Naïve Bayes, SVM, Decision Tree, KNN) were tested, with ensemble learning (voting and stacking) implemented and optimized using GridSearchCV. Results: The stacking ensemble model optimized with GridSearchCV achieved the highest performance with an accuracy of 99.37%, precision and recall above 0.99, and minimal misclassification. This outperformed individual models and previous approaches in the literature. Conclusions: The integration of SMOTE, ensemble methods, and GridSearchCV significantly enhances predictive accuracy for student graduation timeliness. The resulting model provides a robust framework for academic risk detection and early intervention.
Journal: Data and Metadata
Pages: 917
Volume: 4
Year: 2025
DOI: 10.56294/dm2025917
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:917:id:1056294dm2025917

Template-Type: ReDIF-Article 1.0
Author-Name: Roberto Salazar
Author-Name-First: Roberto
Author-Name-Last: Salazar
Author-Name: Lauro Díaz
Author-Name-First: Lauro
Author-Name-Last: Díaz
Author-Name: Jorge Gavilanes
Author-Name-First: Jorge
Author-Name-Last: Gavilanes
Author-Name: Carlos Gallardo Gallardo
Author-Name-First: Carlos Gallardo
Author-Name-Last: Gallardo
Author-Name: José Díaz Díaz
Author-Name-First: José Díaz
Author-Name-Last: Díaz
Author-Name: Diego Jiménez
Author-Name-First: Diego
Author-Name-Last: Jiménez
Title: Energy Analysis of Forced-Air Solar Panels for a Fruit Dehydration Oven
Abstract: This article presents the design, construction, and energy analysis of three forced air solar collectors, which act as an auxiliary energy source for a fruit dehydrator with a capacity of 30 kg. The study began with a review of concepts related to solar energy, including solar collectors and finally the food dehydration process. In the construction stage, the sizing of the collectors is determined by 5.3 m² of black-painted copper for the absorbers, which will allow for the dehydration of batches of 30 kg of pineapple in a period of 18 hours. In the analysis, the results obtained indicate that the implementation of these solar collectors generates an annual savings of $1,135 in the operational costs of the dehydrator, highlighting the efficiency and economic viability of using solar energy in this context.
Journal: Data and Metadata
Pages: 913
Volume: 4
Year: 2025
DOI: 10.56294/dm2025913
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:913:id:1056294dm2025913

Template-Type: ReDIF-Article 1.0
Author-Name: Nur Chamidah
Author-Name-First: Nur
Author-Name-Last: Chamidah
Author-Name: Maylita Hasyim
Author-Name-First: Maylita
Author-Name-Last: Hasyim
Author-Name: Toha Saifudin
Author-Name-First: Toha
Author-Name-Last: Saifudin
Author-Name: Budi Lestari
Author-Name-First: Budi
Author-Name-Last: Lestari
Title: Nonparametric Bi-Response Ordinal Logistic Regression Model for Diabetes Mellitus and Hypertension Risks Based on Multivariate Adaptive Regression Spline
Abstract: This study discusses the application of nonparametric regression for bi-response ordinal logistic modeling based on the Multivariate Adaptive Regression Spline (MARS) estimator in assessing the risk of diabetes mellitus and hypertension. The MARS estimator provides greater flexibility by allowing for nonlinearity and interactions among predictors, making it well-suited for modeling health-related risk factors. Parameter estimation in this study is conducted using the Maximum Likelihood Estimation (MLE) method. However, due to the non-linearity of the first derivative of the log-likelihood function, the Berndt-Hall-Hall-Hausman (BHHH) numerical iteration method is applied to obtain parameter estimates. The complexity of the likelihood function poses challenges in constructing the Hessian matrix, necessitating an approximation of the second derivative using the first derivative in the BHHH method. The analysis identifies Age, Body Mass Index (BMI), and Total Cholesterol as significant predictor variables influencing the risk of diabetes mellitus and hypertension. Model evaluation is carried out using accuracy, the Area Under the Curve (AUC), and the Apparent Error Rate (APER). The results demonstrate an accuracy of 82.44%, indicating strong classification performance. Additionally, the AUC value of 73.42% suggests the model falls within the good category, while the APER value of 17.56% confirms the model’s stability and reliability. The findings suggest that the MARS-based bi-response ordinal logistic regression model effectively captures the relationship between significant risk factors of diabetes mellitus and hypertension.
Journal: Data and Metadata
Pages: 912
Volume: 4
Year: 2025
DOI: 10.56294/dm2025912
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:912:id:1056294dm2025912

Template-Type: ReDIF-Article 1.0
Author-Name: Toto Sugiarto
Author-Name-First: Toto
Author-Name-Last: Sugiarto
Author-Name: Fahmi Rizal
Author-Name-First: Fahmi
Author-Name-Last: Rizal
Author-Name: Wawan Purwanto
Author-Name-First: Wawan
Author-Name-Last: Purwanto
Author-Name: Hasan Maksum
Author-Name-First: Hasan
Author-Name-Last: Maksum
Author-Name: Muhammad Giatman
Author-Name-First: Muhammad
Author-Name-Last: Giatman
Author-Name: Refdinal
Author-Name-First: Refdinal
Author-Name-Last: Refdinal
Title: Development of student psychomotor skill assessment based on performance in service and maintenance of motorcycles with electronic fuel injection: A case study in automotive engineering students
Abstract: Introduction: The present study proposes to develop a psychomotor skill evaluation instrument for servicing and maintenance of motorcycles equipped with electronic fuel injection (EFI). The instrument is designed based on the operational procedure for a motorcycle. The result is the assessment of students' psychomotor abilities. Methods: The development procedure was conducted utilizing the research and development framework. The development guideline was derived from Borg and Gall, which we condensed into six steps. To assess the validity and reliability of the instrument, Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed. Results: The developed psychomotor skill instrument is categorized as valid, practical, and reliable for assessing students' psychomotor skills. Examination revealed that students' performance was inconsistent with respect to standard operating procedures, resulting in findings that failed to meet employment requirements. This study involved two students who, after getting additional instruction, enhanced their work processes and therefore improved their psychomotor skills. Conclusions: The implemented instruments provide valid, reliable, and useful categories for assessing students' psychomotor skills. This measure may evaluate students' ability to integrate cognitive outcomes into psychomotor skills.
Journal: Data and Metadata
Pages: 910
Volume: 4
Year: 2025
DOI: 10.56294/dm2025910
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:910:id:1056294dm2025910

Template-Type: ReDIF-Article 1.0
Author-Name: Amer Muflih Alkufahy
Author-Name-First: Amer
Author-Name-Last: Muflih Alkufahy
Title: The impact of digital marketing in achieving institutional excellence
Abstract: Introduction: Digital marketing has emerged as a crucial topic in research Promotion and marketing Contemporary, addressing different factors across multiple fields. This study aims to investigate the relationship between digital marketing and institutional excellence, as well as the moderating role of digital marketing strategies in this relationship. Using a quantitative research approach, data was collected from business organizations in Jordan, with the participation of 189 Participant, from friends and my manager These companies, contributed to the sample. Methodology followed: method was used Partial least squares structural equation modeling (PLS-SEM) In analytical procedures Main. Results: When studying The impact of digital marketing on achieving institutional excellence in Jordan, It was found that there was a statistically significant effect Between relying on digital marketing and achieving institutional excellence, Average​​Arithmetic (2.13) And deviation normative (0.86) On a big level (0.05). The value is high On a Likert scale. in the first place (Free and natural help to appear in search to improve web searches and thus the continuous development of the products/services offered) And he reached Average Year (2.29) and standard deviation (0.889). In the last order (Online marketing database allows - digital content anytime, anywhere.) On average​​ mathematics (2.01) and standard deviation (0.825). conclusion: This Results confirms that There is a statistical significance for digital marketing in achieving institutional excellence. And There is a moderate positive correlation between digital marketing and achieving institutional excellence (r=0.640) The impact of digital marketing in achieving institutional excellence was equal41%.
Journal: Data and Metadata
Pages: 899
Volume: 4
Year: 2025
DOI: 10.56294/dm2025899
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:899:id:1056294dm2025899

Template-Type: ReDIF-Article 1.0
Author-Name: Ibrahim Radwan Alnsour
Author-Name-First: Ibrahim
Author-Name-Last: Radwan Alnsour
Author-Name: Mohammad Yousef Alghadi
Author-Name-First: Mohammad
Author-Name-Last: Yousef Alghadi
Title: Impact of Cloud Services and Services Quality on Competitive Service Quality of Islamic Banks: Moderating role of consumer e-learning
Abstract: Introduction: The main aim of the current study is to explore the moderating impact of consumer e-learning on the relationship between cloud services, service quality, and competitive service quality in Islamic banks. With the growing reliance on digital banking and cloud technologies, understanding how e-learning can enhance the effectiveness of these services and improve consumer perception is crucial. The research focuses on examining these variables in the context of Islamic banking, where service quality is often directly linked to customer satisfaction and competitive advantage. Methods: To achieve the study's goal, data was collected from e-banking consumers using a structured survey. The researchers applied Structural Equation Modeling (SEM) using AMOS software to analyze the relationships between cloud services, service quality, and competitive service quality, while also examining the moderating role of consumer e-learning. The data sample was obtained from banking consumers in a single country, which may limit the generalizability of the findings. Results: The results of the study indicated that: Cloud services have a significant positive impact on competitive service quality, Service quality has a significant positive impact on competitive service quality, The study’s primary contribution was the confirmation of the moderating effect of consumer e-learning. E-learning in the context of consumer education was found to enhance the relationship between cloud services, service quality, and competitive service quality, making it a key factor for improving customer experiences. Conclusions: The study underscores the importance of consumer e-learning in enhancing the quality and competitiveness of cloud services in Islamic banks. However, certain limitations exist, such as the small sample size and the study being confined to only one country. To build on these findings, future research is recommended to include a larger sample size from diverse geographical regions and consider conducting longitudinal studies to assess the pre- and post-learning effects of consumer education on service quality and competitiveness.
Journal: Data and Metadata
Pages: 898
Volume: 4
Year: 2025
DOI: 10.56294/dm2025898
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:898:id:1056294dm2025898

Template-Type: ReDIF-Article 1.0
Author-Name: Firas Tayseer Ayasrah
Author-Name-First: Firas Tayseer
Author-Name-Last: Ayasrah
Author-Name: Mohammad Abdulsalam Suliman Al Arood
Author-Name-First: Mohammad Abdulsalam
Author-Name-Last: Suliman Al Arood
Author-Name: Hassan Ali Al-Ababneh
Author-Name-First: Hassan Ali
Author-Name-Last: Al-Ababneh
Author-Name: Nidal Al Said
Author-Name-First: Nidal
Author-Name-Last: Al Said
Author-Name: Khaleel Al-Said
Author-Name-First: Khaleel
Author-Name-Last: Al-Said
Author-Name: Suleiman Ibrahim Mohammad
Author-Name-First: Suleiman Ibrahim
Author-Name-Last: Mohammad
Author-Name: Asokan Vasudevan
Author-Name-First: Asokan
Author-Name-Last: Vasudevan
Author-Name: Kabilan N. Maniam
Author-Name-First: Kabilan N.
Author-Name-Last: Maniam
Title: The Impact of 5G Technologies and Technological and Environmental Factors on Educational Performance in Jordanian High Schools: The Role of Parental and Community Support in Enhancing E-Learning Experience
Abstract: Introduction: This study investigates the impact of 5G technologies, technological and environmental factors, and parental and community support on the educational performance of high school students in Jordan. Educational outcomes must be understood because modern technological systems alongside community involvement need assessment during this COVID-19 era of digital learning tool dependency. Researchers investigate here how educational variables enhance student academic achievements under present-day digital learning models. Methods: A survey included 300 students across ten high schools throughout different Jordanian regions. The study relied on Partial Least Squares Structural Equation Modeling as its data analysis foundation through SmartPLS software implementation. A statistical analysis evaluated the link between 5G technologies, technological and environmental factors, and parental and community support which act as independent variables towards educational performance as the main dependent variable. Results: The study showed 5G technologies combined with both technological and environmental factors together with parental and community support have positive effects on educational performance measurement. The analysis through path coefficients and p-values showed that all variables lead to better student learning experiences with positive academic results. Specifically 5G technologies enabled interactive learning but additional student achievement came through digital resource access together with parental involvement. Conclusions: The study summarizes its findings by establishing that inserting 5G technologies together with improved environmental factors and enhanced technological factors plus support from parents and communities actually drives education success. Schools need to focus first on building strong digital infrastructure for learning purposes and secondly on building interconnections between parents and local communities to support student education. The evaluation of these elements' sustained influence on educational outcomes must occur through future investigations which should analyze both cultural diversity and geographical scope while examining how distinct intervention methods build these educational components globally.
Journal: Data and Metadata
Pages: 897
Volume: 4
Year: 2025
DOI: 10.56294/dm2025897
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:897:id:1056294dm2025897

Template-Type: ReDIF-Article 1.0
Author-Name: Malik A. Altayar
Author-Name-First: Malik
Author-Name-Last: A. Altayar
Author-Name: Muhyeeddin Alqaraleh
Author-Name-First: Muhyeeddin
Author-Name-Last: Alqaraleh
Author-Name: Mowafaq Salem Alzboon
Author-Name-First: Mowafaq
Author-Name-Last: Salem Alzboon
Author-Name: Wesam T. Almagharbeh
Author-Name-First: Wesam
Author-Name-Last: T. Almagharbeh
Title: Predicting Blood Type: Assessing Model Performance with ROC Analysis
Abstract: Introduction: Personal identification is a critical aspect of forensic sciences, security, and healthcare. While conventional biometrics systems such as DNA profiling and iris scanning offer high accuracy, they are time-consuming and costly. Objectives: This study investigates the relationship between fingerprint patterns and ABO blood group classification to explore potential correlations between these two traits. Methods: The study analyzed 200 individuals, categorizing their fingerprints into three types: loops, whorls, and arches. Blood group classification was also recorded. Statistical analysis, including chi-square and Pearson correlation tests, was used to assess associations between fingerprint patterns and blood groups. Results: Loops were the most common fingerprint pattern, while blood group O+ was the most prevalent among the participants. Statistical analysis revealed no significant correlation between fingerprint patterns and blood groups (p > 0.05), suggesting that these traits are independent. Conclusions: Although the study showed limited correlation between fingerprint patterns and ABO blood groups, it highlights the importance of future research using larger and more diverse populations, incorporating machine learning approaches, and integrating multiple biometric signals. This study contributes to forensic science by emphasizing the need for rigorous protocols and comprehensive investigations in personal identification.
Journal: Data and Metadata
Pages: 895
Volume: 4
Year: 2025
DOI: 10.56294/dm2025895
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:895:id:1056294dm2025895

Template-Type: ReDIF-Article 1.0
Author-Name: Malik A. Altayar
Author-Name-First: Malik A.
Author-Name-Last: Altayar
Author-Name: Muhyeeddin Alqaraleh
Author-Name-First: Muhyeeddin
Author-Name-Last: Alqaraleh
Author-Name: Mowafaq Salem Alzboon
Author-Name-First: Mowafaq
Author-Name-Last: Salem Alzboon
Author-Name: Wesam T. Almagharbeh
Author-Name-First: Wesam T.
Author-Name-Last: Almagharbeh
Title: Revolutionizing Blood Banks: AI-Driven Fingerprint-Blood Group Correlation for Enhanced Safety
Abstract: Identification of a person is central in forensic science, security, and healthcare. Methods such as iris scanning and genomic profiling are more accurate but expensive, time-consuming, and more difficult to implement. This study focuses on the relationship between the fingerprint patterns and the ABO blood group as a biometric identification tool. A total of 200 subjects were included in the study, and fingerprint types (loops, whorls, and arches) and blood groups were compared. Associations were evaluated with statistical tests, including chi-square and Pearson correlation. The study found that the loops were the most common fingerprint pattern and the O+ blood group was the most prevalent. Discussion: Even though there was some associative pattern, there was no statistically significant difference in the fingerprint patterns of different blood groups. Overall, the results indicate that blood group data do not significantly improve personal identification when used in conjunction with fingerprinting. Although the study shows weak correlation, it may emphasize the efforts of multi-modal based biometric systems in enhancing the current biometric systems. Future studies may focus on larger and more diverse samples, and possibly machine learning and additional biometrics to improve identification methods. This study addresses an element of the ever-changing nature of the fields of forensic science and biometric identification, highlighting the importance of resilient analytical methods for personal identification.
Journal: Data and Metadata
Pages: 894
Volume: 4
Year: 2025
DOI: 10.56294/dm2025894
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:894:id:1056294dm2025894

Template-Type: ReDIF-Article 1.0
Author-Name: Bahaa Kareem Mohammed
Author-Name-First: Bahaa Kareem
Author-Name-Last: Mohammed
Author-Name: Dhurgham Kareem Gharkan
Author-Name-First: Dhurgham Kareem
Author-Name-Last: Gharkan
Author-Name: Hassan Hadi Khayoon
Author-Name-First: Hassan Hadi
Author-Name-Last: Khayoon
Title: Integrating AI and Statistical Models for Climate Time Series Forecasting
Abstract: Climate change is a pressing global challenge, and predicting its future patterns is essential for mitigation strategies. This study integrates synthetic and real-world climate datasets to develop predictive models. Specifically, we apply Long Short-Term Memory (LSTM) networks alongside ARIMA and SARIMA models to forecast global temperature anomalies. Synthetic data were generated using a Gaussian-based data simulator calibrated on historical NOAA/IPCC data, contributing 30% of the training set. Validation included Kolmogorov-Smirnov tests to ensure distributional similarity to real data. Preprocessing involved interpolation for missing values and stationarity checks using the Augmented Dickey-Fuller (ADF) test (p < 0.05), with differencing of order one applied where necessary. LSTM model architecture included two hidden layers with 64 and 32 units, sequence length of 30 days, and a dropout rate of 0.2 to prevent overfitting. Model performance was evaluated using RMSE, MAE, and MAPE. LSTM achieved the lowest RMSE of 1.8 and MAPE of 6.3%, outperforming ARIMA (RMSE: 2.4, MAPE: 8.2%) and SARIMA (RMSE: 2.0, MAPE: 7.1%). Random Forest and SVR models yielded RMSEs of 2.2 and 2.3, respectively, and were included for benchmarking. A Monte Carlo simulation with 10,000 iterations and normal distribution assumptions estimated prediction uncertainty, aligned with IPCC emission scenarios. Scenario-based forecasting (A: status quo, B: 50% emissions cut, C: net-zero) was validated against past reductions post-Kyoto and Paris agreements. Forecasts indicate a potential 1.5°C rise in temperature by 2050 under Scenario A. Compared to baseline mean anomaly of 14.3°C, this reflects a significant trend.
Journal: Data and Metadata
Pages: 893
Volume: 4
Year: 2025
DOI: 10.56294/dm2025893
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:893:id:1056294dm2025893

Template-Type: ReDIF-Article 1.0
Author-Name: Ihsan Fathoni Amri
Author-Name-First: Ihsan Fathoni
Author-Name-Last: Amri
Author-Name: Nur Chamidah
Author-Name-First: Nur
Author-Name-Last: Chamidah
Author-Name: Toha Saifudin
Author-Name-First: Toha
Author-Name-Last: Saifudin
Author-Name: Budi Lestari
Author-Name-First: Budi
Author-Name-Last: Lestari
Author-Name: Dursun Aydin
Author-Name-First: Dursun
Author-Name-Last: Aydin
Title: Forecasting Temperature of Earth Surface in Sragen Regency Using Semiparametric Regression Based on Penalized Fourier Series Estimator
Abstract: Sragen regency that is located in Central Java Province of Indonesia, is one of the areas that feels the direct impact of the high earth surface temperature. The various sectors in Sragen regency, including agriculture, health, and the environment are affected by the high temperature of the earth's surface. The Sragen regency is geographically dominated by agricultural areas, which are very vulnerable to extreme earth surface temperatures. This has a direct effect on agricultural productivity and the availability of water for irrigation. This study examines the use of a semiparametric regression model with a Penalized Least Squares (PLS)-based Fourier Series estimator to analyze the relationship between earth surface temperature and relative humidity in Sragen regency. The combining parametric and nonparametric components, the model effectively addresses complex climate data patterns. A dataset of 100 observations was analyzed under three training data scenarios N = 70, N = 80, and N = 90, yielding optimal Fourier coefficients of 1, 1, 1 and lambda values of 0.035, 0.028, and 0.02. The resulting minimum Generalized Cross Validation (GCV) values of 0.3534871, 0.3711413, and 0.3918924. This model successfully made good predictions for testing data sizes of 30, 20, and 10, with MAPE values of 1.606545, 1.518221, and 1.018482. These results underscore the model's ability to capture the inverse relationship between earth surface temperature and relative humidity. The study highlights the Fourier-based semiparametric approach's effectiveness in dynamic scenarios and recommends applying it to other climate variables or regions to further evaluate its adaptability and robustness.
Journal: Data and Metadata
Pages: 890
Volume: 4
Year: 2025
DOI: 10.56294/dm2025890
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:890:id:1056294dm2025890

Template-Type: ReDIF-Article 1.0
Author-Name: Firas Tayseer Ayasrah
Author-Name-First: Firas Tayseer
Author-Name-Last: Ayasrah
Author-Name: Majida Khalaf Khaleel Alsbou
Author-Name-First: Majida Khalaf
Author-Name-Last: Khaleel Alsbou
Author-Name: Hassan Ali Al-Ababneh
Author-Name-First: Hassan Ali Al-Ababneh
Author-Name-Last: Hassan Ali Al-Ababneh
Author-Name: Nidal Al Said
Author-Name-First: Nidal
Author-Name-Last: Al Said
Author-Name: Khaleel Al-Said
Author-Name-First: Khaleel
Author-Name-Last: Al-Said
Author-Name: Suleiman Ibrahim Mohammad
Author-Name-First: Suleiman Ibrahim
Author-Name-Last: Mohammad
Author-Name: Asokan Vasudevan
Author-Name-First: Asokan
Author-Name-Last: Vasudevan
Author-Name: Khoo Wuan Jing
Author-Name-First: Khoo Wuan
Author-Name-Last: Jing
Title: Educational Performance and the Role of E-Learning, Digital Leadership, and Digital Innovation: A Study of High Schools in Jordan in the Context of 5G
Abstract: Introduction: This study examines the impact of e-learning, digital leadership, and digital innovation on educational performance in high schools, focusing on Jordan's transition to 5G technology. It explores how these factors enhance educational outcomes in a developing country facing technological and infrastructural challenges. Methods: A quantitative approach was used, collecting data from 385 high school teachers in Jordan through a cross-sectional survey. The study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze the relationships between e-learning, digital leadership, digital innovation, and educational performance. Results: The findings indicate that e-learning significantly improves educational performance by offering flexible and interactive learning environments. Digital leadership is crucial for guiding digital transformation and fostering innovation, while digital innovation, including AI, VR, and AR, enhances teaching methods and student engagement, leading to better educational outcomes. Conclusions: The study concludes that integrating e-learning platforms, effective digital leadership, and digital innovation is essential for improving high school performance. Policymakers and school administrators should invest in digital infrastructure, provide teacher training, and promote a culture of innovation to prepare students for a technology-driven future. This research offers valuable insights for enhancing educational practices in developing countries like Jordan.
Journal: Data and Metadata
Pages: 888
Volume: 4
Year: 2025
DOI: 10.56294/dm2025888
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:888:id:1056294dm2025888

Template-Type: ReDIF-Article 1.0
Author-Name: Anber Abraheem Shlash Mohammad
Author-Name-First: Anber Abraheem
Author-Name-Last: Shlash Mohammad
Author-Name: Suleiman Ibrahim Mohammad
Author-Name-First: Suleiman
Author-Name-Last: Ibrahim Mohammad
Author-Name: Khaleel Ibrahim Al- Daoud
Author-Name-First: Khaleel Ibrahim
Author-Name-Last: Al- Daoud
Author-Name: Badrea Al Oraini
Author-Name-First: Badrea
Author-Name-Last: Al Oraini
Author-Name: Menahi Mosallam Alqahtani
Author-Name-First: Menahi
Author-Name-Last: Mosallam Alqahtani
Author-Name: Asokan Vasudevan
Author-Name-First: Asokan
Author-Name-Last: Vasudevan
Author-Name: Mohammad Faleh Ahmmad Hunitie
Author-Name-First: Mohammad Faleh
Author-Name-Last: Ahmmad Hunitie
Title: Riding into the Future: Transforming Jordan’s Public Transportation with Predictive Analytics and Real-Time Data
Abstract: Introduction: This study explores how predictive analytics and real-time data integration can improve efficiency in Jordan’s public transportation network. By addressing scheduling, route optimization, and congestion management, it responds to growing urban transit demands in the region. Methods: Data were collected over three months from official ridership logs, GPS-enabled buses, and traffic APIs. ARIMA-based time-series forecasting captured historical trends, while a Random Forest model incorporated congestion index, average wait times, and other operational variables. Metadata management protocols (JSON/XML) facilitated cross-agency data sharing. Results: ARIMA proved effective for short-term passenger demand projections, although it occasionally underpredicted sudden ridership peaks. The Random Forest approach yielded stronger overall accuracy, explaining roughly 85% of variation when combining real-time congestion data with historical records. Real-time streams further supported dynamic scheduling and route adjustments. Conclusion: Combining predictive models with IoT-based data integration can enhance reliability and user satisfaction in Jordan’s public transit system. Although limited by timeframe and route scope, the findings underscore the importance of multi-agency collaboration and ongoing policy support to sustain data-driven innovations.
Journal: Data and Metadata
Pages: 887
Volume: 4
Year: 2025
DOI: 10.56294/dm2025887
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:887:id:1056294dm2025887

Template-Type: ReDIF-Article 1.0
Author-Name: Nancy Shamaylah
Author-Name-First: Nancy
Author-Name-Last: Shamaylah
Author-Name: Suleiman Ibrahim Mohammad
Author-Name-First: Suleiman
Author-Name-Last: Ibrahim Mohammad
Author-Name: Badrea Al Oraini
Author-Name-First: Badrea
Author-Name-Last: Al Oraini
Author-Name: Jumana Majed Yaseen Al-Gaafreh
Author-Name-First: Jumana Majed
Author-Name-Last: Yaseen Al-Gaafreh
Author-Name: Menahi Mosallam Alqahtani
Author-Name-First: Menahi
Author-Name-Last: Mosallam Alqahtani
Author-Name: Asokan Vasudevan
Author-Name-First: Asokan
Author-Name-Last: Vasudevan
Author-Name: Anber Abraheem Shlash Mohammad
Author-Name-First: Anber Abraheem
Author-Name-Last: Shlash Mohammad
Author-Name: Mohammad Faleh Ahmmad Hunitie
Author-Name-First: Mohammad Faleh
Author-Name-Last: Ahmmad Hunitie
Title: Data-Driven Decision-Making for Employee Training and Development in Jordanian Public Institutions
Abstract: Introduction: AI-driven training and HR analytics have revolutionized employee development by offering personalized learning experiences and optimizing skill enhancement. Public institutions are increasingly leveraging AI-based recommendations and adaptive learning algorithms to improve workforce training. However, the effectiveness and challenges of these approaches in real-world applications require further investigation. Methods: This study employed a descriptive and analytical research design, utilizing both quantitative and qualitative methods. Data was collected from 385 employees in Jordanian public institutions using structured surveys and sentiment analysis of employee feedback. Statistical techniques, including regression analysis, ANOVA, and correlation analysis, were applied to assess the impact of HR data analytics, AI-based recommendations, and training personalization on training effectiveness. Results: The findings indicate that HR data analytics, AI-based recommendations, and training personalization significantly improve training effectiveness. Skill development emerged as the strongest predictor of training success (β = 0.7282, p < 0.001). Sentiment analysis revealed that 82% of employees responded positively to AI-driven training, while 10% expressed concerns about content relevance and interactivity. ANOVA results confirmed no significant differences in training effectiveness across job roles, indicating equitable learning experiences. Conclusion: AI-powered training is widely accepted but requires further refinement to address personalization challenges and employee engagement concerns. Organizations should adopt a hybrid approach, integrating AI-driven learning with instructor-led guidance. Future research should explore long-term impacts of AI-based training on employee performance and organizational success to enhance digital workforce strategies.
Journal: Data and Metadata
Pages: 886
Volume: 4
Year: 2025
DOI: 10.56294/dm2025886
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:886:id:1056294dm2025886

Template-Type: ReDIF-Article 1.0
Author-Name: Suryo Ediyono
Author-Name-First: Suryo
Author-Name-Last: Ediyono
Author-Name: Widodo Aribowo
Author-Name-First: Widodo
Author-Name-Last: Aribowo
Author-Name: Ummi Kulsum
Author-Name-First: Ummi
Author-Name-Last: Kulsum
Author-Name: Soetrisno
Author-Name-First: Soetrisno
Author-Name-Last: Soetrisno
Author-Name: Sri Mulyani
Author-Name-First: Sri
Author-Name-Last: Mulyani
Title: Biocultural Expectancy of Breastfeeding Practice: A Qualitative Content Analysis Using Bibliometric Review
Abstract: This research explores the philosophy of breastfeeding practice as a glory. The research objectives are to discover (explore) the deepest structure of breastfeeding practices, explain (explain) the structures found in the context of general breastfeeding practices, and highlight (expose) the glory of breastfeeding according to a religious view (scriptural view). Research steps: a) find information in the form of central themes found in article(s) contained in Scopus indexed journals; b) data analysis using VOSviewer; c) explain the glory of the practice of breastfeeding. This research found the breastfeeding triad includes: nurturing (biology), self-sacrifice (culture), and moral guidance (expectancy) as the implementation of the glory of breastfeeding practices. The breastfeeding triad is confirmed by findings from religious views (scientific perspective). It is hoped that the results of this research will be a driver for increasing the breastfeeding index, which in turn will support increasing the Human Development Index.
Journal: Data and Metadata
Pages: 880
Volume: 4
Year: 2025
DOI: 10.56294/dm2025880
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:880:id:1056294dm2025880

Template-Type: ReDIF-Article 1.0
Author-Name: Mustafa Abdulirees Jebur
Author-Name-First: Mustafa
Author-Name-Last: Abdulirees Jebur
Author-Name: Seyed Sadra Kashef
Author-Name-First: Seyed
Author-Name-Last: Sadra Kashef
Author-Name: Mc Amirani
Author-Name-First: Mc
Author-Name-Last: Amirani
Author-Name: M.H. Mezher
Author-Name-First: M.H.
Author-Name-Last: Mezher
Title: Design and Fabrication an optical sensor devices base on graphene oxide
Abstract: Contamination of oil, particularly by dissolved water, is a very common problem in the failure of step-down transformers used by electricity providers as this degrades the insulating property of the oil. In this paper, the use of D-shaped optical fibers functionalized with Graphene Oxide is presented to detect the water content in transformer oil. The synthesis of graphene oxide was achieved by a modified version of Hummer's method. Subsequently, the drop-casting process was used to apply the graphene oxide onto the D-shaped fibre. The coating thickness attained in the samples was around 200 nm. Side polishing in a single-mode fiber engages an evanescent field that increases its sensitivity as an optical sensor. A few layers of graphene oxide coating on D-fiber exhibit a quick response time and high sensitivity to moisture content present in transformer oil, which proves to be a hopeful solution in real-time monitoring and maintenance of transformer insulation systems. It manifested that the experimental results had a high sensitivity to different water contents in transformer oil for the D-shaped fiber coated with GO. The GO-coated fibers exhibited a sensitivity of 0.5677 dB/ppm, which is relatively high compared with the sensitivity in the case of uncoated D-shaped fibers.
Journal: Data and Metadata
Pages: 875
Volume: 4
Year: 2025
DOI: 10.56294/dm2025875
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:875:id:1056294dm2025875

Template-Type: ReDIF-Article 1.0
Author-Name: Soufiane El Asri
Author-Name-First: Soufiane
Author-Name-Last: El Asri
Author-Name: Khalid Zebbara
Author-Name-First: Khalid
Author-Name-Last: Zebbara
Author-Name: Mohammed Aftatah
Author-Name-First: Mohammed
Author-Name-Last: Aftatah
Author-Name: Abderrahmane Azaz
Author-Name-First: Abderrahmane
Author-Name-Last: Azaz
Author-Name: Abderrahmane AIT LHOUSSAINE
Author-Name-First: Abderrahmane
Author-Name-Last: AIT LHOUSSAINE
Author-Name: Karim Ait Sidi Lahcen
Author-Name-First: Karim
Author-Name-Last: Ait Sidi Lahcen
Author-Name: Mohamed Baarar
Author-Name-First: Mohamed
Author-Name-Last: Baarar
Author-Name: Oussama BOUBRINE
Author-Name-First: Oussama
Author-Name-Last: BOUBRINE
Title: Computer Vision for Vehicle Detection: A Comprehensive Review
Abstract: The rapid increase in vehicle numbers has exacerbated challenges in modern transportation, leading to traffic congestion, accidents, and operational inefficiencies. Intelligent Transportation Systems (ITS) leverage computer vision techniques for vehicle detection, improving safety and efficiency. This paper aims to provide a comprehensive review of vehicle detection methods in ITS. Traditional image-processing techniques, including Scale-Invariant Feature Transform (SIFT), Viola-Jones (VJ), and Histogram of Oriented Gradients (HOG), are analyzed. Additionally, modern deep learning-based approaches are examined, distinguishing between two-stage methods such as R-CNN and Fast R-CNN, and one-stage methods like YOLO and SSD. Various image acquisition techniques, including Mono-vision, Stereo-vision, Thermal/Infrared Cameras, and Bird’s Eye View, are also reviewed. The analysis highlights the evolution from handcrafted feature-based methods to deep learning techniques, demonstrating significant improvements in detection accuracy and efficiency. One-stage detectors, particularly YOLO and SSD, offer real-time performance, while two-stage methods provide higher precision. The impact of different imaging modalities on detection reliability is also discussed. Advances in deep learning and imaging techniques have significantly enhanced vehicle detection capabilities in ITS. Future research should focus on improving robustness in diverse environmental conditions and optimizing computational efficiency for real-time deployment.
Journal: Data and Metadata
Pages: 873
Volume: 4
Year: 2025
DOI: 10.56294/dm2025873
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:873:id:1056294dm2025873

Template-Type: ReDIF-Article 1.0
Author-Name: Saleem Alzoubi
Author-Name-First: Saleem
Author-Name-Last: Alzoubi
Title: Increasing the operating efficiency of sorting robotic complexes based on multi-projection processing
Abstract: Introduction: This paper explores computer vision techniques for automated sorting of objects based on their geometric shape, color, and brightness. The research addresses two primary scenarios: objects moving along a conveyor belt and objects placed unordered in a common container. Methods: The sorting system utilizes computer vision algorithms that incorporate edge pixel extraction, cellular automata, and the Radon transform. Edge detection is achieved using the Prewitt operator to extract object contours. Cellular automata are employed to generate object backgrounds and define polygonal regions, improving shape recognition. The Radon transform is applied with a hexagonal image grid to produce six projections, aiding in noise reduction and accurate shape and orientation detection. Results: The combined use of six Radon projections and cellular automata enables the system to distinguish individual objects even when they are placed together in a single container. The approach effectively detects and sorts distorted or variably shaped objects with high precision, regardless of their arrangement—either random or orderly. Conclusions: The proposed computer vision-based sorting method is robust and versatile, capable of handling complex object configurations. It offers a reliable solution for sorting objects by shape, color, and brightness in diverse industrial or logistical settings.
Journal: Data and Metadata
Pages: 872
Volume: 4
Year: 2025
DOI: 10.56294/dm2025872
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:872:id:1056294dm2025872

Template-Type: ReDIF-Article 1.0
Author-Name: Hilali Oumaima
Author-Name-First: Hilali
Author-Name-Last: Oumaima
Author-Name: Soulhi Aziz
Author-Name-First: Soulhi
Author-Name-Last: Aziz
Author-Name: Bouami Driss
Author-Name-First: Bouami
Author-Name-Last: Driss
Author-Name: Fouad Amal Wahid
Author-Name-First: Fouad
Author-Name-Last: Amal Wahid
Title: Optimization and designing a hospital using distributed A
Abstract: The Covid crisis has demonstrated the fragility of hospital health systems. Patients monitored for chronic illnesses have had to stop monitoring their illness. The aims of this article is to present an intelligent and dynamic system that allows patients to be redirected to other care units to ensure their medical care and to prevent inequality between the provision of care and the need depending on the region. An interconnected hospital system will restore a better balance between supply and demand for care. In this article, we present the design and architecture of a hospital using distributed AI and especially multi-agent system that has proved its worth in several distributed systems.
Journal: Data and Metadata
Pages: 868
Volume: 4
Year: 2025
DOI: 10.56294/dm2025868
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:868:id:1056294dm2025868

Template-Type: ReDIF-Article 1.0
Author-Name: John Edisson García Peñaloza
Author-Name-First: John Edisson
Author-Name-Last: García Peñaloza
Author-Name: Alexis Ferley Bohórquez
Author-Name-First: Alexis Ferley
Author-Name-Last: Bohórquez
Author-Name: Paula Andrea Solano Balaguera
Author-Name-First: Paula Andrea
Author-Name-Last: Solano Balaguera
Title: Management trends and implementation of AI in university management
Abstract: The objective of this article was to explore managerial trends and the implementation of artificial intelligence in university management, with a particular focus on the Latin American context. To this end, a mixed study was designed, operationalized through a documentary review with bibliometric procedures, a qualitative thematic analysis, a triangulation system, and an integration of data supported by external sources. The results were organized into five management strategies, three emerging trends, five recommendations for managers, and five main themes. These trends reflect significant progress, but also pose challenges, especially in regions with structural inequalities and resource constraints. The data analyzed indicate the need for a balanced approach that combines technological innovation with ethical and social considerations. Furthermore, the findings emphasize the importance of international collaboration and local capacity building to ensure equitable and sustainable implementation of AI. It is concluded that it is cardinal to underline the potential of AI to transform higher education, provided that technical, ethical, and social challenges are addressed comprehensively.
Journal: Data and Metadata
Pages: 866
Volume: 4
Year: 2025
DOI: 10.56294/dm2025866
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:866:id:1056294dm2025866

Template-Type: ReDIF-Article 1.0
Author-Name: Linh Dieu Nguyen
Author-Name-First: Linh
Author-Name-Last: Dieu Nguyen
Title: Digital Divide in Science Education: The Role of Technology Access and Skills in Supporting Underserved Students
Abstract: Introduction: The Digital Divide (DD), refers to the gap among persons with varying levels of access to technology and digital skills, which significantly impacts educational outcomes. Methods: The research examines the impact of technology access and skills on underserved students, focusing on challenges they face in utilizing online learning and digital instruction through a cross-sectional survey of 479 university students. These factors were analyzed to identify how it supports or impede science learning. The descriptive and inferential statistics were used to evaluate responses. Variables, such as device ownership, Internet reliability, prior Online Learning (OL) experience, and technological skill levels were analyzed using regression and Analysis of Variance (ANOVA) models using International Business Machines Statistical Package for the Social Sciences (IBM SPSS) statistics version 17.0 to identify patterns and disparities. Result: The findings revealed significant disparities in technology access and skills, with underserved students reporting lower device ownership and limited digital competence. With p-values of 0.0001 for device ownership, internet dependability, and technological proficiency, and a p-value of 0.004 for previous OL experience, the regression analysis demonstrated significant connections between all factors and OL engagement. ANOVA findings showed a p-value of 0.002 for the between-group variance, indicating significant differences between groups. Conclusion: Technological inequities in online science courses negatively impact underserved students, necessitating targeted institutional support and skill-building programs to improve learning outcomes and ensure equitable educational opportunities.
Journal: Data and Metadata
Pages: 865
Volume: 4
Year: 2025
DOI: 10.56294/dm2025865
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:865:id:1056294dm2025865

Template-Type: ReDIF-Article 1.0
Author-Name: Aiming Pan
Author-Name-First: Aiming
Author-Name-Last: Pan
Title: Application of multi-modal data fusion based on deep learning in diagnosis of depression
Abstract: Depression is a frequent mental condition requiring precise diagnosis in its early onset. Traditional methods are less than accurate and occur late. Following these deficits, this investigates the multi-modal data fusion and Deep Learning (DL) with the purpose of enhancing accuracy for diagnosis. A new DL model, Dynamic Dolphin Echolocation-tuned Effective Temporal Convolutional Networks (DDE-ETCN), is utilized for depression diagnosis. Different sources of data, such as physiological signals (EEG, heart rate), behavioral indicators (facial expressions), and biometric data (activity levels), are fused. Data preprocessing includes wavelet transformation and normalization of biometric and physiological data, and median filtering of behavioral data to provide smooth inputs. Feature extraction is performed through Fast Fourier Transform (FFT) to obtain frequency-domain features of depression indicators. Feature-level fusion is a good fusion of all data sources, which improves the model's performance. The DDE tuning mechanism improves temporal convolution layers to improve the model's ability in detecting sequential changes. The proposed DDE-ETCN model highly improves depression diagnosis when it is developed in Python. The model attains an RMSE of 3.59 and an MAE of 3.09. It has 98.72% accuracy, 98.13% precision, 97.65% F1-score, and 97.81% recall, outperforming conventional diagnostic models and other deep learning-based diagnostic models. The outcomes show the efficiency of the model, rendering a more objective and accurate depression diagnosis. Its higher performance justifies its potential for practical use, providing enhanced accuracy and reliability compared to traditional approaches. This innovation emphasizes the necessity of incorporating deep learning for enhanced mental health evaluations.
Journal: Data and Metadata
Pages: 863
Volume: 4
Year: 2025
DOI: 10.56294/dm2025863
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:863:id:1056294dm2025863

Template-Type: ReDIF-Article 1.0
Author-Name: Elly Pusporani
Author-Name-First: Elly
Author-Name-Last: Pusporani
Author-Name: Ghisella Asy Sifa
Author-Name-First: Ghisella
Author-Name-Last: Asy Sifa
Author-Name: Nurin Faizun
Author-Name-First: Nurin
Author-Name-Last: Faizun
Author-Name: Pressylia Aluisina Putri Widyangga
Author-Name-First: Pressylia Aluisina
Author-Name-Last: Putri Widyangga
Author-Name: Adma Novita Sari
Author-Name-First: Adma
Author-Name-Last: Novita Sari
Author-Name: M. Fariz Fadillah Mardianto
Author-Name-First: M. Fariz
Author-Name-Last: Fadillah Mardianto
Author-Name: Sediono
Author-Name-First: Sediono
Author-Name-Last: Sediono
Title: Comparison of Time Series Regression, Support Vector Regression, Hybrid, and Ensemble Method to Forecast PM2.5
Abstract: Introduction: PM2.5 pollution poses significant health risks, particularly in Jakarta, where levels often exceed safety thresholds. Accurate forecasting models are essential for air quality management and mitigation strategies. Methods: This study compares four forecasting models: Time Series Regression (TSR), Support Vector Regression (SVR), a hybrid TSR-SVR model, and an ensemble approach. The dataset consists of 9,119 hourly PM2.5 observations from January 1, 2023, to January 15, 2024. Missing values were imputed using historical hourly trends. Model performance was evaluated using Root Mean Squared Error (RMSE). Results: The hybrid TSR-SVR model achieved the lowest RMSE (6.829), outperforming TSR (7.595), SVR (7.477), and the ensemble method (7.486). The hybrid approach effectively captures both linear and nonlinear patterns in PM2.5 fluctuations, making it the most accurate model. Conclusions: Integrating statistical and machine learning models improves PM2.5 forecasting accuracy, aiding policymakers in pollution control efforts. Future studies should explore additional external factors to enhance prediction performance.
Journal: Data and Metadata
Pages: 862
Volume: 4
Year: 2025
DOI: 10.56294/dm2025862
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:862:id:1056294dm2025862

Template-Type: ReDIF-Article 1.0
Author-Name: Yassine Zouhair
Author-Name-First: Yassine
Author-Name-Last: Zouhair
Author-Name: Younous ELMRINI
Author-Name-First: Younous
Author-Name-Last: ELMRINI
Author-Name: Mustapha BELAISSAOUI
Author-Name-First: Mustapha
Author-Name-Last: BELAISSAOUI
Title: Framework for ERP implementation in the context of Moroccan SMEs
Abstract: The current and future challenges and the rapid evolution of markets in a context of globalization require companies to acquire the technological tools to manage information flows in order to remain up-to-date and competitive. To do so, it is ready to deploy important means to guarantee its competitiveness and its scalability. This is done mainly through the implementation of software technologies, the most popular of which are known as Enterprise Resource Planning (ERP). ERP is a popular option for small and medium-sized enterprises (SMEs) today that are looking to optimize and integrate their information systems. However, the implementation of ERP is a complex process and remains a challenge for many SMEs, even more so than for large companies, where ERP integration failures have caused some to go bankrupt. In addition, SMEs differ in a number of characteristics that can affect the implementation of ERP, so it is not wise to use the frameworks developed for the large enterprises to implement Enterprise Resource Planning Systems (ERPS) in SMEs. The objective of this research is to develop a framework for implementing an ERP in the context of Moroccan SMEs. The developed framework was used for implementing an ERPS in two Moroccan SMEs, it includes 5 phases, for each one, the objectives, critical success factors, inputs, processes, outputs and risks to be considered. This research study contributed to both practice and research, and the results could help practitioners and Moroccan SMEs when implementing an ERP and to suggest directions for future research.
Journal: Data and Metadata
Pages: 861
Volume: 4
Year: 2025
DOI: 10.56294/dm2025861
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:861:id:1056294dm2025861

Template-Type: ReDIF-Article 1.0
Author-Name: Achmad Shabir
Author-Name-First: Achmad
Author-Name-Last: Shabir
Author-Name: Herwin
Author-Name-First: Herwin
Author-Name-Last: Herwin
Author-Name: Asriadi
Author-Name-First: Asriadi
Author-Name-Last: Asriadi
Author-Name: Riana Nurhayati
Author-Name-First: Riana
Author-Name-Last: Nurhayati
Author-Name: Lantip Diat Prasojo
Author-Name-First: Lantip
Author-Name-Last: Diat Prasojo
Author-Name: Shakila Che Dahalan
Author-Name-First: Shakila
Author-Name-Last: Che Dahalan
Title: Integration of Artificial Intelligence in Virtual Reality-Based Learning
Abstract: The integration of Artificial Intelligence (AI) and Virtual Reality (VR) in education presents a promising opportunity to create immersive and adaptive learning environments, but research on the integration of both is very rare for the case of elementary school education. Most studies only focus on the standalone study of one of the technologies. The research focuses on analyzing the impact and relationship between the integration of AI in VR learning environments over time, hence it will employ longitudinal data collection and time series analysis to understand trends and patterns. The research subjects for this study were fifth grade elementary school students, aged between 11 and 12 years. This research involves observation and data collection in the form of learning outcome tests at several points in time to determine how the integration of AI and VR develops in the classroom and affects learning outcomes. Data was analyzed using trend analysis with a time series approach. The results of the study show that the integration of AI in VR-based learning gives a positive trend to student learning outcomes. The research findings show that the Quadratic Trend Model (QTM) is the most accurate model for measuring student learning outcome trends.
Journal: Data and Metadata
Pages: 859
Volume: 4
Year: 2025
DOI: 10.56294/dm2025859
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:859:id:1056294dm2025859

Template-Type: ReDIF-Article 1.0
Author-Name: Choi Young-Chool
Author-Name-First: Choi
Author-Name-Last: Young-Chool
Author-Name: Yanghoon Song
Author-Name-First: Yanghoon
Author-Name-Last: Song
Author-Name: Ki Seo Kong
Author-Name-First: Ki
Author-Name-Last: Seo Kong
Author-Name: Ahyoung Lee
Author-Name-First: Ahyoung
Author-Name-Last: Lee
Title: Evaluation Of Korea’s Rural Development Oda Projects In Kyrgyzstan Using Neural Network Analysis: Focusing On Local Residents’ Perceptions
Abstract: This study conducted a mid-term evaluation of Korea’s Integrated Rural Development Project (IRDP) implemented in 30 villages of Osh and Batken regions in Kyrgyzstan since 2021, focusing on local residents' perceptions. Particularly, to overcome the limitations of conventional descriptive statistical methods frequently used in previous studies, this research applied neural network analysis to better capture complex and nonlinear relationships among influencing factors. The results indicated that residents generally perceived the Korean rural development ODA project as significantly contributing to local economic development, with the most influential factor being the village-level characteristics. Furthermore, demographic characteristics such as marital status, age, education level, and occupation of residents also had significant effects on their perceived outcomes of the project. This study confirms the usefulness of neural network analysis as an effective method for evaluating ODA project outcomes based on residents’ perceptions and provides meaningful policy implications for enhancing the effectiveness of future Korean rural development ODA projects.
Journal: Data and Metadata
Pages: 858
Volume: 4
Year: 2025
DOI: 10.56294/dm2025858
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:858:id:1056294dm2025858

Template-Type: ReDIF-Article 1.0
Author-Name: César Dilú Sorzano
Author-Name-First: César
Author-Name-Last: Dilú Sorzano
Author-Name: Yohandra Calixto Robert
Author-Name-First: Yohandra
Author-Name-Last: Calixto Robert
Author-Name: Yelena Pereira Perera
Author-Name-First: Yelena
Author-Name-Last: Pereira Perera
Author-Name: José Pérez Trujillo
Author-Name-First: José
Author-Name-Last: Pérez Trujillo
Author-Name: Diana Martín García
Author-Name-First: Diana
Author-Name-Last: Martín García
Author-Name: Gisel Pérez Breff
Author-Name-First: Gisel
Author-Name-Last: Pérez Breff
Author-Name: Gloria Lidia Peña Martínez
Author-Name-First: Gloria Lidia
Author-Name-Last: Peña Martínez
Author-Name: Estela Morales Peralta
Author-Name-First: Estela
Author-Name-Last: Morales Peralta
Author-Name: Paulina Araceli Lantigua Cruz
Author-Name-First: Paulina Araceli
Author-Name-Last: Lantigua Cruz
Author-Name: Haydee Rodríguez Guas
Author-Name-First: Haydee
Author-Name-Last: Rodríguez Guas
Author-Name: Melek Dáger Salomón
Author-Name-First: Melek Dáger
Author-Name-Last: Salomón
Author-Name: Margarita Arguelles Arza
Author-Name-First: Margarita
Author-Name-Last: Arguelles Arza
Author-Name: Roberto Lardoeyt Ferrer
Author-Name-First: Roberto
Author-Name-Last: Lardoeyt Ferrer
Author-Name: Rafael Eduardo Montaño Arrieta
Author-Name-First: Rafael Eduardo
Author-Name-Last: Montaño Arrieta
Author-Name: Norma Elena De León Ojeda
Author-Name-First: Norma Elena
Author-Name-Last: De León Ojeda
Author-Name: Laritza Matínez Rey
Author-Name-First: Laritza
Author-Name-Last: Matínez Rey
Author-Name: Dayana Delgado López
Author-Name-First: Dayana
Author-Name-Last: Delgado López
Author-Name: Noel Taboada Lugo
Author-Name-First: Noel
Author-Name-Last: Taboada Lugo
Author-Name: Daniel Quintana Hernández
Author-Name-First: Daniel
Author-Name-Last: Quintana Hernández
Author-Name: Yamilé Lozada Mengana
Author-Name-First: Yamilé
Author-Name-Last: Lozada Mengana
Author-Name: João Ernesto
Author-Name-First: João
Author-Name-Last: Ernesto
Title: Development and validation of a new artificial intelligence tool (GeneClin) for the clinical diagnosis of genetic diseases
Abstract: Introduction: Advances in the field of Artificial Intelligence (AI) and Machine Learning (ML) have considerable potential to improve the diagnosis and management of rare genetic diseases, due to the human inability to memorize information on a multitude of these diseases, which AI tools could store, analyze and integrate. Objective: to develop and validate a new AI tool for the clinical diagnosis of genetic diseases. Methods: A prospective, cross-sectional, analytical, observational study was conducted at the application level, with a qualitative-quantitative approach and contributing to a technological development project. It was characterized by four stages: selection of the AI ​​tool, selection of the knowledge base, development of the virtual assistant, validation process and implementation in the clinic. Results: A total of 246 patients with genetic diseases and congenital defects were evaluated. The most predominant genetic category was monogenic genetic syndromes with 223 patients who attended the consultation (90.7%). A success rate of 84.1% was obtained and a success/no success ratio of 4.34. The highest percentage of successes was achieved in monogenic or Mendelian syndromes. There were no significant differences between successes and failures in both chromosomal aberrations and congenital defects of environmental etiology. Conclusions: Through this research, an AI virtual assistant has been validated for the clinical diagnosis of genetic diseases with a high percentage of effectiveness of 84%, which confirms its usefulness to support the clinical diagnosis of cases with genetic diseases.
Journal: Data and Metadata
Pages: 857
Volume: 4
Year: 2025
DOI: 10.56294/dm2025857
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:857:id:1056294dm2025857

Template-Type: ReDIF-Article 1.0
Author-Name: Laura María Almeida Rueda
Author-Name-First: Laura María
Author-Name-Last: Almeida Rueda
Author-Name: Maria Andreina Pulido Montes
Author-Name-First: Maria Andreina
Author-Name-Last: Pulido Montes
Title: Importance of mental health nursing care: an ethnographic approach in university professors
Abstract: Introduction: A qualitative study was developed based on nursing care in mental health, which is a pillar in university education and in the performance of the role, since it is a priority to intervene from all the dimensions of the being. Objective: To interpret the meanings about nursing care in mental health held by the professors of the nursing faculty of a university in eastern Colombia. Method: qualitative research with ethnographic approach, use of techniques to obtain data from semi-structured interviews, field diary and observations; ethnographic analysis was carried out according to Clifford Geertz, where categories emerge, resulting in a general matrix that leads to a dense description. Results: in the ethnographic analysis two major categories were identified, one is mental health as a construct, and the other is integral health care, within these a series of subcategories that explain the study phenomenon are deployed, thus achieving a dense description. Conclusion: it was evidenced that the meanings of nursing care in mental health become diverse according to the expertise, it was also identified that the experiences lived by each teacher mark the daily actions in teaching the formation of the nursing role, showing the importance in all cases of assuming nursing care in mental health for the comprehensive care of individuals, families and communities.
Journal: Data and Metadata
Pages: 856
Volume: 4
Year: 2025
DOI: 10.56294/dm2025856
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:856:id:1056294dm2025856

Template-Type: ReDIF-Article 1.0
Author-Name: Pedro Antonio Saltos García
Author-Name-First: Pedro Antonio
Author-Name-Last: Saltos García
Author-Name: Alicia Carmita Vasquez Rodas
Author-Name-First: Alicia Carmita
Author-Name-Last: Vasquez Rodas
Author-Name: César E Morán Castro
Author-Name-First: César E
Author-Name-Last: Morán Castro
Author-Name: Roger Santiago Peñaherrera Andrade
Author-Name-First: Roger Santiago
Author-Name-Last: Peñaherrera Andrade
Title: The quality of higher education in the digital age: indicators, assurance models and the impact of artificial intelligence
Abstract: Artificial intelligence (AI) has become a key element in the transformation of higher education, impacting both teaching models and educational quality assurance. This study uses a mixed approach that combines a literature review with an empirical analysis based on applied research with teachers and students in Latin America. The results indicate a growing use of AI in the academic field, with different opinions about its effectiveness and reliability. While its potential to personalize learning and optimize educational management is widely recognized, concerns also arise regarding regulation, the authenticity of student work, and the need to establish appropriate regulatory frameworks for its implementation. The quantitative results stand out, showing a progressive adoption of AI in educational practices, with 35,2% of students using it daily and 27.3% of teachers integrating it several times a week. In addition, variations in the perception of AI are identified according to academic discipline, level of study, and experience in the use of digital tools. The debate highlights the need to integrate AI into education in a strategic and ethical manner, ensuring that its impact is positive and contributes to strengthening the quality of education in the digital age. Finally, the study highlights the importance of developing regulations that establish clear criteria for the incorporation of AI in the academic field, ensuring a balance between innovation and fundamental pedagogical principles.
Journal: Data and Metadata
Pages: 855
Volume: 4
Year: 2025
DOI: 10.56294/dm2025855
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:855:id:1056294dm2025855

Template-Type: ReDIF-Article 1.0
Author-Name: Carlos Leonidas Yance Carvajal
Author-Name-First: Carlos Leonidas
Author-Name-Last: Yance Carvajal
Author-Name: Freddy Leonardo Garaicoa Fuentes
Author-Name-First: Freddy Leonardo
Author-Name-Last: Garaicoa Fuentes
Author-Name: Xuxa Katherine Cedeño Guillén
Author-Name-First: Xuxa Katherine
Author-Name-Last: Cedeño Guillén
Author-Name: Sandra Edith Rodríguez Bejarano
Author-Name-First: Sandra Edith
Author-Name-Last: Rodríguez Bejarano
Author-Name: Jorge Carlos Morgan Medina
Author-Name-First: Jorge Carlos
Author-Name-Last: Morgan Medina
Title: Innovation of SMEs in Ecuador: An Approach from Socio-emotional Wealth and the Use of Artificial Intelligence
Abstract: Small and medium-sized enterprises (SMEs) in Ecuador face constant challenges to stay competitive in an environment of digital transformation. Artificial intelligence (AI) has become a key tool to optimise processes, while socioemotional wealth influences decision-making and the work environment. This study analysed how the combination of both factors impacts the innovation and competitiveness of Ecuadorian SMEs, identifying strategies to improve their performance in the market. A mixed methodology was applied, combining surveys of 150 workers with interviews of 10 selected SME managers. The surveys, conducted using Google Forms, assess the perception of AI implementation and the importance of socio-emotional values. The interviews, carried out by Zoom, allowed us to delve into strategies, best practices and challenges in the integration of technology and emotional management. The results showed a positive trend in the adoption of AI, highlighting its impact on process optimisation and productivity improvement. It is also confirmed that socioemotional richness influences team cohesion and strategic decision-making. However, barriers such as lack of training and resistance to change were identified. Finally, strategies were proposed to balance technology with emotional well-being and strengthen business competitiveness.
Journal: Data and Metadata
Pages: 854
Volume: 4
Year: 2025
DOI: 10.56294/dm2025854
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:854:id:1056294dm2025854

Template-Type: ReDIF-Article 1.0
Author-Name: Young Chool Choi
Author-Name-First: Young
Author-Name-Last: Chool Choi
Title: Developing an integrated happiness index and analysis of Korea’s happiness level and policy directions
Abstract: This study aims to overcome the limitations of various internationally recognized happiness indices by developing an integrated happiness index to analyze Korea’s happiness level and suggest future policy directions. The scope of the study includes seven major international datasets related to happiness, such as the World Happiness Report, OECD Better Life Index, and the IPSOS Global Happiness Index. Methodologically, literature review and data analysis were conducted, particularly employing Multidimensional Scaling (MDS) to visually analyze characteristics of happiness levels across different countries. The analysis revealed that national happiness rankings are influenced not only by economic performance but also by factors such as social trust, environmental sustainability, and subjective well-being. Applying the developed integrated happiness index to OECD countries, Korea ranked 35th out of 38 nations, highlighting the need for improvements in subjective happiness and environmental sustainability relative to economic performance. Specifically, Korea showed a significant discrepancy between GDP per capita and happiness index, suggesting that long working hours and low social connectedness negatively affect subjective well-being. In conclusion, this study emphasizes the need for a multidimensional approach that encompasses social and cultural factors beyond economic growth to enhance the effectiveness of happiness policies. It further recommends prioritizing policies focused on mental health support and strengthening social ties in Korea alongside continued economic growth.
Journal: Data and Metadata
Pages: 853
Volume: 4
Year: 2025
DOI: 10.56294/dm2025853
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:853:id:1056294dm2025853

Template-Type: ReDIF-Article 1.0
Author-Name: Choi Young-Chool
Author-Name-First: Choi
Author-Name-Last: Young-Chool
Author-Name: Sanghyun Ju
Author-Name-First: Sanghyun
Author-Name-Last: Ju
Author-Name: Gyutae Lee
Author-Name-First: Gyutae
Author-Name-Last: Lee
Author-Name: Sangkun Kim
Author-Name-First: Sangkun
Author-Name-Last: Kim
Author-Name: Sungho Yun
Author-Name-First: Sungho
Author-Name-Last: Yun
Title: Exploring Approaches to Low Fertility through Integrated Application of Big Data-based Topic Modeling and System Dynamics: The Case of South Korea
Abstract: This study examines the multidimensional aspects of low fertility by integrating big data text mining with system dynamics analysis. While previous research primarily utilized macroeconomic, big data discourse, or system dynamics approaches independently, this research combines textual big data analysis and causal loop modeling to address gaps identified in prior methodologies. Specifically, we analyze social discourses and sentiments related to low fertility through text mining of social media data, and then link these qualitative insights with quantitative simulations using system dynamics. Our integrated approach offers a novel methodological framework that enhances understanding of the complex interactions between societal perceptions, policy interventions, and demographic outcomes. The results underscore the importance of capturing both qualitative social trends and quantitative policy feedback loops, providing valuable implications for designing more effective fertility-enhancing policies.
Journal: Data and Metadata
Pages: 852
Volume: 4
Year: 2025
DOI: 10.56294/dm2025852
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:852:id:1056294dm2025852

Template-Type: ReDIF-Article 1.0
Author-Name: DEEPAK V.
Author-Name-First: DEEPAK
Author-Name-Last: V.
Author-Name: X.S. ASHA SHINY
Author-Name-First: X.S.
Author-Name-Last: ASHA SHINY
Author-Name: VIDYABHARATHI DAKSHINAMURTHI
Author-Name-First: VIDYABHARATHI
Author-Name-Last: DAKSHINAMURTHI
Author-Name: S. JOHN JUSTIN THANGARAJ
Author-Name-First: S. JOHN
Author-Name-Last: JUSTIN THANGARAJ
Author-Name: DINESH KUMAR ANGURAJ
Author-Name-First: DINESH
Author-Name-Last: KUMAR ANGURAJ
Title: A proficient recommendation system for athletes utilizing an adaptive learning model integrated with wearable IoT devices
Abstract: In the current digital context, recommendation algorithms must be used. It has found use in various contexts, including music streaming services and athletics. Athletic recommendation systems have received little study attention. Sedentary lifestyles are now the primary cause of many flaws and a significant portion of expenses. Based on user profiles, connections to other users, and histories in the current study, we create a system to suggest daily workout plans to athletes. The created recommendation system uses profiles of users and temporal processes in Adaptive Support Vector Machine (). Additionally, compared to streaming recommendation algorithms, we cannot gather input from athletes using the wearable IoT Devices and sensors for collecting the data of exercise and workout the system is proposed, which sets them apart significantly. As a result, we suggest an active learning process that involves an expert in real. The active learner estimates the recommendation system's level of uncertainty for every user at each successive step and, finally, when it is high, gets assistance from a professional. We construct and use the marginal distance distribution of its probability function in the present research to determine whether to consult subject-matter experts. Our test findings on a real-time dataset demonstrate increased accuracy after incorporating a live and engaged learner into the search engine.
Journal: Data and Metadata
Pages: 851
Volume: 4
Year: 2025
DOI: 10.56294/dm2025851
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:851:id:1056294dm2025851

Template-Type: ReDIF-Article 1.0
Author-Name: PremaLatha V
Author-Name-First: PremaLatha
Author-Name-Last: V
Author-Name: Dinesh Kumar Anguraj
Author-Name-First: Dinesh
Author-Name-Last: Kumar Anguraj
Author-Name: Nikhat Parveen
Author-Name-First: Nikhat
Author-Name-Last: Parveen
Title: Integrated Neural-Hybrid System for Efficient Tumor Detection and Object Reconstruction
Abstract: In computer vision and robotics, reconstructing multi-view 3D images is essential for accurate object representation from 2D data. In the first study, optimised weights through Adaptive School of Fish Optimisation are combined with 2D and 3D networks to introduce a Residual Network-50 model for deep learning-based 3D image reconstruction. On the ShapeNet dataset, this method demonstrates superior accuracy (0.993), F-score (0.734), and IoU (99.3%). Using Concurrent Excited DenseNet (CED)for feature extraction and Attention-Dense GRUs for prediction, the second study introduces the Concurrent Attentional Reconstruction Network(CARN) for reconstructing point clouds from single 2D images, achieving over 99% accuracy with low EMD and CD values. By combining convolutional layers, inception modules, and attention mechanisms with preprocessing steps like Ex_NLMF for noise reduction and Up_FKMA for accurate disease area identification, the third method,Twin Attention-aided Convolutional Inception Capsule Network (TA_CICNet), performs exceptionally well in medical image reconstruction and classification when it comes to diagnosing brain tumours.
Journal: Data and Metadata
Pages: 850
Volume: 4
Year: 2025
DOI: 10.56294/dm2025850
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:850:id:1056294dm2025850

Template-Type: ReDIF-Article 1.0
Author-Name: M Kavitha
Author-Name-First: M
Author-Name-Last: Kavitha
Author-Name: Singaraju Srinivasulu
Author-Name-First: Singaraju
Author-Name-Last: Srinivasulu
Author-Name: P S Latha Kalyampudi
Author-Name-First: P S
Author-Name-Last: Latha Kalyampudi
Author-Name: N. Sunanda
Author-Name-First: N.
Author-Name-Last: Sunanda
Author-Name: M Kalyani
Author-Name-First: M
Author-Name-Last: Kalyani
Author-Name: V Gopikrishna
Author-Name-First: V
Author-Name-Last: Gopikrishna
Author-Name: D Mythrayee
Author-Name-First: D
Author-Name-Last: Mythrayee
Title: Breast Lump Assessment: An IoT-Integrated Framework with Advanced Localization Techniques
Abstract: Internet of Things (IoT) influences many areas such as healthcare, transportation, agriculture, industry control, environment monitoring, and water management. Healthcare is a major area in which the IoT enables a more personalized form of healthcare through smart healthcare systems. Breast cancer is the second leading cause of death among women globally, and its incidence is increasing every year. Early-stage detection of breast cancer is an important research challenge in the medical field. The aim of this article is to design an IoT - Integrated framework with advanced localization techniques for breast lump assessment. Through the proposed framework, breast lumps are monitored periodically using sensor embedded wearable jacket. The lump position and its depth in the breast are evaluated using localization techniques in sensor organization. Model outcome is analysed for six periodic tests data. Results evidence that periodic monitoring of breast health using the designed framework is effective to fix abnormal lumps at the early stage.
Journal: Data and Metadata
Pages: 849
Volume: 4
Year: 2025
DOI: 10.56294/dm2025849
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:849:id:1056294dm2025849

Template-Type: ReDIF-Article 1.0
Author-Name: Nidhya R
Author-Name-First: Nidhya
Author-Name-Last: R
Author-Name: Pavithra D
Author-Name-First: Pavithra
Author-Name-Last: D
Author-Name: Smilarubavathy G
Author-Name-First: Smilarubavathy
Author-Name-Last: G
Author-Name: Mythrayee D
Author-Name-First: Mythrayee
Author-Name-Last: D
Title: Automated Weed Detection in Crop Fields Using Convolutional Neural Networks: A Deep Learning Approach for Smart Farming
Abstract: Deep learning is a part of modern machine learning that includes deep belief networks, deep neural networks, and recurrent neural networks. Computer vision, audio processing, and language comprehension are the most important sectors of deep learning. In many instances, these applications exceed human performance. In smart agriculture, deep learning gives novel ideas for increasing productivity and efficiency. Weed identification is an important application in crop areas that improves farming. This technology improves crop yields by identifying weeds. Also, it reduces resource wastage in agricultural practices. This paper presents a Convolutional Neural Network (CNN) model specifically designed to accurately identify and classify weeds using images of crop fields, augmented by the ImageNet dataset for enhanced feature extraction and model training. The model identifies essential characteristics, such as dimensions, form, spectral reflectance, and texture, to distinguish between crops and weeds. Unlike existing systems, our CNN-based approach achieves a high accuracy of 98%. This improvement enhances weed identification efficiency and reduces pesticide usage, therefore it minimising environmental impact.
Journal: Data and Metadata
Pages: 848
Volume: 4
Year: 2025
DOI: 10.56294/dm2025848
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:848:id:1056294dm2025848

Template-Type: ReDIF-Article 1.0
Author-Name: You He
Author-Name-First: You
Author-Name-Last: He
Author-Name: Mohd Jaki Bin Mamat
Author-Name-First: Mohd Jaki
Author-Name-Last: Bin Mamat
Author-Name: Li Sun
Author-Name-First: Li
Author-Name-Last: Sun
Title: Adaptation Potential Evaluation of Ornamental Motifs in Huizhou Heritage Buildings under Contemporary Context: An AHP-Fuzzy Comprehensive Evaluation Model Approach
Abstract: The ornamental motifs in the structural elements of traditional Huizhou dwellings, listed as a World Heritage Site, embody the convergence of Confucian philosophy and geomantic principles. They integrated into wooden, stone, and brick carvings, reflect the local populace’s aspirations for prosperity, longevity, and familial harmony, possessing important inheritance significance. This study aims to establish a model to evaluate the adaptation potential of ornamental motifs under contemporary context in traditional Huizhou dwellings by investigating their development history, categories, and cultural meanings. This model quantifies the factors influencing the adaptation potential of these ornamental motifs, calculates the weight of each factor. The analysis results indicate that, among all the factors, the most important primary indicator is cultural connotation (0,3118). The secondary indicators are visual appeal (0.2300), compatibility with modern aesthetic needs (0.0988) and noble, elegant (0.0836). Additionally, the overall grade of adaptation potential was determined by scoring and ranking motif samples based on the fuzzy comprehensive evaluation method. This approach enhances the objectivity, scientific rigor, and accuracy of the selection of ornamental motifs, which not only provides theoretical support and practical guidance for the sustainable development of Huizhou cultural symbols, but also serves methodological reference for related fields in other countries and regions.
Journal: Data and Metadata
Pages: 1104
Volume: 4
Year: 2025
DOI: 10.56294/dm2025847
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1104:id:1056294dm2025847

Template-Type: ReDIF-Article 1.0
Author-Name: José Seijas-Díaz
Author-Name-First: José
Author-Name-Last: Seijas-Díaz
Author-Name: Karla Martell-Alfaro
Author-Name-First: Karla
Author-Name-Last: Martell-Alfaro
Author-Name: John Ruiz-Cueva
Author-Name-First: John
Author-Name-Last: Ruiz-Cueva
Author-Name: Angélica Sanchez-Castro
Author-Name-First: Angélica
Author-Name-Last: Sanchez-Castro
Author-Name: Enrique Barbachán-Ruales
Author-Name-First: Enrique
Author-Name-Last: Barbachán-Ruales
Author-Name: Roberto Sánchez-Colina
Author-Name-First: Roberto
Author-Name-Last: Sánchez-Colina
Title: Sustainable e-commerce strategy as an alternative to preserve indigenous crafts
Abstract: Due to the limitations of handicraft trade in indigenous communities, we propose an e-commerce strategy as an alternative for preservation. The research adopted an ethnographic approach focused on understanding the economic dynamics of handicraft trade in four Shawi indigenous communities in the Peruvian Amazon. The e-commerce strategy is based on four fundamental pillars: brand, product, team, and marketing. Each pillar includes components that must be developed in a structured and coordinated manner to ensure the sustainability of the marketing process of indigenous handicrafts. The study presents an innovative approach by proposing a sustainable e-commerce strategy specifically designed for indigenous handicrafts.
Journal: Data and Metadata
Pages: 844
Volume: 4
Year: 2025
DOI: 10.56294/dm2025844
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:844:id:1056294dm2025844

Template-Type: ReDIF-Article 1.0
Author-Name: Deixy Ximena Ramos Rivadeneira
Author-Name-First: Deixy Ximena
Author-Name-Last: Ramos Rivadeneira
Author-Name: Javier Alejandro Jiménez Toledo
Author-Name-First: Javier Alejandro
Author-Name-Last: Jiménez Toledo
Title: Educational Robotics in STEM Education: Approaches and Trends from an Educational Management Perspective
Abstract: Introduction. Educational robotics was consolidated as a key tool in STEM education (Science, Technology, Engineering, and Mathematics), by fostering technical, cognitive, and socio-emotional skills in students. Objectives. This study aimed to conduct a systematic review of approaches and trends in the use of educational robotics within STEM learning contexts, incorporating a perspective focused on educational management. Methods. A structured literature review methodology was applied, using recognized academic databases to identify relevant studies published between 2020 and 2024. The analysis included implementation strategies, technological tools, pedagogical models, reported benefits, and challenges. Results. The findings showed that educational robotics strengthened computational thinking, problem-solving abilities, student motivation, and collaborative learning. However, persistent challenges were identified, such as unequal access to technology, the need for teacher training, and the lack of unified curricular standards, which posed significant issues for institutional management. Conclusions. It was concluded that, in addition to constructivist and inquiry-based learning approaches, emerging trends included the integration of artificial intelligence and adaptive learning environments. The findings provided valuable insights for school leaders, educators, and researchers interested in incorporating educational robotics in STEM contexts from a strategic management perspective.
Journal: Data and Metadata
Pages: 843
Volume: 4
Year: 2025
DOI: 10.56294/dm2025843
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:843:id:1056294dm2025843

Template-Type: ReDIF-Article 1.0
Author-Name: John Jairo Rivera Minayo
Author-Name-First: John Jairo
Author-Name-Last: Rivera Minayo
Author-Name: Javier Alejandro Jiménez Toledo
Author-Name-First: Javier Alejandro
Author-Name-Last: Jiménez Toledo
Author-Name: Deixy Ximena Ramos Rivadeneira
Author-Name-First: Deixy Ximena
Author-Name-Last: Ramos Rivadeneira
Author-Name: Jorge Albeiro Rivera Rosero
Author-Name-First: Jorge Albeiro
Author-Name-Last: Rivera Rosero
Title: Model for discovering knowledge about academic and administrative aspects for students at driving schools in San Juan De Pasto
Abstract: This paper proposes a comprehensive methodology for knowledge discovery in databases (KDD) applied to driving schools. The usefulness of clustering algorithms such as K-means and K-prototype to identify patterns in administrative and academic procedures was explored. During the study, three main stages were developed: process characterization, experimental design based on machine learning, and evaluation of the generated models. The results showed that K-prototype is particularly effective in handling mixed data, providing key recommendations to optimize both training processes and internal management. In addition, an application was designed to implement the model, highlighting the impact of educational data mining on dynamic analysis and informed decision making.
Journal: Data and Metadata
Pages: 842
Volume: 4
Year: 2025
DOI: 10.56294/dm2025842
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:842:id:1056294dm2025842

Template-Type: ReDIF-Article 1.0
Author-Name: Armando Sofonías Muñoz del Castillo
Author-Name-First: Armando Sofonías
Author-Name-Last: Muñoz del Castillo
Author-Name: Gladys Patricia Torres Murillo
Author-Name-First: Gladys Patricia
Author-Name-Last: Torres Murillo
Author-Name: Juan Carlos Salazar Losada
Author-Name-First: Juan Carlos
Author-Name-Last: Salazar Losada
Author-Name: Deixy Ximena Ramos Rivadeneira
Author-Name-First: Deixy Ximena
Author-Name-Last: Ramos Rivadeneira
Author-Name: Javier Alejandro Jiménez Toledo
Author-Name-First: Javier Alejandro
Author-Name-Last: Jiménez Toledo
Title: Computational Thinking: Empowering Elementary School Teachers to Transform the Classroom
Abstract: The effectiveness of a curricular proposal designed to develop Computational Thinking competencies in primary school teachers in Colombia was evaluated. The main objective was to determine whether the educational intervention was able to improve the level of competence of teachers. A quasi-experimental design was used with a group of 99 teachers in training and in practice, through a series of reflective workshops based on the curricular proposal, their learning process was intervened. A pretest and a posttest were applied to evaluate the level of competence. The results showed a significant increase in the level of competence. Substantial improvements were observed in the understanding of fundamental concepts and in the ability to solve problems using computational tools. It is important to recognize some limitations. The quasi-experimental design and the sample size could limit the generalizability of the findings. In addition, the duration of the intervention might not be sufficient to evaluate the long-term impact. Future studies with more robust designs and large samples are recommended to corroborate these results and explore the impact. Furthermore, it is suggested that quantitative analyses be complemented with qualitative studies to gain a deeper understanding of teachers' learning processes. Despite these limitations, the results support the effectiveness of the curricular proposal in developing CT skills in primary school teachers and suggest the need to implement similar programs in other educational contexts.
Journal: Data and Metadata
Pages: 837
Volume: 4
Year: 2025
DOI: 10.56294/dm2025837
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:837:id:1056294dm2025837

Template-Type: ReDIF-Article 1.0
Author-Name: Katherine Rincón Romero
Author-Name-First: Katherine
Author-Name-Last: Rincón Romero
Author-Name: Diana Isabel Cáceres Rivera
Author-Name-First: Diana Isabel
Author-Name-Last: Cáceres Rivera
Author-Name: Luis Alberto López Romero
Author-Name-First: Luis Alberto
Author-Name-Last: López Romero
Author-Name: Maria Andreina Pulido Montes
Author-Name-First: Maria Andreina
Author-Name-Last: Pulido Montes
Title: Content validation of the questionnaire to measure knowledge, attitudes and practices (KAP) in postpartum women based on the Colombian maternal-perinatal route
Abstract: Introducción: La ruta de atención materno-perinatal en Colombia busca garantizar la atención integral de las mujeres durante el embarazo, el parto y el puerperio. Para fortalecer esta atención, se requieren herramientas que recopilen los conocimientos, actitudes y prácticas (CAP) de las mujeres en el puerperio. La validación de estas herramientas permite el desarrollo de intervenciones eficaces que contribuyan a la reducción de la morbilidad y la mortalidad materna y perinatal. Objetivo: Validar cuestionario para medir los conocimientos actitudes y practicas (CAP) en mujeres puérperas basado en la ruta materno – perinatal colombiana. Metodología: Estudio descriptivo de validación de aspecto y de contenido de cuestionario para medir los conocimientos actitudes y practicas (CAP) basado en la ruta materno – perinatal, se distribuye en tres fases fase 1: revisión de literatura y diseño de la encuesta tipo CAP fase 2: validación contenido por 5 expertos. Resultados: Las pruebas de compresibilidad aplicadas a los 5 expertos reportaron una validez de contenido aceptable ya que CVC está por encima de 0.58. Conclusión: El cuestionario para medir los conocimientos actitudes y practicas (CAP) en mujeres puérperas basado en la ruta materno – perinatal para es válido para la aplicación clínica y ambulatoria de las mujeres que reciben atención en el marco de la ruta materno perinatal en el contexto colombiano. Este instrumento es un insumo para futuras investigaciones que propendan fortalecer las acciones para evitar la morbimortalidad materna-perinatal.
Journal: Data and Metadata
Pages: 834
Volume: 4
Year: 2025
DOI: 10.56294/dm2025834
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:834:id:1056294dm2025834

Template-Type: ReDIF-Article 1.0
Author-Name: Fausto Salazar-Fierro
Author-Name-First: Fausto
Author-Name-Last: Salazar-Fierro
Author-Name: Karla Herrera-Mayorga
Author-Name-First: Karla
Author-Name-Last: Herrera-Mayorga
Author-Name: Cayo Léon Fernández
Author-Name-First: Cayo Léon
Author-Name-Last: Fernández
Author-Name: Carpio Pineda-Manosalvas
Author-Name-First: Carpio
Author-Name-Last: Pineda-Manosalvas
Author-Name: Irving Reascos
Author-Name-First: Irving
Author-Name-Last: Reascos
Title: Comparison of tools for the creation of VLO for the subject of Algorithms. UTN case
Abstract: This paper compares the performance of three authoring tools, eXeLearning, H5P and Xerte, used to create Virtual Learning Objects (VLOs). The research stems from the need to identify the most appropriate tool for creating VLOs. The study aimed to evaluate these tools through an objective comparison based on 15 pre-defined criteria. For this purpose, a methodological approach of the UP4VED methodology for virtual environments was used in addition to implementing the ADDIE methodology for developing VLOs. The results showed that eXeLearning achieved the highest score with 10.38 points, followed by Xerte with 8.83 and H5P with 7.74. Subsequently, DeLone & McLean's success model was applied to assess students' perceptions of using OVAs in an online course, with a minimum favourability index of 81.92% for quality of service and a maximum of 92.80% for quality of information. These results confirm the acceptability and effectiveness of OVAs as digital resources that enhance self-learning in educational environments.
Journal: Data and Metadata
Pages: 771
Volume: 4
Year: 2025
DOI: 10.56294/dm2025771
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:771:id:1056294dm2025771

Template-Type: ReDIF-Article 1.0
Author-Name: Blanca Segovia-Rosero
Author-Name-First: Blanca
Author-Name-Last: Segovia-Rosero
Author-Name: Jaime Michilena-Calderón
Author-Name-First: Jaime
Author-Name-Last: Michilena-Calderón
Author-Name: Carlos Vásquez-Ayala
Author-Name-First: Carlos
Author-Name-Last: Vásquez-Ayala
Author-Name: Alejandra Pinto-Erazo
Author-Name-First: Alejandra
Author-Name-Last: Pinto-Erazo
Author-Name: Luis Suárez-Zambrano
Author-Name-First: Luis
Author-Name-Last: Suárez-Zambrano
Title: Microclimate condition monitoring system for the prevention of methane contamination in the methane contamination in compost production in Microfarms
Abstract: This work focuses on the development and implementation of a microclimate variable control system to prevent microbial contaminants in compost production, with the objective of investigating composting methods and how they can help reduce the production of methane, a greenhouse gas, and thus contribute to environmental care. The Action-Research methodology is used with the use of sensors that monitor data on environmental variables of ambient temperature, relative humidity and soil moisture, which are sent to an IoT platform where the necessary data are processed and generated. A specific infrastructure is designed for compost production, which includes a closed box lined with greenhouse plastic, a container for the compost, a piping system to maintain humidity, a heater to raise the temperature and a protective box for the sensors. Also included is the development and training of a neural network model that predicts methane production based on the above variables. The data show that composting at temperatures between 55-65 degrees Celsius, using aerobic biological methods, significantly reduces methane production by eliminating bacteria responsible for methane generation. The data collected and model predictions can be monitored remotely through the IoT platform. At the conclusion of the work, the compost generated was found to be suitable for micronization.
Journal: Data and Metadata
Pages: 770
Volume: 4
Year: 2025
DOI: 10.56294/dm2025770
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:770:id:1056294dm2025770

Template-Type: ReDIF-Article 1.0
Author-Name: Jaime G. Alvarado
Author-Name-First: Jaime G.
Author-Name-Last: Alvarado
Author-Name: Edgar V. Lema
Author-Name-First: Edgar V.
Author-Name-Last: Lema
Author-Name: Luis A. Cuaical
Author-Name-First: Luis A.
Author-Name-Last: Cuaical
Author-Name: Alexis D. Alvarado
Author-Name-First: Alexis D.
Author-Name-Last: Alvarado
Title: Regression Models for the Analysis of Telecommunications Data in Ecuador
Abstract: The research highlights the importance of mathematical models for better planning in both state and private companies, specifically through curvilinear regressions, to forecast future activities of the State Telecommunications Regulation and Control Agency of Ecuador (ARCOTEL), by analyzing variables such as the number of internet service users. The study was based on data preprocessing, which included homogeneity analysis and scale changes. Statistical tests such as the Mann-Kendall Test and the Helmert Test were applied to evaluate trends in time series. Subsequently, the data were fitted from a linear model to a polynomial one. Evaluation metrics included absolute, mean, and quadratic percentage errors, as well as coefficients of determination and correlation. The analysis showed that the sixth-degree polynomial fitting provided an adequate adjustment for the time series, with high correlation coefficients and relatively low absolute and mean percentage errors, suggesting acceptable accuracy between the fitted and actual values. Scaling the data facilitated comparison and analysis, eliminating biases. The research emphasized the importance of effective planning using mathematical models to predict economic activity in companies. The sixth-degree polynomial fitting proved to be effective in representing time series, with low errors and high accuracy. These methods were useful for planning and forecasting in the telecommunications sector, as exemplified by the analysis of ARCOTEL users.
Journal: Data and Metadata
Pages: 769
Volume: 4
Year: 2025
DOI: 10.56294/dm2025769
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:769:id:1056294dm2025769

Template-Type: ReDIF-Article 1.0
Author-Name: Arley Chuquin
Author-Name-First: Arley
Author-Name-Last: Chuquin
Author-Name: Brizeida Gámez
Author-Name-First: Brizeida
Author-Name-Last: Gámez
Author-Name: Marco Naranjo
Author-Name-First: Marco
Author-Name-Last: Naranjo
Author-Name: David Ojeda
Author-Name-First: David
Author-Name-Last: Ojeda
Title: Effect of 3D printing parameters on the mechanical characteristics of carbon fiber- reinforced PLA
Abstract: The comparative results of the mechanical behavior of carbon fiber-reinforced Polylactic Acid (PLA FC) specimens of two brands of filaments for printing by the Fused Deposition Modeling (FDM) process are presented. The experiments were carried out according to ASTM D638 14, using Type I specimens with the established dimensions. For the generation of the 3D model, parameters such as printing temperature, printing speed, density, and filling pattern were set. Cubic, gyroid, and triangular filling patterns were used, with filling densities of 40%, 60%, and 80%. For each configuration, a G-code was generated and used for the fabrication of each specimen. A total of 90 specimens were used, which were divided into two groups according to the brand. Subsequently, tensile tests were carried out to determine the mechanical properties by analyzing the stress-strain curves under the established conditions. Comparative analysis revealed that SUNLU's PLA FC filament achieves higher ultimate stress values, while Artillery's filament has a better ability to withstand deformation. Likewise, the filler pattern that withstood the greatest load was the cubic one.
Journal: Data and Metadata
Pages: 768
Volume: 4
Year: 2025
DOI: 10.56294/dm2025768
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:768:id:1056294dm2025768

Template-Type: ReDIF-Article 1.0
Author-Name: Fernando Ramírez-Paredes
Author-Name-First: Fernando
Author-Name-Last: Ramírez-Paredes
Author-Name: Victor Montenegro Simancas
Author-Name-First: Victor
Author-Name-Last: Montenegro Simancas
Author-Name: Doris Ascanta Otacoma
Author-Name-First: Doris
Author-Name-Last: Ascanta Otacoma
Title: Application of Systems Thinking in Scientific Research in Engineering and Science: An Interdisciplinary Approach
Abstract: This study analyzes the integration of systems thinking with the quantitative scientific method in the teaching of research in engineering and applied sciences. It demonstrates that systems thinking enables a better understanding of complex problems by considering the interrelation between variables, facilitating system modeling in various fields. The results obtained through statistical analysis indicate a significant improvement in students' academic performance after implementing the systems approach. The Wilcoxon test showed a p-value of 5.539 × 10⁻¹⁰, confirming that grades significantly improved in the second evaluation. Additionally, the Shapiro-Wilk normality test revealed that the analyzed variables (nota-1, nota-2, dt1, dt2) do not follow a normal distribution, justifying the use of non-parametric methods. Overall, systems thinking-based teaching reduces learning variability, helping students acquire a more structured knowledge. The findings suggest that this approach can be a valuable tool for teaching research methodology in engineering and applied sciences.
Journal: Data and Metadata
Pages: 767
Volume: 4
Year: 2025
DOI: 10.56294/dm2025767
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:767:id:1056294dm2025767

Template-Type: ReDIF-Article 1.0
Author-Name: Galo Hurtado Crespo
Author-Name-First: Galo
Author-Name-Last: Hurtado Crespo
Author-Name: Gina Pamela Novillo
Author-Name-First: Gina Pamela
Author-Name-Last: Novillo
Author-Name: Ana C. Umaquinga-Criollo
Author-Name-First: Ana C.
Author-Name-Last: Umaquinga-Criollo
Author-Name: Juan Marcelo Pérez
Author-Name-First: Juan Marcelo
Author-Name-Last: Pérez
Title: Development of Intelligent Therapeutic Devices: Integration of New Technologies for theCare of Elderly Adults in Cuenca: ARTRI Phase 2
Abstract: Population aging and the increasing prevalence of osteoarthritis pose a significant challenge for geriatric care. According to the VIII Population and Housing Census of Ecuador (2023), older adults constitute 9% of the national population, highlighting the need to implement innovative technologies for rehabilitation. Cuenca, Ecuador, is internationally recognized as one of the preferred cities for foreign older adults as a place of residence. Additionally, according to the National Institute of Statistics and Censuses (INEC), it has a high life expectancy of 79 years, making it an ideal environment for implementing technological solutions in geriatric rehabilitation. This study presents the evolution of ARTRI, an intelligent therapeutic device designed for motor stimulation in older adults with osteoarthritis. The new version incorporates an optimized electronic board with ESP32, improving processing capacity, connectivity, and energy efficiency. Additionally, a digital lock was implemented for code protection, and an acrylic structure was designed to enhance durability and ergonomics. The software has been upgraded with structured databases, cloud storage, and real-time monitoring, enabling efficient therapy supervision. The SCRUM methodology ensured an iterative and agile development process, while a demographic heat map facilitated the strategic distribution of the device in key institutions such as the University of Older Adults in Cuenca. The results demonstrate significant improvements in effectiveness, security, and user acceptance, establishing ARTRI as a scalable and innovative solution in digital health and geriatric rehabilitation.
Journal: Data and Metadata
Pages: 766
Volume: 4
Year: 2025
DOI: 10.56294/dm2025766
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:766:id:1056294dm2025766

Template-Type: ReDIF-Article 1.0
Author-Name: Jhonny Barzola
Author-Name-First: Jhonny
Author-Name-Last: Barzola
Author-Name: Francisco Naranjo
Author-Name-First: Francisco Naranjo
Author-Name-Last: Francisco Naranjo
Author-Name: Julio Guerra
Author-Name-First: Julio
Author-Name-Last: Guerra
Author-Name: Carlos Morán
Author-Name-First: Carlos
Author-Name-Last: Morán
Title: A Lead-Acid Battery Discharge Emulator with a Hardware-in-the-Loop System for Low-Power General Applications
Abstract: This study addresses the critical need for efficient laboratory methods to test battery performance, identified through a bibliometric analysis of research trends in battery technologies, integration challenges, lifespan, and recovery. A key focus is the detailed evaluation of lead-acid batteries and battery emulators in electronic applications. The study highlights the significance of lead-acid battery discharge emulators as cost-effective and safe alternatives to actual batteries in laboratory testing, enabling controlled testing conditions. The system behavior was validated by employing a resistive load module and making comparisons with manufacturer data. Using this system and a resistive load module, its behavior was verified by comparing it with the data provided by the manufacturer. The next phase of this work involved selecting components to emulate the battery's behavior using a switched-mode power supply controlled by a current source and a mathematical model chosen from the Matlab-Simulink tool through a Hardware-in-the-loop (HIL) system that interprets the battery's state of charge (SoC) to match the pre-configured model response to the lead-acid battery manufacturer's data. The emulator circuit was thoroughly evaluated against the model's expected responses to various charge levels, culminating in the implementation of an integrated prototype that simulates the discharge of lead-acid batteries in low-power applications and introduces a user-friendly interface, facilitating its application in general engineering studies. The work offers a valuable tool for battery research and development, promoting advancements in the study of lead-acid battery discharge in low-power applications.
Journal: Data and Metadata
Pages: 765
Volume: 4
Year: 2025
DOI: 10.56294/dm2025765
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:765:id:1056294dm2025765

Template-Type: ReDIF-Article 1.0
Author-Name: Julio Guerra
Author-Name-First: Julio
Author-Name-Last: Guerra
Author-Name: Isabel Quinde
Author-Name-First: Isabel
Author-Name-Last: Quinde
Author-Name: Gerardo Collaguazo
Author-Name-First: Gerardo
Author-Name-Last: Collaguazo
Author-Name: Andrés Martínez
Author-Name-First: Andrés
Author-Name-Last: Martínez
Author-Name: Germán León
Author-Name-First: Germán
Author-Name-Last: León
Title: Determining the Predominant Materials for Triboelectric Nanogenerator Fabrication: A Bibliometric and a Systematic Analysis
Abstract: Introduction: Triboelectric Nanogenerators (TENGs) have gained considerable attention as efficient energy-harvesting devices based on the triboelectric effect and electrostatic induction. Their performance is highly dependent on the materials used, which influence charged generation efficiency, durability, and application potential. Despite significant advancements in material design, a comprehensive analysis of the most frequently used materials and their impact on output performance remains limited. Methods: A bibliometric and systematic review was conducted to identify the predominant materials in TENG fabrication. Data was collected from Scopus and Web of Science, analyzing publication trends, material co-occurrence, and performance metrics. A co-occurrence network analysis was performed using VOSviewer, and experimental studies were systematically reviewed to evaluate the correlation between material selection and output voltage (Voc). Results: The analysis revealed that PTFE, FEP, PVDF, PDMS, and carbon-based nanomaterials are the most frequently utilized materials due to their high triboelectric polarity and electrical stability. The highest reported Voc values exceeded 400 V, with hybrid materials, nanostructured interfaces, and electrode engineering significantly enhancing TENG performance. Additionally, China, the United States, and South Korea were identified as the leading contributors to TENG research. Conclusions: This study quantitatively assesses TENG material trends and their impact on electrical performance. The findings offer valuable insights for researchers and engineers working on next-generation TENGs, facilitating the optimization of material selection for self-powered devices and large-scale energy harvesting applications.
Journal: Data and Metadata
Pages: 764
Volume: 4
Year: 2025
DOI: 10.56294/dm2025764
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:764:id:1056294dm2025764

Template-Type: ReDIF-Article 1.0
Author-Name: Gonzalo Espinosa
Author-Name-First: Gonzalo
Author-Name-Last: Espinosa
Author-Name: Julio Guerra
Author-Name-First: Julio
Author-Name-Last: Guerra
Author-Name: Francisco Naranjo
Author-Name-First: Francisco
Author-Name-Last: Naranjo
Author-Name: Luis Mosquera
Author-Name-First: Luis
Author-Name-Last: Mosquera
Title: Technological Innovations in Raptor Conservation: A Systematic Review of Methods and Applications
Abstract: Introduction: Raptors play a critical role in ecosystem stability, yet many species face significant population declines due to habitat loss, climate change, and human-induced mortality. Technological advancements such as satellite telemetry, machine learning, bioacoustics, and radar tracking have transformed raptor research, enabling precise monitoring and data-driven conservation strategies. Methods: A systematic review using the PRISMA methodology was conducted on the most relevant methodologies and technologies used in raptor research. Data from multiple studies employing satellite telemetry, habitat modeling, genetic analysis, bioacoustics, and conservation management tools were synthesized to evaluate their effectiveness. Results: Findings indicated that satellite telemetry remains the most widely used tool for tracking raptor movements, while machine learning and bioacoustics are emerging as powerful methods for habitat assessment. Population viability models frequently overlook key demographic factors, such as the age of first breeding, which can significantly impact conservation outcomes. Conclusions: Integrating advanced technologies with standardized methodologies is essential for improving raptor conservation. Future research should focus on refining predictive models, enhancing data-sharing platforms, and ensuring technological advancements translate into effective conservation policies.
Journal: Data and Metadata
Pages: 763
Volume: 4
Year: 2025
DOI: 10.56294/dm2025763
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:763:id:1056294dm2025763

Template-Type: ReDIF-Article 1.0
Author-Name: Gabriela Erazo
Author-Name-First: Gabriela
Author-Name-Last: Erazo
Author-Name: Carlos Benalcázar
Author-Name-First: Carlos
Author-Name-Last: Benalcázar
Author-Name: Daisy Imbaquingo
Author-Name-First: Daisy
Author-Name-Last: Imbaquingo
Author-Name: Julio Guerra
Author-Name-First: Julio
Author-Name-Last: Guerra
Title: Global Evolution of Research Productivity on Educational Policies Before and After COVID-19: A Bibliometric Analysis
Abstract: Introduction: The COVID-19 pandemic triggered unprecedented disruptions in education, catalyzing shifts in policy research worldwide. This study offers a comprehensive bibliometric analysis of educational policy scholarship from 2014 to 2024, focusing on changes in research output, thematic trends, and geographic patterns before and after the onset of COVID-19. Methods: A systematic search was conducted in Scopus and Web of Science using the keywords “educational legislation,” “educational law,” “educational policy,” and “education regulation” while excluding terms related to COVID-19 or the pandemic to avoid bias. Data were filtered by open-access status, exported, and deduplicated. Bibliometric indicators were calculated to examine publication volume and top publishing countries. VOSviewer was employed for keyword co-occurrence analysis. Results: The findings reveal a notable increase in publication output after 2020, particularly in countries like the United States, Spain, and emerging contributors like Chile and Brazil. Co-occurrence analysis indicates heightened attention to digital learning, equity, and public health dimensions in post-pandemic research. Conclusions: The pandemic has amplified existing themes of equity and curriculum reform and introduced new focal points, including distance education and socioemotional well-being. These insights inform policymakers and researchers seeking to develop resilient and inclusive educational frameworks in a rapidly changing global context.
Journal: Data and Metadata
Pages: 762
Volume: 4
Year: 2025
DOI: 10.56294/dm2025762
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:762:id:1056294dm2025762

Template-Type: ReDIF-Article 1.0
Author-Name: Carlos Sam
Author-Name-First: Carlos
Author-Name-Last: Sam
Author-Name: Daisy Imbaquingo
Author-Name-First: Daisy
Author-Name-Last: Imbaquingo
Author-Name: Roxana Albarracín
Author-Name-First: Roxana
Author-Name-Last: Albarracín
Author-Name: Verónica Massón
Author-Name-First: Verónica
Author-Name-Last: Massón
Author-Name: Julio Guerra
Author-Name-First: Julio
Author-Name-Last: Guerra
Title: Challenges to Accessibility in Virtual Distance Education: A Bibliometric Study
Abstract: Introduction: The study provided a comprehensive bibliometric analysis addressing the challenges for virtual distance education, primarily among populations facing significant barriers, such as individuals with disabilities, web accessibility constraints, and those affected by diverse socioeconomic or geopolitical factors. Methods: The analysis encompassed publications from the last ten years. A co-occurrence approach was employed to identify thematic trends, conceptual relationships, and geographic contributions. Data were filtered for relevance and subjected to quantitative indicators (e.g., publication counts by year) to track research evolution. Results: The findings indicated a marked increase in scholarly attention to enhancing accessibility in virtual education, as evidenced by rising annual publication volumes. The United States, China, and the United Kingdom emerged as the leading contributors to this scientific discourse. Key challenges identified included technological barriers, socio-economic disparities, and the need for inclusive pedagogical strategies targeting marginalized groups. Conclusions: The analysis underscored the importance of a multidimensional and inclusive approach to advancing digital education. By highlighting persistent obstacles and emerging solutions, the study informed policy, technological innovation, and instructional design aimed at fostering equitable participation in virtual distance education for underrepresented communities.
Journal: Data and Metadata
Pages: 761
Volume: 4
Year: 2025
DOI: 10.56294/dm2025761
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:761:id:1056294dm2025761

Template-Type: ReDIF-Article 1.0
Author-Name: Irving Reascos
Author-Name-First: Irving
Author-Name-Last: Reascos
Author-Name: Diego Trejo
Author-Name-First: Diego
Author-Name-Last: Trejo
Author-Name: Mauricio Rea
Author-Name-First: Mauricio
Author-Name-Last: Rea
Author-Name: Jayli De La Torre
Author-Name-First: Jayli
Author-Name-Last: De La Torre
Title: Descriptive Framework for Project Management for the Implantation of Enterprise IT Applications in SMEs
Abstract: Adopting an Enterprise IT Application (EITA) is a complex process that requires effective management to ensure success. Studies show that failure rates for such projects range from 30% to 70%, with 57% of implementations taking longer than expected, 54% exceeding budget, and 41% failing to achieve the anticipated benefits. This paper proposes a framework for managing EITA implementation projects in small and medium-sized enterprises (SMEs). The framework was developed by coding and analyzing interviews with 18 professionals who have experience with EITA implementations. The interviews were transcribed and analyzed using the qualitative analysis software MaxQDA. The outcome is a descriptive framework that outlines the necessary activities for each phase of an EITA implementation project, taking into account the specific limitations and constraints of SMEs. This framework is designed to be a practical tool for project managers overseeing EITA implementations in SMEs, as well as for professionals new to this type of project, providing them with a foundational structure for effective project management.
Journal: Data and Metadata
Pages: 760
Volume: 4
Year: 2025
DOI: 10.56294/dm2025760
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:760:id:1056294dm2025760

Template-Type: ReDIF-Article 1.0
Author-Name: Guillermo Mosquera
Author-Name-First: Guillermo
Author-Name-Last: Mosquera
Author-Name: Vladimir Bonilla
Author-Name-First: Vladimir
Author-Name-Last: Bonilla
Author-Name: Sofía Vergara
Author-Name-First: Sofía
Author-Name-Last: Vergara
Author-Name: Christian Rueda
Author-Name-First: Christian
Author-Name-Last: Rueda
Author-Name: Marcelo Moya
Author-Name-First: Marcelo
Author-Name-Last: Moya
Title: Design and Comparison of Controllers for a Robotic Transfemoral Prosthesis
Abstract: This study investigates the performance of four control strategies—Proportional-Derivative (PD), Feedforward-Feedback PD (FF-FB PD), Linear Quadratic Regulator (LQR), and Feedforward-Feedback LQR (FF-FB LQR)—implemented on a robotic transfemoral prosthesis. The performance metrics, including overshoot, settling time, trajectory tracking accuracy, and torque requirements, were evaluated using simulation models. The results indicate that the FF-FB LQR controller demonstrated superior performance, achieving the lowest overshoot (4.98%) and near-zero trajectory tracking error. All controllers required approximately 8.6 Nm of torque, suggesting consistent energy requirements across strategies despite their performance differences. The LQR controller exhibited the best stability, minimizing overshoot and improving overall system response. These findings highlight the advantages of feedforward-feedback control strategies, particularly the FF-FB LQR, for controlling robotic transfemoral prostheses with enhanced stability and accuracy.
Journal: Data and Metadata
Pages: 759
Volume: 4
Year: 2025
DOI: 10.56294/dm2025759
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:759:id:1056294dm2025759

Template-Type: ReDIF-Article 1.0
Author-Name: Muhyeeddin Alqaraleh
Author-Name-First: Muhyeeddin
Author-Name-Last: Alqaraleh
Author-Name: Mowafaq Salem Alzboon
Author-Name-First: Mowafaq
Author-Name-Last: Salem Alzboon
Author-Name: Mohammad Subhi Al-Batah
Author-Name-First: Mohammad
Author-Name-Last: Subhi Al-Batah
Author-Name: Lana Yasin Al Aesa
Author-Name-First: Lana Yasin
Author-Name-Last: Al Aesa
Author-Name: Mohammed Hasan Abu-Arqoub
Author-Name-First: Mohammed
Author-Name-Last: Hasan Abu-Arqoub
Author-Name: Rashiq Rafiq Marie
Author-Name-First: Rashiq
Author-Name-Last: Rafiq Marie
Author-Name: Firas Hussein Alsmadi
Author-Name-First: Firas
Author-Name-Last: Hussein Alsmadi
Title: Machine Learning-Based Quantification of Vesicoureteral Reflux with Enhancing Accuracy and Efficiency
Abstract: Vesicoureteral reflux (VUR) is traditionally assessed using subjective grading systems, leading to variability in diagnosis. This study explores the potential of machine learning to enhance diagnostic accuracy by analysing voiding cystourethrogram (VCUG) images. The objective is to develop predictive models that provide an objective and consistent approach to VUR classification. A total of 113 VCUG images were reviewed, with experts grading them based on VUR severity. Nine distinct image features were selected to build six predictive models, which were evaluated using 'leave-one-out' cross-validation. The analysis identified renal calyces’ deformation patterns as key indicators of high-grade VUR. The models—Logistic Regression, Tree, Gradient Boosting, Neural Network, and Stochastic Gradient Descent—achieved precise classifications with no false positives or negatives. High sensitivity to subtle patterns characteristic of different VUR grades was confirmed by substantial Area Under the Curve (AUC) values. This study demonstrates that machine learning can address the limitations of subjective VUR assessments, offering a more reliable and standardized grading system. The findings highlight the significance of renal calyces’ deformation as a predictor of severe VUR cases. Future research should focus on refining methodologies, exploring additional image features, and expanding the dataset to enhance model accuracy and clinical applicability.
Journal: Data and Metadata
Pages: 756
Volume: 4
Year: 2025
DOI: 10.56294/dm2025756
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:756:id:1056294dm2025756

Template-Type: ReDIF-Article 1.0
Author-Name: Mohammad Subhi Al-Batah Al-batah
Author-Name-First: Mohammad Subhi Al-Batah
Author-Name-Last: Al-batah
Author-Name: Mowafaq Salem Alzboon
Author-Name-First: Mowafaq Salem
Author-Name-Last: Alzboon
Author-Name: Muhyeeddin Alqaraleh
Author-Name-First: Muhyeeddin
Author-Name-Last: Alqaraleh
Author-Name: Mohammed Hasan Abu-Arqoub
Author-Name-First: Mohammed Hasan
Author-Name-Last: Abu-Arqoub
Author-Name: Rashiq Rafiq Marie
Author-Name-First: Rashiq
Author-Name-Last: Rafiq Marie
Title: Classifying Dental Care Providers Through Machine Learning with Features Ranking
Abstract: This study investigates the application of machine learning (ML) models for classifying dental providers into two categories—standard rendering providers and safety net clinic (SNC) providers—using a 2018 dataset of 24,300 instances with 20 features. The dataset, characterized by high missing values (38.1%), includes service counts (preventive, treatment, exams), delivery systems (FFS, managed care), and beneficiary demographics. Feature ranking methods such as information gain, Gini index, and ANOVA were employed to identify critical predictors, revealing treatment-related metrics (TXMT_USER_CNT, TXMT_SVC_CNT) as top-ranked features. Twelve ML models, including k-Nearest Neighbors (kNN), Decision Trees, Support Vector Machines (SVM), Stochastic Gradient Descent (SGD), Random Forest, Neural Networks, and Gradient Boosting, were evaluated using 10-fold cross-validation. Classification accuracy was tested across incremental feature subsets derived from rankings. The Neural Network achieved the highest accuracy (94.1%) using all 20 features, followed by Gradient Boosting (93.2%) and Random Forest (93.0%). Models showed improved performance as more features were incorporated, with SGD and ensemble methods demonstrating robustness to missing data. Feature ranking highlighted the dominance of treatment service counts and annotation codes in distinguishing provider types, while demographic variables (AGE_GROUP, CALENDAR_YEAR) had minimal impact. The study underscores the importance of feature selection in enhancing model efficiency and accuracy, particularly in imbalanced healthcare datasets. These findings advocate for integrating feature-ranking techniques with advanced ML algorithms to optimize dental provider classification, enabling targeted resource allocation for underserved populations.
Journal: Data and Metadata
Pages: 755
Volume: 4
Year: 2025
DOI: 10.56294/dm2025755
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:755:id:1056294dm2025755

Template-Type: ReDIF-Article 1.0
Author-Name: Fadi Ahmad Mohammad Abutabanjh
Author-Name-First: Fadi Ahmad
Author-Name-Last: Mohammad Abutabanjh
Author-Name: Abdel Rahman Khaled Mahmoud Alghzawi
Author-Name-First: Abdel Rahman
Author-Name-Last: Khaled Mahmoud Alghzawi
Title: Integrating Interior Design and Project Management: The Mediator's Role in Enhancing Organizational Creativity and Efficiency
Abstract: Introduction: The combination of interior design and project management is key to improving organizational creativity and efficiency. As firms compete for differentiation, it becomes necessary to optimize the design and management of workspaces. Methods: This research seeks to verify the hypothesis of the relationships between interior design quality, project management effectiveness, and organizational creativity and efficiency with the mediating effect of integration. A close-ended structured questionnaire was administered among 350 managers of Jordanian project management companies quantitatively to collect data. For the analysis the study conducted structural equation modeling (SEM) using Smart PLS 4. Results: The results shed light to confirm the existence of significant positive relationships between IDQ and OCE, PME and OCE, IDQ and INT, and PME and INT. Moreover, integration (INT) serves as a partial mediator between IDQ, PME and OCE. Conclusions: The study suggests that there is a need for a paradigm shift in project management approaches to promote the application of modern interior design techniques for improved organizational innovation and efficiency. Further studies should investigate these findings in other industries and other cultures.
Journal: Data and Metadata
Pages: 752
Volume: 4
Year: 2025
DOI: 10.56294/dm2025752
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:752:id:1056294dm2025752

Template-Type: ReDIF-Article 1.0
Author-Name: Khalil Omar
Author-Name-First: Khalil
Author-Name-Last: Omar
Author-Name: Izzeddin Matar
Author-Name-First: Izzeddin
Author-Name-Last: Matar
Author-Name: Jamal Zraqou
Author-Name-First: Jamal
Author-Name-Last: Zraqou
Author-Name: Hussam Fakhouri
Author-Name-First: Hussam
Author-Name-Last: Fakhouri
Author-Name: Jorge Marx Gómez
Author-Name-First: Jorge
Author-Name-Last: Marx Gómez
Title: AI for All: Bridging Accessibility and Usability Through User-Centered AI Design
Abstract: Artificial Intelligence (AI) technologies are promised to improve digital services and automate tasks. However, there are still significant barriers to ensuring that AI technologies are accessible and usable by a broad range of users. As AI solutions proliferate across mainstream systems and applications, design-based approaches that explicitly bring in inclusive and human-centric values have become critical. This paper provides a concerted look at user-centered design at the intersection of AI, accessibility, and usability, proposing a framework that cuts across technological, social, and regulatory challenges. Contributions include identifying existing work and current literature gaps, key research questions, and a methodology to explore how to optimize AI systems for the widest possible range of users. We anchor our recommendations with a use-inspired case of an AI-driven public transportation assistant for individuals with diverse physical and cognitive abilities to demonstrate how our framework could benefit real-world applications. On the basis of existing standards and theoretical insights, this paper argues that the design process should be proactive, iterative, and implemented with the participation of multiple stakeholders. In their design of AI systems, this is meant to make the systems adaptive to users, rather than users being adaptive to the AI systems, thus revealing that “AI for all” can indeed be a realistic and realizable paradigm.
Journal: Data and Metadata
Pages: 751
Volume: 4
Year: 2025
DOI: 10.56294/dm2025751
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:751:id:1056294dm2025751

Template-Type: ReDIF-Article 1.0
Author-Name: Mohammad Subhi Al-Batah
Author-Name-First: Mohammad
Author-Name-Last: Subhi Al-Batah
Author-Name: Muhyeeddin Alqaraleh
Author-Name-First: Muhyeeddin
Author-Name-Last: Alqaraleh
Author-Name: Mowafaq Salem Alzboon
Author-Name-First: Mowafaq
Author-Name-Last: Salem Alzboon
Author-Name: Abdullah Alourani
Author-Name-First: Abdullah
Author-Name-Last: Alourani
Title: Comparative performance of ensemble models in predicting dental provider types: insights from fee-for-service data
Abstract: Dental provider classification plays a crucial role in optimizing healthcare resource allocation and policy planning. Effective categorization of providers, such as standard rendering providers and safety net clinic (SNC) providers, enhances service delivery to underserved populations. To evaluate the performance of machine learning models in classifying dental providers using a 2018 dataset. A dataset of 24,300 instances with 20 features was analyzed, including beneficiary and service counts across fee-for-service (FFS), Geographic Managed Care, and Pre-Paid Health Plans. Providers were categorized by delivery system and patient age groups (0–20 and 21+). Despite 38.1% missing data, multiple machine learning algorithms were tested, including k-Nearest Neighbors (kNN), Decision Trees, Support Vector Machines (SVM), Stochastic Gradient Descent (SGD), Random Forest, Neural Networks, and Gradient Boosting. A 10-fold cross-validation approach was applied, and models were evaluated using AUC, classification accuracy (CA), F1-score, precision, and recall. Neural Networks achieved the highest AUC (0.975) and CA (94.1%), followed by Random Forest (AUC: 0.948, CA: 93.0%). These models effectively handled imbalanced data and complex feature interactions, outperforming traditional classifiers like Logistic Regression and SVM. Advanced machine learning techniques, particularly ensemble and deep learning models, significantly enhance dental workforce classification. Their integration into healthcare analytics can improve provider identification and resource distribution, benefiting underserved populations.
Journal: Data and Metadata
Pages: 750
Volume: 4
Year: 2025
DOI: 10.56294/dm2025750
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:750:id:1056294dm2025750

Template-Type: ReDIF-Article 1.0
Author-Name: Israa Musa Al-Momani Al-Momani
Author-Name-First: Israa Musa Al-Momani
Author-Name-Last: Al-Momani
Title: The impact of social networks on social relations in Jordan from a jurisprudential perspective
Abstract: Introduction: The research reviews the impact of social networks on social relations from a jurisprudential perspective, as it discusses the positive and negative changes these platforms have brought about in traditional relationship patterns. On the one hand, networks contribute to strengthening family ties, facilitating communication between friends, and spreading Islamic values through awareness campaigns, and on the other hand, their excessive use leads to social isolation and a decline in real interaction, in addition to the spread of immoral content that negatively affects values. Methods: The study used the descriptive analytical approach based on previous studies and data analysis. The research relies on Islamic jurisprudence as a reference to provide legal controls that control the use according to the principle of "warding off evils and bringing interests", where he stressed the importance of balancing benefits and harms. Results: The study found that moderate use of these networks enhances social relationships, while excessive leads to family disintegration and weakened real ties. The research recommends promoting community awareness about responsible use, developing Sharia standards, and supporting real-world interaction through social activities, with a focus on disseminating meaningful and positive content. Conclusions: It concluded that communication networks represent a dual-impact tool that requires managing them wisely to ensure public benefit and avoid negative effects, with the need to integrate Islamic values into digital use to enhance social relations and control digital behavior.
Journal: Data and Metadata
Pages: 748
Volume: 4
Year: 2025
DOI: 10.56294/dm2025748
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:748:id:1056294dm2025748

Template-Type: ReDIF-Article 1.0
Author-Name: Mohammad Musa Al-Momani
Author-Name-First: Mohammad Musa
Author-Name-Last: Al-Momani
Author-Name: Ahmad Awadallah
Author-Name-First: Ahmad
Author-Name-Last: Awadallah
Author-Name: Bilal Alnassar
Author-Name-First: Bilal
Author-Name-Last: Alnassar
Author-Name: AbdelRahman Ismail
Author-Name-First: AbdelRahman
Author-Name-Last: Ismail
Author-Name: Mohammed Nassoura
Author-Name-First: Mohammed
Author-Name-Last: Nassoura
Author-Name: Nabil Abudarwish
Author-Name-First: Nabil
Author-Name-Last: Abudarwish
Title: The Role of Business Intelligence in Supply Chain Optimization A Case Study of the Carrefour Market in Jordan
Abstract: Introduction: This study examines the role of Business Intelligence (BI) in the supply chain operations of Carrefour Market, a Jordanian retail leader. It uses a case study approach to assess the effectiveness of BI tools in inventory management, demand planning, and supplier development, aiming to improve efficiency and competitiveness. Methods: The study demonstrates how BI tools, such as real-time analytics, data visualization, and predictive modeling, have been used to solve key supply chain problems. The study also explores the process of implementing BI, including staff training and fostering a data intelligence culture. Results: The findings show that BI has positively impacted the company's key performance indicators (KPIs), such as inventory turnover rates, order fulfillment accuracy, and supply chain resiliency. Conclusions: The study concludes that BI resolves operational processes and provides strategic perspectives for development and responsiveness to the evolving retail climate. The case study suggests initiatives for other retailers to improve supply chain performance using BI technologies.
Journal: Data and Metadata
Pages: 747
Volume: 4
Year: 2025
DOI: 10.56294/dm2025747
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:747:id:1056294dm2025747

Template-Type: ReDIF-Article 1.0
Author-Name: Juan Manuel Andrade-Navia
Author-Name-First: Juan Manuel
Author-Name-Last: Andrade-Navia
Author-Name: William Alejandro Orjuela-Garzón
Author-Name-First: William Alejandro
Author-Name-Last: Orjuela-Garzón
Author-Name: Carlos Eduardo Aguirre Rivera
Author-Name-First: Carlos Eduardo
Author-Name-Last: Aguirre Rivera
Title: The impact of information systems on SME economic performance
Abstract: The objective of the article was to evaluate the relationship between information systems and the economic performance of SMEs in the Surcolombian region. For the above, the theoretical constructs of the variables were approached from a review of related literature. The study was of a quantitative nature, was approached from the deductive method and was a correlational type of research. The study population corresponded to managers, area chiefs, process directors and, in general, collaborators who occupy positions that involve decision making, while a non-probabilistic convenience sampling was used. A total of 160 surveys were applied. For the information systems and economic performance variables, instruments were elaborated by the researchers in a literature review. The reliability of the research was evaluated with Cronbach's alpha and composite reliability, while the validity was determined with the Mean Explained Variance and Confirmatory Factor Analysis, in all cases was satisfactory. The results obtained showed high levels of the information systems and economic performance variables, evidencing the relevance of these aspects in management. Likewise, the positive and significant relationship between information systems and economic performance in SMEs was verified. It is concluded that the relationship between the variables is strong in the processes that allow operational efficiency and the management of customers and marketing.
Journal: Data and Metadata
Pages: 746
Volume: 4
Year: 2025
DOI: 10.56294/dm2025746
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:746:id:1056294dm2025746

Template-Type: ReDIF-Article 1.0
Author-Name: Yasna Sandoval
Author-Name-First: Yasna
Author-Name-Last: Sandoval
Author-Name: Juana Roman-Zubeldia
Author-Name-First: Juana
Author-Name-Last: Roman-Zubeldia
Author-Name: Soledad Sacheri
Author-Name-First: Soledad
Author-Name-Last: Sacheri
Title: Public opinion towards stuttering: The differentiated beliefs and reactions between Chilean men and women.
Abstract: Introduction: Stuttering is a disorder that affects fluency and is associated with social stigma and negative beliefs. Public opinion about stuttering is fundamental to understanding the social and psychological dynamics faced by people who stutter. Beliefs and reactions to stuttering have been documented to vary across cultures, gender and age, which may influence understanding of the condition. Objective: To explore beliefs and reactions to stuttering in chilean men and women, and to assess whether gender influences public opinion about the condition. Method: Quantitative, descriptive, exploratory study. The culturally adapted survey 'the public opinion survey on human attributes-stuttering' was administered to 400 Chileans. Results: A high percentage of men (92.7%) and women (96.0%) believe that people with stuttering should hide their condition. Both sexes also share stigmatizing beliefs, although they recognize that people with stuttering can lead normal lives. In terms of reactions, both women and men expressed concern when someone stuttered, but also showed a willingness to behave normally in conversation. Conclusions: Beliefs and reactions to stuttering in Chile reflect a persistent stigma. Differences in perceptions may be influenced by socio-demographic factors such as gender, suggesting the need for educational interventions to promote better understanding of the condition.
Journal: Data and Metadata
Pages: 744
Volume: 4
Year: 2025
DOI: 10.56294/dm2025744
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:744:id:1056294dm2025744

Template-Type: ReDIF-Article 1.0
Author-Name: Hayder Najm
Author-Name-First: Hayder
Author-Name-Last: Najm
Author-Name: Mohammed Salih Mahdi
Author-Name-First: Mohammed
Author-Name-Last: Salih Mahdi
Author-Name: Sanaa Mohsin
Author-Name-First: Sanaa
Author-Name-Last: Mohsin
Title: Novel Key Generator-Based SqueezeNet Model and Hyperchaotic Map
Abstract: Cybersecurity threats are evolving at a very high rate, thus requiring the use of new methods to enhance the encryption of data and the communication process. In this paper, we propose a new key generation algorithm using the simultaneous use of the SqueezeNet deep learning model and hyperchaotic map to improve the hallmark of cryptographic security. The method employed in the proposed approach is built around the SqueezeNet model, which is lighter and faster in extracting features from the input image, and a hyperchaotic map, which is the main source of dynamic and non-trivial keys. The hyperchaotic map enhances complexity and randomness, securing the new cryptosystem against brute force and statistical attacks, and the key length depends on the number of features in the image. All our experiments prove that the proposed key generator works well in generating long, random, high entropy keys and is highly resistant to all typical cryptographic attacks. The promising profound synergy of deep learning and chaotic systems provides directions for the development of secure and effective methods of cryptography amid the exacerbated cyber threats. The technique was found to meet all the 15 criteria as tested through the NIST statistical test suite.
Journal: Data and Metadata
Pages: 743
Volume: 4
Year: 2025
DOI: 10.56294/dm2025743
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:743:id:1056294dm2025743

Template-Type: ReDIF-Article 1.0
Author-Name: Ola Ali Obead
Author-Name-First: Ola
Author-Name-Last: Ali Obead
Author-Name: Hakem Beitollahi
Author-Name-First: Hakem
Author-Name-Last: Beitollahi
Title: Hybrid Intrusion detection model-based density clustering approach and deep learning for detection of malicious traffic over network
Abstract: Intrusion detection in modern network environments poses significant challenges due to the increasing volume and complexity of cyber-attacks. This study proposes a hybrid approach integrating density-based clustering with deep learning to identify malicious traffic over the network. The proposed framework consists of two steps: clustering and classifying data. in clustering, the proposed model uses density clustering techniques to pre-process and segment network traffic into coherent clusters, thereby reducing data noise within clusters. The deep learning model analyses these clusters, accurately distinguishing between benign and malicious activities. The proposed model was tested over the benchmark dataset CIRA-CIC-DoHBrw-2020. The performance of the proposed model compared with standard machine learning models and the number of states of the artworks. The experiment result demonstrates that our hybrid model significantly improves detection accuracy and reduces false-positive rates compared to existing methods .
Journal: Data and Metadata
Pages: 739
Volume: 4
Year: 2025
DOI: 10.56294/dm2025739
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:739:id:1056294dm2025739

Template-Type: ReDIF-Article 1.0
Author-Name: Idam Ragil Widianto Atmojo
Author-Name-First: Idam Ragil Widianto
Author-Name-Last: Atmojo
Author-Name: Fadhil Purnama Adi
Author-Name-First: Fadhil
Author-Name-Last: Purnama Adi
Author-Name: Matsuri
Author-Name-First: Matsuri
Author-Name-Last: Matsuri
Author-Name: Moh Salimi
Author-Name-First: Moh
Author-Name-Last: Salimi
Author-Name: Lilia Halim
Author-Name-First: Lilia
Author-Name-Last: Halim
Author-Name: Ruslinawati Mohd Roslan
Author-Name-First: Ruslinawati
Author-Name-Last: Mohd Roslan
Author-Name: Dwi Yuniasih Saputri
Author-Name-First: Dwi Yuniasih
Author-Name-Last: Saputri
Author-Name: Devira Nur Pratama
Author-Name-First: Devira Nur
Author-Name-Last: Pratama
Author-Name: Roy Ardiansyah
Author-Name-First: Roy
Author-Name-Last: Ardiansyah
Author-Name: Fajar Danur Isnantyo
Author-Name-First: Fajar
Author-Name-Last: Danur Isnantyo
Title: Analysis of students' computational thinking skills in science and mathematics subject in fifth grade of elementary school
Abstract: Introduction: This study aimed to explore the components of computational thinking skills at the End of Term Summative Assessment (ETSA) and End of Year Summative Assessment (EYSA) questions as well as the profile of computational thinking skills in science and mathematics subjects of fifth-grade students in Surakarta City, Indonesia. Method: This was a qualitative study with a case study approach. Data were collected through analysis of ETSA and EYSA documents, which include test instruments and student answers. Sampling was carried out using purposive sampling by considering the level of cognitive development of students. Results: The results showed that each ETSA question included components of decomposition, pattern recognition, abstraction, and algorithm design. Meanwhile, each EYSA question included different components from one question to another. The profile of students' computational thinking skills showed variation in the success of answering questions, with significant differences between components and types of questions. Conclusions: This study concludes that ETSA questions are more consistent in covering all components of computational thinking skills compared to EYSA. The profile of student skills varies, with some components showing lower results. The implications of these findings include the need for adjustments in the curriculum and teaching methods to emphasize more evenly distributed mastery of computational thinking skills. Further research should explore the causes of variation in mastery of these skills and develop more comprehensive evaluation instruments.
Journal: Data and Metadata
Pages: 738
Volume: 4
Year: 2025
DOI: 10.56294/dm2025738
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:738:id:1056294dm2025738

Template-Type: ReDIF-Article 1.0
Author-Name: Abdul Razzak Alshehadeh
Author-Name-First: Abdul
Author-Name-Last: Razzak Alshehadeh
Author-Name: Murad Al-Zaqeba
Author-Name-First: Murad
Author-Name-Last: Al-Zaqeba
Author-Name: Ali Qtaishat
Author-Name-First: Ali
Author-Name-Last: Qtaishat
Author-Name: Haneen Al-khawaja
Author-Name-First: Haneen
Author-Name-Last: Al-khawaja
Author-Name: Eman Al-Wreikat
Author-Name-First: Eman
Author-Name-Last: Al-Wreikat
Title: Digitalization and Sustainable Development Goals: Enhancing Electronic Financial Reports Quality in Banking
Abstract: Introduction: This study captures the effect of digitalization and Sustainable Development Goals (SDGs) on the quality of electronic financial reports in one developing nation, specifically the Jordanian banking sector. Given the ever-evolving landscape where financial institutions embrace digital technologies while integrating sustainability principles into their operations, it is essential to examine the interplay of both trends in enhancing the transparency and accuracy of their financial reporting. Methods: Data were collected from the banking professionals in Jordan using a structured questionnaire. The responses of two hundred and four valid respondents were analyzed accordingly using the Partial Least Squares Structural Equation Modeling (PLS-SEM). It tested the relationships of digitization, SDG integration, and the quality of electronic financial reporting. Results: The results indicate that digitalization and SDG integration positively affect the quality of electronic financial reports. On the other hand, SDG integration (Coefficient = 0.214) was more substantial than digitalization (Coefficient = 0.150), which indicates that good governance, environmental, and social improvements add value to financial reporting by providing more transparency and accuracy. Moreover, these digital technologies facilitate the finance departments' data governance, reporting, and regulatory compliance. Conclusions: This study contributes to the literature on the importance of digitalization and sustainability integration for SME financial reporting in Jordanian banks. Banks that adopt digital tools and align with SDGs are also better prepared to meet stakeholder expectations and comply with regulatory requirements. Further research can investigate these factors in determining the long-term economic sustainability of these variables and how they ultimately shape the financial reporting standards of these developing economies.
Journal: Data and Metadata
Pages: 734
Volume: 4
Year: 2025
DOI: 10.56294/dm2025734
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:734:id:1056294dm2025734

Template-Type: ReDIF-Article 1.0
Author-Name: M. Fariz Fadillah Mardianto
Author-Name-First: M. Fariz
Author-Name-Last: Fadillah Mardianto
Author-Name: Izyan A. Wahab
Author-Name-First: Izyan A.
Author-Name-Last: Wahab
Author-Name: Deshinta Arrova Dewi
Author-Name-First: Deshinta
Author-Name-Last: Arrova Dewi
Author-Name: Theofillus Vebriano
Author-Name-First: Theofillus
Author-Name-Last: Vebriano
Author-Name: Ilham Jaya Saputra
Author-Name-First: Ilham
Author-Name-Last: Jaya Saputra
Title: Perceptions of Paramedics and Non-paramedics Related to Existence of Telemedicine in ASEAN Based on SEM-PLS
Abstract: Introduction: this article explored the potential of telemedicine in ASEAN amidst challenges regarding health facilities. This article analyzed perceptions of paramedics and non-paramedics regarding the implementation of telemedicine in ASEAN to further improve health services, as stated in the third Sustainable Development Goals (SDGs). Methods: perceptions of paramedics and non-paramedics were analyzed using Structural Equation Modeling-Partial Least Square (SEM-PLS). A sample of 500 were taken from paramedics and non-paramedics across ASEAN countries using purposive sampling technique. The data was analyzed descriptively and the outer and inner model was evaluated. Results: outer model evaluation showed the indicators were valid and reliable. Meanwhile, inner model evaluation showed a moderate influence of the indicators on Telemedicine satisfaction and hypothesis testing showed that Doctor Reliability, Responsiveness, and Assurance variables significantly influence paramedics and non-paramedics perceptions of Telemedicine satisfaction. Conclusions: an application is needed that provides telemedicine services universally in ASEAN by strengthening features regarding Doctor Reliability, Responsiveness, and Assurance, to connect patients with the nearest doctor. This application can strengthen Telemedicine usage in Southeast Asia.
Journal: Data and Metadata
Pages: 732
Volume: 4
Year: 2025
DOI: 10.56294/dm2025732
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:732:id:1056294dm2025732

Template-Type: ReDIF-Article 1.0
Author-Name: Amer Hani Al-Qassem
Author-Name-First: Amer Hani
Author-Name-Last: Al-Qassem
Author-Name: Hazim Ryad Momani
Author-Name-First: Hazim
Author-Name-Last: Ryad Momani
Author-Name: Zeyad Alkhazali
Author-Name-First: Zeyad
Author-Name-Last: Alkhazali
Author-Name: Rawan Alshawabkeh
Author-Name-First: Rawan
Author-Name-Last: Alshawabkeh
Author-Name: Lina Hamdan Al-Abbadi
Author-Name-First: Lina Hamdan
Author-Name-Last: Al-Abbadi
Author-Name: Sufian Nathmi Al Sheyab
Author-Name-First: Sufian Nathmi
Author-Name-Last: Al Sheyab
Author-Name: Trad EshIwI JIzza Anawarseh
Author-Name-First: Trad EshIwI
Author-Name-Last: JIzza Anawarseh
Author-Name: Mohammad Alzoubi
Author-Name-First: Mohammad
Author-Name-Last: Alzoubi
Author-Name: Ahmad Bani Ahmad
Author-Name-First: Ahmad
Author-Name-Last: Bani Ahmad
Title: The Impact of Technological Advancements on Human Resource Management Practices: Adapting to the Digital Era
Abstract: Introduction: In today's digital era, the process of digitalization has increasingly become a significant factor for organizations striving to enhance productivity, efficiency, and competitiveness. The adoption of technologies such as Artificial Intelligence (AI), automation, and cloud platforms has revolutionized various business operations, especially in Human Resource Management (HRM). These technologies have been pivotal in transforming HR practices by improving data management, enhancing staff training, and streamlining communication processes. This research aims to examine the role and impact of digital technologies on HRM practices, with a focus on making these processes more efficient and faster in the digital age. Methods: A mixed-methods approach was adopted for this research. Qualitative data was collected through a review of journals and articles accessed via Google Scholar, which provided insights into the broader trends of digital technology use in HRM. For quantitative data, a survey was conducted using Google Forms, targeting 200 HR managers across different industries, including retail, automobile, and others. The survey consisted of 10 close-ended questions to capture the extent to which digital technologies impact HRM practices. The qualitative data was analyzed using thematic analysis, identifying recurring themes and patterns in the responses, while the quantitative data was processed through statistical analysis using the SPSS tool. Results: The study revealed that digital technologies play a vital role in transforming HRM practices in various industries. These technologies streamline key HR functions such as recruitment and talent acquisition, enabling faster and more informed decision-making. Additionally, they contribute significantly to automating training and development processes, as well as performance management. Overall, digital technologies have become essential for improving the efficiency, effectiveness, and strategic capabilities of HRM. Conclusions: This research underscores the critical role that digital technologies play in enhancing HRM practices across diverse industries. By automating and streamlining HR functions, these technologies enable HR managers to make more informed decisions, reduce operational costs, and improve overall business performance. Organizations should continue to embrace digital transformation to remain competitive and meet the evolving demands of the workforce. The findings offer valuable insights into the significant benefits of digital technologies for HRM and provide a foundation for further exploration into the integration of such technologies within organizations.
Journal: Data and Metadata
Pages: 731
Volume: 4
Year: 2025
DOI: 10.56294/dm2025731
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:731:id:1056294dm2025731

Template-Type: ReDIF-Article 1.0
Author-Name: T K Shoukath
Author-Name-First: T K
Author-Name-Last: Shoukath
Author-Name: Midhun Chakkaravarthy
Author-Name-First: Midhun
Author-Name-Last: Chakkaravarthy
Title: Predictive analytics in education: machine learning approaches and performance metrics for student success – a systematic literature review
Abstract: Higher education institutions rely on student performance to improve grades and enhance academic outcomes. Universities face challenges in evaluating student achievement, providing high-quality instruction, and analyzing performance in a dynamic and competitive context. However, due to limited research on prediction techniques and the critical factors influencing performance, making accurate forecasts is challenging. The utilization of educational data and machine learning has the potential to improve the learning environment. Ensemble models in educational data mining enhance accuracy and robustness by combining predictions from multiple models. Approaches such as bagging and boosting effectively mitigate the risk of overfitting. Machine learning techniques, including Support Vector Machines, Random Forests, K-Nearest Neighbors, Artificial neural networks, Decision Trees, and convolutional neural networks, have been employed in performance prediction. In this study, we examined 85 papers that focused on student performance prediction using machine learning, data mining, and deep learning techniques. The thorough analysis underscores the importance of various factors in forecasting academic performance, offering valuable insights for improving educational strategies and interventions in higher education contexts.
Journal: Data and Metadata
Pages: 730
Volume: 4
Year: 2025
DOI: 10.56294/dm2025730
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:730:id:1056294dm2025730

Template-Type: ReDIF-Article 1.0
Author-Name: Darío Cervantes
Author-Name-First: Darío
Author-Name-Last: Cervantes
Author-Name: Edgar Morales
Author-Name-First: Edgar
Author-Name-Last: Morales
Title: Perception and use of ChatGPT among university students. Analysis based on K-Modes
Abstract: The rapid advancement of artificial intelligence has transformed the educational landscape, with tools such as ChatGPT gaining popularity among university students. This study analyses the perception and use of ChatGPT in Ecuadorian higher education, exploring its impact on academic performance and associated ethical concerns. The objective was to examine how students from various universities and majors perceive the usefulness of ChatGPT in their academic activities. A non-probabilistic cross-sectional approach was employed, using a structured survey applied to 256 students from six Ecuadorian universities. The methodology included the use of the K-Modes algorithm to cluster the students' categorical responses, visualised through principal component analysis. The results revealed significant differences in the use of ChatGPT according to gender, university and career. Female and engineering students showed higher adoption. Four distinct groups of users were identified, reflecting diverse perceptions of the usefulness and risks of ChatGPT. The discussion addressed variability in ChatGPT adoption, highlighting factors such as technological access and ethical concerns. A tension was noted between perceived benefits and potential risks, such as over-reliance and plagiarism. In conclusion, the study highlights the importance of understanding students' perceptions of ChatGPT to effectively integrate these tools into higher education, while simultaneously addressing ethical concerns and encouraging responsible use of AI in academia.
Journal: Data and Metadata
Pages: 729
Volume: 4
Year: 2025
DOI: 10.56294/dm2025729
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:729:id:1056294dm2025729

Template-Type: ReDIF-Article 1.0
Author-Name: Edgar Rolando Morales Caluña
Author-Name-First: Edgar Rolando
Author-Name-Last: Morales Caluña
Author-Name: Dario Javier Cervantes Diaz
Author-Name-First: Dario Javier
Author-Name-Last: Cervantes Diaz
Author-Name: Cristian Ismael Morales Caluña
Author-Name-First: Cristian Ismael
Author-Name-Last: Morales Caluña
Author-Name: Fernando Xavier Altamirano Capelo
Author-Name-First: Fernando Xavier
Author-Name-Last: Altamirano Capelo
Title: Factors Influencing Satisfaction and Continued Use Intention of ChatGPT in the Academic Context: Analysis Using Structural Equation Modeling
Abstract: Artificial intelligence tools like ChatGPT have transformed higher education by facilitating academic tasks and improving autonomous learning. However, their acceptance and continued use depend on factors such as compatibility, efficiency, satisfaction, and intention to use. This study applies Structural Equation Modeling (SEM) to evaluate these relationships. To analyze how these factors influence user satisfaction and continued use intentions of ChatGPT among university students. Study involved 210 students from Ecuadorian universities. Validated surveys were used to assess six constructs: compatibility, efficiency, perceived ease of use, perceived usefulness, satisfaction, and continued use intention. Data were analyzed using Exploratory and Confirmatory Factor Analysis, followed by SEM for model adjustment. The findings identified a four-factor structure explaining 63% of the variance. Fit indices were acceptable (CFI = 0.876, SRMR = 0.064), with significant factor loadings (p<0.001). However, high correlations among factors suggested conceptual redundancy. ChatGPT is perceived as a useful, satisfying tool aligned with students' learning styles, promoting its continued adoption. Nonetheless, refining the factor structure could improve the model.
Journal: Data and Metadata
Pages: 728
Volume: 4
Year: 2025
DOI: 10.56294/dm2025728
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:728:id:1056294dm2025728

Template-Type: ReDIF-Article 1.0
Author-Name: Tarimantan Sanberto Saragih
Author-Name-First: Tarimantan
Author-Name-Last: Sanberto Saragih
Author-Name: Ratminto
Author-Name-First: Ratminto
Author-Name-Last: Ratminto
Author-Name: Achmad Djunaedi
Author-Name-First: Achmad
Author-Name-Last: Djunaedi
Author-Name: Hakimul Ikhwan
Author-Name-First: Hakimul
Author-Name-Last: Ikhwan
Author-Name: Arief Dahyan
Author-Name-First: Arief
Author-Name-Last: Dahyan
Author-Name: An Nisa Pramasanti
Author-Name-First: An
Author-Name-Last: Nisa Pramasanti
Author-Name: Fergie Stevi Mahaganti
Author-Name-First: Fergie
Author-Name-Last: Stevi Mahaganti
Title: Unlocking Digital Potential: Technological Capability as a Key Moderator-Mediator in Migrant Workers' Use of JMO Mobile
Abstract: This study aims to examine the factors influencing technology adoption (TA) among Indonesian migrant workers, particularly in the use of the JMO Mobile application. The research integrates technological capability (TC) as both a moderating and mediating variable within the TAM to provide a more comprehensive understanding of adoption behavior. Specifically, the study investigates the impact of Perceived Ease of Use (PEOU), Perceived Benefits (PB), and organizational support on TC and TA. The research employs a quantitative approach using a survey method, collecting data from Indonesian migrant workers who use the JMO Mobile application. PLS-SEM is applied to analyze the links among the variables. The findings reveal that PEOU, PB, and organizational support significantly influence both TC and TA. Furthermore, TC serves as a moderator, strengthening the link between PEOU and TA, as well as between PB and TA. Additionally, TC functions as a mediator between PEOU and TA, and between organizational support and TA, indicating its critical role in facilitating the adoption process. These findings have practical implications for improving the technological engagement of Indonesian migrant workers. By enhancing user-friendly features, providing clear benefits, and offering organizational support through training programs, applications like JMO Mobile can better meet migrant workers' needs. The study contributes to the theoretical expansion of the TAM by incorporating TC as a key factor influencing adoption. The originality of this research lies in its focus on Indonesian migrant workers, a group that has received limited attention in TA studies, and its integration of TC as both a moderating and mediating variable.
Journal: Data and Metadata
Pages: 727
Volume: 4
Year: 2025
DOI: 10.56294/dm2025727
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:727:id:1056294dm2025727

Template-Type: ReDIF-Article 1.0
Author-Name: Ade Oktarino
Author-Name-First: Ade
Author-Name-Last: Oktarino
Author-Name: Sarjon Defit
Author-Name-First: Sarjon
Author-Name-Last: Defit
Author-Name: YUhandri
Author-Name-Last: YUhandri
Title: Development of a Hybrid CNN-BiLSTM Architecture to Enhance Text Classification Accuracy
Abstract: Introduction: Natural Language Processing (NLP) has experienced significant advancements to address the growing demand for efficient and accurate text classification. Despite numerous methodologies, achieving a balance between high accuracy and model stability remains a critical challenge. This research aims to explore the implementation of a hybrid architecture integrating Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) with FastText embeddings, targeting effective text classification. Methods: The proposed hybrid architecture combines the CNN's ability to capture local patterns and BiLSTM's temporal feature extraction capabilities, enhanced by FastText embeddings for richer word representation. Regulatory mechanisms such as Dropout and Early Stopping were employed to mitigate overfitting. Comparative experiments were conducted to evaluate the performance of the model with and without Early Stopping. Results: The experimental findings reveal that the model without Early Stopping achieved a remarkable accuracy of 99%, albeit with a higher susceptibility to overfitting. Conversely, the implementation of Early Stopping resulted in a stable accuracy of 73%, demonstrating enhanced generalization capabilities while preventing overfitting. The inclusion of Dropout further contributed to model regularization and stability. Conclusions: This study underscores the significance of balancing accuracy and stability in deep learning models for text classification. The proposed hybrid architecture effectively combines the strengths of CNN, BiLSTM, and FastText embeddings, providing valuable insights into the trade-offs between achieving high accuracy and ensuring robust generalization. Future work could further explore optimization techniques and datasets for broader applicability.
Journal: Data and Metadata
Pages: 726
Volume: 4
Year: 2025
DOI: 10.56294/dm2025726
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:726:id:1056294dm2025726

Template-Type: ReDIF-Article 1.0
Author-Name: Re Chen
Author-Name-First: Re
Author-Name-Last: Chen
Author-Name: Heidi Tan Yeen Ju
Author-Name-First: Heidi Tan
Author-Name-Last: Yeen Ju
Author-Name: Neo Mai
Author-Name-First: Neo
Author-Name-Last: Mai
Title: Applied research on data analysis in creative multimedia courses in universities
Abstract: Creative multimedia has become a key component of innovation in today's quickly changing digital world, blending technology and artistry to provide captivating, interactive, visual, and aural experiences. Universities worldwide are offering specialized courses in creative multimedia to equip students with skills for industries like entertainment, advertising, education, and digital communication. This course integrates graphic design, animation, video production, game development, and virtual reality, fostering a holistic knowledge atmosphere. The purpose of the research is to establish the application of data analysis in creative multimedia courses in universities to enhance student achievement evaluation and foster innovative and technical development in university-level graphic design courses focused on packaging design. The Efficient African Buffalo Tuned Adaptive Random Forest (EAB-ARF) is applied to assess student performance based on various criteria, including creativity, technical proficiency, and the effectiveness of packaging designs. Data collection includes student performance, design samples, teacher ratings, and packaging design. The data was preprocessed using data cleaning and normalization from the acquired data. EAB is used to select the features from data, and ARF is employed to assess student performance and enhance creativity. The recommended EAB-ARF outperforms all other models with the highest accuracy values of 95.8%, (95.6%) precision, (99.2%) recall, and (97.6%) F1-score. This illustrates how EAB-ARF performs well across all evaluation categories and has a superior ability to forecast student results.
Journal: Data and Metadata
Pages: 725
Volume: 4
Year: 2025
DOI: 10.56294/dm2025725
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:725:id:1056294dm2025725

Template-Type: ReDIF-Article 1.0
Author-Name: Lamyae Benhlima
Author-Name-First: Lamyae
Author-Name-Last: Benhlima
Author-Name: Mohammed El Haj Tirari
Author-Name-First: Mohammed
Author-Name-Last: El Haj Tirari
Title: Advanced Weighted Approach for Class Imbalance Learning
Abstract: Predictive models derived from statistical learning techniques often assume that data originate from simple random sampling, thus assigning equal weight to all individuals. However, this assumption faces two significant challenges: it overlooks the complexity of real samples, where individuals may have different sampling weights, and it introduces a bias toward the majority class in imbalanced datasets. In this study, we propose an innovative approach that introduces differentiated weights for individuals by adjusting sample weights through calibration. This method aims to address class imbalance issues while improving the representativeness of samples. We applied it to the Support Vector Machine. Additionally, we developed an improved adjusted weighting approach to further enhance model performance, particularly for the minority class. This improved version combines two widely used techniques for handling class imbalances (resampling and cost-sensitive learning) by first balancing the classes through resampling, then applying adjusted sample weights during training. We evaluated the performance of our approach on real datasets with varying levels of imbalance using multiple evaluation metrics. The results were compared with various conventional methods commonly employed to address class imbalance. Our findings demonstrate the relevance and generalizability of our proposed algorithms, which often achieve performance equal to or better than that of established competing methods. Overall, our methodology not only corrects sample imbalances but also ensures a more accurate representation of the target population in the model, making it a robust and flexible solution for real-world imbalanced classification challenges.
Journal: Data and Metadata
Pages: 719
Volume: 4
Year: 2025
DOI: 10.56294/dm2025719
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:719:id:1056294dm2025719

Template-Type: ReDIF-Article 1.0
Author-Name: Nora Abia
Author-Name-First: Nora
Author-Name-Last: Abia
Author-Name: Hanane Sadeq
Author-Name-First: Hanane
Author-Name-Last: Sadeq
Author-Name: Aziz Soulhi
Author-Name-First: Aziz
Author-Name-Last: Soulhi
Author-Name: Ibtissam MEDARHRI
Author-Name-First: Ibtissam
Author-Name-Last: MEDARHRI
Title: Modeling Entrepreneurial Intentions in Moroccan Higher Education: Bridging Academia and Entrepreneurship with Artificial Neural Networks.
Abstract: This study explores the determinants influencing entrepreneurial intentions among higher education students in Morocco, utilizing both traditional statistical methods and Artificial Neural Networks (ANN) to predict entrepreneurial intention. The research focuses on variables such as desirability, social norms, self-concept, and academic context, and assesses their impact on students' propensity toward entrepreneurship. A survey was conducted with 300 engineering and master's students from Higher School of Textile and Clothing Industries (ESITH) in Casablanca. The statistical analysis revealed significant relationships between entrepreneurial intention and factors such as desirability, social norms, and self-concept, while the feasibility factor showed a limited influence. ANN was employed to model the complex, non-linear relationships between these variables, providing deeper insights into the predictive dynamics of entrepreneurial intentions. The ANN model demonstrated high accuracy, highlighting the importance of desirability and social norms as primary drivers, followed by self-concept and academic context. The study concludes with recommendations to enhance entrepreneurial intention through targeted educational strategies, emphasizing the role of practical experiences and skill-building programs. This research contributes a novel approach to understanding and fostering entrepreneurship in academic settings through the integration of ANN, offering predictive modeling capabilities that could inform future educational policies and entrepreneurial programs.
Journal: Data and Metadata
Pages: 722
Volume: 4
Year: 2025
DOI: 10.56294/dm2025722
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:722:id:1056294dm2025722

Template-Type: ReDIF-Article 1.0
Author-Name: Mohammed MHMOOD Al matalka
Author-Name-First: Mohammed
Author-Name-Last: MHMOOD Al matalka
Author-Name: Khaled (Yousef Issa) Alshboul
Author-Name-First: Khaled
Author-Name-Last: (Yousef Issa) Alshboul
Title: The Role of Big Data in Enhancing Communication Language Among Students: The Impact of Digital Systems in Private Universities
Abstract: Introduction: This research investigates how Big Data, in conjunction with digital systems, enhances student communication and social interactions at private universities. The increasing integration of Big Data with e-learning platforms presents significant opportunities for fostering better academic collaboration and student engagement in the digital era. Methods: A sample of 275 students from various private universities was selected for this study. The research employed a mixed-methods approach to explore the phenomena of academic communication and collaboration in relation to Big Data. Quantitative data were gathered via surveys to assess students' perceptions of Big Data’s impact on their academic communication. Structural Equation Modeling (SEM) was used as the analytical framework to examine the relationships between Big Data, digital systems, and communication practices. Qualitative data were collected through interviews to further validate the numerical findings. Results: The research revealed that students' communication skills were significantly improved when they engaged with Big Data and digital systems. The integration of advanced technological systems within universities mediates academic interaction by connecting Big Data with student relationships, resulting in improved collaboration and communication patterns. Conclusions: The study highlights the critical role of Big Data and digital systems in transforming student communication and academic collaboration at private universities. The findings suggest that these technologies not only enhance students' language and communication skills but also play a pivotal role in fostering better academic relationships. This research provides valuable insights into how Big Data and digital systems can be utilized to improve higher education practices and student engagement in the future.
Journal: Data and Metadata
Pages: 720
Volume: 4
Year: 2025
DOI: 10.56294/dm2025720
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:720:id:1056294dm2025720

Template-Type: ReDIF-Article 1.0
Author-Name: Chandupatla Deepika
Author-Name-First: Chandupatla
Author-Name-Last: Deepika
Author-Name: Swarna Kuchibhotla
Author-Name-First: Swarna
Author-Name-Last: Kuchibhotla
Title: Enhanced Speech Emotion RecognitionUsing AudioSignal Processing with CNN Assistance
Abstract: Abstract: The important form human communicating is speech, which can also be used as a potential means of human-computer interaction (HCI) with the use of a microphone sensor. An emerging field of HCI research uses these sensors to detect quantifiable emotions from speech signals. This study has implications for human-reboot interaction, the experience of virtual reality, actions assessment, Health services, and Customer service centres for emergencies, among other areas, to ascertain the speaker's emotional state as shown by their speech. We present significant contributions for; in this work. (i) improving Speech Emotion Recognition (SER) accuracy in comparison in the most advanced; and (ii) lowering computationally complicated nature of the model SER that is being given. We present a plain nets strategy convolutional neural network (CNN) architecture with artificial intelligence support to train prominent and distinguishing characteristics from speech signal spectrograms were improved in previous rounds to get better performance. Rather than using a pooling layer, convolutional layers are used to learn local hidden patterns, whereas Layers with complete connectivity are utilized to understand global discriminative features and Speech emotion categorization is done using a soft-max classifier. The suggested method reduces the size of the model by 34.5 MB while improving the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and Interactive Emotional Dyadic Motion Capture (IEMDMC) datasets, respectively, increasing accuracy by 4.5% and 7.85%. It shows how the proposed SER technique can be applied in real-world scenarios and proves its applicability and efficacy.
Journal: Data and Metadata
Pages: 715
Volume: 4
Year: 2025
DOI: 10.56294/dm2025715
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:715:id:1056294dm2025715

Template-Type: ReDIF-Article 1.0
Author-Name: Aqeel H. Al-Fatlawi
Author-Name-First: Aqeel H.
Author-Name-Last: Al-Fatlawi
Author-Name: Seyed Sadra Kashef
Author-Name-First: Seyed Sadra
Author-Name-Last: Kashef
Author-Name: Yaqeen Sabah Mezaal
Author-Name-First: Yaqeen Sabah
Author-Name-Last: Mezaal
Author-Name: Morteza Valizadeh
Author-Name-First: Morteza
Author-Name-Last: Valizadeh
Title: Design of a Compact Microstrip Band Pass Filter for IoT and S-Band Radar Applications
Abstract: This paper presents firstly the literature review and then investigates the design, simulation, and performance analysis of a compact microstrip bandpass filter (BPF) for wireless communication, IoT, and radar systems as candidate for TWIN OPEN LOOP RESONATORS in our future studies of filter and diplexers. Using ROGER RT (RO3003), an open-loop square with two SIRS at the top of the resonator structure is investigated for the filter, which is optimized to operate at the center frequency of 3,82 GHz with an external quality factor of 57,88. Regarding S-parameter results, the performance is good, with an insertion loss of 0,41 dB, while a high return loss is achieved at 21,66 dB. Such characteristics guarantee good selectivity and low signal distortion at an upper band of 83,74 dB /GHz with a transition band of 0,269 GHz. The proposed BPF has a transmission zero of 71,53 dB at 4,427 GHz with a compact size of 18 mm × 18 mm.
Journal: Data and Metadata
Pages: 714
Volume: 4
Year: 2025
DOI: 10.56294/dm2025714
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:714:id:1056294dm2025714

Template-Type: ReDIF-Article 1.0
Author-Name: Ahmed Mahdi Abdulkadium
Author-Name-First: Ahmed
Author-Name-Last: Mahdi Abdulkadium
Author-Name: Asaad Sabah Hadi
Author-Name-First: Asaad
Author-Name-Last: Sabah Hadi
Title: A Compherence Approach to Collaborative Academic Paper’s Ontology Based on Existing Linking Graph Prediction
Abstract: The current study describes the technological and methodological approach to collaborative ontology development in inter-organizational settings. It depends on formalisation of ontology development cooperation by means of an explicit editorial process, coordinating change proposals between ontology editors in a flexible manner. Added to this is the presence of novel distributed change management of ontologies style, models, and methods. We introduce the Academic Paper Citation Ontology (APCO) as an new layer-style approach to presenting ontologies towards highest independence of the underlying language of the ontologies. We also have attendant manipulation, versioning, capture, storage, and maintenance approaches and methods that exist and which rely on existing notions that are at the cutting-edge. Additionally, we provide a suite of change propagation techniques for supporting the consistency maintenance of distributed replicas of the same ontology. Finally, to increase the domain coverage of FOAF, we have extended it by extracting social interaction facts and relationships from emerging ontology. One specific problem that arises from time to time in enriching and merging ontologies is what this paper is all about: choosing which of the several ontologies available best relates to a particular piece of text associated with an input domain. Artificial Neural Networks (ANNs), more specifically their application in the research field of Natural Language Processing (NLP), are the foundation of the approach proposed. Consider calculating the ontologies' relevance score by combining neural networks and natural language processing.
Journal: Data and Metadata
Pages: 713
Volume: 4
Year: 2025
DOI: 10.56294/dm2025713
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:713:id:1056294dm2025713

Template-Type: ReDIF-Article 1.0
Author-Name: Rami Hanandeh
Author-Name-First: Rami
Author-Name-Last: Hanandeh
Author-Name: Zeyad Alkhazali
Author-Name-First: Zeyad
Author-Name-Last: Alkhazali
Author-Name: Khaled M.K. Alhyasat
Author-Name-First: Khaled M.K.
Author-Name-Last: Alhyasat
Author-Name: Ali M. Mistarihi
Author-Name-First: Ali M.
Author-Name-Last: Mistarihi
Author-Name: Qais AL Kilani
Author-Name-First: Qais AL
Author-Name-Last: Kilani
Title: The Impact of Data-Driven Decision-Making, Real-Time Analytics, and Ethical Data Practices on HR Performance and Employee Satisfaction
Abstract: Introduction: The importance of this study is to investigate decision-making by making decisions about data and real-time analytics and practicing ethical data on human resource performance and employee satisfaction. Objective: The study was conducted at Zain Telecommunications Company Jordan through designing a questionnaire for a segmentation research in the telecommunications and exhibitions company and 220 suitable samples were removed to analyze the structural equation modeling program method SEM. Method: The diversity of independent studies was indicated through the contracts indicating it, so multiple choices were used as evidence, workforce turnover forecasts, performance measures were available to indicate the correct decision-making to the data. Its employees were used in real time, tracking the productivity of dynamic workforce workers, and instant questionnaire mechanisms to indicate real-time analytics. Result: Transparency in its data use policies, implementation of data privacy standards, and algorithmic fairness were used in innovative processes to indicate ethical data practices. Through the questionnaire that was distributed, these parties' studies were conducted on improving the performance of human resources and employee satisfaction. Conclusion: His studies have concluded by integrating his three main areas of accurate decision making of his research, real-time analysis and practice of creative data and performance significantly improves the HR outcomes he chooses from employee satisfaction by choosing his specialty on data keeping pace with organizational goals by choosing his evidence.
Journal: Data and Metadata
Pages: 712
Volume: 4
Year: 2025
DOI: 10.56294/dm2025712
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:712:id:1056294dm2025712

Template-Type: ReDIF-Article 1.0
Author-Name: Mahmoud Alghizzawi
Author-Name-First: Mahmoud
Author-Name-Last: Alghizzawi
Author-Name: Muhammad Ussama Majeed
Author-Name-First: Muhammad
Author-Name-Last: Ussama Majeed
Author-Name: Zahid Hussain
Author-Name-First: Zahid
Author-Name-Last: Hussain
Author-Name: Sumaira Aslam
Author-Name-First: Sumaira
Author-Name-Last: Aslam
Author-Name: Nawaf Aljundi
Author-Name-First: Nawaf
Author-Name-Last: Aljundi
Author-Name: Ibrahim A. Abu-AlSondos
Author-Name-First: Ibrahim A.
Author-Name-Last: Abu-AlSondos
Author-Name: Abd Alrahman Ratib Ezmigna
Author-Name-First: Abd Alrahman
Author-Name-Last: Ratib Ezmigna
Title: Bridging the Gap: The Role of Innovation in Connecting Design Thinking, Entrepreneurship Education, and Business Success
Abstract: Introduction: Innovation is a critical driver of business success, especially in today’s dynamic global economy. This study investigates how design thinking (DT) and entrepreneurship education (EE) impact business success (BS) in Pakistani institutions, with a focus on the mediating role of innovation. The research highlights the synergy between DT and EE in fostering environments where students can develop innovative business ideas. Methods: A systematic sampling approach was used, involving 260 students from various universities to ensure a comprehensive assessment of the impact of EE and DT on BS. SPSS and Smart PLS was used to analyze the data. Results: The study’s findings emphasize innovation’s crucial role in linking entrepreneurship education and design thinking with business success. By identifying how innovation bridges these components, educational institutions can enhance their curricula to better equip students with entrepreneurial skills Conclusions: This research provides valuable recommendations for refining educational strategies to cultivate innovative mindsets, preparing students for the challenges of the modern business world.
Journal: Data and Metadata
Pages: 711
Volume: 4
Year: 2025
DOI: 10.56294/dm2025711
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:711:id:1056294dm2025711

Template-Type: ReDIF-Article 1.0
Author-Name: Ali Alqudah
Author-Name-First: Ali
Author-Name-Last: Alqudah
Author-Name: Khalid Thaher Amayreh
Author-Name-First: Khalid
Author-Name-Last: Thaher Amayreh
Author-Name: Hassan Al_Wahshat
Author-Name-First: Hassan
Author-Name-Last: Al_Wahshat
Author-Name: Omar Alqudah
Author-Name-First: Omar
Author-Name-Last: Alqudah
Title: Developing an Intelligent Model for Construction Project Management Using Artificial Intelligence and Big Data Analysis to Improve Scheduling and Reduce Delays
Abstract: Introduction: Traditional construction project management approaches have consistently struggled to address the key challenges of delays, budget overruns, and operational inefficiencies. These persistent issues highlight the need for more advanced methodologies. The integration of Artificial Intelligence (AI) with Big Data Analytics has emerged as a promising solution, aiming to improve scheduling accuracy, reduce delays, and enhance operational effectiveness in construction projects. Methods: A survey was conducted with 176 construction industry professionals, including project managers, engineers, and contractors, to assess the impact of AI and Big Data Analytics on construction project management. The survey focused on the use of AI-driven systems, including machine learning and predictive analytics, to improve project scheduling and delivery. Additionally, the application of Big Data Analytics in decision-making and risk assessment was explored. Results: The findings revealed that AI-powered systems, particularly those incorporating machine learning and predictive analytics, significantly outperform traditional construction management methods in terms of scheduling accuracy and delivery speed. Furthermore, the use of Big Data Analytics provided stakeholders with a deeper understanding of large datasets, facilitating more informed decisions and more accurate risk assessments. Quality execution and delivery were also found to be closely tied to effective communication and collaboration among teams and contractors, ensuring stakeholder satisfaction. Conclusions: This research demonstrates that AI and Big Data Analytics have the potential to transform construction project management by improving scheduling precision, reducing delays, and enhancing operational efficiency. The study underscores the importance of clear communication between teams and contractors to ensure the successful delivery of projects. While challenges related to infrastructure costs and ethical production remain, the integrated framework presented in this research provides valuable academic insights and practical solutions for stakeholders and project management personnel in the construction industry.
Journal: Data and Metadata
Pages: 709
Volume: 4
Year: 2025
DOI: 10.56294/dm2025709
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:709:id:1056294dm2025709

Template-Type: ReDIF-Article 1.0
Author-Name: Mohammed MHMOOD Al matalka
Author-Name-First: Mohammed MHMOOD
Author-Name-Last: Al matalka
Author-Name: Khaled Yousef Issa Alshboul
Author-Name-First: Khaled Yousef
Author-Name-Last: Issa Alshboul
Title: The Impact of Digital System Tools on Project Management Efficiency in Educational Institutions: The Mediating Role of Communication Quality within the Team (Language)
Abstract: Introduction: This research explores the impact of digital system tools on project management efficiency in educational institutions in Jordan and Saudi Arabia, with a particular focus on the role of team communication quality. In the context of educational project management, the study examines how digital tools influence project outcomes and communication practices within teams. Methods: The study is based on an experimental design with three hypotheses, which were tested using questionnaire data collected from 176 respondents. The first hypothesis evaluates the positive effects of digital system tools on project management efficiency. The second hypothesis investigates the effects of digital tools on the quality of team communication. The third hypothesis examines the intervening role that communication quality plays in the relationship between digital tool usage and project efficiency. Results: The research confirms all three hypotheses. It demonstrates that the use of digital systems significantly enhances both project management efficiency and team communication quality. Furthermore, communication quality is found to act as a mediator in the relationship between digital system tools and project efficiency, amplifying the impact of digital tools on project outcomes. Conclusions: The findings suggest that educational institutions in Jordan and Saudi Arabia should prioritize the adoption of digital tools and focus on improving team communication methods. By doing so, they can significantly enhance project management efficiency and achieve better project results. The study underscores the importance of integrating digital tools and fostering strong communication practices to improve educational project management.
Journal: Data and Metadata
Pages: 708
Volume: 4
Year: 2025
DOI: 10.56294/dm2025708
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:708:id:1056294dm2025708

Template-Type: ReDIF-Article 1.0
Author-Name: Yasmine El Belghiti
Author-Name-First: Yasmine
Author-Name-Last: El Belghiti
Author-Name: Abdelfattah Mouloud
Author-Name-First: Abdelfattah
Author-Name-Last: Mouloud
Author-Name: Mehdi El Bouchti
Author-Name-First: Mehdi
Author-Name-Last: El Bouchti
Author-Name: Samir Tetouani
Author-Name-First: Samir
Author-Name-Last: Tetouani
Author-Name: Aziz Soulhi
Author-Name-First: Aziz
Author-Name-Last: Soulhi
Title: Application of the Lean Six Sigma Methodology Enhanced by Fuzzy Logic Optimizing Mold Changeover Times in the Automotive Injection Industry
Abstract: Minimizing mold changeover time is a critical challenge in the plastic injection molding industry, as it directly impacts productivity, operational efficiency, and competitiveness. This study introduces an integrated approach that combines Lean Manufacturing tools, the DMAIC methodology (Define, Measure, Analyze, Improve,Control), and Single Minute Exchange of Dies (SMED) techniques, enhanced by fuzzy logic and artificial intelligence (AI). The methodology focuses on improving mold changeover processes for the NEGRI BOSSI 650 machine by identifying bottlenecks, transforming internal tasks into external ones, and optimizing workflows to reduce downtime and improve overall efficiency. Key phases of the study included identifying the root causes of inefficiencies through data collection and analysis, streamlining task sequences using real-time process data, and balancing the production line by redistributing workloads and reducing bottlenecks. Fuzzy logic and AI technologies were employed to support decision-making and enhance optimization, ensuring a robust and adaptable framework for continuous improvement. The results obtained were of high impact a 65% reduction in mold changeover time and a 46.8% improvement in Process Cycle Efficiency (PCE) with significant improvements in terms of the global line balancing. These findings validate the effectiveness of combining Lean principles with advanced technologies such as fuzzy logic in solving Industry challenges, improving resource utilization, and ensuring long-term operational performance. This study just goes to prove that a structured Lean Manufacturing approach combined with innovative tools and automation can drive significant improvement in the plastic injection molding industry, establishing a scalable and competitive strategy for operational excellence.
Journal: Data and Metadata
Pages: 703
Volume: 4
Year: 2025
DOI: 10.56294/dm2025703
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:703:id:1056294dm2025703

Template-Type: ReDIF-Article 1.0
Author-Name: Najwa Alsuwais
Author-Name-First: Najwa
Author-Name-Last: Alsuwais
Author-Name: Haya Awawdeh
Author-Name-First: Haya
Author-Name-Last: Awawdeh
Author-Name: Yaser Altaamneh
Author-Name-First: Yaser
Author-Name-Last: Altaamneh
Author-Name: Mays Shatnawi
Author-Name-First: Mays
Author-Name-Last: Shatnawi
Author-Name: Hanadi Abulaila
Author-Name-First: Hanadi
Author-Name-Last: Abulaila
Author-Name: Heba Awawdeh
Author-Name-First: Heba
Author-Name-Last: Awawdeh
Author-Name: Ibrahim Siam
Author-Name-First: Ibrahim
Author-Name-Last: Siam
Author-Name: Mohammad Alzoubi
Author-Name-First: Mohammad
Author-Name-Last: Alzoubi
Title: The Impact of Digital Financial Technology in Achieving Digital Entrepreneurship: Business Intelligence as a Modifying Variable in Jordanian Banks
Abstract: Introduction: This study aims to examine the relationships between digital financial technologies, business intelligence, and digital entrepreneurship, focusing on how business intelligence influences the relationship between digital financial technologies and the emergence of digital entrepreneurship. Methods: Data were collected through a questionnaire distributed to senior management in four Jordanian banks, targeting a total of 270 individuals. A total of 170 questionnaires were distributed, 165 were returned, and 159 were valid for analysis. Results: The findings revealed significant relationships between digital financial technologies, business intelligence, and the emergence of digital entrepreneurship. Business intelligence was found to play a crucial role as a moderating variable in linking digital financial technologies to digital entrepreneurship. Conclusions: The study highlights the importance of digital financial technologies and business intelligence in fostering digital entrepreneurship in the banking sector. The results provide valuable insights for banking institutions to enhance their digital strategies and support entrepreneurship initiatives.
Journal: Data and Metadata
Pages: 701
Volume: 4
Year: 2025
DOI: 10.56294/dm2025701
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:701:id:1056294dm2025701

Template-Type: ReDIF-Article 1.0
Author-Name: Anber Abraheem Shlash Mohammad
Author-Name-First: Anber Abraheem
Author-Name-Last: Shlash Mohammad
Author-Name: Suleiman Ibrahim Shelash Mohammad
Author-Name-First: Suleiman Ibrahim
Author-Name-Last: Shelash Mohammad
Author-Name: Badrea Al Oraini
Author-Name-First: Badrea
Author-Name-Last: Al Oraini
Author-Name: Ayman Hindieh
Author-Name-First: Ayman Hindieh
Author-Name-Last: Ayman Hindieh
Author-Name: Asokan Vasudevan
Author-Name-First: Asokan Vasudevan
Author-Name-Last: Asokan Vasudevan
Author-Name: Muhammad Turki Alshurideh
Author-Name-First: Muhammad
Author-Name-Last: Turki Alshurideh
Title: Decoding Consumer Behaviour: Leveraging Big Data and Machine Learning for Personalized Digital Marketing
Abstract: Introduction Big data analytics and machine learning have transformed digital marketing by enabling data-driven insights for personalization. This study investigates the role of engagement metrics, sentiment analysis, and consumer segmentation in enhancing marketing effectiveness. Specifically, it examines how these technologies process consumer interaction data to uncover actionable insights, segment audiences, and drive purchase conversions. Method The study employed a mixed-methods approach, integrating big data analytics and machine learning techniques. Descriptive statistics highlighted engagement patterns, while k-means clustering segmented consumers based on behavioural and emotional data. Sentiment analysis, conducted using Natural Language Processing (NLP), captured consumer emotions as positive, neutral, or negative. Regression analysis evaluated the influence of social media activity, click-through rates, session duration, and sentiment scores on purchase conversion rates. Results Descriptive analysis revealed significant variability in consumer engagement and sentiment, with 37.5% of consumers expressing positive sentiment. Clustering identified three distinct consumer segments, reflecting differences in engagement and sentiment. Regression analysis showed that sentiment had a positive but statistically insignificant relationship with purchase conversions, while other metrics, such as click-through rates and session duration, exhibited minimal impact. The overall explanatory power of the regression model was low (R-squared = 0.001), indicating the need for additional factors to understand purchase behaviour. Conclusion The findings emphasize the potential of big data analytics and machine learning in consumer segmentation and sentiment analysis. However, their direct impact on purchase conversion is limited without integrating broader variables. A holistic, adaptive framework combining behavioural, emotional, and contextual insights is essential for maximizing marketing personalization and driving outcomes in dynamic digital environments.
Journal: Data and Metadata
Pages: 700
Volume: 4
Year: 2025
DOI: 10.56294/dm2025700
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:700:id:1056294dm2025700

Template-Type: ReDIF-Article 1.0
Author-Name: Suhaila Abuowaida
Author-Name-First: Suhaila
Author-Name-Last: Abuowaida
Author-Name: Hamza Abu Owida
Author-Name-First: Hamza
Author-Name-Last: Abu Owida
Author-Name: Suleiman Ibrahim Shelash Mohammad
Author-Name-First: Suleiman Ibrahim
Author-Name-Last: Shelash Mohammad
Author-Name: Nawaf Alshdaifat
Author-Name-First: Nawaf
Author-Name-Last: Alshdaifat
Author-Name: Esraa Abu Elsoud
Author-Name-First: Esraa
Author-Name-Last: Abu Elsoud
Author-Name: Raed Alazaidah
Author-Name-First: Raed
Author-Name-Last: Alazaidah
Author-Name: Asokan Vasudevan
Author-Name-First: Asokan
Author-Name-Last: Vasudevan
Author-Name: Muhammad Turki Alshurideh
Author-Name-First: Muhammad
Author-Name-Last: Turki Alshurideh
Title: Evidence Detection in Cloud Forensics: Classifying Cyber-Attacks in IaaS Environments using machine learning
Abstract: Introduction: Cloud computing is considered a remarkable paradigm shift in Information Technology (IT), offering scalable and virtualized resources to end users at a low cost in terms of infrastructure and maintenance. These resources offer an exceptional degree of flexibility and adhere to established standards, formats, and networking protocols while being managed by several management entities. However, the existence of flaws and vulnerabilities in underlying technology and outdated protocols opens the door for malicious network attacks. Methods: This study addresses these vulnerabilities by introducing a method for classifying attacks in Infrastructure as a Service (IaaS) cloud environments, utilizing machine learning methodologies within a digital forensics framework. Various machine learning algorithms are employed to automatically identify and categorize cyber-attacks based on metrics related to process performance. The dataset is divided into three distinct categories—CPU usage, memory usage, and disk usage—to assess each category’s impact on the detection of attacks within cloud computing systems. Results: Decision Tree and Neural Network models are recommended for analyzing disk-related features due to their superior performance in detecting attacks with an accuracy of 90% and 87.9%, respectively. Neural Network is deemed more suitable for identifying CPU behavior, achieving an accuracy of 86.2%. For memory-related features, K-Nearest Neighbor (KNN) demonstrates the best False Negative Rate (FNR) value of 1.8%. Discussion: Our study highlights the significance of customizing the selection of classifiers based on the specific system feature and the intended focus of detection. By tailoring machine learning models to particular system features, the detection of malicious activities in IaaS cloud environments can be enhanced, offering practical insights into effective attack classification.
Journal: Data and Metadata
Pages: 699
Volume: 4
Year: 2025
DOI: 10.56294/dm2025699
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:699:id:1056294dm2025699

Template-Type: ReDIF-Article 1.0
Author-Name: Richar Jacobo Posso Pacheco
Author-Name-First: Richar Jacobo
Author-Name-Last: Posso Pacheco
Author-Name: Laura Cristina Barba Miranda
Author-Name-First: Laura Cristina
Author-Name-Last: Barba Miranda
Author-Name: Raquel Arcenia Tenorio Sánchez
Author-Name-First: Raquel Arcenia
Author-Name-Last: Tenorio Sánchez
Author-Name: Rosangela Caicedo-Quiroz
Author-Name-First: Rosangela
Author-Name-Last: Caicedo-Quiroz
Author-Name: Giceya Maqueira-Caraballo
Author-Name-First: Giceya
Author-Name-Last: Maqueira-Caraballo
Author-Name: Julio Barzola-Monteses
Author-Name-First: Julio
Author-Name-Last: Barzola-Monteses
Title: PRISMA Guidelines: Methodological Adaptation for Systematic Reviews in Education
Abstract: Introduction: Systematic reviews in education require methodological adaptations due to the complexity of educational data and contexts, making it necessary to adjust the PRISMA guidelines, initially designed for health, to meet the needs of the educational field. Objective: To adapt the PRISMA guidelines for their methodological implementation in systematic reviews within the educational domain. Methodos: A documentary and comparative analysis between PRISMA and educational studies was conducted, complemented by consultations with specialists, allowing the development of an adapted framework based on rigorous criteria and methodological tools tailored to education. Results: An adapted framework was proposed, including inclusion and exclusion criteria, methodological procedures, and tools for quality assessment, positively impacting the relevance, applicability, and rigor of educational systematic reviews. Conclusions: The adaptation of PRISMA to the educational field will enhance the quality of systematic reviews, strengthening their impact on policies, pedagogical strategies, and evidence-based teaching practices in complex contexts such as Latin America.
Journal: Data and Metadata
Pages: 698
Volume: 4
Year: 2025
DOI: 10.56294/dm2025698
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:698:id:1056294dm2025698

Template-Type: ReDIF-Article 1.0
Author-Name: Saeed Ali Faris Alketbi
Author-Name-First: Saeed Ali
Author-Name-Last: Faris Alketbi
Author-Name: Massudi bin Mahmuddin
Author-Name-First: Massudi bin
Author-Name-Last: Mahmuddin
Author-Name: Mazida Binti Ahmad
Author-Name-First: Mazida Binti
Author-Name-Last: Ahmad
Title: Blockchain Technology and Smart Cities: A Technological Framework for Innovation and Sustainability in the UAE and Beyond
Abstract: Introduction: Blockchain technology has emerged as a cornerstone for innovation in the field of information systems, offering secure, decentralized, and transparent solutions to address the complex challenges of smart city development. This paper explores the transformative potential of blockchain in advancing smart cities, focusing on its ability to integrate with Internet of Things (IoT) systems, enable secure data management, and optimize urban services. Key challenges, such as scalability, interoperability, and regulatory frameworks, are analyzed alongside innovative solutions, including second-layer protocols, cross-chain communication, and energy-efficient consensus mechanisms. The study introduces the International Certification Layer (ICL) as a novel framework designed to enhance regulatory oversight while maintaining blockchain’s decentralized integrity. Additionally, Dubai’s Blockchain Strategy serves as a pioneering case study, showcasing how strategic investment in blockchain technology can streamline governance, enhance citizen trust, and support the achievement of sustainability goals. By addressing critical challenges and identifying future research directions, this paper underscores the role of blockchain as a transformative enabler for sustainable and efficient urban ecosystems.
Journal: Data and Metadata
Pages: 697
Volume: 4
Year: 2025
DOI: 10.56294/dm2025697
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:697:id:1056294dm2025697

Template-Type: ReDIF-Article 1.0
Author-Name: Motteh S. Al Shibly
Author-Name-First: Motteh S.
Author-Name-Last: Al Shibly
Author-Name: Mahmoud Hussein Abu Joma
Author-Name-First: Mahmoud
Author-Name-Last: Hussein Abu Joma
Author-Name: Sahar Moh’d Abu Bakir
Author-Name-First: Sahar Moh’d
Author-Name-Last: Abu Bakir
Author-Name: Bashar khaled Almagharbeh
Author-Name-First: Bashar
Author-Name-Last: khaled Almagharbeh
Author-Name: Jamil Alotoum Firas
Author-Name-First: Jamil Alotoum
Author-Name-Last: Firas
Author-Name: Salman M Abu lehyeh
Author-Name-First: Salman M
Author-Name-Last: Abu lehyeh
Author-Name: Mahmoud Alghizzawi
Author-Name-First: Mahmoud
Author-Name-Last: Alghizzawi
Author-Name: Qais Hammouri
Author-Name-First: Qais
Author-Name-Last: Hammouri
Title: Exploring the Influence of Green Human Resource Management on Risk Management: The Mediating Effect of Agile Leadership
Abstract: This study explores the relationship between Green Human Resource Management (GHRM) practices, agile leadership, and organizational risk management. The study collected data from 501 managers in 130 businesses registered on the Amman Stock Exchange in Jordan. It used a questionnaire to gather information on their organization's GHRM practices, agile leadership style, and risk management strategies. SPSS and Amos were used to analyze the data. The results show that GHRM practices positively impact risk management, supporting previous research on the influence of GHRM on fostering sustainable practices in organizations. Furthermore, the study finds that the agile leadership style moderates the relationship between GHRM practices and risk management, highlighting the importance of leadership in increasing the efficiency of sustainable practices in organizations. The findings have implications for managers and policymakers, emphasizing the need for organizations to prioritize GHRM practices and cultivate agile leadership to improve their risk management strategies, expand their innovation skills, and encourage sustainable practices. Policymakers can also use the results to support sustainability efforts and urge businesses to follow good governance and risk management practices. The findings show the significance of agile leadership as a mediating variable and emphasize the relevance of organizations prioritizing GHRM practices to achieve sustainable results.
Journal: Data and Metadata
Pages: 696
Volume: 4
Year: 2025
DOI: 10.56294/dm2025696
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:696:id:1056294dm2025696

Template-Type: ReDIF-Article 1.0
Author-Name: Mohammed Mhmood Al Matalka
Author-Name-First: Mohammed Mhmood
Author-Name-Last: Al Matalka
Author-Name: Hazim Ryad Momani
Author-Name-First: Hazim
Author-Name-Last: Ryad Momani
Author-Name: Mohammad Khasawneh
Author-Name-First: Mohammad
Author-Name-Last: Khasawneh
Author-Name: Salim Khanfar
Author-Name-First: Salim
Author-Name-Last: Khanfar
Author-Name: Zaid Akram AL-Malahmeh
Author-Name-First: Zaid Akram
Author-Name-Last: AL-Malahmeh
Author-Name: Amer Hani Al-Qassem
Author-Name-First: Amer Hani
Author-Name-Last: Al-Qassem
Author-Name: Ammar Mohammad Al-Ramadan
Author-Name-First: Ammar Mohammad
Author-Name-Last: Al-Ramadan
Author-Name: Mohammad Alzoubi
Author-Name-First: Mohammad
Author-Name-Last: Alzoubi
Author-Name: Ashraf Alfandi
Author-Name-First: Ashraf
Author-Name-Last: Alfandi
Title: The Factors That Affect Electronic Learning Students' Behavioural Intentions In The Higher Education Tourism And Hospitality Disciplines
Abstract: Introduction: This study aims to explore the factors influencing the intention of hospitality and tourism students in the UAE to adopt e-learning using the Technology Acceptance Model (TAM). E-learning has become an essential tool in higher education, particularly in response to the COVID-19 pandemic. The research seeks to identify the key determinants that affect students' willingness to engage with e-learning platforms. Methods: A cross-sectional survey was conducted in two phases, involving 278 undergraduate students from a UAE university. The survey assessed various TAM constructs such as perceived usefulness, ease of use, system characteristics, and hedonic motivation. Data were analyzed using SmartPLS software and Structural Equation Modeling (SEM) to test the relationships between the variables. Results: The study found that perceived usefulness and ease of use were the most significant factors influencing students' intention to adopt e-learning. Other influential factors included e-learning resources, platform functionality, subjective norms, and e-learning support. Additionally, hedonic motivation played an important role in enhancing students' engagement with e-learning. Conclusions: The findings suggest that higher education institutions should focus on improving the perceived usefulness and ease of use of e-learning platforms while ensuring robust system functionality and support. The study contributes to the understanding of technology adoption in non-technical fields, offering insights that can inform e-learning strategies, especially in the context of future pandemics or disruptions.
Journal: Data and Metadata
Pages: 691
Volume: 4
Year: 2025
DOI: 10.56294/dm2025691
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:691:id:1056294dm2025691

Template-Type: ReDIF-Article 1.0
Author-Name: Saleem Alzoubi
Author-Name-First: Saleem
Author-Name-Last: Alzoubi
Title: Image encryption based on simple shift, permutation and transformation operations on bit layers
Abstract: Introduction: This paper explores image encryption techniques that leverage transformation, shifting, and permutation operations. The primary focus is on enhancing the security and quality of encrypted raster images by manipulating the individual bit layers of color images. Methods: To encrypt a raster image, the color image is decomposed into binary layers, each representing pixel bits at varying levels of significance. The least significant bits are placed in the least significant layers, while the most significant bits are positioned in the most significant layers. Transformation operations are performed on the bits or their arrays, reconfiguring them into different bit arrangements. Shifting operations are applied to bits across rows and columns within each bit layer, with shifts between layers carried out separately. Additionally, permutation operations are used to further rearrange bit arrays both within individual layers and between layers themselves. Results: Through experimentation, two encryption scenarios have been identified that provide high-quality results for images with different structures. These scenarios produce distinct encrypted images based on the combinations of operations and their sequence, yet maintain a high standard of encryption quality. Conclusions: The proposed method demonstrates an effective approach to encrypting raster images without relying on external encryption tools, minimizing the risk of information loss during decryption. The combination of transformation, shifting, and permutation operations ensures robust encryption, making the technique suitable for a wide range of image types.
Journal: Data and Metadata
Pages: 690
Volume: 4
Year: 2025
DOI: 10.56294/dm2025690
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:690:id:1056294dm2025690

Template-Type: ReDIF-Article 1.0
Author-Name: Hugo Humberto Paz-León
Author-Name-First: Hugo Humberto
Author-Name-Last: Paz-León
Author-Name: Marco Hjalmar Velasco-Arrellano
Author-Name-First: Marco Hjalmar
Author-Name-Last: Velasco-Arrellano
Author-Name: Lenin Santiago Orozco-Cantos
Author-Name-First: Lenin Santiago
Author-Name-Last: Orozco-Cantos
Author-Name: Lidia Castro-Cepeda
Author-Name-First: Lidia
Author-Name-Last: Castro-Cepeda
Title: Use of mathematical modeling as a methodological proposal for the development of cognitive competences in the subject of differential equations
Abstract: This study proposes to integrate mathematical modeling as an active methodology with a pedagogical approach oriented toward competency development. The objective is to use modeling activities as a bridge that connects problem-solving with the strengthening of cognitive competencies. The model contrasts with traditional methods, which typically focus on the execution of algorithms and the memorization of theorems and formulas, limiting mathematical learning to obtaining results without exploring their application to engineering problems or practical contexts. The research is conducted in an educational environment, where students could analyze, describe, formulate hypotheses, contrast them, reflect, argue, and communicate their ideas. The research design is quasi-experimental, descriptive-correlational, and the research employs scientific, inductive-deductive, and analytical-synthetic methods, basing the methodology on problem-solving in accordance with the progress of the Differential Equations course syllabus. To evaluate the proposal, techniques such as direct observation, teamwork, multiple-choice tests, and feedback were used with second-semester students. The results show that methodology promotes greater development of cognitive skills compared to the traditional approach based on mechanical problem-solving. It can be concluded that mathematical modeling allows students to develop cognitive skills such as critical thinking, creativity, and problem-solving through the analysis, synthesis, and evaluation of information.
Journal: Data and Metadata
Pages: 685
Volume: 4
Year: 2025
DOI: 10.56294/dm2025685
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:685:id:1056294dm2025685

Template-Type: ReDIF-Article 1.0
Author-Name: Anber Abraheem Shlash Mohammad
Author-Name-First: Anber Abraheem
Author-Name-Last: Shlash Mohammad
Author-Name: Ammar Mohammad Al-Ramadan
Author-Name-First: Ammar Mohammad
Author-Name-Last: Al-Ramadan
Author-Name: Suleiman Ibrahim Mohammad
Author-Name-First: Suleiman
Author-Name-Last: Ibrahim Mohammad
Author-Name: Badrea Al Oraini
Author-Name-First: Badrea
Author-Name-Last: Al Oraini
Author-Name: Asokan Vasudevan
Author-Name-First: Asokan
Author-Name-Last: Vasudevan
Author-Name: Muhammad Turki Alshurideh
Author-Name-First: Muhammad
Author-Name-Last: Turki Alshurideh
Author-Name: Qian Chen
Author-Name-First: Qian
Author-Name-Last: Chen
Author-Name: Imad Ali
Author-Name-First: Imad
Author-Name-Last: Ali
Title: Enhancing Metadata Management And Data-Driven Decision-Making In Sustainable Food Supply Chains Using Blockchain And AI Technologies
Abstract: Sustainability in food supply chains is a critical global challenge, particularly in resource-constrained regions like Jordan, where operational inefficiencies and environmental concerns are prevalent. This study explores the integration of blockchain and artificial intelligence (AI) technologies to enhance metadata management, forecast sustainability metrics, and support decision-making in Jordan’s food supply chains. Blockchain's ability to improve metadata accuracy, standardization, and traceability, combined with AI’s predictive capabilities, offers a powerful solution for addressing sustainability challenges. Methods The research employed a mixed-methods approach, combining real-time data from blockchain transaction logs, AI-generated forecasts, and stakeholder surveys. Blockchain data from platforms like Hyperledger Fabric and Ethereum provided insights into metadata accuracy and traceability. AI models were developed using machine learning techniques, such as linear regression, to forecast food waste reduction, carbon footprint reduction, and energy efficiency. Multi-Criteria Decision Analysis (MCDA), using AHP and TOPSIS, was applied to evaluate trade-offs among sustainability goals. Results The results revealed significant improvements in metadata accuracy (from 83% to 96.66%) and reductions in traceability time (from 4.0 to 2.35 hours) following blockchain implementation. AI models demonstrated high predictive accuracy, explaining 88%, 81%, and 76% of the variance in food waste reduction, carbon footprint reduction, and energy efficiency, respectively. Conclusion This study underscores the transformative potential of blockchain and AI technologies in achieving sustainability goals. By fostering transparency, predictive insights, and data-driven decision-making, these innovations can address key challenges in Jordan’s food supply chains, offering actionable strategies for stakeholders.
Journal: Data and Metadata
Pages: 683
Volume: 4
Year: 2025
DOI: 10.56294/dm2025683
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:683:id:1056294dm2025683

Template-Type: ReDIF-Article 1.0
Author-Name: Abril Yuriko Herrera Rios
Author-Name-First: Abril Yuriko
Author-Name-Last: Herrera Rios
Author-Name: Pamela Herrera Ríos
Author-Name-First: Pamela
Author-Name-Last: Herrera Ríos
Author-Name: Reyna Christian Sánchez Parra
Author-Name-First: Reyna Christian
Author-Name-Last: Sánchez Parra
Title: Estimation of GHG emissions and costs in Sinaloa: Towards sustainable economic and environmental policies
Abstract: Introduction: Climate change and its impacts have created the need for policies that balance economic growth with environmental sustainability. This study focuses on the relationship between economic growth and greenhouse gas (GHG) emissions in the state of Sinaloa, located in northwestern Mexico, during the period 2003–2021. Methods: Using historical energy consumption data and the State Quarterly Economic Activity Indicator, GHG emissions were estimated through an official national tool for emissions calculation. An economic cost was assigned using rates proposed by a center specialized in environmental and economic analysis in Mexico. Results: The findings reveal a strong positive correlation (R=0.893) between the State Quarterly Economic Activity Indicator and emissions, highlighting that Sinaloa's economic growth heavily depends on energy-intensive activities. Over the period analyzed, emissions increased by 83.8%. Conclusions: This study underscores the importance of designing public policies that reduce emissions without hindering economic development, promoting sustainable strategies that contribute to Mexico's commitments under the Paris Agreement.
Journal: Data and Metadata
Pages: .682
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.682
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.682:id:1056294dm2024682

Template-Type: ReDIF-Article 1.0
Author-Name: Mahrus Ali
Author-Name-First: Mahrus
Author-Name-Last: Ali
Author-Name: Rahmat Gernowo
Author-Name-First: Rahmat
Author-Name-Last: Gernowo
Author-Name: Budi Warsito
Author-Name-First: Budi
Author-Name-Last: Warsito
Author-Name: Faliha Muthmainah
Author-Name-First: Faliha
Author-Name-Last: Muthmainah
Title: Markov Switching Autoregressive In Information Systems For Improving Islamic Banks
Abstract: Sharia banks operate in several Muslim-majority countries, offering an alternative financial system aligned with Islamic principles. This study aims to develop a web-based information system for measuring the Maqasid Sharia Index (SMI) in real-time to assess the Islamization level of Sharia banks. Financial data from Indonesian sharia banks before and after their merger in 2021 (January 2013–February 2022) were analyzed. The Markov Switching Autoregressive (MSAR) method, with a margin of error of 0.107, was used to predict SMI values several years into the future. The results indicate that while the SMI value is predicted to decrease by 2025, the financial value of the banks is expected to increase. These findings provide valuable insights for improving the operational effectiveness of Sharia-compliant banking systems.
Journal: Data and Metadata
Pages: .681
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.681
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.681:id:1056294dm2024681

Template-Type: ReDIF-Article 1.0
Author-Name: Haneen A. Al-khawaja
Author-Name-First: Haneen A.
Author-Name-Last: Al-khawaja
Author-Name: Abdul Razzak Alshehadeh
Author-Name-First: Abdul Razzak
Author-Name-Last: Alshehadeh
Author-Name: Asad Aburub Faisal
Author-Name-First: Asad Aburub
Author-Name-Last: Faisal
Author-Name: Ali Matar
Author-Name-First: Ali
Author-Name-Last: Matar
Author-Name: Osaid Hasan Althnaibat
Author-Name-First: Osaid Hasan
Author-Name-Last: Althnaibat
Title: Solutions for Insider Trading and Regulatory Challenges in Financial Governance
Abstract: Insider trading and regulatory inconsistencies have important historical challenges to the integrity and stability of global financial markets. These issues challenge trust, transparency, and fairness are requiring solutions. In this study, we introduce a novel artificial intelligence (AI)-driven system that carefully addressing these challenges. The proposed system employs machine learning models for insider trading detection, natural language processing (NLP) for sentiment analysis, and graph neural networks (GNNs) to detect irregular patterns in blockchain transactions. Moreover, reinforcement learning techniques are utilized here to complement regulatory standards dynamically, enhancing policy flexibility and market agreement. Explainable AI (XAI) were used here as well to ensure the transparency and trust in decision-making processes, this helps stakeholders to take actions. Experimental evaluations prove the system efficiency, with promising precision and recall percentages, enhanced governance in decentralized systems, and robust cross-jurisdictional regulatory alignment. This research contributes to knowledge by proving the transformative prospective of AI in strengthening regulatory frameworks and improving governance mechanisms in financial systems. The achievements here provide a roadmap for policymakers, financial institutions, and technology developers to build reasonable, efficient, and resistant markets. El tráfico de información privilegiada y las inconsistencias regulatorias han sido desafíos históricos importantes para la integridad y estabilidad de los mercados financieros globales. Estos problemas desafían la confianza, la transparencia y la equidad y requieren soluciones. En este estudio, presentamos un nuevo sistema impulsado por inteligencia artificial (IA) que aborda cuidadosamente estos desafíos. El sistema propuesto emplea modelos de aprendizaje automático para la detección de tráfico de información privilegiada, procesamiento del lenguaje natural (NLP) para el análisis de sentimientos y redes neuronales gráficas (GNN) para detectar patrones irregulares en transacciones de blockchain. Además, aquí se utilizan técnicas de aprendizaje de refuerzo para complementar los estándares regulatorios de forma dinámica, mejorando la flexibilidad de las políticas y el acuerdo del mercado. Aquí también se utilizó IA explicable (XAI) para garantizar la transparencia y la confianza en los procesos de toma de decisiones, lo que ayuda a las partes interesadas a tomar medidas. Las evaluaciones experimentales prueban la eficiencia del sistema, con porcentajes prometedores de precisión y recuperación, una gobernanza mejorada en sistemas descentralizados y una sólida alineación regulatoria interjurisdiccional. Esta investigación contribuye al conocimiento al demostrar la perspectiva transformadora de la IA en el fortalecimiento de los marcos regulatorios y la mejora de los mecanismos de gobernanza en los sistemas financieros. Los logros aquí alcanzados proporcionan una hoja de ruta para que los responsables de las políticas, las instituciones financieras y los desarrolladores de tecnología construyan mercados razonables, eficientes y resistentes.
Journal: Data and Metadata
Pages: 680
Volume: 4
Year: 2025
DOI: 10.56294/dm2025680
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:680:id:1056294dm2025680

Template-Type: ReDIF-Article 1.0
Author-Name: Jamal Zraqou
Author-Name-First: Jamal
Author-Name-Last: Zraqou
Author-Name: Riyad Alrosan
Author-Name-First: Riyad
Author-Name-Last: Alrosan
Author-Name: Najem Sirhan
Author-Name-First: Najem
Author-Name-Last: Sirhan
Author-Name: Hussam Fakhouri
Author-Name-First: Hussam
Author-Name-Last: Fakhouri
Author-Name: Khalil Omar
Author-Name-First: Khalil
Author-Name-Last: Omar
Author-Name: Jawad Alkhateeb
Author-Name-First: Jawad
Author-Name-Last: Alkhateeb
Title: Robust Deep Learning Approach for Automating the Epithelial Dysplasia Detection in Histopathology Images
Abstract: Automated image analysis using deep learning techniques helped diagnose epithelial dysplasia in normal tissues. This study examined a hybrid approach that combined traditional image processing methods with deep learning for accurate tissue classification. A diverse, annotated dataset of epithelial dysplasia histology images was created and processed. To mitigate overfitting, a pre-trained convolutional neural network (CNN) model was finetuned with optimized hyperparameters. Performance metrics, including accuracy and precision, were assessed using an independent test dataset. The Structural Similarity Index (SSIM) was applied to enhance image contrast. The optimized deep learning model outperformed conventional methods in diagnostic accuracy. The hybrid approach demonstrated significant effectiveness in distinguishing epithelial dysplasia in medical images. The results highlighted the potential of integrating deep learning algorithms with traditional image processing techniques for automated medical diagnostics. This method showed promise for future applications in enhancing diagnostic accuracy and efficiency.
Journal: Data and Metadata
Pages: 679
Volume: 4
Year: 2025
DOI: 10.56294/dm2025679
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:679:id:1056294dm2025679

Template-Type: ReDIF-Article 1.0
Author-Name: Sandeep Raghuwanshi
Author-Name-First: Sandeep
Author-Name-Last: Raghuwanshi
Author-Name: Arif Hasan
Author-Name-First: Arif
Author-Name-Last: Hasan
Author-Name: Sushma R
Author-Name-First: Sushma
Author-Name-Last: R
Author-Name: Reetika Agrawal
Author-Name-First: Reetika
Author-Name-Last: Agrawal
Author-Name: Ardhendu Shekhar Singh
Author-Name-First: Ardhendu
Author-Name-Last: Shekhar Singh
Author-Name: Neeraj Kumar Dubey
Author-Name-First: Neeraj Kumar
Author-Name-Last: Dubey
Author-Name: Prabhat Kumar
Author-Name-First: Prabhat
Author-Name-Last: Kumar
Title: Thematic analysis: exploring teacher and student perspectives on utilizing chatgpt for content generation
Abstract: Introduction: The research investigated the effects of ChatGPT, an AI-driven language model, on students and academic institutions. The analysis incorporated viewpoints from academics, research scholars, and graduate or postgraduate students. The increasing use of AI in education requires a comprehensive understanding of its potential benefits and drawbacks, especially within higher education and research. Methods: A thematic content analysis was used to investigate the viewpoints of 46 graduate and postgraduate students, 8 research scholars, and 4 educators. The investigation sought to find repeating themes and principal concepts concerning the influence of AI in educational environments. Results: The research examined remarks regarding the function of ChatGPT for students, researchers, and educators, pinpointing eight major themes. The most prevalent were Content Writing (45 mentions), Creation of Thought (35 references), and Collection of Information (33 mentions), underscoring ChatGPT’s influence on content development, ideation, and data organization. Additional themes encompassed Language Utilization, Innovation Generation, Model Development, Idea Formation, and Supportive Tools. The results demonstrated that ChatGPT is perceived as revolutionary for writing, cognitive processes, and information acquisition. Conclusions: The research determined that ChatGPT has considerable ramifications for students and universities, as revealed by thematic content analysis. It emphasized eight primary themes: content, creativity, language, tools, models, information, generations, and ideas. It highlighted AI as an augmentation of the human intellect while acknowledging the significance of human traits. The results highlighted the necessity for additional research into privacy issues, ethical considerations, and optimal procedures for incorporating AI in education. The report emphasized the necessity of recognizing both the benefits and drawbacks of AI in current research and higher education.
Journal: Data and Metadata
Pages: 676
Volume: 4
Year: 2025
DOI: 10.56294/dm2025676
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:676:id:1056294dm2025676

Template-Type: ReDIF-Article 1.0
Author-Name: Oksana Desyatnyuk
Author-Name-First: Oksana
Author-Name-Last: Desyatnyuk
Author-Name: Olena Ptashchenko
Author-Name-First: Olena
Author-Name-Last: Ptashchenko
Author-Name: Iryna Murenets
Author-Name-First: Iryna
Author-Name-Last: Murenets
Author-Name: Kyrylo Oliinyk
Author-Name-First: Kyrylo
Author-Name-Last: Oliinyk
Author-Name: Olga Kyrylenko
Author-Name-First: Olga
Author-Name-Last: Kyrylenko
Title: Ensuring Financial Security: Approaches to Risk Management and Protection in the Digital Economy
Abstract: Introduction: The rapid development of digital technologies and their integration into financial systems, accompanied by the emergence of new types of risks and threats, necessitates the use of effective strategies aimed at minimizing financial risks. Objective: The purpose of this article is to analyse risk protection and risk management strategies in the digital economy to ensure financial security. Methods: During the study, the author analysed the literature, which made it possible to identify relevant strategies for protecting and managing risks in the digital economy. As part of the study, an expert survey was conducted among 20 scientists, the results of which allowed for a correlation analysis in the JASP software to determine the effectiveness of financial risk management strategies. Results: The outcomes of the correlation analysis revealed that the standardization of digital technologies reduces credit risk and cyber risk (r = -0.549, p = 0.01), while increasing reputational risk (r = -0.742, p = 0.001); the regulation of digital assets leads to an increase in counterparty and inflation risks (r = -0.742, p = 0.001); and the development of financial literacy reduces counterparty risk (r = -0.645, p = 0.002), reputational risk (r = -0.833, p = 0.001), and inflation risk (r = -0.645, p = 0.002). Conclusions: Based on the findings of the study, different risk management strategies in the digital economy demonstrate different effectiveness in mitigating specific financial risks, which emphasizes the need for a comprehensive approach to ensure overall financial stability.
Journal: Data and Metadata
Pages: 674
Volume: 4
Year: 2025
DOI: 10.56294/dm2025674
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:674:id:1056294dm2025674

Template-Type: ReDIF-Article 1.0
Author-Name: Haitham Yousef Ali Yousef Ali
Author-Name-First: Haitham Yousef Ali
Author-Name-Last: Yousef Ali
Author-Name: Yaser Mohd Hamshari
Author-Name-First: Yaser Mohd
Author-Name-Last: Hamshari
Author-Name: Mohammad Ahmad Alqam
Author-Name-First: Mohammad Ahmad
Author-Name-Last: Alqam
Author-Name: Abdelkarim Fawwaz Albataineh
Author-Name-First: Abdelkarim Fawwaz
Author-Name-Last: Albataineh
Title: Effects of corporate governance at information asymmetry on the presence of covid 19 pandemic
Abstract: This research was conducted in the presence of the COVID-19 epidemic, and its goal was to examine the effect of certain corporate governance mechanisms on information asymmetry. Company governance mechanisms include board size, board independence, block holders, family ownership, government ownership, and managerial ownership. The study spans the monthly interval from March of 2020 to December of 2021. Companies listed on the Amman Stock Exchange were randomly sampled using a statistically valid and reliable process of elimination. In total, 48 company-years were used to compile this sample. This study's experimental nature, its foundation in the financial statements of actual businesses, and its practical end-use all place it firmly within the realm of accounting solid evidence research. We examined the connection between corporate governance mechanisms and information asymmetry using a System GMM dynamic panel technique. The study discovered that information asymmetry is significantly associated with corporate governance mechanisms. Furthermore, the significance level at 1% of the lagged factors of information asymmetry indicates that the asymmetry persists over time. Lastly, the corporate governance mechanisms in our study are based on a wide range of aspects of corporate governance and point companies and their shareholders toward governance practices that reduce information asymmetry in firms.
Journal: Data and Metadata
Pages: 672
Volume: 4
Year: 2025
DOI: 10.56294/dm2025672
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:672:id:1056294dm2025672

Template-Type: ReDIF-Article 1.0
Author-Name: Anber Abraheem Shlash Mohammad
Author-Name-First: Anber Abraheem
Author-Name-Last: Shlash Mohammad
Author-Name: Zeyad Alkhazali
Author-Name-First: Zeyad
Author-Name-Last: Alkhazali
Author-Name: Suleiman Ibrahim Shelash Mohammad
Author-Name-First: Suleiman Ibrahim
Author-Name-Last: Shelash Mohammad
Author-Name: Badrea Al Oraini
Author-Name-First: Badrea
Author-Name-Last: Al Oraini
Author-Name: Asokan Vasudevan
Author-Name-First: Asokan
Author-Name-Last: Vasudevan
Author-Name: Menahi Mosallam Alqahtani
Author-Name-First: Menahi Mosallam
Author-Name-Last: Alqahtani
Author-Name: Muhammad Turki Alshurideh
Author-Name-First: Muhammad Turki
Author-Name-Last: Alshurideh
Title: Machine Learning Models for Predicting Employee Attrition: A Data Science Perspective
Abstract: Introduction: Employee attrition poses significant challenges for organizations, impacting productivity and profitability. This study explores attrition patterns using machine learning models, integrating predictive analytics with established human resource theories to identify key drivers of workforce turnover. Methods: The research analysed a dataset comprising demographic, job-related, and engagement factors. Logistic Regression was employed as the baseline model to interpret linear relationships, while Random Forest and Decision Trees captured non-linear interactions. Performance metrics such as accuracy, precision, recall, F1-score, and AUC-ROC were used to evaluate model effectiveness, alongside feature importance analysis for actionable insights. Results: Results revealed that job satisfaction, tenure, departmental dynamics, and engagement levels are critical predictors of attrition. Random Forest emerged as the most effective model, achieving an accuracy of 92% and an AUC-ROC of 94%, highlighting its capability to capture complex patterns. Decision Trees provided interpretable decision rules, offering practical thresholds for HR interventions. Logistic Regression complemented these models by offering insights into direct, linear relationships between predictors and attrition. Conclusion: The study finds that machine learning improves attrition analysis by identifying complex patterns and enabling proactive retention strategies. Predictive analytics strengthens traditional theories, providing a structured approach to reducing employee turnover. Organizations can use these insights to enhance workforce stability and performance. Future research could incorporate qualitative methods and longitudinal studies to refine strategies and assess long-term impacts.
Journal: Data and Metadata
Pages: 669
Volume: 4
Year: 2025
DOI: 10.56294/dm2025669
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:669:id:1056294dm2025669

Template-Type: ReDIF-Article 1.0
Author-Name: Khalid Thaher Amayreh
Author-Name-First: Khalid
Author-Name-Last: Thaher Amayreh
Author-Name: Mohammad Ali Alqudah
Author-Name-First: Mohammad
Author-Name-Last: Ali Alqudah
Author-Name: Firas Rashed Wahsheh
Author-Name-First: Firas
Author-Name-Last: Rashed Wahsheh
Author-Name: Khaled Yousef Issa Alshbou
Author-Name-First: Khaled
Author-Name-Last: Yousef Issa Alshbou
Author-Name: Tariq Abdelhamid Ali Mussalam
Author-Name-First: Tariq
Author-Name-Last: Abdelhamid Ali Mussalam
Author-Name: Omar Mohammad Ali Alqudah
Author-Name-First: Omar
Author-Name-Last: Mohammad Ali Alqudah
Author-Name: Mohammad Alzoubi
Author-Name-First: Mohammad
Author-Name-Last: Alzoubi
Title: Analysing the artificial intelligence of e-marketing adoption in the b2b enterprise market
Abstract: Introduction: This research examines the factors influencing the adoption of e-marketing by B2B organizations in Jordan and its impact on overall business performance. It draws on relationship marketing, innovation adoption theories, and integrates concepts from Artificial Intelligence Driven Big Data Analytics (AI DBDA) and Cognitive Service Analytics (CSA). The study is framed around the Diffusion of Innovation (DoI) theory and the Technology Acceptance Model (TAM), providing a comprehensive view of the determinants that drive e-marketing adoption. Methods: A quantitative research approach was employed, using Structural Equation Modelling (SEM) for data analysis. A survey was administered via a Google Form, collecting 226 valid responses from B2B organizations in Jordan. The theoretical framework was tested using advanced statistical techniques to evaluate the relationships between e-marketing adoption and various influencing factors. Results: The findings indicate that e-marketing adoption is significantly influenced by AI-DBDA, CSA, perceived compatibility, relative advantage, perceived ease of use, and market performance. These factors were found to be crucial in determining the extent to which B2B organizations in Jordan embrace e-marketing. Conclusions: This study emphasizes the importance of environmental factors, such as technological capabilities and organizational perceptions, in the adoption of e-marketing. The results contribute to the limited empirical research on e-marketing adoption in developing countries, offering insights into how these factors enhance business performance. These findings suggest that for successful e-marketing implementation, organizations need to focus on technological innovations and alignment with their business objectives.
Journal: Data and Metadata
Pages: 667
Volume: 4
Year: 2025
DOI: 10.56294/dm2025667
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:667:id:1056294dm2025667

Template-Type: ReDIF-Article 1.0
Author-Name: Redouane Messnaoui
Author-Name-First: Redouane
Author-Name-Last: Messnaoui
Author-Name: Mhammed El Bakkali
Author-Name-First: Mhammed
Author-Name-Last: El Bakkali
Author-Name: Mostafa Elkhaoudi
Author-Name-First: Mostafa
Author-Name-Last: Elkhaoudi
Author-Name: Omar Cherkaoui
Author-Name-First: Omar
Author-Name-Last: Cherkaoui
Author-Name: Aziz Soulhi
Author-Name-First: Aziz
Author-Name-Last: Soulhi
Title: Predicting the tensile strength of a new fabric using artificial intelligence (fuzzy logic)
Abstract: One of the most important characteristics of a warp and weft fabric is its tensile strength. The aim of this research is to develop a practical fuzzy logic model that could anticipate the ideal tensile strength of new fabrics by modifying only the weave structure. An experimental part was carried out on different weave structures to obtain the results that will enable the development of this new model. We then used the fuzzy logic model to compare its results with those of the experimental tests. The calculated mean absolute error of the fuzzy model was 1.83% for tensile strength in the warp direction and 1.99% for tensile strength in the weft direction. This result also confirmed that the fuzzy model was not only effective, but also reliable in predicting the strength of the new fabric.
Journal: Data and Metadata
Pages: 666
Volume: 4
Year: 2025
DOI: 10.56294/dm2025666
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:666:id:1056294dm2025666

Template-Type: ReDIF-Article 1.0
Author-Name: Abdullah Mohammad Al-zoubi
Author-Name-First: Abdullah Mohammad
Author-Name-Last: Al-zoubi
Author-Name: SaqerSuliman Al-Tahat
Author-Name-First: SaqerSuliman
Author-Name-Last: Al-Tahat
Title: The Impact of External Auditors Practice of Electronic Auditing on Fraud Detection: External Auditors' Awareness of the Fundamental Principles of Ethics for Members Providing Forensic Accounting Service and Forensic Accountant Skills as Moderate Variables
Abstract: The objective of the study was to measure the impact of external auditors' electronic auditing practice on fraud detection, and to measure the moderating impact of external auditors' awareness of the fundamental principles of ethics for members providing forensic accounting services and the skills of the forensic accountant on the relationship between external auditors' electronic auditing practice and fraud detection. The descriptive-analytical approach was used to achieve the objective of the study. A questionnaire was designed to collect data on the fundamental principles of ethics, the skills of the forensic accountant, the electronic auditing practice of external auditors and fraud detection. The study population comprised practicing external statutory auditors totaling (521) auditors. A sample of (267) auditors was selected, with (250) valid questionnaires retrieved and analyzed; several statistical tests were performed, including factor analysis, Cronbach's alpha. Smart PLS software was used to test the hypotheses. The study concluded that external auditors' knowledge of the fundamental principles of ethics for members providing forensic accounting services, as well as their knowledge of the skills of forensic accountants, significantly impacts the relationship between external auditors' electronic audit practice and fraud detection. This, in turn, improved the relationship, increasing the likelihood of detecting fraud. The study recommends that external auditors formally adopt the fundamental principles of ethics for members providing forensic accounting services and the skills of forensic accountants in their work, together with the holding of training sessions to ensure the optimal application of these principles and skills in their professional duties.
Journal: Data and Metadata
Pages: 664
Volume: 4
Year: 2025
DOI: 10.56294/dm2025664
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:664:id:1056294dm2025664

Template-Type: ReDIF-Article 1.0
Author-Name: Muslim
Author-Name-First: Muslim
Author-Name-Last: Muslim
Author-Name: Ambiyar
Author-Name-First: Ambiyar
Author-Name-Last: Ambiyar
Author-Name: Arwizet Karudin Karudin
Author-Name-First: Arwizet Karudin
Author-Name-Last: Karudin
Author-Name: Muhammad Syafiq Hazwan Ruslan
Author-Name-First: Muhammad Syafiq
Author-Name-Last: Hazwan Ruslan
Author-Name: Hsu-Chan Kuo
Author-Name-First: Hsu-Chan
Author-Name-Last: Kuo
Author-Name: Doni Tri Putra Yanto
Author-Name-First: Doni Tri
Author-Name-Last: Putra Yanto
Title: Project-based Augmented Reality (PjBAR): Evaluation for Vocational Education Effectiveness
Abstract: Introduction: The Industrial Revolution 4.0 requires vocational education to adopt innovative learning approaches that integrate advanced technology with real work practices. This study aims to analyze the effectiveness of the Project-based Augmented Reality (PjBAR) model in improving the quality of learning in vocational education. Methods: Data were collected through a trial implementation of the PjBAR model compared to direct instruction. The effectiveness of the model was analyzed using effect size to determine how much influence the PjBAR model had on learning outcomes. Results: This study revealed a significant difference between the PjBAR model class and the direct instruction method. The average learning outcomes of the PjBAR class were superior to those of the direct instruction class. Effect size analysis indicated that the PjBAR model had a strong impact on improving student learning outcomes. Conclusions: This model not only improves learning outcomes and the quality of education but is also able to provide a more interesting learning experience through the integration of augmented reality technology, making it relevant to meet the needs of 21st-century learning.
Journal: Data and Metadata
Pages: .661
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.661
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.661:id:1056294dm2024661

Template-Type: ReDIF-Article 1.0
Author-Name: Srinivas Adapa
Author-Name-First: Srinivas
Author-Name-Last: Adapa
Author-Name: Vamsidhar Enireddy
Author-Name-First: Vamsidhar
Author-Name-Last: Enireddy
Title: Systematic Review: Recent Advancements in Deep Learning Techniques for Facial Feature Recognition
Abstract: Deep Learning is a rapidly evolving field with critical contributions to various domains including security, healthcare, and human — computer interaction, etc. It reviews the significant developments in the area of facial recognition using deep learning techniques. It explains deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and Generative Adversarial Networks (GANs), as well as hybrid models and transfer learning uses. It also addresses technical, ethical, and legal challenges that arise for facial analysis systems and emphasizes the need for real-time processing, multi-modal systems, and robust algorithms to improve the technical accuracy and fairness of facial analysis.
Journal: Data and Metadata
Pages: 658
Volume: 4
Year: 2025
DOI: 10.56294/dm2025658
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:658:id:1056294dm2025658

Template-Type: ReDIF-Article 1.0
Author-Name: Eva Haddad
Author-Name-First: Eva
Author-Name-Last: Haddad
Title: Impact of global project coordination, policy efficiency, and organizational cultural diversity on the development of project leadership and skill performance: A Case Study of Jordanian Ministry of Planning and International Cooperation
Abstract: This research focused on studying the impact of global project coordination, policy efficiency, and organizational cultural diversity on project leadership development and skills performance through choosing the Ministry of Planning and International Cooperation in Jordan to collect all needed information and data as the research community. As a key governmental body implementing dynamic strategic initiatives, the Ministry’s effectiveness has relied on incorporating different cultural perspectives, meticulous project coordination, and policy efficiency principles to reinforce strong leadership and skill enhancement. This study explored how cultural diversity enhanced leadership perspectives and decision-making, how structured project coordination confirmed goal alignment and efficiency, and how policy efficiency enhanced resource exploitation while minimizing waste. By analyzing data collected from 200 employees within the Ministry using PLS-SEM, the study examined the interactions among cultural diversity, project coordination, policy efficiency, and their combined impact on leadership development and performance skills. Results revealed that these elements significantly encouraged leadership skills and skill performance, highlighting the importance of a varied, well-coordinated, and policy-focused approach to managing projects. The study concluded that a cohesive application of cultural diversity, strategic coordination, and policy efficiency was essential for achieving high-performance leadership and fostering skill development within the organization.
Journal: Data and Metadata
Pages: 657
Volume: 4
Year: 2025
DOI: 10.56294/dm2025657
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:657:id:1056294dm2025657

Template-Type: ReDIF-Article 1.0
Author-Name: Ezequiel Martínez-Rojas
Author-Name-First: Ezequiel
Author-Name-Last: Martínez-Rojas
Author-Name: Cristian Zahn-Muñoz
Author-Name-First: Cristian
Author-Name-Last: Zahn-Muñoz
Title: New forms of fraud in science: Deceptive practices such as article mills, fraudulent peer review, and automatic content generation
Abstract: Introduction: The study analyzes emerging trends in scientific fraud, focusing on article mills, fraudulent peer reviews, and randomly generated content, practices that have transformed the dynamics of scientific retractions. Methods: With a descriptive and transversal approach, 37,480 retracted documents were analyzed between 2015 and 2024, using data from the Retraction Watch database. Information was collected on authors, countries of affiliation, dates, areas of knowledge, and reasons for retraction. Results: The results reveal a notable change in the causes of retraction. Between 2015 and 2019, plagiarism (21.6%) and duplication (14%) led, while between 2020 and 2024 they dropped to 6.8% and 4%, respectively. In this last period, article mills (30.1%), fake peer reviews (19.9%), and randomly generated content (23.3%) increased. These practices mainly affected Business, Technology and Social Sciences, with China and India leading in these fraudulent activities. Conclusions: The study concludes that these new forms of scientific fraud represent a critical challenge to the integrity of the publications system. It underscores the need to strengthen editorial policies, implement advanced screening tools, and promote ethics education to protect the credibility of global science.
Journal: Data and Metadata
Pages: 655
Volume: 4
Year: 2025
DOI: 10.56294/dm2025655
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:655:id:1056294dm2025655

Template-Type: ReDIF-Article 1.0
Author-Name: Fahrul Adit
Author-Name-First: Fahrul
Author-Name-Last: Adit
Author-Name: Jianli Aryanto
Author-Name-First: Jianli
Author-Name-Last: Aryanto
Author-Name: Adi Fitra Andikos
Author-Name-First: Adi
Author-Name-Last: Fitra Andikos
Author-Name: Lesis Andre
Author-Name-First: Lesis
Author-Name-Last: Andre
Title: Practicality Of Developing The Work Based Learning Higher Order Thinking Skills Employability (Wbl-Hotse) Model To Improve Critical Thinking Ability In 3T Regional Vocational High Schools
Abstract: In world of education, critical thinking skills students are very much needed. This This based on based on the information found by researchers during observations at the State Vocational High School (SMKN) 01 Mentawai Islands, the critical thinking skills of students are still low. The purpose of this research This is done to produce a learning model that can improve students' critical thinking skills by implementing a valid Work Based Learning Higher Order Thinking Skills Employability (Wbl-Hotse) model. And practical for video editing technique elements in class XI SMK. Type The research carried out by researchers is development research that applies a model R&D by Borg and Gall. The practicality data collection instruments in this research are in the form of student response questionnaires and teacher activity observation sheets. The practicality of the learning device in the teacher and instructor respondent trials is the Practicality of the Learning Model 85%, the Practicality of the WBL-HOTSE Learning Model Book 90%, the Practicality of the Teacher's Guide Book 80%. The conclusion of this research is that the learning device developed by implementing the WBL-HOTSE model is practical to improve students' critical thinking skills.
Journal: Data and Metadata
Pages: 654
Volume: 4
Year: 2025
DOI: 10.56294/dm2025654
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:654:id:1056294dm2025654

Template-Type: ReDIF-Article 1.0
Author-Name: Hartanto
Author-Name-First: Hartanto
Author-Name-Last: Hartanto
Author-Name: Dimas Adi Prasetyo
Author-Name-First: Dimas Adi
Author-Name-Last: Prasetyo
Author-Name: Adi Fitra Andikos
Author-Name-First: Adi Fitra
Author-Name-Last: Andikos
Author-Name: Lesis Andre
Author-Name-First: Lesis
Author-Name-Last: Andre
Author-Name: Elvi Syofiana
Author-Name-First: Elvi
Author-Name-Last: Syofiana
Author-Name: Muhammad Amin
Author-Name-First: Muhammad
Author-Name-Last: Amin
Title: The Effectiveness of the Work Based Learning Higher Order Thinking Skills Employability (Wbl-Hotse) Model on Student Learning Outcomes for 3T Regional Vocational High Schools
Abstract: Learning outcomes serve as a key indicator of the effectiveness of the teaching and learning process. In Vocational High Schools (SMK) located in underdeveloped, frontier, and outermost (3T) regions, students' knowledge-based learning outcomes remain relatively low. This study aims to examine the effect of implementing the Work-Based Learning Higher Order Thinking Skills Employability (WBL-HOTSE) model on students’ cognitive learning outcomes during practice-based instruction in vocational schools situated in 3T areas. The research employed a quasi-experimental method using a nonequivalent control group design. One class was taught using the scientific approach as the control group, while the experimental class received instruction through the WBL-HOTSE model. The research subjects consisted of all 11th-grade students at SMKN 1 Kepulauan Mentawai. Data were collected through a test instrument. Based on hypothesis testing using the t-test, the results showed a significant difference in learning outcomes between students who were taught using the WBL-HOTSE model and those who received instruction through the scientific approach. These findings indicate that the WBL-HOTSE model can be effectively applied in practice-based learning to improve students' higher-order thinking skills and learning outcomes.
Journal: Data and Metadata
Pages: 651
Volume: 4
Year: 2025
DOI: 10.56294/dm2025651
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:651:id:1056294dm2025651

Template-Type: ReDIF-Article 1.0
Author-Name: Danny Velasco-Silva
Author-Name-First: Danny
Author-Name-Last: Velasco-Silva
Author-Name: Genesis Chafla-Espinoza
Author-Name-First: Genesis
Author-Name-Last: Chafla-Espinoza
Author-Name: Lidia Castro-Cepeda
Author-Name-First: Lidia
Author-Name-Last: Castro-Cepeda
Author-Name: Alex Asitimbay-Chamba
Author-Name-First: Alex
Author-Name-Last: Asitimbay-Chamba
Author-Name: Alex Buñay-Yuquilema
Author-Name-First: Alex
Author-Name-Last: Buñay-Yuquilema
Author-Name: Fabián Bastidas-Alarcón
Author-Name-First: Fabián
Author-Name-Last: Bastidas-Alarcón
Author-Name: Andrés Noguera-Cundar
Author-Name-First: Andrés
Author-Name-Last: Noguera-Cundar
Author-Name: Javier Albuja-Jácome
Author-Name-First: Javier
Author-Name-Last: Albuja-Jácome
Title: Integration of the double diamond methodology of design thinking and devops for the optimization of software development processes
Abstract: The integration of Double Diamond Design Thinking methodologies with DevOps seeks to optimize software development processes. This hybrid approach combines the user-centered design principles of the Double Diamond methodology, which emphasizes understanding and addressing user needs through a structured four-phase process (Discover, Define, Develop, and Deliver), with the collaborative and iterative nature of DevOps, which aims to improve efficiency and value delivery in the software lifecycle, in order to improve development quality by reducing response times, increasing adaptability to changes, and ensuring that final products are functional, scalable, and aligned with user needs and business objectives. A mixed-approach methodology was used. For the qualitative analysis, 120 researchers were surveyed, divided into two groups: control and experimental, to analyze the variables efficiency, product quality, adaptability and user satisfaction. On the other hand, the quantitative variables of system load and performance were analyzed. It was shown that the automated system reduced registration and follow-up times, improved the perception of ease of use and decreased the need for technical support. In addition, its efficiency was validated with 300 simultaneous requests, showing an optimized use of resources and average response times of 200 ms. It was concluded that this methodological integration improves efficiency, quality and user satisfaction, offering a replicable model in other technological contexts
Journal: Data and Metadata
Pages: .650
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.650
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.650:id:1056294dm2024650

Template-Type: ReDIF-Article 1.0
Author-Name: Ihsanul Insan Aljundi
Author-Name-First: Ihsanul Insan
Author-Name-Last: Aljundi
Author-Name: Dony Novaliendry
Author-Name-First: Dony
Author-Name-Last: Novaliendry
Author-Name: Yeka Hendriyani
Author-Name-First: Yeka
Author-Name-Last: Hendriyani
Author-Name: Syafrijon
Author-Name-First: Syafrijon
Author-Name-Last: Syafrijon
Title: Mobile-Based Skin Cancer Classification System Using Convolutional Neural Network
Abstract: Introduction: Skin cancer is a growing concern worldwide, often exacerbated by limited awareness and accessibility to diagnostic tools. Early detection is critical for improving survival rates and patient outcomes. This study developed a convolutional neural network (CNN) algorithm integrated into a mobile application to address this issue. Methods: The researchers employed an agile methodology to design and implement a CNN-based skin cancer detection system using the VGG16 architecture. A dataset of skin cancer images from the International Skin Imaging Collaboration (ISIC) was used, consisting of 1,500 images divided into six classes. The model was trained on 1,200 images and tested on 300 images. Preprocessing steps included resizing images to 224x224 pixels, normalization, and image augmentation to enhance model generalization. Results: The trained model achieved a test accuracy of 86.67% in classifying skin cancer types, with the highest performance for healthy skin (100% accuracy) and melanoma (98% recall). The mobile application allows users to upload or capture images of skin lesions and receive automated classification results, including lesion characteristics such as asymmetry, border, color, and diameter. Additional features include user authentication and history tracking, enhancing usability and accessibility. Conclusions: The study successfully developed a reliable CNN-based skin cancer detection system integrated into a user-friendly mobile application. The application provides a valuable tool for early detection and awareness of skin cancer. Future work should focus on clinical validation, expanding the dataset to include diverse populations, and optimizing the system for mobile deployment
Journal: Data and Metadata
Pages: .649
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.649
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.649:id:1056294dm2024649

Template-Type: ReDIF-Article 1.0
Author-Name: Muslim
Author-Name-First: Muslim
Author-Name-Last: Muslim
Author-Name: Ambiyar
Author-Name-First: Ambiyar
Author-Name-Last: Ambiyar
Author-Name: Arwizet Karudin
Author-Name-First: Arwizet
Author-Name-Last: Karudin
Author-Name: Muhammad Syafiq Hazwan Ruslan
Author-Name-First: Muhammad Syafiq
Author-Name-Last: Hazwan Ruslan
Author-Name: Hsu-Chan Kuo
Author-Name-First: Hsu-Chan
Author-Name-Last: Kuo
Author-Name: Donny Fernandez
Author-Name-First: Donny
Author-Name-Last: Fernandez
Author-Name: Doni Tri Putra Yanto
Author-Name-First: Doni Tri
Author-Name-Last: Putra Yanto
Author-Name: Nuzul Hidayat
Author-Name-First: Nuzul
Author-Name-Last: Hidayat
Title: Augmented Reality-Enhanced 5-Step Project-Based Learning Framework for Advancing Technical Education
Abstract: Introduction: In facing the challenges of the Industrial Revolution 4.0, vocational education requires a learning model that can integrate technology with real work practices. This study aims to develop, test the validity of the construction, and evaluate the practicality of a project-based learning model assisted by augmented reality in engineering education. Methods: The development model used is the ADDIE model. The construction validity test uses Confirmatory Factor Analysis to evaluate the model syntax, while the model's practicality is tested through a survey given to lecturers and students. Results: The results of the CFA test show that all PjBAR model syntaxes have good construction validity and reliability. In addition, the results of the practicality test show that this model is very practical to use, with an average score of more than 80% of the responses of lecturers and students. Conclusion: The PjBAR model developed shows good validity and reliability as well as high practicality, so it is worthy of being applied in engineering education to improve the quality of learning through AR technology
Journal: Data and Metadata
Pages: .647
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.647
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.647:id:1056294dm2024647

Template-Type: ReDIF-Article 1.0
Author-Name: Nani Rohaeni
Author-Name-First: Nani
Author-Name-Last: Rohaeni
Author-Name: Meutia
Author-Name-First: Meutia
Author-Name-Last: Meutia
Author-Name: Ina Indriana
Author-Name-First: Ina
Author-Name-Last: Indriana
Author-Name: Yeni Januarsi
Author-Name-First: Yeni
Author-Name-Last: Januarsi
Title: The Effect Of Capital Structure And Liquidity On Financial Sustainability Through Performance
Abstract: Introduction: This study was conducted to determine whether there is a relationship between capital structure and liquidity to financial sustainability through financial performance as a mediating variable. Because in some cases capital structure has an important role in business activities in a company. Objective: This study aims to analyze the effect of capital structure and liquidity on financial sustainability with financial performance as a mediating variable. Capital structure is measured using the Debt to Equity Ratio (DER), liquidity is measured using the Loan to Deposit Ratio (LDR), financial performance is measured using Return on Equity (ROE), and financial sustainability is measured by the Sustainable Growth Rate (SGR). Method: This study uses a quantitative approach with a causal design. Secondary data were obtained from the financial statements of companies listed on the Indonesia Stock Exchange (IDX) in the banking sector during the period 2018–2023. The data analysis methods used in this study are classical assumption tests and multiple linear regression to analyze direct effects and to analyze mediation effects. Result: The findings of this study prove that capital structure and liquidity have a positive and significant effect on financial performance. Capital structure and liquidity have a positive and significant effect on financial sustainability. Financial performance is proven to have an effect on financial sustainability. Financial performance is also proven to mediate the effect of capital structure and liquidity on financial sustainability. Conclusion: These findings indicate that optimal management of capital structure and liquidity, accompanied by improved financial performance, can improve the financial sustainability of the company.
Journal: Data and Metadata
Pages: .646
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.646
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.646:id:1056294dm2024646

Template-Type: ReDIF-Article 1.0
Author-Name: Nursanda Rizki Adhari
Author-Name-First: Nursanda
Author-Name-Last: Rizki Adhari
Author-Name: Dadang Sundawa
Author-Name-First: Dadang
Author-Name-Last: Sundawa
Author-Name: Cecep Darmawan
Author-Name-First: Cecep
Author-Name-Last: Darmawan
Author-Name: Syaifullah
Author-Name-First: Syaifullah
Author-Name-Last: Syaifullah
Title: The Impact of Youth Empowerment Programs on Patriotism-Driven Defense Readiness: A Meta-Analysis
Abstract: Introduction: The lack of comprehensive research evaluating the impact of youth empowerment programs on patriotism-driven defense readiness among youth underlies this study. Accordingly, this study aimed to analyze the influence of youth empowerment programs on patriotism-driven defense readiness. Methods: This study utilized a quantitative approach of meta-analysis type. The eligible criteria for the reviewed articles included: (1) relevant topics of discussion; (2) empirical research results; (3) having values ​​(r), (t), or (F); (4) N ≥ 30; (5) using an internationally recognized language; (6) indexed by Scopus, Web of Science, SINTA, or at least Google Scholar; and (7) searchable in national and international online journal databases. Data collection techniques included the use of various related keywords in well-known journal databases such as PubMed, Scopus, Web of Science, ERIC (Education Resources Information Center), and Google Scholar. The software used in this data analysis was JASP 19.2.0. Results: The results of this study revealed several findings as follows: (1) the heterogeneity test shows that the analyzed studies come from a heterogeneous population; (2) the effect size test shows the estimated effect size value of 0.845 (p < 0.001) indicating a significant level of influence and in the high category; and (3) the publication bias test using the Precision-effect test and precision-effect estimate with standard errors (PET-PEESE) t value of 0.644 and p-value of 0.524 indicate no publication bias was found. Conclusions: This study concludes that youth empowerment programs have a significant impact on patriotism-based defense readiness, with studies originating from heterogeneous populations and without any detected publication bias.
Journal: Data and Metadata
Pages: .645
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.645
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.645:id:1056294dm2024645

Template-Type: ReDIF-Article 1.0
Author-Name: Abdelfattah MOULOUD
Author-Name-First: Abdelfattah
Author-Name-Last: MOULOUD
Author-Name: Yasmine EL BELGHITI
Author-Name-First: Yasmine
Author-Name-Last: EL BELGHITI
Author-Name: Samir TETOUANI
Author-Name-First: Samir
Author-Name-Last: TETOUANI
Author-Name: Aziz SOULHI
Author-Name-First: Aziz
Author-Name-Last: SOULHI
Title: Optimization of Order Scheduling in the Moroccan Garment Industry for Fast Fashion: A Clustering-Based Approach
Abstract: The Moroccan garment industry plays a crucial role in the global fast fashion market, requiring efficient, flexible, and timely production to meet evolving consumer demands. However, the scheduling of small order batches presents significant challenges, as it demands skilled operators and strict adherence to On-Time Delivery (OTD) targets. Traditional scheduling methods based on product family groupings often result in frequent and time-consuming changeovers, increasing downtime and reducing operational efficiency by up to 15-20%. This paper introduces a novel clustering-based scheduling methodology that organizes production lines by technological times rather than product families. By grouping garments with similar operational requirements, this approach aims to minimize changeover times, streamline production transitions, and reduce downtime by an average of 30-35%. A case study conducted in a Moroccan garment factory validates the effectiveness of the proposed approach. The factory, with an average order size of 50-100 units per batch, achieved a 20% reduction in lead time and a 15% increase in operator productivity after implementing the clustering-based scheduling. Additionally, the use of clustering methods such as K-Means facilitated the grouping of garments with minimal operational variability, further enhancing planning flexibility and resource utilization. This study highlights how technological clustering enhances production scheduling in the garment industry. It emphasizes aligning production processes with operational needs to optimize resources and competitiveness in fast fashion. The methodology provides a framework for reducing changeover downtime, boosting productivity, and maintaining agility in a dynamic market.
Journal: Data and Metadata
Pages: .644
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.644
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.644:id:1056294dm2024644

Template-Type: ReDIF-Article 1.0
Author-Name: Ikram Khadir
Author-Name-First: Ikram
Author-Name-Last: Khadir
Author-Name: Mohamed Saadi
Author-Name-First: Mohamed
Author-Name-Last: Saadi
Author-Name: El Hamdouni Ikram
Author-Name-First: El Hamdouni
Author-Name-Last: Ikram
Title: Hydraulic modeling of Sebou tributaries for flood prevention in the el Gharb plain - Morocco
Abstract: Flooding is one of the most unpredictable natural hazards. In Morocco, the El Gharb plain is the most affected. The Rharb basin receives between 500 and 600mm of precipitation and includes 30% of Morocco's water resources. All the factors that make the Rharb plain a vulnerable area are: climatic factors, lithology, geomorphology, the limited number of natural outlets for water drainage towards the Atlantic Ocean. The methodology adopted is based on the determination of flood zones and the hydraulic modeling of the main tributaries of the Oued Sebou and the main sanitation channels, in order to monitor the evolution of flood zones and evaluate the flow of the Oued Sebou to understand the functioning of the hydrographic network and the overflow points. According to the hydrographs established by the Gharb plain flood protection department, the maximum flow at the entrance to the city of Kenitra was estimated at 2,600 m3/s and the flow of the Oued at this level is of the order of 1,600 m3/s, which explains the overflows recorded at the level of the left bank of the Oued Sebou, the dead arm of the Oued. The results of these studies as well as the analysis of the history of the floods of the Oued Sebou, show that one to two major floods occur every 10 years and that the overflows reach upstream of the highway to the dead arm of the Oued and cover Merja-Fouarate such as the case of the flooding of the city of Kenitra in 2010.
Journal: Data and Metadata
Pages: 643
Volume: 4
Year: 2025
DOI: 10.56294/dm2025643
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:643:id:1056294dm2025643

Template-Type: ReDIF-Article 1.0
Author-Name: Yarisda Ningsih
Author-Name-First: Yarisda
Author-Name-Last: Ningsih
Author-Name: Lufri
Author-Name-First: Lufri
Author-Name-Last: Lufri
Author-Name: I Made Arnawa
Author-Name-First: I Made
Author-Name-Last: Arnawa
Author-Name: Dony Novaliendry
Author-Name-First: Dony
Author-Name-Last: Novaliendry
Author-Name: Rahmat Fadillah
Author-Name-First: Rahmat
Author-Name-Last: Fadillah
Title: Problem, realistic, technology in mathematics (protectim) learning model founded on blended learning
Abstract: Esta investigación aborda la necesidad de modelos de aprendizaje innovadores, en concreto el aprendizaje basado en problemas, para superar los retos en la formación de maestros de primaria. El estudio propone el modelo de aprendizaje PROTECTIM, un marco basado en el aprendizaje combinado diseñado para mejorar las competencias pedagógicas de los estudiantes de Magisterio de Primaria. El objetivo principal de este estudio es desarrollar un modelo de aprendizaje PROTECTIM válido, práctico y eficaz que apoye las competencias pedagógicas de los futuros maestros de primaria. El estudio emplea una metodología de investigación basada en proyectos, evaluando la idoneidad del modelo en términos de contenido, estructura y lenguaje. El estudio utiliza como referencia materiales de muestra, incluidos libros de texto, cuadernos de trabajo del profesor y cuadernos de trabajo del alumno. Los participantes en la investigación son alumnos del PGSD que han completado cursos de geometría y medición de nivel elemental. El modelo PROTECTIM incorpora una sintaxis estructurada que comprende los siguientes pasos: clarificación del problema, planteamiento del problema, comprensión, experiencias de los alumnos, presentación, reflexión y evaluación. Los resultados indican que el modelo PROTECTIM desarrollado cumple los criterios de validez teórica en cuanto a contenido, gráficos y lenguaje. Además, cumple los criterios prácticos, demostrando facilidad de uso, utilidad para el aprendizaje y atractivo. En particular, el modelo PROTECTIM, basado en los principios del aprendizaje combinado, ha resultado ser un instrumento eficaz para mejorar las competencias pedagógicas de los estudiantes de PGSD, contribuyendo así de forma sustancial al ámbito de la formación del profesorado de primaria
Journal: Data and Metadata
Pages: .641
Volume: 3
Year: 2025
DOI: 10.56294/dm2024.641
Handle: RePEc:dbk:datame:v:3:y:2025:i::p:.641:id:1056294dm2024641

Template-Type: ReDIF-Article 1.0
Author-Name: María-Mercedes Yeomans-Cabrera
Author-Name-First: María-Mercedes
Author-Name-Last: Yeomans-Cabrera
Author-Name: Jonathan Martínez-Líbano
Author-Name-First: Jonathan
Author-Name-Last: Martínez-Líbano
Title: Evaluating teacher induction programs: a systematic review
Abstract: Introduction: Teacher induction programs ensure novice teachers transition smoothly into their roles, fostering confidence, organizational culture, and professional identity. These programs enhance teacher satisfaction, retention, and productivity, benefiting educators and institutions. Despite their importance, the evaluation of induction programs often lacks generalizability, with existing studies constrained by local policies, specific methodologies, or limited scopes. Objective: The purpose of this study was to systematically review and analyze existing teacher induction program evaluation instruments to identify their applicability, limitations, and gaps. Method: A systematic review was conducted in July 2021 following PRISMA guidelines, with searches performed in the Web of Science (WoS) and Scopus databases. Keywords such as “induction program,” “program evaluation,” and “programme assessment” were used with Boolean operators to ensure a comprehensive search. Inclusion criteria required articles to evaluate induction programs and be published in English or Spanish. Out of 22 initially identified publications, six met the eligibility criteria. Results: The selected studies showcased diverse methodologies, including quantitative, qualitative, and mixed approaches. However, most were limited by contextual specificity, focusing on local policies or isolated variables like online learning communities or immersive programs. Conclusions: The E-Tip was the only instrument demonstrating generalizability in teacher induction program evaluation. However, it assesses the presence of quality criteria without evaluating their degree of implementation. The review highlights the need for a robust, qualitative tool to complement existing quantitative measures, which would provide a comprehensive framework for assessing the effectiveness of teacher induction programs across diverse educational settings.
Journal: Data and Metadata
Pages: .640
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.640
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.640:id:1056294dm2024640

Template-Type: ReDIF-Article 1.0
Author-Name: M. Fariz Fadillah Mardianto
Author-Name-First: M. Fariz
Author-Name-Last: Fadillah Mardianto
Author-Name: Adnan Syawal Adilaha Sadikin
Author-Name-First: Adnan Syawal
Author-Name-Last: Adilaha Sadikin
Author-Name: Grace Lucyana Koesnadi
Author-Name-First: Grace Lucyana
Author-Name-Last: Koesnadi
Author-Name: Elly Pusporani
Author-Name-First: Elly
Author-Name-Last: Pusporani
Author-Name: Goh Khang Wen
Author-Name-First: Goh
Author-Name-Last: Khang Wen
Title: Time Series Clustering for Stock Exchange in Asean Based on Non-Hierarchical Methods
Abstract: Introduction: This study explores the impact of global economic volatility, particularly influenced by the Russia-Ukraine and Israel-Palestine conflicts, on the ASEAN stock markets. The research aims to analyze stock price patterns and trends to support sustainable economic planning and improve market stability. Methods: The study employed non-hierarchical clustering techniques, including K-Means and K-Medoids, to analyze time series data from 18 ASEAN stocks over a 10-year period. Data preprocessing involved Min-Max normalization, and Principal Component Analysis (PCA) was utilized for dimensionality reduction. The clustering performance was evaluated using silhouette coefficients, and the Elbow Method determined the optimal number of clusters. Results: K-Means demonstrated superior clustering performance with a silhouette coefficient of 0.63362 compared to K-Medoids (0.37133). The K-Means method identified seven distinct clusters, effectively grouping stocks with similar temporal patterns. The results revealed significant trends in price stability and volatility across different sectors. Conclusions: The findings highlight the value of clustering techniques in understanding market dynamics and provide actionable insights for policymakers and investors. The study recommends the development of real-time market monitoring systems to mitigate price fluctuations and support sustainable economic growth in ASEAN. Future research could explore integrating machine learning models for enhanced market analysis
Journal: Data and Metadata
Pages: .639
Volume: 3
Year: 2025
DOI: 10.56294/dm2024.639
Handle: RePEc:dbk:datame:v:3:y:2025:i::p:.639:id:1056294dm2024639

Template-Type: ReDIF-Article 1.0
Author-Name: Ezequiel Martínez-Rojas
Author-Name-First: Ezequiel
Author-Name-Last: Martínez-Rojas
Author-Name: Cristian Zahn-Muñoz
Author-Name-First: Cristian
Author-Name-Last: Zahn-Muñoz
Author-Name: Ricardo Espinaza-Solar
Author-Name-First: Ricardo
Author-Name-Last: Espinaza-Solar
Title: RETRACTION OF SCIENTIFIC LITERATURE: Analyzing the reasons for retractions across different areas of knowledge in Latin America
Abstract: The article analyzes scientific retractions as essential tools to correct faulty literature, highlighting their increase in recent years. Although this phenomenon has been widely studied in health sciences, there is little research in other areas of knowledge and in regions such as Latin America. The objective of the study was to identify the reasons for retraction of scientific publications in the region between 1987 and 2024, using data from the Retraction Watch database. Using a transversal and descriptive approach, 614 documents were analyzed, classifying the reasons as misconduct, unintentional error and others. The results revealed that misconduct is the predominant cause in all areas, reaching its highest incidence in Business and Technology (91,9 %), while unintentional errors were more frequent in experimental disciplines. The study concludes with recommendations to reduce retractions and ensure greater.
Journal: Data and Metadata
Pages: 638
Volume: 4
Year: 2025
DOI: 10.56294/dm2025638
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:638:id:1056294dm2025638

Template-Type: ReDIF-Article 1.0
Author-Name: Ahmad Hanandeh
Author-Name-First: Ahmad
Author-Name-Last: Hanandeh
Author-Name: Saleh Yahya ALFreijat
Author-Name-First: Saleh Yahya
Author-Name-Last: ALFreijat
Author-Name: Rania J. Qutieshat
Author-Name-First: Rania J.
Author-Name-Last: Qutieshat
Author-Name: Hamzeh yuosef Alsha’ar
Author-Name-First: Hamzeh yuosef
Author-Name-Last: Alsha’ar
Author-Name: Qais AL Kilani
Author-Name-First: Qais AL
Author-Name-Last: Kilani
Author-Name: Mohamad Ahmad Saleem Khasawneh
Author-Name-First: Mohamad Ahmad
Author-Name-Last: Saleem Khasawneh
Title: Implementing AI Accuracy, Learning Rate, Inference Time on enhancing Big Data Analysis and Strategic Plan
Abstract: Introduction: This study aims to focus on the role of artificial intelligence tools and capabilities such as artificial intelligence accuracy, learning rate and inference time in influencing big data analysis and building strategic plans at Zain Jordan Telecommunications Company. Objective: The review explores how increasing the ability of organizations to maintain competitive capabilities in an era of continuous change and development in the field of information technology, most organizations focus on adopting new tactics and increasing features to improve organizational performance, improve services provided to customers, simplify administrative and operational processes, improve operational efficiency and make strategic decisions. Method: A research questionnaire was distributed to study the impact and measure the impact of artificial intelligence tools such as artificial intelligence accuracy, learning rate and inference time on increasing big data analysis capabilities and building strategic plans. 163 valid questionnaires were received for analysis and the data were analyzed using the PLSSIM system. Result: Artificial intelligence tools such as artificial intelligence accuracy, learning rates and inference time positively affect increasing big data analysis and building strategic plans. Conclusion: this study allows for a deeper understanding of the impact of artificial intelligence tools and capabilities in influencing big data analysis and building strategic plans.
Journal: Data and Metadata
Pages: 637
Volume: 4
Year: 2025
DOI: 10.56294/dm2025637
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:637:id:1056294dm2025637

Template-Type: ReDIF-Article 1.0
Author-Name: Marcela Hechenleitner-Carvallo
Author-Name-First: Marcela
Author-Name-Last: Hechenleitner-Carvallo
Author-Name: Jacqueline Ibarra-Peso
Author-Name-First: Jacqueline
Author-Name-Last: Ibarra-Peso
Title: Telehealth and telemedicine projections in the post-covid-19 era. A scoping review
Abstract: Introduction: Before the COVID-19 pandemic, telemedicine and telehealth faced legal, technological, and cultural regulatory limitations. The health crisis boosted its massive adoption, enhancing its continuity over time. The objective of this review is to determine the projections of telehealth and telemedicine in the post-COVID-19 era and the factors that condition its growth. Methods: A systematic review was carried out following the PRISMA-ScR guidelines. The databases consulted were PubMed, Web of Science, and Scopus. 19 relevant studies were selected from an initial total of 96. Results: The pandemic accelerated the adoption of telemedicine, maintaining its use in areas such as mental health and chronic diseases. Factors associated with the use and development of technologies, added to cultural and economic aspects, have hindered its growth. Conclusions: Telehealth and telemedicine have improved access to health, but their sustainability requires resolving technological inequalities, in addition to guaranteeing privacy and security standards.
Journal: Data and Metadata
Pages: 633
Volume: 4
Year: 2025
DOI: 10.56294/dm2025633
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:633:id:1056294dm2025633

Template-Type: ReDIF-Article 1.0
Author-Name: Gabriela de Jesús Vásquez Espinoza
Author-Name-First: Gabriela de Jesús
Author-Name-Last: Vásquez Espinoza
Author-Name: Kathiusca Paola Echeverría Caicedo
Author-Name-First: Kathiusca Paola
Author-Name-Last: Echeverría Caicedo
Author-Name: Juliana Karina Zapa Cedeño
Author-Name-First: Juliana Karina
Author-Name-Last: Zapa Cedeño
Author-Name: Guadalupe Saldarriaga Jiménez
Author-Name-First: Guadalupe
Author-Name-Last: Saldarriaga Jiménez
Title: Practices, Risks, and Regulations of Self-Medication in Ecuador, Analysis of Prevalence, Determinant Factors, and Patterns
Abstract: Self-medication was identified as a significant global public health issue, particularly in regions with fragmented healthcare systems and economic disparities. This practice posed risks such as antimicrobial resistance, adverse drug reactions, and delayed diagnoses of serious conditions. This study aimed to analyze the prevalence, patterns, and drivers of self-medication in Latin America and compare these findings with other global contexts. A mixed-methods approach was employed, integrating quantitative data from secondary sources and qualitative analysis of cultural and regulatory influences. Data from Ecuador, Peru, Colombia, Brazil, and Spain were analyzed, revealing a prevalence range from 35% in Brazil to 82.9% in Ecuador. Antibiotics and analgesics were the most commonly used drugs, with their misuse contributing to increased public health risks, particularly antimicrobial resistance. Economic barriers, cultural norms, and healthcare access disparities were identified as key drivers. In Spain, stricter pharmaceutical regulations corresponded to a lower prevalence (40%), highlighting the role of policy enforcement. The findings underscored the need for effective interventions, including stricter regulations, public education campaigns, and improved healthcare access, to mitigate risks and improve health outcomes.
Journal: Data and Metadata
Pages: 632
Volume: 4
Year: 2025
DOI: 10.56294/dm2025632
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:632:id:1056294dm2025632

Template-Type: ReDIF-Article 1.0
Author-Name: Robinson Dueñas Casallas
Author-Name-First: Robinson
Author-Name-Last: Dueñas Casallas
Author-Name: Cristina Crespo Soler
Author-Name-First: Cristina
Author-Name-Last: Crespo Soler
Author-Name: Vicente Mateo Ripoll Feliu
Author-Name-First: Vicente Mateo
Author-Name-Last: Ripoll Feliu
Author-Name: Carlos A. Álvarez Moreno
Author-Name-First: Carlos A.
Author-Name-Last: Álvarez Moreno
Title: Management and efficiency in highly complex public healthcare: an analysis of financial ratios and non-parametric statistic
Abstract: Introduction: the objective of the study was to evaluate the management and technical efficiency of public health, taking as a sample the 25 specialized public hospitals in Colombia as well as the data of their annual financial statements and income statements from 2017 to 2022. Method: a total of 28 financial ratios were developed for each hospital and year, then a correlation test was carried out, selecting nine of the best results to determine those with the greatest contribution to the data and their changes. To evaluate the management and technical efficiency, the SPSS and R software were used for the statistical analysis. Results: according to Kruskall-Wallis’ test, they do not have technical efficiency, the results are below average and mostly negative, which allowed identifying opportunities for improvement of the financial and operational management systems, efficiency and productivity. Therefore, the research hypothesis is rejected. Conclusion: there is no technical efficiency in the hospitals analyzed, high degrees of management asymmetry are observed in most of the ratios analyzed, there are possibilities of operational and financial risk, hence it is suggested to enhance management control, and thereby some recommendations and new research are given.
Journal: Data and Metadata
Pages: .630
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.630
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.630:id:1056294dm2024630

Template-Type: ReDIF-Article 1.0
Author-Name: Aazelarab Boughaleb
Author-Name-First: Aazelarab
Author-Name-Last: Boughaleb
Author-Name: Mounir Jerry
Author-Name-First: Mounir
Author-Name-Last: Jerry
Title: Economic Measures for Risk Control and Cost Containment in Healthcare in Morocco: An Exploratory Study
Abstract: Background: Controlling healthcare expenditures is a major challenge for Morocco, particularly with the expansion of universal health coverage. This reform raises issues of financial sustainability, equity, and efficiency of financing mechanisms, requiring an evaluation of the implemented economic measures. Objectives: To analyze and assess the effectiveness of economic measures adopted to control financial risks and healthcare expenditures. To identify key initiatives, challenges, and recommendations to strengthen the sector's financial governance. Method: A qualitative approach combining documentary analysis and a semi-structured survey conducted with 18 healthcare sector experts via an online questionnaire. Thematic analysis of responses identified effective measures, limitations, and areas for improvement. Results: Coordinated Care Pathways (CCPs), Health Technology Assessment (HTA), and the drug reimbursement strategy are perceived as effective. However, challenges remain, including lack of coordination, an inadequate regulatory framework, and fragmented information systems. Recommendations include strengthening financial governance, integrating digital tools, and optimizing regulatory and pricing mechanisms. Conclusion: The study highlights the need for an integrated approach to improve healthcare expenditure management in Morocco. Adopting financial risk management technologies, modernizing regulatory tools, and strengthening primary care are essential to ensuring sustainable universal health coverage.
Journal: Data and Metadata
Pages: 627
Volume: 4
Year: 2025
DOI: 10.56294/dm2025627
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:627:id:1056294dm2025627

Template-Type: ReDIF-Article 1.0
Author-Name: Karla Martell Alfaro
Author-Name-First: Karla
Author-Name-Last: Martell Alfaro
Author-Name: José Seijas-Díaz
Author-Name-First: José
Author-Name-Last: Seijas-Díaz
Author-Name: Very Rengifo-Hidalgo
Author-Name-First: Very
Author-Name-Last: Rengifo-Hidalgo
Author-Name: Cinthya Torres-Silva
Author-Name-First: Cinthya
Author-Name-Last: Torres-Silva
Author-Name: Jessica Cabel-Rabines
Author-Name-First: Jessica
Author-Name-Last: Cabel-Rabines
Author-Name: Seidy Vela-Reátegui
Author-Name-First: Seidy
Author-Name-Last: Vela-Reátegui
Author-Name: Lloy Pinedo
Author-Name-First: Lloy
Author-Name-Last: Pinedo
Title: Design of a virtual platform for the promotion and trade of utilitarian ceramics from indigenous communities
Abstract: Digital commerce offers opportunities for the promotion of handicraft products, especially those with a strong cultural component such as utilitarian ceramics made by indigenous communities. This study aimed to design a virtual platform for the promotion and commercialization of ceramics inspired by the iconography of the Shawi indigenous communities, preserving their cultural traditions and improving their economic opportunities. The methodology employed included the application of Kanban as an agile approach for the design of the platform. Technological tools such as PHP, MySQL, HTML and CSS were used to ensure the functionality and scalability of the system. The platform included functionalities such as an interactive catalog, a shopping cart and an educational section on the history of the pieces. In addition, we worked in collaboration with the artisans to integrate cultural elements into the design. The results showed that the platform not only facilitates access to digital markets, but also strengthens the cultural valorization of Shawi ceramics. In conclusion, this model represents an effective solution for linking technology, commerce and culture, with practical implications for economic development and cultural preservation of indigenous communities.
Journal: Data and Metadata
Pages: 625
Volume: 4
Year: 2025
DOI: 10.56294/dm2025625
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:625:id:1056294dm2025625

Template-Type: ReDIF-Article 1.0
Author-Name: Maureen Marsenne
Author-Name-First: Maureen
Author-Name-Last: Marsenne
Author-Name: Tubagus Ismail
Author-Name-First: Tubagus
Author-Name-Last: Ismail
Author-Name: Muhamad Taqi
Author-Name-First: Muhamad
Author-Name-Last: Taqi
Author-Name: Imam Abu Hanifah
Author-Name-First: Imam Abu
Author-Name-Last: Hanifah
Title: Harnessing Big Data and AI for Predictive Insights: Assessing Bankruptcy Risk in Indonesian Stocks
Abstract: Introduction: This research aims to investigate the use of financial Big Data and artificial intelligence (AI) in predicting the bankruptcy risk of companies listed on the Indonesia Stock Exchange (BEI), with the Altman Z-Score model as the main framework. Objective: In this research, an intervening variable in the form of financial data quality is introduced to assess the role of mediation in increasing the accuracy of bankruptcy predictions.. Method: The research method used is quantitative with the analytical method used is Structural Equation Modeling Partial Least Squares (SEM-PLS), which allows analysis of the relationship between independent variables (Big Data and AI), intervening variables (quality of financial data), and dependent variables (bankruptcy risk prediction). Result: The research results show that the integration of financial Big Data and AI significantly increases the accuracy of company bankruptcy risk predictions on the IDX, with the quality of financial data acting as an intervening variable that strengthens this relationship. The influence of Big Data and AI on bankruptcy prediction through the quality of financial data has also been proven to provide more precise and faster results compared to the conventional Altman Z-Score model. Conclusion: These findings confirm that the quality of financial data is a key factor that must be considered in optimizing bankruptcy predictions in the capital market. This research has implications for the development of financial technology (Fintech) and risk management strategies in public companies, especially in identifying bankruptcy risks more effectively by utilizing the latest technology.
Journal: Data and Metadata
Pages: .622
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.622
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.622:id:1056294dm2024622

Template-Type: ReDIF-Article 1.0
Author-Name: Rahmiati
Author-Name-First: Rahmiati
Author-Name-Last: Rahmiati
Author-Name: Nizwardi Jalinus
Author-Name-First: Nizwardi
Author-Name-Last: Jalinus
Author-Name: Hansi Effendi
Author-Name-First: Hansi
Author-Name-Last: Effendi
Author-Name: Rahmat Fadillah
Author-Name-First: Rahmat
Author-Name-Last: Fadillah
Author-Name: Rizki Ema Wulansari
Author-Name-First: Rizki Ema
Author-Name-Last: Wulansari
Title: Assessing the Impact of a STEM Learning Project Model on Artificial Intelligence Education in Higher Learning Institutions
Abstract: This study investigates the effectiveness of the STEM Learning Project model in enhancing student outcomes in Artificial Intelligence (AI) courses at higher education institutions. The research aimed to assess the model’s impact on students’ cognitive, affective, and psychomotor skills, with a focus on fostering active participation, problem-solving, and interdisciplinary knowledge integration. Employing a mixed-methods approach, the study utilized both qualitative and quantitative data collection methods. The experimental group engaged in the STEM Learning Project, while the control group followed a traditional AI curriculum. Changes in student knowledge and engagement were measured using pre- and post-test surveys, complemented by qualitative insights obtained from interviews and focus group discussions. The results demonstrated progress in both groups, though the experimental group achieved a greater increase in post-test scores (29,87) compared to the control group (29,21). Statistical analyses confirmed that the data satisfied normality and homogeneity assumptions, allowing for parametric testing. An independent sample t-test revealed a significant difference in post-test scores between the two groups, highlighting the effectiveness of the STEM Learning Project model in enhancing students' AI-related skills. This approach notably improved students' cognitive abilities and interdisciplinary knowledge in AI education, establishing it as a promising strategy for preparing students to address the demands of the AI industry. Future research could explore the model's long-term impact on career readiness and its applicability to other technology-driven educational settings.
Journal: Data and Metadata
Pages: .623
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.623
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.623:id:1056294dm2024623

Template-Type: ReDIF-Article 1.0
Author-Name: Arip Ramadan
Author-Name-First: Arip
Author-Name-Last: Ramadan
Author-Name: Dwi Rantini
Author-Name-First: Dwi
Author-Name-Last: Rantini
Author-Name: Yohanes Manasye Triangga
Author-Name-First: Yohanes
Author-Name-Last: Manasye Triangga
Author-Name: Ratih Ardiati Ningrum
Author-Name-First: Ratih Ardiati
Author-Name-Last: Ningrum
Author-Name: Fazidah Othman
Author-Name-First: Fazidah
Author-Name-Last: Othman
Title: Space-Time Autoregressive Integrated Moving Average (STARIMA) Modeling for Predicting Criminal Cases of Motor Vehicle Theft in Surabaya, Indonesia
Abstract: Introduction: Motor vehicle theft poses significant challenges in urban areas, particularly in large metropolitan cities like Surabaya, Indonesia's second-largest city. Surabaya's strategic economic role makes it a hotspot for criminal activities, including motor vehicle theft, driven by various socio-economic factors. Methods: This study utilizes the Space-Time Autoregressive Integrated Moving Average (STARIMA) model to predict motor vehicle theft cases across five sub-regions in Surabaya, covering the period from January 2019 to December 2023. The STARIMA model, which incorporates both temporal and spatial dependencies, offers a more robust framework for crime prediction compared to traditional models like ARIMA. The results show that STARIMA effectively captures the spatio-temporal dynamics of crime, providing valuable insights for law enforcement to develop targeted strategies that enhance public safety. Results: The model's performance was evaluated using the Root Mean Square Error (RMSE), indicating its suitability for accurate and actionable crime forecasting in Surabaya. Based on the RMSE value, the best model obtained is STARIMA (1,1,2) with a Uniform Location weighting matrix. Conclusions: This STARIMA (1,1,2) model, it is used to predict motor vehicle theft incidents in West Surabaya, Central Surabaya, South Surabaya, East Surabaya, and North Surabaya. The forecast value carried out is for a period of five months into the future. Case predictions for the next five months show fluctuations in each region of Surabaya, with the highest regions in succession being North Surabaya, East Surabaya, and South Surabaya
Journal: Data and Metadata
Pages: .621
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.621
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.621:id:1056294dm2024621

Template-Type: ReDIF-Article 1.0
Author-Name: Endang Purwaningsih
Author-Name-First: Endang
Author-Name-Last: Purwaningsih
Author-Name: Muslikh
Author-Name-First: Muslikh
Author-Name-Last: Muslikh
Author-Name: Muhamad Fathurahman
Author-Name-First: Muhamad
Author-Name-Last: Fathurahman
Author-Name: Basrowi
Author-Name-Last: Basrowi
Title: Optimization of Branding and Value Chain Mapping Using Artificial Intelligence for the Batik Village Clusters in Indonesia to Achieve Competitive Advantage
Abstract: This research investigates the role of artificial intelligence (AI) in optimizing branding and mapping value chains to strengthen the competitive advantage of Batik Village Clusters in Indonesia. Employing a quantitative approach, the study analyzes survey data from stakeholders in the batik industry, focusing on their perceptions of AI's impact on branding and value chain processes. The study reveals that AI has a significant positive impact on branding optimization (t-statistic = 29.249, p = 0.000) and value chain mapping (t-statistic = 15.066, p = 0.000). Additionally, both branding optimization (t-statistic = 8.621) and value chain mapping (t-statistic = 16.853) were found to positively affect the competitive advantage of batik clusters. These findings suggest that AI can enhance branding efforts, improve value chain efficiency, and elevate the competitive positioning of Batik Village Clusters. The study provides actionable recommendations for batik entrepreneurs and policymakers, emphasizing the need to incorporate AI technologies to improve global competitiveness and ensure long-term sustainability in the batik industry
Journal: Data and Metadata
Pages: .620
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.620
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.620:id:1056294dm2024620

Template-Type: ReDIF-Article 1.0
Author-Name: Alejandra Holguin Avila
Author-Name-First: Alejandra
Author-Name-Last: Holguin Avila
Author-Name: Luis Asunción Pérez Domínguez
Author-Name-First: Luis Asunción
Author-Name-Last: Pérez Domínguez
Author-Name: Roberto Romero Lopez
Author-Name-First: Roberto
Author-Name-Last: Romero Lopez
Author-Name: David Luviano Cruz
Author-Name-First: David Luviano
Author-Name-Last: Cruz
Title: The role of multicriteria decision making in the supply chain: Literature review
Abstract: Introduction: The evaluation of supply chain performance has gained significant relevance due to recent events that have transformed its operational dynamics, as well as the advent of Industry 5.0. This new era introduces advanced technologies, such as digital twins, which, when combined with multicriteria models, can identify and prioritize key factors to enhance performance evaluation. These tools have the potential to optimize strategic decision-making in an increasingly dynamic and competitive environment. Methods: A systematic literature review was conducted following the PRISMA framework, analyzing 45 articles published between 2019 and 2024. The sources included scientific databases such as SCOPUS and Web of Science. The search employed terms related to multicriteria models, supply chain, Industry 4.0, and digital twins. Articles were selected based on predefined inclusion and exclusion criteria. Results: Findings revealed that multicriteria methods are widely used to evaluate efficiency, sustainability, and resilience in supply chains. Additionally, digital twins emerged as key tools for real-time monitoring, risk management, and process simulation. However, technological, financial, and regulatory barriers were identified, hindering their practical implementation. Conclusions: The combination of advanced technologies with multicriteria approaches represents a promising solution for improving supply chain performance. Future research should focus on developing hybrid models, promoting organizational training, and establishing international standards to ensure effective adoption. These initiatives will enable organizations to address the challenges of an increasingly complex global environment, strengthening the resilience and sustainability of supply chains.
Journal: Data and Metadata
Pages: 619
Volume: 4
Year: 2025
DOI: 10.56294/dm2025619
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:619:id:1056294dm2025619

Template-Type: ReDIF-Article 1.0
Author-Name: Verenice Sánchez-Castillo
Author-Name-First: Verenice
Author-Name-Last: Sánchez-Castillo
Title: Analysis of Colombian scientific production around Agroecology in Scopus
Abstract: Introduction: This article presents a detailed analysis of Colombian scientific production in agroecology indexed in Scopus between 2013 and 2023. It addresses the main trends, themes and methodological approaches in the research. Methodology: Using a mixed methodology, a bibliometric analysis was carried out to identify patterns in productivity, collaboration and impact, which was complemented with a qualitative analysis of articles on the topic. Results: The results revealed a significant increase in scientific production, marked by the incorporation of participatory and interdisciplinary approaches, in which collaboration between local and international institutions played a crucial role. Furthermore, a transition was observed towards studies that integrate environmental sustainability with agroecological practice. This reflects the commitment to the resilience of production systems. However, challenges persist in the visibility and citation of Colombian research at a global level, which indicates the need to strengthen its dissemination and alignment with theoretical frameworks of international scope. This analysis provides a comprehensive understanding of the state of agroecology in Colombia and highlights its potential to guide sustainable agricultural policies
Journal: Data and Metadata
Pages: .618
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.618
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.618:id:1056294dm2024618

Template-Type: ReDIF-Article 1.0
Author-Name: Angelo J. Soto-Vergel
Author-Name-First: Angelo J.
Author-Name-Last: Soto-Vergel
Author-Name: Oriana A. Lopez-Bustamante
Author-Name-First: Oriana A.
Author-Name-Last: Lopez-Bustamante
Author-Name: Byron Medina-Delgado
Author-Name-First: Byron
Author-Name-Last: Medina-Delgado
Title: Experimental Data-Driven Estimation of Impulse Response in Audio Systems Using Parametric and Non-Parametric Methods
Abstract: The impulse response is a fundamental tool for characterizing linear time-invariant (LTI) systems, enabling the derivation of a mathematical model that accurately describes system dynamics under arbitrary input conditions. This study used experimental data to estimate the impulse response of an audio system—comprising an amplifier, a speaker, a room, and a microphone. Four methods were employed: two parametric and two non-parametric approaches, applied in both the time and frequency domains. The methods were evaluated quantitatively using the Root Mean Square Error (RMSE) metric and qualitatively through a perceptual analysis with six participants. The parametric frequency-domain method achieved the best perceptual results, with 75% of participants rating the output as good. While this method exhibited slightly higher RMSE compared to other techniques, its low filter order (8) resulted in superior computational efficiency. The findings highlight that perceptual alignment often diverges from purely mathematical error minimization. Real-time implementation of the selected impulse response further demonstrated its practical application in audio processing systems. This research bridges quantitative metrics and human auditory perception, emphasizing the need for balanced decision-making in audio system modeling. The results contribute to advancing data-driven methodologies in acoustics, offering insights into both experimental design and computational efficiency
Journal: Data and Metadata
Pages: .617
Volume: 3
Year: 2024
DOI: 10.56294/dm2025.617
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.617:id:1056294dm2025617

Template-Type: ReDIF-Article 1.0
Author-Name: Een Yayah Haenilah
Author-Name-First: Een Yayah
Author-Name-Last: Haenilah
Author-Name: Handoko
Author-Name-First: Handoko
Author-Name-Last: Handoko
Author-Name: Mustofa Abi Hamid
Author-Name-First: Mustofa Abi
Author-Name-Last: Hamid
Author-Name: Tiyas Abror Huda
Author-Name-First: Tiyas Abror
Author-Name-Last: Huda
Author-Name: Muhammad Nurtanto
Author-Name-First: Muhammad
Author-Name-Last: Nurtanto
Author-Name: Radinal Fadli
Author-Name-First: Radinal
Author-Name-Last: Fadli
Author-Name: Muhammad Hakiki
Author-Name-First: Muhammad
Author-Name-Last: Hakiki
Title: Mapping publication trend of teacher mindfulness: a visualization and bibliometric analysis using Scopus databases
Abstract: Introduction: This study aimed to analyze the bibliometric characteristics and trends of research with the theme of teacher mindfulness on the Scopus database, including co-authorship, co-occurrence by keywords, citation, co-citation, and bibliographic coupling in the last decade (from 2014 to 2024). Methods: This study utilizes a qualitative research methodology through a bibliometric literature analysis strategy. The Scopus database is used to identify research trends related to teacher mindfulness and teacher professionalism from 2014 to 2024. The data was structured according to the PRISMA approach, yielding a total of 783 papers that were further processed for the purpose of mapping and visualizing research trends. Results: There has been significant growth in the quantity of articles on teacher mindfulness subjects during the last decade. A total of 783 papers were analyzed, published across 159 journals, with Mindfulness being the most popular journal. Collaboration among authors occurred across 74 different countries, resulting in 16 clusters, with the United States and the United Kingdom as the leading contributors to the publication of scholarly works on the subject of mindfulness. Jennings, P.A. is the author who most contributed to the publication. Furthermore, topics about teacher mindfulness, trait mindfulness, mindfulness in teaching, psychological stress, teacher well-being, job satisfaction, and teacher professionalism, which have great opportunities for further research, relate to the theme of teacher mindfulness. Conclusions: The topic of teacher mindfulness is becoming increasingly significant with current challenges, providing opportunities for future research aligned with current trends in mindfulness studies.
Journal: Data and Metadata
Pages: .616
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.616
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.616:id:1056294dm2024616

Template-Type: ReDIF-Article 1.0
Author-Name: Re Chen
Author-Name-First: Re
Author-Name-Last: Chen
Author-Name: Heidi Tan Yeen Ju
Author-Name-First: Heidi
Author-Name-Last: Tan Yeen Ju
Author-Name: Neo Mal
Author-Name-First: Neo
Author-Name-Last: Mal
Title: Effect analysis of AI-assisted multimedia creation platform in college teaching
Abstract: Introduction：Recent advancements in educational technologies, particularly the integration of artificial intelligence (AI), have transformed the creation, distribution, and consumption of educational materials. This study explores the impact of an AI-assisted multimedia creation platform (AI-MCP) on college teaching, emphasizing its role in enhancing the teaching and learning experience by utilizing AI's capabilities alongside rich multimedia content. Methods:The AI-MCP architecture was developed to facilitate interaction with students through features such as speech recognition, visual perception, and intelligent behavior, all of which foster a humanistic approach to learning. The framework incorporates the Advanced Monkey Search Algorithm (AMSA) to manage and optimize the teaching system across diverse records, data, and multimedia elements. Traditional assessment methods were employed to compare student performance outcomes and evaluation metrics against conventional teaching methods. Results:The implementation of the AI-MCP demonstrated significant improvements in student engagement and understanding across various information technology disciplines. Performance metrics indicated enhanced learning outcomes, suggesting that the integration of AI technologies can effectively support instructional design and student interaction. Conclusion:This study confirms that AI-assisted multimedia platforms can significantly enhance college teaching by improving student performance and engagement. The findings advocate for the broader adoption of AI-MCP in educational settings, highlighting its potential to revolutionize the teaching and learning process in higher education.
Journal: Data and Metadata
Pages: .615
Volume: 3
Year: 2024
DOI: 10.56294/dm2024615
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.615:id:1056294dm2024615

Template-Type: ReDIF-Article 1.0
Author-Name: Eryd Saputra
Author-Name-First: Eryd
Author-Name-Last: Saputra
Author-Name: Nizwardi Jalinus
Author-Name-First: Nizwardi
Author-Name-Last: Jalinus
Author-Name: Asmar Yulastri
Author-Name-First: Asmar
Author-Name-Last: Yulastri
Author-Name: M Giatman
Author-Name-First: M
Author-Name-Last: Giatman
Author-Name: Remon Lapisa
Author-Name-First: Remon
Author-Name-Last: Lapisa
Author-Name: Kasmita
Author-Name-First: Kasmita
Author-Name-Last: Kasmita
Author-Name: Bambang Heriyadi
Author-Name-First: Bambang
Author-Name-Last: Heriyadi
Title: Validation of the integrated progressive internship model syntax in tourism polytechnic
Abstract: In the era of globalization, the gap between university graduates' competencies and industry requirements remains a significant challenge. Internship programs often fail to optimally bridge the gap between academic theories and workplace practices. This study aims to validate the development of an integrated progressive internship model designed to enhance the quality of industrial internship programs for students. The model was developed based on the Four D’s framework (Define, Design, Develop, Disseminate), with key syntax elements including Coaching Clinic, Input, Process, Evaluation, and Dissemination. Validation was conducted involving 8 experts through Focus Group Discussions (FGDs) and data analysis using Confirmatory Factor Analysis (CFA) based on Structural Equation Modeling. The findings reveal that the model demonstrates high validity and reliability, with R-Square values ranging from 0.938 to 0.939. It can be concluded that this internship model holds significant potential for implementation in reducing the gap between theory and practice while enhancing students’ preparedness to meet the demands of the workforce.
Journal: Data and Metadata
Pages: .614
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.614
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.614:id:1056294dm2024614

Template-Type: ReDIF-Article 1.0
Author-Name: Abrar Alhomaid
Author-Name-First: Abrar
Author-Name-Last: Alhomaid
Author-Name: Qais Hammouri
Author-Name-First: Qais
Author-Name-Last: Hammouri
Title: Examining the Mediating Role of Social Media Engagement on Brand Loyalty: A Case Study of iPhone Users
Abstract: This study investigates the mediating role of social media engagement on the relationship between brand-related factors and brand loyalty among iPhone users in Saudi Arabia. Specifically, it examines how perceived brand quality, customer satisfaction, and brand trust influence brand loyalty, both directly and indirectly through social media engagement. Using a quantitative approach, data was collected from 344 iPhone users and analyzed using Smart PLS-SEM. The findings support all ten proposed hypotheses, demonstrating that perceived brand quality, customer satisfaction, and brand trust positively affect brand loyalty. Furthermore, these factors also positively influence social media engagement, which in turn strengthens their relationship with brand loyalty. The study confirms the significant mediating role of social media engagement, highlighting its importance in building and maintaining brand loyalty among iPhone users. These findings offer valuable insights for marketers seeking to leverage social media strategies to enhance brand loyalty in the Saudi Arabian market.
Journal: Data and Metadata
Pages: 612
Volume: 4
Year: 2025
DOI: 10.56294/dm2025612
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:612:id:1056294dm2025612

Template-Type: ReDIF-Article 1.0
Author-Name: Maunah Setyawati
Author-Name-First: Maunah
Author-Name-Last: Setyawati
Author-Name: Nur Chamidah
Author-Name-First: Nur
Author-Name-Last: Chamidah
Author-Name: Ardi Kurniawan
Author-Name-First: Ardi
Author-Name-Last: Kurniawan
Author-Name: Dursun Aydin
Author-Name-First: Dursun
Author-Name-Last: Aydin
Title: Confidence Interval for Semiparametric Regression Model Parameters Based on Truncated Spline with Application to COVID-19 Dataset in Indonesia
Abstract: This study proposed a method for constructing confidence intervals for parameters in a semiparametric regression model using a truncated spline estimator, tailored for multiresponse and multipredictor longitudinal data. The semiparametric model integrated parametric and nonparametric components, facilitating the analysis of complex relationships. Confidence intervals were estimated using a pivotal quantity method.The approach was applied to COVID-19 data from Indonesia, exploring the associations between Time, Temperature, and Sunlight Intensity with the Case Increase Rate (CIR) and Case Fatality Rate (CFR). Data spanning April to November 2020 were sourced from 10 provinces with the highest CIR and CFR, obtained from http://kawalcovid.com/ and https://power.larc.nasa.gov/.The analysis identified an optimal Generalized Cross-Validation (GCV) value of 220, with one knot at 24.35°C for Temperature and two knots at 11.33 and 13 units for Sunlight Intensity. Confidence interval estimation demonstrated that all parametric components associated with Time were statistically significant, reflecting a consistent decline in CIR and CFR over time. For the nonparametric components, four parameters significantly influenced CIR, while three parameters significantly affected CFR, contingent on the knot points.The findings underscored the role of environmental factors in shaping COVID-19 dynamics and provided a robust analytical framework for future pandemic modeling. This study highlighted the utility of semiparametric regression with truncated splines in addressing complex epidemiological data, offering valuable insights for policymakers to design evidence-based mitigation strategies
Journal: Data and Metadata
Pages: .609
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.609
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.609:id:1056294dm2024609

Template-Type: ReDIF-Article 1.0
Author-Name: Badr MACHKOUR
Author-Name-First: Badr
Author-Name-Last: MACHKOUR
Author-Name: Ahmed ABRIANE
Author-Name-First: Ahmed
Author-Name-Last: ABRIANE
Title: Internet of Things in Education: Transforming Learning Environments, Enhancing Pedagogy, and Optimizing Resource Management
Abstract: In the educational sector, Internet of Things (IoT) is poised to redefine learning environments by facilitating interactive and personalized experiences tailored to individual learner needs. Additionally, it offers transformative solutions for managing educational infrastructures, optimizing resources, and enhancing administrative efficiency. This study provides a comprehensive examination of IoT's development, definitions, and emerging trends, with a specific focus on its applications within the educational domain. Utilizing a methodological framework based on an analysis of academic literature, the research traces the evolution of IoT-related studies and assesses its implications for teaching and learning. The findings reveal a significant surge in IoT-related publications since 2010, underscoring its applicability across diverse sectors, including education. In this context, IoT technologies are shown to enrich learning experiences by offering adaptive, student-centered approaches while improving the operational efficiency of educational institutions. Despite its transformative potential, the study also highlights persistent challenges, such as technical limitations, data privacy concerns, and ethical considerations, which require careful attention to ensure responsible and equitable implementation. As IoT adoption continues to expand, the research underscores its potential to reshape the educational landscape and contribute to a more connected and technologically advanced future.
Journal: Data and Metadata
Pages: .602
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.602
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.602:id:1056294dm2024602

Template-Type: ReDIF-Article 1.0
Author-Name: Anber Abraheem Shlash Mohammad
Author-Name-First: Anber Abraheem
Author-Name-Last: Shlash Mohammad
Author-Name: Suleiman Ibrahim Mohammad
Author-Name-First: Suleiman Ibrahim
Author-Name-Last: Mohammad
Author-Name: Badrea Al Oraini
Author-Name-First: Badrea
Author-Name-Last: Al Oraini
Author-Name: Ayman Hindieh
Author-Name-First: Ayman
Author-Name-Last: Hindieh
Author-Name: Asokan Vasudevan
Author-Name-First: Asokan
Author-Name-Last: Vasudevan
Author-Name: Mohammad Faleh Ahmmad Hunitie
Author-Name-First: Mohammad Faleh
Author-Name-Last: Ahmmad Hunitie
Author-Name: Hongli Long
Author-Name-First: Hongli
Author-Name-Last: Long
Author-Name: Imad Ali
Author-Name-First: Imad
Author-Name-Last: Ali
Title: Leveraging Predictive Analytics and Metadata Integration for Strategic Talent Management in Jordan
Abstract: Introduction Talent management is critical for organizational performance. This research explored the use of predictive analytics on employee metadata, including profile analysis, performance history, training records, and career progression scores, to optimize retention and promotion strategies in Jordanian organizations. The study provided insights into the effectiveness of integrating predictive tools with metadata to enhance talent management outcomes. Methods A quantitative research design, incorporating descriptive and correlational approaches, was employed. Data were collected from 257 HR professionals and decision-makers using structured questionnaires and organizational records. Statistical techniques such as linear and logistic regression, correlation analysis, and machine learning models were used to examine the predictive influence of variables like age, training hours, performance ratings, and career progression scores. Results The results indicated that training hours, performance ratings, and career progression scores are good predictors of retention rates while age and tenure were strong predictors of success promotion. Machine learning models strongly predicted retention outcomes with an attainment of an R-squared score of 0.671, so predictive analytics can enhance efficiency in decision-making. Moderate use of Predictive analytic tools was related to improved promotion outcomes suggests a balance between data-driven and human judgment approaches. Conclusion The study contributed to the growing discourse on data-driven HR practices, contextualizing findings within Jordanian organizations. It highlighted the ethical and cultural considerations necessary for implementing metadata-driven tools. The results underscored the potential of predictive analytics to improve talent management processes, ultimately supporting the achievement of strategic HR goals
Journal: Data and Metadata
Pages: .599
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.599
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.599:id:1056294dm2024599

Template-Type: ReDIF-Article 1.0
Author-Name: El Mehdi IYOUBI
Author-Name-First: El Mehdi
Author-Name-Last: IYOUBI
Author-Name: RAJA EL BOQ
Author-Name-First: RAJA
Author-Name-Last: EL BOQ
Author-Name: KENZA IZIKKI
Author-Name-First: KENZA
Author-Name-Last: IZIKKI
Author-Name: SAMIR TETOUANI
Author-Name-First: SAMIR
Author-Name-Last: TETOUANI
Author-Name: OMAR CHERKAOUI
Author-Name-First: OMAR
Author-Name-Last: CHERKAOUI
Author-Name: AZIZ SOULHI
Author-Name-First: AZIZ
Author-Name-Last: SOULHI
Title: Revolutionizing Smart Agriculture: Enhancing Apple Quality with Machine Learning
Abstract: Agriculture 4.0 is a field that has spread widely around the world in this century, as it has undergone an exceptionally rapid evolution, especially when it comes to fruit recognition. Decisions about their quality are crucial to maximize profits and meet customer expectations. In the past, apples or even other fruits were based solely on visual assessments by experts, which led to errors. These old methods no longer consider the genetic evolution of apples, as they only consider their size, color, and skin imperfections. Digitizing this process saves energy and reduces costs and human error as well. Recent technological advances, which combine AI and CAO at the same time for fruit sorting, make it possible to achieve high levels of quality and meet the growing challenges of food safety on a global scale. This study proposes a machine learning-based multiclass model to improve the accuracy and efficiency of apple quality assessment. The model is trained on a large image dataset of three apple varieties: Gala, Fuji, and Golden Delicious (G.D). The model automatically classifies apples based on attributes such as color, shape, and imperfections, and evaluates their conformity. Experimental results demonstrate the effectiveness of this model, which achieves 97% accuracy in identifying apple varieties and assessing their quality. This approach significantly reduces inspection time and errors, optimizing operations in the production chain.
Journal: Data and Metadata
Pages: .592
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.592
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.592:id:1056294dm2024592

Template-Type: ReDIF-Article 1.0
Author-Name: Mohammad Subhi Al-Batah
Author-Name-First: Mohammad
Author-Name-Last: Subhi Al-Batah
Author-Name: Mowafaq Alzboon
Author-Name-First: Mowafaq
Author-Name-Last: Alzboon
Author-Name: Muhyeeddin Alqaraleh
Author-Name-First: Muhyeeddin
Author-Name-Last: Alqaraleh
Title: Optimizing Genetic Algorithms with Multilayer Perceptron Networks for Enhancing TinyFace Recognition
Abstract: This study conducts an empirical examination of MLP networks investigated through a rigorous methodical experimentation process involving three diverse datasets: TinyFace, Heart Disease, and Iris. Study Overview: The study includes three key methods: a) a baseline training using the default settings for the Multi-Layer Perceptron (MLP), b) feature selection using Genetic Algorithm (GA) based refinement c) Principal Component Analysis (PCA) based dimension reduction. The results show important information on how such techniques affect performance. While PCA had showed benefits in low-dimensional and noise-free datasets GA consistently increased accuracy in complex datasets by accurately identifying critical features. Comparison reveals that feature selection and dimensionality reduction play interdependent roles in enhancing MLP performance. The study contributes to the literature on feature engineering and neural network parameter optimization, offering practical guidelines for a wide range of machine learning tasks
Journal: Data and Metadata
Pages: .594
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.594
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.594:id:1056294dm2024594

Template-Type: ReDIF-Article 1.0
Author-Name: Farida Nur Kumala
Author-Name-First: Farida Nur
Author-Name-Last: Kumala
Author-Name: Arnelia Dwi Yasa
Author-Name-First: Arnelia Dwi
Author-Name-Last: Yasa
Author-Name: Moh Salimi
Author-Name-First: Moh
Author-Name-Last: Salimi
Author-Name: Layli Hidayah
Author-Name-First: Layli
Author-Name-Last: Hidayah
Author-Name: Dina Asmaul Chusniyah
Author-Name-First: Dina
Author-Name-Last: Asmaul Chusniyah
Title: Hybrid Learning Microsite Project STEAMER: Computational Thinking and Creative Thinking Abilities of Prospective Elementary School Teachers
Abstract: Introduction: Prospective teachers' computational and creative thinking skills show quite low results because classroom learning is less innovative. This requires the use of innovative models. This study was conducted to determine the effectiveness of the Hybrid Learning Microsite Project STEAMER in improving prospective elementary school teachers' Computational and Creative Thinking Skills. Methods: The study subjects were prospective elementary school teachers from 10 Educational Personnel Education Institutions in six provinces. This method of study used a mixed approach. Data were collected through tests, interviews, and observations. Data were analyzed quantitatively and qualitatively. Quantitative data were analyzed using Multivariate statistics, SEM LISREL 8.80, while Miles and Huberman data analysis techniques were used to analyze qualitative data. Results: This study shown that the average post-test score in the experimental class increased by 69.95 and in the control class by 55.65. This study concludes that the application of the learning model has implications for the variables of creative and computational thinking abilities by 29.6% and 10.6%. Conclusions: The implementation of the STEAMER Hybrid Learning Project has influenced students' computational and creative thinking abilities through a series of model stages, such as reflection, conducting research, finding strategies, implementing design results, and communicating the results of the developed project.
Journal: Data and Metadata
Pages: .591
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.591
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.591:id:1056294dm2024591

Template-Type: ReDIF-Article 1.0
Author-Name: Cristian Zahn-Muñoz
Author-Name-First: Cristian
Author-Name-Last: Zahn-Muñoz
Author-Name: Claudio Rivera-Mercado
Author-Name-First: Claudio
Author-Name-Last: Rivera-Mercado
Author-Name: César López-Ojeda
Author-Name-First: César
Author-Name-Last: López-Ojeda
Author-Name: Ezequiel Martínez-Rojas
Author-Name-First: Ezequiel
Author-Name-Last: Martínez-Rojas
Title: Scientific production in education in latin america: bibliometric analysis of latin american education journals, period 2017-2022
Abstract: This study analyzes scientific production in Latin American education journals indexed in SCOPUS during the period 2017-2022, with the aim of characterizing it by identifying patterns of collaboration, citation and productivity, to understand its dynamics and regional impact. Using a descriptive approach with bibliometric indicators, 22 educational journals were selected from the Scimago Journal Rank (SJR) and SCOPUS databases, to place a total of 6,488 documents, including research articles and bibliographic reviews, which recorded 15,651 signatures of 11,911. authors. The results highlight the leadership of Brazil, which concentrates 54.5% of the documents and 11 of the 22 journals analyzed. In addition, an increase of 16.5% in the annual production of publications and a growing trend in collaboration between authors was identified, with an average collaboration index of 2.41. However, the average citation impact is moderate, reaching 2.2 citations per document. In conclusion, the study shows a dynamic and constantly evolving panorama, characterized by Brazil's leadership, growing internationalization and the strengthening of academic networks in the region. However, it is necessary to diversify analysis sources and optimize visibility strategies to increase the global impact of educational research in Latin America.
Journal: Data and Metadata
Pages: 590
Volume: 4
Year: 2025
DOI: 10.56294/dm2025590
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:590:id:1056294dm2025590

Template-Type: ReDIF-Article 1.0
Author-Name: Hassan Bousnguar
Author-Name-First: Hassan
Author-Name-Last: Bousnguar
Author-Name: Lotfi NAJDI
Author-Name-First: Lotfi
Author-Name-Last: NAJDI
Author-Name: Amal BATTOU
Author-Name-First: Amal
Author-Name-Last: BATTOU
Title: A new hybrid approach based on machine learning for more efficient time series forecasting
Abstract: Introduction: Forecasting new student enrollment in bachelor's degree programs has emerged as a critical need for higher education institutions. Accurate enrollment predictions are essential for improving the student-teacher ratio and optimizing resource allocation. Methods: A hybrid approach combining statistical and machine learning techniques was proposed to develop accurate forecasting models. The study utilized the historical enrollment database of Ibn Zohr University, which included data from over twenty institutions dating back to 2003. This dataset was used to train and validate the proposed models. Results: The hybrid approach demonstrated superior accuracy compared to standalone statistical and machine learning models. The results indicated that the proposed method effectively captured enrollment trends and provided reliable forecasts. Conclusions: The study concluded that the hybrid approach offers a robust solution for enrollment forecasting in higher education. It highlighted the potential of combining statistical and machine learning techniques to improve prediction accuracy, thereby aiding institutions in better planning and resource management..
Journal: Data and Metadata
Pages: 589
Volume: 4
Year: 2025
DOI: 10.56294/dm2025589
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:589:id:1056294dm2025589

Template-Type: ReDIF-Article 1.0
Author-Name: Carlos Arturo Vides Herrera
Author-Name-First: Carlos Arturo
Author-Name-Last: Vides Herrera
Author-Name: Aldo Pardo García
Author-Name-First: Aldo
Author-Name-Last: Pardo García
Author-Name: Ivaldo Torres Chávez
Author-Name-First: Ivaldo
Author-Name-Last: Torres Chávez
Title: Fuzzy control to maximize the performance of a two-degree-of-freedom photovoltaic solar tracker
Abstract: Introduction: The need to reduce global warming is increasing every day and is a priority for the governments of our people and for organizations that support the environment, which is why it is proposed to contribute to increasing the performance of photovoltaic solar systems, using emerging technologies. Methods: For this work, control techniques are used through intelligent computing, more specifically a fuzzy control system for the search for the maximum power point in a photovoltaic solar tracker. Results: As a result, a fuzzy controller was designed and implemented that allows obtaining the point of maximum solar efficiency at any time during the day. The solar tracker is oriented at the maximum power point (MPPT) at each instant in time, thus increasing energy production and reducing system losses due to the orientation of the PV panel. Conclusions: The use of computational intelligence techniques such as fuzzy logic allows for an increase in the performance of photovoltaic solar tracking systems, which was verified by implementing a programmed fuzzy controller in an embedded system
Journal: Data and Metadata
Pages: 588
Volume: 4
Year: 2025
DOI: 10.56294/dm2025588
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:588:id:1056294dm2025588

Template-Type: ReDIF-Article 1.0
Author-Name: Francisco Javier Acevedo Velandia
Author-Name-First: Francisco Javier
Author-Name-Last: Acevedo Velandia
Author-Name: Georgina Isunza Vizuet
Author-Name-First: Georgina
Author-Name-Last: Isunza Vizuet
Title: Decomposition of the Gini Coefficient by Income Sources: An Analysis of Determinant Sources for Colombia (2002-2023)
Abstract: This study analyzed how the different sources of income that constitute total household income have a differentiated effect on various segments of the population. The inequality measured by the Gini, Atkinson, and Theil indices for Colombia reflects the distributive efforts to reduce inequality between 2002 and 2023. To conduct this analysis, the Gini coefficient was decomposed by sources of income using the methodology proposed by Lerman, Yitzhaki, and Shorrocks. The results revealed that despite efforts in redistributive policy, the gap between the richest 10% and the poorest 10% widened. The income structure showed that households diversify their income, with institutional assistance being crucial for the lower deciles, while income from primary activities is most representative in the higher deciles. Additionally, from 2020 onwards, there has been increased dependence on institutional assistance among the lowest deciles. The article concluded that, although there have been improvements in per capita income overall, inequalities persist and have been exacerbated in times of crisis. It emphasized the need for more effective policies to address income disparities concerning the main sources and to promote a more equitable distribution in Colombia
Journal: Data and Metadata
Pages: 587
Volume: 4
Year: 2025
DOI: 10.56294/dm2025587
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:587:id:1056294dm2025587

Template-Type: ReDIF-Article 1.0
Author-Name: Young-Chool Choi
Author-Name-First: Young-Chool
Author-Name-Last: Choi
Title: Machine Learning-based Classification of Developing Countries and Exploration of Country-Specific ODA Strategies
Abstract: Introduction: This study aimed to develop a systematic methodology for classifying recipient countries using machine learning, with the premise that tailoring mid- to long-term ODA strategies to country characteristics is essential. Additionally, it sought to propose ODA policy directions considering the unique attributes of classified developing countries. Methods: The research analyzed 166 countries, including both developed and developing nations, using SDG scores and GDP per capita as key indicators. Machine learning techniques, specifically neural network analysis and decision tree analysis, were employed for classification. Results: The analysis resulted in the classification of the 166 countries into 12 distinct groups, with seven nodes representing developing countries. Each group exhibited unique characteristics that informed the development of country-specific ODA strategies Conclusions: This study successfully developed a systematic classification methodology for recipient countries using machine learning. The resulting classification and proposed ODA strategies for each group provide a foundation for more targeted and effective ODA policies. This approach enables policymakers to tailor their strategies to the specific needs and characteristics of different developing country groups, potentially improving the impact and efficiency of ODA efforts.
Journal: Data and Metadata
Pages: .586
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.586
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.586:id:1056294dm2024586

Template-Type: ReDIF-Article 1.0
Author-Name: Serafeim A. Triantafyllou
Author-Name-First: Serafeim A.
Author-Name-Last: Triantafyllou
Author-Name: Theodosios Sapounidis
Author-Name-First: Theodosios
Author-Name-Last: Sapounidis
Author-Name: Yousef Farhaoui
Author-Name-First: Yousef
Author-Name-Last: Farhaoui
Title: Students’ development of Computational Thinking and Teachers’ professional development via Bebras through Gamification: A systematic literature review
Abstract: The Bebras Challenge is an international initiative that promotes computational thinking among students through fun and engaging challenges. It incorporates gamification elements, which play a significant role in making learning more interactive and motivating. For teachers, it offers valuable professional development opportunities, helping them to incorporate these concepts into their teaching practices. However, so far, a limited number of studies have been conducted to investigate Bebras Educational Competition and Gamification for the development of students’ computational thinking in secondary education. Also, while the Bebras Challenge is widely recognized for its role in promoting computational thinking through engaging tasks, the specific intersection of Bebras, gamification, and teacher development is a relatively underexplored research area. Specifically, for this paper seven databases were searched, and 33 papers were finally selected for this review. The findings seem to shed light on whether Bebras competition might enhance the development of students’ computational thinking, and to present what could be the potential impact and effectiveness of a gamified learning approach included in Bebras initiative for promoting computational thinking skills among students, especially in secondary education. A significant conclusion stemming from findings of this review, is that the learning of teachers at a professional level, and the development of their expertise, leads them to changes in teaching practices that have as a final result the improvement of student learning and the development of students’ computational thinking skills
Journal: Data and Metadata
Pages: .582
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.582
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.582:id:1056294dm2024582

Template-Type: ReDIF-Article 1.0
Author-Name: Abdulkader Hajjouz
Author-Name-First: Abdulkader
Author-Name-Last: Hajjouz
Author-Name: Elena Avksentieva
Author-Name-First: Elena
Author-Name-Last: Avksentieva
Title: Optimizing Intrusion Detection for DoS, DDoS, and Mirai Attacks Subtypes Using Hierarchical Feature Selection and CatBoost on the CICIoT2023 Dataset
Abstract: Introduction: Modern networks suffer until unheard of vulnerabilities that need for advanced intrusion detection systems (IDS) given the growing danger presented by DoS, DDoS, and Mirai attacks. Research on the identification of certain attack subtypes is still lacking even with the CICIoT2023 dataset, which offers a complete basis for evaluating these cyber hazards. Usually, aggregating attacks into more general categories, existing research neglects the complex characteristics of specific subtypes, therefore reducing the detection effectiveness. Methods: This work presents a novel IDS model aiming at high accuracy detection of DoS, DDoS, and Mirai attack subtypes. Using hierarchical feature selection and the CatBoost algorithm on the CICIoT2023 dataset, our model addresses the problems of high-dimensional data and emphasizes on keeping the most important features by means of advanced preprocessing methods including Spearman correlation and hierarchical clustering. Furthermore, used is stratified sampling to guarantee in the training and testing stages fair representation of attack types, both common and uncommon. Results: With an amazing Prediction Time per Network Flow of 7.16e-07 seconds, our model shows a breakthrough in intrusion detection performance by means of rigorous stratified cross-valuation, thereby attaining outstanding outcomes in accuracy, recall, and precision. Conclusions: Our method not only closes a significant gap in current knowledge but also establishes a new benchmark in cybersecurity by providing very detailed protection mechanisms against advanced threats. This study marks major progress in network security as it gives companies a more efficient instrument to recognize and minimize certain cyber risks with better precision and effectiveness
Journal: Data and Metadata
Pages: 577
Volume: 3
Year: 2024
DOI: 10.56294/dm2024577
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:577:id:1056294dm2024577

Template-Type: ReDIF-Article 1.0
Author-Name: Deviana Ridhani
Author-Name-First: Deviana
Author-Name-Last: Ridhani
Author-Name: Krismadinata
Author-Name-Last: Krismadinata
Author-Name: Dony Novaliendry
Author-Name-First: Dony
Author-Name-Last: Novaliendry
Author-Name: Ambiyar
Author-Name-Last: Ambiyar
Author-Name: Hansi Effendi
Author-Name-First: Hansi
Author-Name-Last: Effendi
Title: Development of An Intelligent Learning Evaluation System Based on Big Data
Abstract: The increasing need for effective learning evaluation in higher education is driving the development of big data-based systems to provide comprehensive insights. This research aims at developing an Intelligent Learning Evaluation System (ILES) to support team teaching and monitor the effectiveness of the learning process through pre-tests, post-tests and periodic evaluations. The system was developed using the Agile methodology, including iterative stages of requirements gathering, design, development, testing and implementation. Codeigniter is used for backend development, PostgreSQL as database. This system enables dynamic monitoring and evaluation of teaching performance and student learning outcomes. The finding showed that the Real-time data visualization and user-friendly dashboards improve decision making for faculty and administrators. Testing shows increased engagement and actionable insights for performance improvement. ILES demonstrates the potential of big data in higher education by enabling data-based decision making and driving continuous improvement in teaching and learning. Future research will explore integration with broader institutional systems and its scalability
Journal: Data and Metadata
Pages: .569
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.569
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.569:id:1056294dm2024569

Template-Type: ReDIF-Article 1.0
Author-Name: Yose Indarta
Author-Name-First: Yose
Author-Name-Last: Indarta
Author-Name: Ambiyar
Author-Name-First: Ambiyar
Author-Name-Last: Ambiyar
Author-Name: Fadhillah
Author-Name-First: Fadhillah
Author-Name-Last: Fadhillah
Author-Name: Agariadne Dwinggo Samala
Author-Name-First: Agariadne
Author-Name-Last: Dwinggo Samala
Author-Name: Afif Rahman Riyanda
Author-Name-First: Afif
Author-Name-Last: Rahman Riyanda
Author-Name: Fadhli Ranuharja
Author-Name-First: Fadhli
Author-Name-Last: Ranuharja
Author-Name: Firas Tayseer Ayasrah
Author-Name-First: Firas
Author-Name-Last: Tayseer Ayasrah
Author-Name: Angel Torres Toukoumidis
Author-Name-First: Angel Torres
Author-Name-Last: Toukoumidis
Title: Transforming Vocational Education through Augmented Reality: A Systematic Review of Current Trends, Challenges, and Future Opportunities
Abstract: In recent years, the potential of Augmented Reality (AR) to revolutionize vocational education has garnered significant attention, offering innovative solutions to bridge the gap between theoretical knowledge and practical skills. This systematic literature review aims to explore the latest trends, challenges, and opportunities related to the implementation of AR in vocational training over the past decade. The data utilized in this study were sourced from articles published between 2014 and 2024, extracted from the Scopus and Web of Science (WoS) databases to ensure comprehensive and high-quality coverage. The PRISMA method was applied to guarantee a transparent and reproducible process, consisting of several stages: identifying relevant studies, filtering based on predefined inclusion and exclusion criteria, assessing study quality, and extracting data. Following the PRISMA protocol, 67 research papers were initially identified, and after multiple stages of refinement, 24 papers were selected for detailed analysis. The findings indicate that Augmented Reality (AR) can enhance engagement and learning effectiveness in vocational education. However, its implementation is still hindered by limitations in infrastructure and the need for improved teacher training. To fully harness the potential of AR, further research is essential to develop more inclusive pedagogical models and support the integration of AR into vocational education curricula
Journal: Data and Metadata
Pages: 578
Volume: 4
Year: 2025
DOI: 10.56294/dm2025578
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:578:id:1056294dm2025578

Template-Type: ReDIF-Article 1.0
Author-Name: John Byron Tuazon
Author-Name-First: John
Author-Name-Last: Byron Tuazon
Author-Name: Michelle Renee Ching
Author-Name-First: Michelle
Author-Name-Last: Renee Ching
Title: Exploring Digital Banking and Financial Services Inclusivity: A Study of Social Media Interactions in an Online Community
Abstract: The study explores the role of digital banking and financial services (DBFS) in enhancing financial inclusivity through social media interactions within an online community. It highlights how innovations like mobile technology have significantly improved financial inclusion, particularly in the Philippines. The research focuses on a Facebook group discussing digital banking, aiming to understand user perceptions of accessibility, usability, trust, security, financial literacy, and inclusivity. Key findings reveal that user-friendly interfaces and accessibility features are crucial for adoption. At the same time, trust and security concerns are prevalent but mitigated through shared experiences and reassurances within the community. Financial literacy is enhanced through exchanging advice and user guides, and inclusivity is promoted by providing opportunities for previously unbanked individuals. The study also emphasizes the importance of continuous improvement in digital infrastructure and financial literacy to ensure equitable access to digital financial services. By analyzing social media content and conducting interviews, the research offers valuable insights into how online communities can foster digital inclusivity in banking. It underscores the need to address challenges such as the digital divide and data privacy concerns to maintain consumer trust and ensure that all individuals, regardless of socio-economic status, can benefit from digital banking and financial services advancements.
Journal: Data and Metadata
Pages: 575
Volume: 4
Year: 2025
DOI: 10.56294/dm2025575
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:575:id:1056294dm2025575

Template-Type: ReDIF-Article 1.0
Author-Name: William Alejandro Orjuela-Garzón
Author-Name-First: William Alejandro
Author-Name-Last: Orjuela-Garzón
Author-Name: Henry Cárdenas-Roa
Author-Name-First: Henry
Author-Name-Last: Cárdenas-Roa
Author-Name: Daniel Bustos-Vanegas
Author-Name-First: Daniel
Author-Name-Last: Bustos-Vanegas
Author-Name: Juan Manuel Andrade-Navia
Author-Name-First: Juan Manuel
Author-Name-Last: Andrade-Navia
Title: Data ecosystem framework proposal to implement Food Informatics systems in agri-food chains
Abstract: Agri-food chains face permanent climate change, population growth, and water and input access challenges. These impact production, processing, and marketing processes, making the capture, processing, and analysis of the data generated by each link more complex, isolated, and independent. Extracting this information for intelligent analysis to allow the optimization of agri-food chains based on data analytics is called Food Informatics. The study paradigm has given rise to the concept of data ecosystems in agri-food chains. The aim of this study is to design a data ecosystem model for the implementation of Food Informatics systems in agri-food chains. The PRISMA methodology was implemented for the identification, screening, eligibility, and inclusion of studies from the Scopus and Clarivate databases. A total of 26 records were included in the in-depth analysis, identifying two data ecosystem types: those with integrated bidirectional views that facilitate link interoperability and others of an individual nature focused on one link. The proposed integrated data ecosystem model has as its core an ETL in GCP for Data Issued in Batches with a Data Catalog and Data Mesh-type structure, which integrates a physical and a digital layer and data infrastructure for storage, processing, visualization, curation, and interaction with the user
Journal: Data and Metadata
Pages: 572
Volume: 4
Year: 2025
DOI: 10.56294/dm2025572
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:572:id:1056294dm2025572

Template-Type: ReDIF-Article 1.0
Author-Name: Mohammad Subhi Al-Batah
Author-Name-First: Mohammad
Author-Name-Last: Subhi Al-Batah
Author-Name: Muhyeeddin Alqaraleh
Author-Name-First: Muhyeeddin
Author-Name-Last: Alqaraleh
Author-Name: Mowafaq Salem Alzboon
Author-Name-First: Mowafaq
Author-Name-Last: Salem Alzboon
Title: Improving Oral Cancer Outcomes Through Machine Learning and Dimensionality Reduction
Abstract: Oral cancer presents a formidable challenge in oncology, necessitating early diagnosis and accurate prognosis to enhance patient survival rates. Recent advancements in machine learning and data mining have revolutionized traditional diagnostic methodologies, providing sophisticated and automated tools for differentiating between benign and malignant oral lesions. This study presents a comprehensive review of cutting-edge data mining methodologies, including Neural Networks, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and ensemble learning techniques, specifically applied to the diagnosis and prognosis of oral cancer. Through a rigorous comparative analysis, our findings reveal that Neural Networks surpass other models, achieving an impressive classification accuracy of 93.6% in predicting oral cancer. Furthermore, we underscore the potential benefits of integrating feature selection and dimensionality reduction techniques to enhance model performance. These insights underscore the significant promise of advanced data mining techniques in bolstering early detection, optimizing treatment strategies, and ultimately improving patient outcomes in the realm of oral oncology
Journal: Data and Metadata
Pages: .570
Volume: 3
Year: 2025
DOI: 10.56294/dm2024.570
Handle: RePEc:dbk:datame:v:3:y:2025:i::p:.570:id:1056294dm2024570

Template-Type: ReDIF-Article 1.0
Author-Name: Nur Chamidah
Author-Name-First: Nur
Author-Name-Last: Chamidah
Author-Name: Toha Saifudin
Author-Name-First: Toha
Author-Name-Last: Saifudin
Author-Name: Riries Rulaningtyas
Author-Name-First: Riries
Author-Name-Last: Rulaningtyas
Author-Name: Adam Anargya Mawardi
Author-Name-First: Adam Anargya
Author-Name-Last: Mawardi
Author-Name: Puspa Wardhani
Author-Name-First: Puspa
Author-Name-Last: Wardhani
Author-Name: I Nyoman Budiantara
Author-Name-First: I Nyoman
Author-Name-Last: Budiantara
Author-Name: Naufal Ramadhan Al Akhwal Siregar
Author-Name-First: Naufal Ramadhan Al
Author-Name-Last: Akhwal Siregar
Title: Classification of Malaria Parasite Plasmodium Falciparum Based on Blood Smear Images Using Support Vector Machine Approach
Abstract: Malaria remained a significant global health issue, particularly in tropical and subtropical regions. The disease resulted in a substantial number of clinical cases and deaths each year, with high-risk groups including infants, toddlers, and pregnant women. Accurate and prompt diagnosis was a key factor in managing the disease. To address this issue, the research aimed to develop an automated system for the classification of Plasmodium falciparum malaria parasites based on blood smear images. The methods employed included image feature selection using Principal Component Analysis (PCA) and the Support Vector Machine (SVM) approach for classification. The research findings indicated that in the image feature selection process, the category of normal malaria exhibited distinctive characteristics with PC1 and PC2 values that tended to be negative and dispersed, whereas the category of parasitic malaria displayed greater variability in both PC1 and PC2 components. Furthermore, the evaluation of the classification system's accuracy using SVM with three different kernel types showed promising results. The average accuracy through K-fold cross-validation for the polyinomial, linear, and radial basis function kernels was 96.7%, 98.9%, and 94.4%, respectively. These results highlighted the significant potential of SVM utilization in the classification of Plasmodium falciparum malaria parasites based on blood smear images.
Journal: Data and Metadata
Pages: 568
Volume: 4
Year: 2025
DOI: 10.56294/dm2025568
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:568:id:1056294dm2025568

Template-Type: ReDIF-Article 1.0
Author-Name: Evan Asfoura
Author-Name-First: Evan
Author-Name-Last: Asfoura
Author-Name: Gamal Kassem
Author-Name-First: Gamal
Author-Name-Last: Kassem
Title: Decision supporting approach based on suitable chatbot system for big data analytics
Abstract: Introduction: The increasing reliance of organizational decision-makers on advanced information systems and analytical tools highlights the transformative potential of big data analytics in modern business environments. As organizations accumulate vast amounts of data, the ability to harness this information effectively has become critical for informed decision-making and strategic planning. However, the complexity of big data analytics and the evolving demands of business environments pose challenges, particularly for managers navigating data-driven cultures. Effective utilization of these tools requires comprehensive training and support, especially for newly appointed managers Objective: . This paper presents a chatbot-based system designed to bridge the gap between decision-makers and big data analytics. By leveraging natural language processing (NLP) and machine learning, the proposed chatbot facilitates interactive learning and real-time engagement with analytical insights. This system empowers decision-makers to navigate analytical outputs efficiently, fostering improved decision-making processes. Methods: The research adopts a design science methodology to develop and evaluate this innovative approach. Initial findings suggest that the chatbot enhances accessibility and usability of analytics tools, reduces the technical burden on managers, and promotes a more effective data-driven decision-making culture Results: chatbot-based decision support solution demonstrated its potential to transform decision-making processes in data-driven organizations. By addressing the feedback gathered during this evaluation phase, future iterations of the system can further enhance its utility and effectiveness. Conclusions: This study contributes to the growing discourse on integrating artificial intelligence tools in organizational decision-making and highlights their potential to transform managerial practices in a data-intensive era.
Journal: Data and Metadata
Pages: .564
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.564
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.564:id:1056294dm2024564

Template-Type: ReDIF-Article 1.0
Author-Name: Fivia Eliza
Author-Name-First: Fivia
Author-Name-Last: Eliza
Author-Name: Radinal Fadli
Author-Name-First: Radinal
Author-Name-Last: Fadli
Author-Name: Yayuk Hidayah
Author-Name-First: Yayuk
Author-Name-Last: Hidayah
Author-Name: M. Aghpin Ramadhan
Author-Name-First: M. Aghpin
Author-Name-Last: Ramadhan
Author-Name: Abdulnassir Yassin
Author-Name-First: Abdulnassir
Author-Name-Last: Yassin
Author-Name: Mohammad Bhanu Setyawan
Author-Name-First: Mohammad
Author-Name-Last: Bhanu Setyawan
Author-Name: Sutrisno
Author-Name-First: Sutrisno
Author-Name-Last: Sutrisno
Title: Building a Secure Digital Future: Investigating Cyber Hygiene Levels of Accounting, Finance, and Business Students
Abstract: Abstract structured in: Introduction: This study aims to investigate the level of cyber hygiene among accounting, finance and business students, to identify strengths and weaknesses to inform the development of cybersecurity in education. Methods: A quantitative research design was employed, utilizing an objective online test to assess cyber hygiene knowledge. The instrument was validated through tests of validity, difficulty level, discriminatory power, and reliability. The study sample consisted of students in finance, administration and business. Data analysis involved statistical methods to compare awareness levels across the three student groups. Results: The results indicated that administration students had the highest overall cyber hygiene awareness, particularly in areas such as Rules & Laws, Access & Password, and Security Settings. Business students showed moderate awareness, while accounting students demonstrated significant gaps, especially in Web Access and Social Media Safety. The findings highlighted the need for targeted educational interventions to address specific weaknesses in each group. Conclusions: This study underscores the importance of cyber hygiene education, especially for accounting, finance, and business students, to prevent cyber incidents. The findings provide actionable insights for the development of curricula and training programs, which contribute to a safer digital environment in professional settings. Further research should expand sample sizes, incorporate qualitative methods, and explore the long-term effectiveness of cyber hygiene education
Journal: Data and Metadata
Pages: .554
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.554
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.554:id:1056294dm2024554

Template-Type: ReDIF-Article 1.0
Author-Name: Afif Rahman Riyanda
Author-Name-First: Afif Rahman
Author-Name-Last: Riyanda
Author-Name: Ika Parma Dewi
Author-Name-First: Ika
Author-Name-Last: Parma Dewi
Author-Name: Nizwardi Jalinus
Author-Name-First: Nizwardi
Author-Name-Last: Jalinus
Author-Name: Ahyanuardi
Author-Name-Last: Ahyanuardi
Author-Name: Margaretha Karolina Sagala
Author-Name-First: Margaretha Karolina
Author-Name-Last: Sagala
Author-Name: Daniel Rinaldi
Author-Name-First: Daniel
Author-Name-Last: Rinaldi
Author-Name: Rian Andri Prasetya
Author-Name-First: Rian Andri
Author-Name-Last: Prasetya
Author-Name: Fitri Yanti
Author-Name-First: Fitri
Author-Name-Last: Yanti
Title: Digital Skills and Technology Integration Challenges in Vocational High School Teacher Learning
Abstract: This study evaluates the digital skills, level of technology integration in teaching, and challenges faced by vocational high school (SMK) teachers in Solok City, Indonesia. A total of 105 SMK teachers participated in this descriptive-correlational study, which used a four-point Likert scale questionnaire to assess these areas. The findings reveal that SMK teachers exhibit very high digital competencies, with an average score of 3.42, especially in digital literacy, collaboration, creativity, and problem-solving. Technology integration in teaching also shows a very high level, with an average score of 3.52, particularly in multimedia usage and collaborative tools. Despite these positive results, key challenges include limited access to technological devices, lack of technical support, and insufficient digital learning resources. The study found no significant differences in digital skills based on age, but gender differences were observed, with female teachers performing better in certain domains. Additionally, teachers’ digital skills are positively correlated with their educational attainment and participation in training. The study suggests that improving technological infrastructure, offering practice-based continuous training, and providing technical support at the school level are essential for overcoming existing barriers and ensuring the effective integration of technology in teaching. These recommendations are vital to preparing students for the demands of the 21st-century workforce
Journal: Data and Metadata
Pages: 553
Volume: 4
Year: 2025
DOI: 10.56294/dm2025553
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:553:id:1056294dm2025553

Template-Type: ReDIF-Article 1.0
Author-Name: Shadi Altarifi
Author-Name-First: Shadi
Author-Name-Last: Altarifi
Author-Name: Jamal Zraqou
Author-Name-First: Jamal
Author-Name-Last: Zraqou
Author-Name: Firas Omar
Author-Name-First: Firas
Author-Name-Last: Omar
Author-Name: Raed Momani
Author-Name-First: Raed
Author-Name-Last: Momani
Title: New Strategic Deployment of Augmented and Virtual Reality for Enhancing Purchase Intentions and Brand Attitudes
Abstract: This study examines how Augmented Reality (AR) and Virtual Reality (VR) influence consumer decision-making in experiential retail. Through three experiments and qualitative feedback, AR proved more effective in driving purchase intentions by emphasizing product-focused mental imagery, while VR significantly enhanced brand attitudes through context-focused immersion. Results further indicated that presenting AR before VR yielded stronger outcomes, as AR’s product clarity primed consumers for the immersive brand narrative of VR. Qualitative insights underscored the emotional resonance and detailed visualization each technology provides, highlighting their complementary roles in shaping consumer perceptions. Employing a mixed methods approach, the research integrated statistical analyses of mental imagery, purchase intentions, and brand attitudes with participant narratives. While limitations include convenience sampling, controlled settings, and self-report measures, the findings offer a solid foundation for strategic AR-VR deployment. Future research should explore diverse contexts, user demographics, and long-term effects to deepen understanding of these emerging technologies.
Journal: Data and Metadata
Pages: 552
Volume: 4
Year: 2025
DOI: 10.56294/dm2025552
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:552:id:1056294dm2025552

Template-Type: ReDIF-Article 1.0
Author-Name: MR Oubellouch Hicham
Author-Name-First: MR Oubellouch
Author-Name-Last: Hicham
Author-Name: MR Soulhi Aziz
Author-Name-First: MR Soulhi
Author-Name-Last: Aziz
Title: Evaluating the Efficacy of Artificial Intelligence Techniques for Proactive Risk Assessment in Oil and Gas: A Focus on Predictive Accuracy and Real Time Decision Support
Abstract: The oil and gas industry operates within a landscape of complex, high-stakes risks that span operational, environmental, and safety domains. Traditional risk assessment methodologies, while foundational, are constrained by their static nature and limited capacity to process dynamic, large-scale data. This dissertation investigates the application of artificial intelligence (AI) methodologies—specifically fuzzy logic and machine learning—to enhance risk assessment frameworks in the oil and gas sector. By systematically evaluating key performance criteria, including predictive accuracy, data processing capabilities, and user interactivity, this research establishes a comprehensive framework for integrating AI-driven approaches into risk management systems. The findings demonstrate that AI-based models significantly enhance the ability to anticipate and mitigate risks through real-time decision support and advanced predictive analytics. This work further introduces a scalable decision-making model leveraging fuzzy inference to handle uncertainty and improve the robustness of risk assessments. The proposed framework offers a pathway for transitioning from reactive to proactive safety management strategies, ensuring resilience and sustainability in increasingly complex industrial environments.
Journal: Data and Metadata
Pages: .532
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.532
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.532:id:1056294dm2024532

Template-Type: ReDIF-Article 1.0
Author-Name: Zaynab Boujelb
Author-Name-First: Zaynab
Author-Name-Last: Boujelb
Author-Name: Ahmed Idrissi
Author-Name-First: Ahmed
Author-Name-Last: Idrissi
Author-Name: Achraf Benba
Author-Name-First: Achraf
Author-Name-Last: Benba
Author-Name: El Mahjoub Chakir
Author-Name-First: El Mahjoub
Author-Name-Last: Chakir
Title: Detecting hemorrhagic stroke from computed tomographic scans using machine learning models comparison
Abstract: Introduction: Stroke is the most leading cause of death and disability worldwide, with hemorrhagic stroke being the most dangerous due to bleeding in the brain. To minimize the impacts, early detection is crucial for effective management and timely intervention. This is precisely the motivation behind our research, which aims to develop a reliable and rapid diagnostic support system. Methods: In this study, the authors combined machine learning (ML) models to detect stroke using a dataset of computerized tomography (CT) images. The study was conducted on a real database containing CT images collected from Moroccan patients. The method used in data organization and preprocessing were performed, followed by feature extraction from each image, such as intensity, grayscale, and histogram characteristics. These extracted features were then compressed using several algorithms, including Principal Component Analysis (PCA). The processed data were fed into the most robust machine learning classifiers based on existing literature. Results: As a result, the XGBoost model achieved the highest classification accuracy, with 93% precision, using a Leave-One-Subject-Out (LOSO) validation scheme. Conclusion: This study is a step forward in improving patient healthcare by enabling early detection, which could lead to timely, potentially life-saving interventions.
Journal: Data and Metadata
Pages: .548
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.548
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.548:id:1056294dm2024548

Template-Type: ReDIF-Article 1.0
Author-Name: Mowafaq Salem Alzboon
Author-Name-First: Mowafaq
Author-Name-Last: Salem Alzboon
Author-Name: Muhyeeddin Alqaraleh
Author-Name-First: Muhyeeddin
Author-Name-Last: Alqaraleh
Author-Name: Mohammad Subhi Al-Batah
Author-Name-First: Mohammad
Author-Name-Last: Subhi Al-Batah
Title: Diabetes Prediction and Management Using Machine Learning Approaches
Abstract: Diabetes has emerged as a significant global health issue, especially with the increasing number of cases in many countries. This trend Underlines the need for a greater emphasis on early detection and proactive management to avert or mitigate the severe health complications of this disease. Over recent years, machine learning algorithms have shown promising potential in predicting diabetes risk and are beneficial for practitioners. Objective: This study highlights the prediction capabilities of statistical and non-statistical machine learning methods over Diabetes risk classification in 768 samples from the Pima Indians Diabetes Database. It consists of the significant demographic and clinical features of age, body mass index (BMI) and blood glucose levels that greatly depend on the vulnerability against Diabetes. The experimentation assesses the various types of machine learning algorithms in terms of accuracy and effectiveness regarding diabetes prediction. These algorithms include Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Naive Bayes, Support Vector Machine, Gradient Boosting and Neural Network Models. The results show that the Neural Network algorithm gained the highest predictive accuracy with 78.57%, and then the Random Forest algorithm had the second position with 76.30% accuracy. These findings show that machine learning techniques are not just highly effective. Still, they also can potentially act as early screening tools in predicting Diabetes within a data-driven fashion with valuable information on who is more likely to get affected. In addition, this study can help to realize the potential of machine learning for timely intervention over the longer term, which is a step towards reducing health outcomes and disease burden attributable to Diabetes on healthcare systems.
Journal: Data and Metadata
Pages: 545
Volume: 4
Year: 2025
DOI: 10.56294/dm2025545
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:545:id:1056294dm2025545

Template-Type: ReDIF-Article 1.0
Author-Name: Marcela Hechenleitner-Carvallo
Author-Name-First: Marcela
Author-Name-Last: Hechenleitner-Carvallo
Author-Name: Jacqueline Ibarra-Peso
Author-Name-First: Jacqueline
Author-Name-Last: Ibarra-Peso
Title: Interdisciplinarity in the Effectiveness of Telehealth: Challenges, Opportunities, and Necessary Competencies
Abstract: Introduction: Telehealth has become a key tool to improve access to healthcare, particularly in contexts with geographical barriers. Its effective implementation relies on integrating technology with clinical knowledge, which requires specific competencies and interdisciplinary collaboration to ensure equitable care. Objective: To describe how interdisciplinarity influences the implementation and effectiveness of telehealth, identifying the challenges, opportunities, and necessary competencies from the perspective of healthcare professionals in the Biobío region, Chile. Methodology: A qualitative study was conducted through a focus group with 14 healthcare professionals from various disciplines. Data were analyzed using thematic analysis and co-occurrence analysis to identify relationships among key competencies. Results: The findings indicate that interdisciplinarity optimizes telehealth by combining clinical and technological knowledge, although it faces challenges such as the need for training in communication and adaptability competencies. The importance of skilled human resources and robust digital infrastructure is also highlighted. Conclusions: Interdisciplinary collaboration is essential for the success of telehealth, enabling patient-centered care and promoting health equity. Ongoing training in specific competencies and adequate technological support are necessary to ensure the sustainability and effectiveness of telehealth in the region
Journal: Data and Metadata
Pages: 542
Volume: 4
Year: 2025
DOI: 10.56294/dm2025542
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:542:id:1056294dm2025542

Template-Type: ReDIF-Article 1.0
Author-Name: Sutejo
Author-Name-First: Sutejo
Author-Name-Last: Sutejo
Author-Name: Refdinal
Author-Name-First: Refdinal
Author-Name-Last: Refdinal
Author-Name: Wakhinuddin
Author-Name-First: Wakhinuddin
Author-Name-Last: Wakhinuddin
Author-Name: Yogi Ersan Fadrial
Author-Name-First: Yogi
Author-Name-Last: Ersan Fadrial
Author-Name: Yogi Yunefri
Author-Name-First: Yogi
Author-Name-Last: Yunefri
Title: Ready to Master Data Structures? Discover How Eduplay and Problem-Based Learning Elevate Computational Thinking and Real-World Problem Solving
Abstract: Data structures therefore should be conquered for optimal development of computational thinking and general problem solving prowess. Nevertheless, traditional classroom approaches can hardly meet learners’ interest and enlighten them on how the acquired knowledge could be applied in practice. This paper aims to analyze how the introduced Eduplay, an application that comprises educational games and interactive exercises, can be applied to Problem-Based Learning, focusing on problem-solving. The purpose of the study is to determine the extent to which active learning methods contribute to the improvement of students’ interest, comprehension and skill in data structures. This methodology comprised pre- and post-tests with regard to CT and qualitative approach employed in observing the learning participants’ engagement and interactions. The study also shows that with the integration of Eduplay and PBL, students become more engaged effectively and independently, they think critically, and effectively and independently apply the learnt concepts. The above results imply that combining Eduplay and PBL offers not only the transition from classroom knowledge to working experience but also learning in its entirety. This approach presents educators with a new direction that they can use to enhance the learning outcomes of their students in data structures and other computational fields
Journal: Data and Metadata
Pages: 540
Volume: 3
Year: 2024
DOI: 10.56294/dm2024540
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:540:id:1056294dm2024540

Template-Type: ReDIF-Article 1.0
Author-Name: Ala’a M. Al-Momani
Author-Name-First: Ala’a M.
Author-Name-Last: Al-Momani
Author-Name: Mohammed A. Al-Sharafi
Author-Name-First: Mohammed A.
Author-Name-Last: Al-Sharafi
Author-Name: Mufleh Amin AL Jarrah
Author-Name-First: Mufleh Amin
Author-Name-Last: AL Jarrah
Author-Name: Omar Wassef Hijaeen
Author-Name-First: Omar Wassef
Author-Name-Last: Hijaeen
Title: Is Big Data Adoption Shaping Business Landscapes? An Overview of Current Hotspots and Future Trends
Abstract: Introduction: Most bibliometrics reviews in the prior studies have focused on tracking the evolution, applications, and implications of Big Data in business through different sectors using Web of Science or Scopus databases. Moreover, none of these studies has addressed the differences between developed and developing countries. These gaps indicate that we need a bibliometric review that can identify current trends and unexplored areas. Objectives: This study aims to use a bibliometric approach to examine how Big Data is used in businesses using WoS and Scopus databases. Methods: A Systematic Literature Review was conducted based on the country's economic status using the SPAR-4-SLR protocol for this research. Results: The results show a significant growth in publications since 2013 among developed countries and since 2014 among developing ones such as the United States and the United Kingdom, along with China and India, respectively. Also, Machine Learning Overlaps Artificial Intelligence alongside Analytics, fueling innovative data-driven business processes around Big Data. Conclusions: This article explores the transformative power of Big Data across domains, stressing its ability to cause substantial breakthroughs within the digital economy
Journal: Data and Metadata
Pages: 536
Volume: 4
Year: 2025
DOI: 10.56294/dm2025536
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:536:id:1056294dm2025536

Template-Type: ReDIF-Article 1.0
Author-Name: Erna Piantari
Author-Name-First: Erna
Author-Name-Last: Piantari
Author-Name: Dwi Novia Al Husaeni
Author-Name-First: Dwi Novia Al
Author-Name-Last: Husaeni
Author-Name: Eddy Prasetyo Nugroho
Author-Name-First: Eddy Prasetyo
Author-Name-Last: Nugroho
Author-Name: Ani Anisyah
Author-Name-First: Ani
Author-Name-Last: Anisyah
Author-Name: Irawan Afrianto
Author-Name-First: Irawan
Author-Name-Last: Afrianto
Author-Name: Irham Jundurrahman
Author-Name-First: Irham
Author-Name-Last: Jundurrahman
Author-Name: Ibrahim Danial Bisulthon
Author-Name-First: Ibrahim Danial
Author-Name-Last: Bisulthon
Title: Blockchain for Managing Citizens’ Data: Bibliometric Analysis and Systematic Literature Review
Abstract: Introduction: Blockchain is an advanced technology that ensures a high level of data transparency and security by utilizing a distributed mechanism in storing and managing data. While currently blockchain is mostly utilized to facilitate cryptocurrency transactions due to its capability in securing data, blockchain also has the potential to store and manage various data, including government data. This research aims to reflect in the literature on trends in the application of blockchain technology in citizen data management and e-Government systems, focusing on trends, architectural design and the sector of the implementation of this technology. This research will also investigate data integrity and population accountability. This study seeks to provide insight into the challenges and application of blockchain technology in governance systems through several research questions. Methods: The PRISM method is used as a guide for conducting literature review analysis starting from defining literature eligibility criteria, defining information sources, conducting filtering, and data analysis. Results: Based on the results of the literature review, several previous studies have integrated Blockchain with IoT to increase supply chain transparency. Meanwhile, in e-government systems, several Blockchain implementations are used for decentralized identity verification to ensure the integrity and privacy of demographic data. Conclusions: Previous research shows that Blockchain technology has the potential to improve efficiency, security and transparency in various fields, especially in data management and governance.
Journal: Data and Metadata
Pages: .537
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.537
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.537:id:1056294dm2024537

Template-Type: ReDIF-Article 1.0
Author-Name: Tri Murwaningsih
Author-Name-First: Tri
Author-Name-Last: Murwaningsih
Title: The Impact of Teachers' Professional Development on The Internet Self-Efficacy and ICT Competencies
Abstract: Introduction: In the dynamic realm of education, the incorporation of Information and Communication Technology (ICT) has ushered in transformative shifts in the methods of teaching and learning. This study explores the impact of teacher professional development on the interconnected dimensions of internet self-efficacy and ICT competencies within the context of vocational high schools. Methods: The research employs a carefully designed survey instrument featuring 56 items, adapted from previous studies. Targeting vocational high school teachers across six Regencies in Central Java Province, Indonesia, the study utilized a clustered random sampling technique to select a sample size of 150 teachers in vocational high schools. The study employed Partial Least Square Structural Equation Modelling (PLS-SEM) to analyze the relationships among teacher professional development, internet self-efficacy, and ICT competencies. Results: This study yields four notable results. First, teachers' professional development positively impacts their internet self-efficacy. Second, teachers' professional development positively impacts their ICT competencies. Third, Teachers' internet self-efficacy influences their ICT competencies. Fourth, there is a mediated link between teacher professional development and ICT competencies through internet self-efficacy. Conclusions: This study underscores the importance of teacher personal professional development for improving internet self-efficacy and ICT competencies in vocational high schools. Policymakers should consider tailored training programs to enhance teacher skills, promoting better technology integration for improved vocational education.
Journal: Data and Metadata
Pages: 531
Volume: 4
Year: 2025
DOI: 10.56294/dm2025531
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:531:id:1056294dm2025531

Template-Type: ReDIF-Article 1.0
Author-Name: FX. Risang Baskara
Author-Name-First: FX. Risang
Author-Name-Last: Baskara
Title: Conceptualizing Digital Literacy for the AI Era: A Framework for Preparing Students in an AI-Driven World
Abstract: Introduction: As artificial intelligence (AI) has become increasingly integrated into daily life, traditional digital literacy frameworks must be revised to address the modern challenges. This study aimed to develop a comprehensive framework that redefines digital literacy in the AI era by focusing on the essential competencies and pedagogical approaches needed in AI-driven education. Methods: This study employed a constructivist and connectivist theoretical approach combined with Jabareen's methodology for a conceptual framework analysis. A systematic literature review from 2010-2024 was conducted across education, computer science, psychology, and ethics domains, using major databases including ERIC, IEEE Xplore, and Google Scholar. The analysis incorporated a modified Delphi technique to validate the framework’s components. Results: The developed framework comprises four key components: technical understanding of AI systems, practical implementation skills, critical evaluation abilities, and ethical considerations. These components are integrated with traditional digital literacy standards through a meta-learning layer that emphasises adaptability and continuous learning. This framework provides specific guidance for curriculum design, pedagogical approaches, assessment strategies, and teacher development. Conclusions: This framework offers a structured approach for reconceptualising digital literacy in the AI era, providing educational institutions with practical guidelines for implementation. Integrating technical and humanistic aspects creates a comprehensive foundation for preparing students for an AI-driven world, while identifying areas for future empirical validation.
Journal: Data and Metadata
Pages: 530
Volume: 4
Year: 2025
DOI: 10.56294/dm2025530
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:530:id:1056294dm2025530

Template-Type: ReDIF-Article 1.0
Author-Name: Carlos Julio Rojas-Hermida
Author-Name-First: Carlos Julio
Author-Name-Last: Rojas-Hermida
Author-Name: John Edisson García Peñaloza
Author-Name-First: John Edisson
Author-Name-Last: García Peñaloza
Author-Name: Ricardo León Castro Zamora
Author-Name-First: Ricardo León
Author-Name-Last: Castro Zamora
Title: Scientific production on risks and financial instruments for commodity management
Abstract: Introduction: Commodity management benefits from a variety of financial instruments and tools that allow companies to mitigate risks and optimize their operations. Among the most used are futures contracts, contracts for difference, risk management tools such as insurance and investment funds. The objective of this article is to analyze the scientific production on risks and financial instruments for the management of raw materials. Methodology: The research paradigm is mixed, through the combination of qualitative and quantitative methods. A bibliometric analysis was carried out which was complemented with a documentary review. The study was synthesized in three stages and was carried out in the Google Scholar, Scielo and SCOPUS databases, during the period from 1991 to 2024, without limitations in language. Results: The literature review shows an increase in the adoption of digital technologies to improve internal communication and employee engagement, with Brazil as a leader in the Latin American region. In addition, endomarketing is identified as a key element for attracting and retaining talent, especially in sectors with high competition and a shortage of specific skills. Likewise, its contribution to organizational sustainability is highlighted, focusing on the social dimension by promoting the well-being and development of employees. Conclusion: These findings reflect how endomarketing has been integrated into business strategies to align corporate objectives with the values ​​of social responsibility and job satisfaction, consolidating itself as an essential tool in improving productivity and long-term commitment.
Journal: Data and Metadata
Pages: 529
Volume: 3
Year: 2024
DOI: 10.56294/dm2024529
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:529:id:1056294dm2024529

Template-Type: ReDIF-Article 1.0
Author-Name: Any Tsalasatul Fitriyah
Author-Name-First: Any
Author-Name-Last: Tsalasatul Fitriyah
Author-Name: Nur Chamidah
Author-Name-First: Nur
Author-Name-Last: Chamidah
Author-Name: Toha Saifudin
Author-Name-First: Toha
Author-Name-Last: Saifudin
Title: Prediction of Paddy Production in Indonesia Using Semiparametric Time Series Regression Least Square Spline Estimator
Abstract: Support for one of the points of Sustainable Development Goals (SDGs), namely Zero Hunger, is by supporting sustainable agricultural empowerment. Indonesia is one of the countries with the fourth largest rice consumption according to the United States Department of Agriculture. 90% of Indonesians consume rice as a staple food. In this study, we model paddy production in Indonesia using a semiparametric time series regression approach based on least square spline estimator (LSSE). Where spline is used to overcome data that tends to fluctuate in monthly paddy production data. Monthly data on paddy production in Indonesia over a certain period of time is used to build a model. The use of a semiparametric regression approach by combining parametric components and nonparametric components for analyzing factors that affect paddy production. In this study, the parametric component is paddy production in the previous period lag-1 and the nonparametric components are the potential area of crop failure and the generative area. For predicting paddy production in Indonesia using Semiparametric Time Series Regression Model (STSRM) approach based on LSSE, we determine the order and optimal knot points based on the smallest Generalized Cross Validation (GCV) value. The results of the study show that the Mean Absolute Percentage Error (MAPE) value of 18.05% is less than 20%. It means that prediction of paddy production in Indonesia using STSRM based on LSSE is a good prediction
Journal: Data and Metadata
Pages: 527
Volume: 4
Year: 2025
DOI: 10.56294/dm2025527
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:527:id:1056294dm2025527

Template-Type: ReDIF-Article 1.0
Author-Name: Titi Sriwhyuni
Author-Name-First: Titi
Author-Name-Last: Sriwhyuni
Author-Name: Ambiyar
Author-Name-First: Ambiyar
Author-Name-Last: Ambiyar
Author-Name: Elfi Tasrif
Author-Name-First: Elfi
Author-Name-Last: Tasrif
Author-Name: Afif Rahman Riyanda
Author-Name-First: Afif Rahman
Author-Name-Last: Riyanda
Author-Name: Hafizul Fahri Hanafi
Author-Name-First: Hafizul Fahri
Author-Name-Last: Hanafi
Author-Name: Yolanda Idha Hanafi
Author-Name-First: Yolanda Idha
Author-Name-Last: Hanafi
Author-Name: Fitri Yanti
Author-Name-First: Fitri
Author-Name-Last: Yanti
Title: Android-Based Digital Learning Media: Improving Interactivity in Analysis and Design of Systems Course
Abstract: This study aims to develop an Android-based digital learning media to enhance student engagement and understanding in the Analysis and System Design course at Universitas Negeri Padang (UNP). The development of this media was carried out using the Multimedia Development Life Cycle (MDLC) method, which includes the stages of concept, design, material collection, assembly, testing, and distribution. Validation was conducted by experts in pedagogy, design, and technology to ensure the quality of the media, with the validation results showing an average Aiken V value of 0.84, categorized as valid. Additionally, Black Box Testing was performed to ensure that the application's features, such as navigation, content delivery, and task submission, functioned properly. The results of the study indicate that this Android-based learning media is effective in supporting learning, providing flexible access, and increasing student interaction and engagement with the material in the Analysis and System Design course. This media has proven to be a useful tool for improving learning outcomes by presenting multimedia content that can be accessed through Android devices. Future research is expected to develop the compatibility of this media on other platforms and assess its impact on student learning performance.
Journal: Data and Metadata
Pages: 523
Volume: 4
Year: 2025
DOI: 10.56294/dm2025523
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:523:id:1056294dm2025523

Template-Type: ReDIF-Article 1.0
Author-Name: Anber Abraheem Shlash Mohammad
Author-Name-First: Anber Abraheem
Author-Name-Last: Shlash Mohammad
Author-Name: Sulieman Ibraheem Shelash Al-Hawary
Author-Name-First: Sulieman Ibraheem
Author-Name-Last: Shelash Al-Hawary
Author-Name: Ayman Hindieh
Author-Name-First: Ayman
Author-Name-Last: Hindieh
Author-Name: Asokan Vasudevan
Author-Name-First: Asokan
Author-Name-Last: Vasudevan
Author-Name: Hussam Mohd Al-Shorman
Author-Name-First: Hussam
Author-Name-Last: Mohd Al-Shorman
Author-Name: Ahmad Samed Al-Adwan
Author-Name-First: Ahmad Samed
Author-Name-Last: Al-Adwan
Author-Name: Muhammad Turki Alshurideh
Author-Name-First: Muhammad
Author-Name-Last: Turki Alshurideh
Author-Name: Imad Ali
Author-Name-First: Imad
Author-Name-Last: Ali
Title: Intelligent Data-Driven Task Offloading Framework for Internet of Vehicles Using Edge Computing and Reinforcement Learning
Abstract: Introduction: The Internet of Vehicles (IoV) was enabled through innovative developments featuring advanced automotive networking and communication to fulfill the need for real-time applications that are latency-sensitive, such as autonomous driving and emergency management. Given that the servers were much farther away from the actual site of operation, traditional cloud computing faced huge delays in processing. Mobile Edge Computing (MEC) resolved this challenge by enabling localized data processing, reducing latency and enhancing resource utilization. Methods: This study proposed an Efficient Mobile Edge Computing-based Internet of Vehicles Task Offloading Framework (EMEC-IoVTOF). The framework integrated deep reinforcement learning (DRL) to optimize task offloading decisions, focusing on minimizing latency and energy consumption while accounting for bandwidth and computational constraints. Offloading costs were calculated using mathematical modeling and further optimized through Particle Swarm Optimization (PSO). An adaptive inertia weight mechanism was implemented to avoid local optimization and enhance task allocation decisions. Result: The proposed framework was thus proved effective for any latency reduction and energy consumption optimization in efficiently improving the overall system performance. DRL and MEC together facilitate scalability in task distribution by ensuring robust performance in dynamic vehicular environments. Integration with PSO further enhances the decision-making process and makes the system highly adaptable to dynamic task demands and network conditions. Discussion:The findings highlighted the potential of EMEC-IoVTOF to address key challenges in IoV systems, including latency, energy efficiency, and bandwidth utilization. Future research could explore real-world deployment and adaptability to complex vehicular scenarios, further validating its scalability and reliability.
Journal: Data and Metadata
Pages: 521
Volume: 4
Year: 2025
DOI: 10.56294/dm2025521
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:521:id:1056294dm2025521

Template-Type: ReDIF-Article 1.0
Author-Name: N Hari Babu
Author-Name-First: N Hari
Author-Name-Last: Babu
Author-Name: Enireddy Vamsidhar
Author-Name-First: Enireddy
Author-Name-Last: Vamsidhar
Title: Fusion enhancement and learning model for histopathological image analysis using learning approaches
Abstract: Breast cancer is the most prevalent form of the disease and the primary cause of cancer-related deaths among women globally. Early detection plays a pivotal role in substantially diminishing both the morbidity and mortality rates associated with this disease in women. Consequently, the development of an automated diagnostic system holds promise in enhancing the precision of diagnoses. To automatically classify breast cancer microscopy images stained into two distinct classifications—normal tissue and benign lesions—this study introduces a graph-based convolutional neural network with hybrid optimization (G-CNN) that makes use of a dataset that was specially selected for this purpose. The network layer is capitalized in our suggested model to extract reliable and abstract information from input photos. Initially, we used 5-fold cross-validation (CV) to optimize the suggested model on the original dataset. Our framework demonstrated a 98% accuracy rate and a 0.969 kappa score. It also received an average AUC-ROC score of 0.998 and a mean AUC-PR value of 0.995. In specific terms, it displayed 96% and 99% sensitivity, respectively, about the supplied photographs. Examining normalized photos, the suggested architecture outperformed the other approaches in terms of colour normalization methodology performance. These findings underscore the superior performance of our proposed model compared to both the baseline approaches and established prevailing models using default settings. Furthermore, it becomes evident that while existing normalization techniques delivered competitive performance, they fell short of surpassing the results obtained from the original dataset
Journal: Data and Metadata
Pages: 511
Volume: 4
Year: 2025
DOI: 10.56294/dm2025511
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:511:id:1056294dm2025511

Template-Type: ReDIF-Article 1.0
Author-Name: Dinesh Kumar Anguraj
Author-Name-First: Dinesh Kumar
Author-Name-Last: Anguraj
Author-Name: Sivaneasan Bala Krishnan
Author-Name-First: Sivaneasan Bala
Author-Name-Last: Krishnan
Author-Name: T Sathish Kumar
Author-Name-First: T Sathish
Author-Name-Last: Kumar
Author-Name: Prasun Chakrabarti
Author-Name-First: Prasun
Author-Name-Last: Chakrabarti
Author-Name: Tulika Chakrabarti
Author-Name-First: Tulika
Author-Name-Last: Chakrabarti
Author-Name: Martin Margala
Author-Name-First: Martin
Author-Name-Last: Margala
Author-Name: Siva Shankar S
Author-Name-First: Siva
Author-Name-Last: Shankar S
Title: Hybrid weighted sequential learnong technique for structural health monitoring using learning approaches
Abstract: Abstract- Structural Health Monitoring (SHM) plays a vital role in damage detection, offering significant maintenance and failure prevention benefits. Establishing effective SHM systems for damage identification (DI) traditionally requires extensive experimental datasets collected under varied operating and environmental conditions, which can be resource-intensive. This study introduces a novel approach to SHM by leveraging a Hybrid Weighted Sequential Learning Technique (HWSLT) classifier, which uses Finite Element (FE) computed responses to simulate structural behaviors under both healthy and damaged states. Initially, an optimal FE model representing a healthy, benchmark linear beam structure is developed and updated using experimental validation data. The HWSLT classifier is trained on SHM vibration data generated from this model under simulated load cases with uncertainty. This allows for minimal real-world experimental intervention while ensuring robust damage detection. Results demonstrate that the HWSLT classifier, trained with optimal FE model data, achieves high accuracy in predicting damage states in the benchmark structure, even when mixed with random disturbances. Conversely, data from non-ideal FE models produced unreliable classifications, underscoring the importance of model accuracy. These findings suggest that the integration of ideal FE models and deep learning offers a promising pathway for future SHM applications, with potential for reduced experimental costs and enhanced damage localization capabilities
Journal: Data and Metadata
Pages: 510
Volume: 4
Year: 2025
DOI: 10.56294/dm2025510
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:510:id:1056294dm2025510

Template-Type: ReDIF-Article 1.0
Author-Name: Yiming Zhang
Author-Name-First: Yiming
Author-Name-Last: Zhang
Author-Name: Ajmera Mohan Singh
Author-Name-First: Ajmera
Author-Name-Last: Mohan Singh
Title: Leveraging Digital Intelligence for the Design and Fabrication of Urban Sculpture Art
Abstract: With the rise of digital intelligent technology, its application fields are more and more extensive, including urban sculpture. This study addresses the critical factors of durability and maintenance associated with the digital components used in outdoor urban sculptures. The primary objective of this research is to employ cutting-edge digital intelligent technologies in the conceptualization and realization of urban sculpture. We introduce an innovative Efficient Generative Adversarial Network (EGAN), enhanced by fruit fly optimization (FFO), which facilitates the generation of unique patterns and designs for urban sculptures through smart sensor integration. This approach leverages a variety of data collected by smart sensors, which is subsequently preprocessed through data cleaning and normalization techniques. We apply Principal Component Analysis (PCA) for effective feature extraction, allowing for the development of intelligent digital frameworks for urban sculpture design models. Our results demonstrate that the proposed method significantly enhances design efficiency (25 hours), resolution (600 dpi), material strength (35 MPa), environmental adaptability (high), and overall durability (10 years) of urban sculpture patterns derived from smart sensor data. The digital intelligent technology-based design approach surpasses traditional methodologies in meeting the stringent standards set for urban sculpture design, thereby contributing to the future of urban art installations.
Journal: Data and Metadata
Pages: 501
Volume: 4
Year: 2025
DOI: 10.56294/dm2025501
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:501:id:1056294dm2025501

Template-Type: ReDIF-Article 1.0
Author-Name: Ahmed En-nhaili
Author-Name-First: Ahmed
Author-Name-Last: En-nhaili
Author-Name: Adil Hachmoud
Author-Name-First: Adil
Author-Name-Last: Hachmoud
Author-Name: Anwar Meddaoui
Author-Name-First: Anwar
Author-Name-Last: Meddaoui
Author-Name: Abderrahim Jrifi
Author-Name-First: Abderrahim
Author-Name-Last: Jrifi
Title: Enhancing product predictive quality control using Machine Learning and Explainable AI
Abstract: The integration of predictive quality and eXplainable Artificial Intelligence (XAI) in product quality classification marks a significant advancement in quality control processes. This study examines the application of Machine Learning (ML) models and XAI techniques in managing product quality, using a case study in the agri-food industry quality as an example. Predictive quality models leverage historical and real-time data to anticipate potential quality issues, thereby improving detection accuracy and efficiency. XAI ensures transparency and interpretability, facilitating trust in the model’s decisions. This combination enhances quality management, supports informed decision-making, and ensures regulatory compliance. The case study demonstrates how ML models, particularly Artificial Neural Network (ANN), can accurately predict product quality, with XAI providing clarity on the reasoning behind these predictions. The study suggests future research directions, such as expanding datasets, exploring advanced ML techniques, implementing real-time monitoring, and integrating sensory analysis, to further improve the accuracy and transparency of quality control in various industries.
Journal: Data and Metadata
Pages: 500
Volume: 4
Year: 2025
DOI: 10.56294/dm2025500
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:500:id:1056294dm2025500

Template-Type: ReDIF-Article 1.0
Author-Name: Pornnisa Wattanasiri
Author-Name-First: Pornnisa
Author-Name-Last: Wattanasiri
Author-Name: Paiboon Manorom
Author-Name-First: Paiboon
Author-Name-Last: Manorom
Author-Name: Wirapong Chansanam
Author-Name-First: Wirapong
Author-Name-Last: Chansanam
Title: Emerging Themes, Leaders, and Collaboration in Library and Information Science Research
Abstract: Introduction: This study uses bibliometric methods to evaluate research articles within the library and information science (LIS) domain. The focus is to uncover trends and patterns in social network analysis related to LIS, particularly examining research collaborations and content within highly cited articles. By analyzing these aspects, the study seeks to identify influential authors, prominent research themes, and key contributors in the LIS field. Methods: A dataset of 14,517 articles published between 1954 and 2023 was extracted from the Scopus database for bibliometric analysis. The study concentrated on publications in the LIS domain, focusing on the journal Library Philosophy and Practice. Multiple Correspondence Analysis (MCA) was used to identify clusters within the research field, while content analysis was performed to determine prevalent topics and disciplinary influences within the articles. Results: The analysis revealed that China is home to many of the most influential authors in the LIS domain, with the United States, China, and the United Kingdom identified as the top contributing countries to LIS research. Common research themes include information science, bibliometrics, academic libraries, information literacy, and LIS education. Two main clusters emerged from the MCA: one focused on information-related concepts and the other on bibliometrics and scholarly communication. Content analysis indicated a significant presence of topics from physics, computer science, and information technology within LIS research. Conclusions: This study highlights key trends and patterns in LIS research, with academic libraries, information literacy, LIS education, and librarians' roles identified as critical areas for future exploration. Expanding databases and refining keyword searches are recommended to enhance knowledge dissemination and educational adaptability in the LIS field. The findings aim to support LIS researchers, facilitate research planning, and promote global interinstitutional cooperation
Journal: Data and Metadata
Pages: 497
Volume: 4
Year: 2025
DOI: 10.56294/dm2025497
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:497:id:1056294dm2025497

Template-Type: ReDIF-Article 1.0
Author-Name: Irvin David Bonilla Solís
Author-Name-First: Irvin David
Author-Name-Last: Bonilla Solís
Author-Name: Luis Asunción Pérez-Domínguez
Author-Name-First: Luis Asunción
Author-Name-Last: Pérez-Domínguez
Author-Name: Rosa Patricia Ramírez Delgado
Author-Name-First: Rosa Patricia
Author-Name-Last: Ramírez Delgado
Author-Name: Marling Carolina Cordero Díaz
Author-Name-First: Marling Carolina
Author-Name-Last: Cordero Díaz
Title: Web Application Development for TODIM Method Automation and Alternatives Evaluation
Abstract: The TODIM method (Tomada de Decisao Interativa Multicriterio), which in Portuguese means “Interactive and Multicriteria Decision Making”, is a process of evaluation alternatives, with various academic and professional uses. The current project focuses on the first approach, considering students, professors and investigators as the main beneficiaries and target audience. There is a shortage of software that automate the TODIM method, therefore it is proposed to develop a web application mainly using Python, HTML and JavaScript programming languages that can satisfy this necessity. To guarantee a uniformity in the results obtained by this program, samples of results obtained by this method by other researchers are taken as a basis and guide. To develop the application, the complete functioning of TODIM as an alternative evaluation approach must be understood. In a general view, TODIM uses pairwise comparisons between decision criteria while eliminating the inconsistencies that arise from such comparisons. Thus, the main idea is to measure the degree of dominance in each alternative over the others using the prospective value function. As result, it calculates the overall and partial degrees of dominance of each alternative to finally lead to a classification or “ranking” of the best alternatives.
Journal: Data and Metadata
Pages: 492
Volume: 4
Year: 2024
DOI: 10.56294/dm2025492
Handle: RePEc:dbk:datame:v:4:y:2024:i::p:492:id:1056294dm2025492

Template-Type: ReDIF-Article 1.0
Author-Name: Yanjun Wang
Author-Name-First: Yanjun
Author-Name-Last: Wang
Author-Name: Ajmera Mohan Singh
Author-Name-First: Ajmera
Author-Name-Last: Mohan Singh
Title: Application of computer simulation technology in traditional building protection
Abstract: Background: Computer simulation technology, especially virtual reality (VR) technology, offers an innovative solution for participating in architectural design by providing an immersive and interactive experiences. Aim: This research aims to provide the VR application for the protection of traditional buildings, focusing on how this technology can enhance stakeholder participation in the protection and preservation of historical structures. The aim is to evaluate the effectiveness of VR in facilitating a bottom-up approach to decision-making, thereby preserving cultural heritage. Method: To gather data, a random sample of 136 participants, including both local residents and architectural professionals, were engaged in VR simulations of renovation for traditional buildings. The VR environment presented two design schemes: one reflecting a traditional architectural style and the other featuring a modern approach. Participants interacted with both schemes using VR, and their feedback was collected through structured surveys. Statistical methods were employed to evaluate the quality of VR experiences and their impact on participant preferences and decision-making. Result: It indicate that VR technology significantly improves stakeholder engagement, with a majority of participants expressing a strong preference for traditional designs in terms of cultural protection. The immersive nature of VR was found to effectively replace traditional review methods, offering clearer insights into design intentions and facilitating informed decisions. Conclusion: VR technology proves to be a valuable tool in the protection of traditional buildings by enhancing participant engagement and supporting informed decision-making processes
Journal: Data and Metadata
Pages: 491
Volume: 4
Year: 2025
DOI: 10.56294/dm2025491
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:491:id:1056294dm2025491

Template-Type: ReDIF-Article 1.0
Author-Name: Redouane Touil
Author-Name-First: Redouane
Author-Name-Last: Touil
Author-Name: Rachid Marrakh
Author-Name-First: Rachid
Author-Name-Last: Marrakh
Author-Name: Taoufiq Belhoussine Drissi
Author-Name-First: Taoufiq
Author-Name-Last: Belhoussine Drissi
Author-Name: Bahloul Bensassi
Author-Name-First: Bahloul
Author-Name-Last: Bensassi
Title: Improve customer service quality, reduce operating expenses, and improve energy sales by the K-MEANS method and path optimization algorithms
Abstract: Managing consumer expectations was essential to maintaining customer satisfaction throughout the electricity contract. However, the service provided to customers was based on the location of electrical meters. In the absence of addressing in rural areas, it was too difficult to ensure a comprehensive survey of electrical meter indexes and intervene in time for troubleshooting. The method adopted was the choice of a site with a significant number of meters and energy transformers and the geolocation of electrical installations by a GPS that allowed the assignment of a universal address to electrical installations and facilitated the location of facilities for maintenance and emergency response. The study of optimization algorithms has directed the choice toward the algorithm of the nearest neighbor that remains fast and aims to other algorithms whose number of iterations can be exponential (n! iterations) but less exact. The integration of route optimization algorithms has improved the reactivity of technicians, reduced operational costs, and ensured accurate reading of indexes and transparent billing. The study presents a specific case in Morocco, where route optimization based on GPS coordinates of électric meters in reading indexes has significantly improved efficiency and customer satisfaction. In addition, the K-Means method was used to determine the centroids and clusters that represent respectively the transformation stations and the groups of electrical meters. These groupings allow the calculation of energy sales by transformer station to increase them by reducing energy losses
Journal: Data and Metadata
Pages: 489
Volume: 4
Year: 2025
DOI: 10.56294/dm2025489
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:489:id:1056294dm2025489

Template-Type: ReDIF-Article 1.0
Author-Name: Jie Bai
Author-Name-First: Jie
Author-Name-Last: Bai
Author-Name: Ajmera Mohan Singh
Author-Name-First: Ajmera
Author-Name-Last: Mohan Singh
Title: Exploring Computer-Aided Environmental Art Design: A Course Overview
Abstract: Computer aided education is transforming with the integration of technology. In the context of advancing art education, there is a pressing need for innovation to enhance student engagement and learning outcomes. This study introduces an innovative approach by employing an Adaptive Kookaburra Optimized Dynamic Recurrent Neural Network (AKO-DRNN) with the framework of computer-aided environmental art design courses. The traditional methods of teaching art are being complemented by computer-aided tools and intelligent systems. This research explores the application of AKO-DRNN in revolutionizing art education, focusing on environmental art design. The primary goal is to develop an instructional system that leverages advanced algorithms to offer personalized, accurate aesthetic guidance, enhance creative exploration, and elevate students' practical skills in environmental art design. This study integrates AKO-DRNN into the course structure, which combines deep learning (DL) models with environmental design principles. The AKO-DRNN model utilizes dynamic recurrent networks optimized by a Kookaburra-inspired optimization algorithm to effectively analyze and predict artistic styles and features. This model provides real-time feedback and adaptive learning paths tailored to individual student needs. Implementation of the suggested model has demonstrated significant improvements in students’ design quality, creativity, and skill acquisition. The adaptive nature of the model enhances learning outcomes and engagement. The established framework offers a robust solution for modernizing art education in environmental design, fostering greater innovation and practical skills among students.
Journal: Data and Metadata
Pages: 488
Volume: 4
Year: 2025
DOI: 10.56294/dm2025488
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:488:id:1056294dm2025488

Template-Type: ReDIF-Article 1.0
Author-Name: Ambreen Iftikhar
Author-Name-First: Ambreen
Author-Name-Last: Iftikhar
Author-Name: Suleiman Ibrahim Mohammad
Author-Name-First: Suleiman Ibrahim
Author-Name-Last: Mohammad
Author-Name: Mohammad N. Alqudah
Author-Name-First: Mohammad
Author-Name-Last: N. Alqudah
Author-Name: Ahmad Samed Al-Adwan
Author-Name-First: Ahmad
Author-Name-Last: Samed Al-Adwan
Author-Name: Asokan Vasudevan
Author-Name-First: Asokan
Author-Name-Last: Vasudevan
Author-Name: Imad Ali
Author-Name-First: Imad
Author-Name-Last: Ali
Author-Name: Mohd Farhan
Author-Name-First: Mohd
Author-Name-Last: Farhan
Title: Evaluating Inclusivity and Fairness of AI Recruitment Tools for Hiring People with Disabilities in the United Arab Emirates(UAE)
Abstract: Introduction This research aims to explore the effectiveness and inclusivity of AI-powered recruitment tools in hiring people with disabilities within the United Arab Emirates. Such is the situation where AI integration into the arena of recruitment is increasingly rapid, while there are vital issues on the side of bias, accessibility, and fairness for applicants of diverse needs. Methods This study was a mixed-methods approach, examining sentiment analysis, emotion detection, and HR analytics of feedback from applicants with a disability, 415 in total. The research focused on scores referring to sentiment, the progression rate, and the outcome of the final hiring. Results The sentiment score varied significantly across disability types (p-value <0.05). The applicants with cognitive disability expressed the highest sentiment sore while applicants with hearing impairment had the lowest, which indicated the varying adaptability of AI. The emotion analysis depicted a mix of positive and negative emotions. A few applicants liked technology and have trust in it, while others report fear. Clearly, the applicants, both disabled and non-disabled did not differ in their rate of progression (p-value >0.05), hence never indicating any significant difference within the initial steps of the process. The final hiring stage showed significant differences in results with (p-value <0.05), where the proportionate number of disabled applicants was recorded to be lower than that of non-disabled applicants
Journal: Data and Metadata
Pages: 487
Volume: 4
Year: 2025
DOI: 10.56294/dm2025487
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:487:id:1056294dm2025487

Template-Type: ReDIF-Article 1.0
Author-Name: Mariam ATWANI
Author-Name-First: Mariam
Author-Name-Last: ATWANI
Author-Name: Mustapha HLYAL
Author-Name-First: Mustapha
Author-Name-Last: HLYAL
Author-Name: Jamila EL ALAMI
Author-Name-First: Jamila
Author-Name-Last: EL ALAMI
Title: A hybrid hw-rfr forecasting model: case of moroccan pharmaceutical sector
Abstract: Sales forecasting is an essential element of effective supply chain management, particularly in the pharmaceutical sector where continuous availability of drugs is crucial. This article examines sales forecasts for fluoxetine, an antidepressant available on the Moroccan market under six trade names and 14 different forms. The main objective of this study is to compare the effectiveness of four forecasting models, namely Prophet Facebook, ARIMA, GRU and Holt-Winters through their accuracy, and to propose a hybrid model that will contribute to improving the accuracy of demand forecasts. Each model was applied individually to predict future sales, and evaluated using MAPE, MAE and RMSE metrics. Next, a hybrid model, integrating Holt-Winters and Random Forest Regressor methods, was developed to leverage the robustness of traditional models while improving predictive performance through machine learning techniques. The results of the study show that traditional models, such as ARIMA and Holt-Winters, offer a solid basis for sales forecasting. However, the hybrid HW-RFR (Holt-Winters Random Forest Regressor) model stands out for a significant improvement in forecast accuracy, demonstrating great robustness to fluctuations in fluoxetine demand. This article highlights the potential of hybrid models for forecasting pharmaceutical sales. The improved forecast accuracy achieved with the HW-RFR model provides stakeholders with more reliable information, enabling them to make informed decisions to optimize pharmaceutical supply chain management
Journal: Data and Metadata
Pages: 483
Volume: 4
Year: 2025
DOI: 10.56294/dm2025483
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:483:id:1056294dm2025483

Template-Type: ReDIF-Article 1.0
Author-Name: Mustapha RAZZOUKI
Author-Name-First: Mustapha
Author-Name-Last: RAZZOUKI
Author-Name: Rachid ZAMMAR
Author-Name-First: Rachid
Author-Name-Last: ZAMMAR
Author-Name: Adil GAROHE
Author-Name-First: Adil
Author-Name-Last: GAROHE
Author-Name: Karima SALAH-EDDINE
Author-Name-First: Karima
Author-Name-Last: SALAH-EDDINE
Title: The mediating effect of blue ocean strategy on the relationship between management control system and competitive advantage
Abstract: This research examines the impact of management control systems on the competitive advantage of industrial companies, with a focus on the role of the blue ocean strategy as a mediating variable in this relationship. To achieve these objectives, a quantitative methodology was employed. Data was gathered through a structured questionnaire distributed online via Google Forms, and the analysis was performed using Smart PLS Version 4 software. The sample consisted of 405 Moroccan industrial companies. The analysis revealed that management control systems significantly impact competitive advantage, and the blue ocean strategy mediates this relationship. Based on these findings, future research should explore various approaches to implementing blue ocean strategies and their effects on company performance.
Journal: Data and Metadata
Pages: 482
Volume: 4
Year: 2025
DOI: 10.56294/dm2025482
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:482:id:1056294dm2025482

Template-Type: ReDIF-Article 1.0
Author-Name: V Sivamurugan
Author-Name-First: V
Author-Name-Last: Sivamurugan
Author-Name: N Radha
Author-Name-First: N
Author-Name-Last: Radha
Author-Name: R Swathika
Author-Name-First: R
Author-Name-Last: Swathika
Title: Detection and segmentation of meningioma tumors using improved cloud empowered visual geometry group (cloud-ivgg) deep learning structure
Abstract: Detection and segmentation of meningioma brain tumor is a complex process due to its similar textural pattern with other tumors. In this paper Meningioma Tumor Detection System (MTDS) approach is proposed to detect and classify the meningioma brain images from the healthy brain images. The training work flow of the proposed MTDS approach consists of Spatial Gabor Transform (SGT), feature computations and deep learning structure. The features are computed from the meningioma brain image dataset images and the normal brain image dataset images and these features are fed into the classification architecture. In this paper, the proposed CLOUD-IVGG architecture is derived from the existing Cloud empowered Visual Geometry Group (VGG) architecture to improve the detection rate of the proposed system and to decrease the computational time complexity. The testing work flow of the proposed system is also consist of SGT, feature computation and the CLOUD-IVGG architecture to produce the classification result of the source brain images into either normal or meningioma. Further, the tumor regions in this meningioma image have been located using the Morphological segmentation algorithm. In this research work, two independent resource brain imaging datasets has been involved to estimate and validate the performance efficiency of the proposed MTDS. The datasets are Kaggle Brain Imaging (KBI) and BRATS Imaging 2020 (BI20). The performance efficiency has been analyzed with respect to detection rate, precision, recall and Jaccard index
Journal: Data and Metadata
Pages: 478
Volume: 4
Year: 2025
DOI: 10.56294/dm2025478
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:478:id:1056294dm2025478

Template-Type: ReDIF-Article 1.0
Author-Name: Resi Elfitri
Author-Name-First: Resi
Author-Name-Last: Elfitri
Author-Name: Ambiyar
Author-Name-First: Ambiyar
Author-Name-Last: Ambiyar
Author-Name: Fahmi Rizal
Author-Name-First: Fahmi
Author-Name-Last: Rizal
Author-Name: Rijal Abdullah
Author-Name-First: Rijal
Author-Name-Last: Abdullah
Author-Name: Asrul Huda
Author-Name-First: Asrul
Author-Name-Last: Huda
Author-Name: Rizky Ema Wulansari
Author-Name-First: Rizky Ema
Author-Name-Last: Wulansari
Title: Breaking Grounds in Civil Engineering Education: The Potential of Building Information Modelling Software as Catalyst for Improved Interior Design Learning
Abstract: The Industrial Revolution 4.0 in the 21st century has necessitated a shift in learning experiences and self-development to adapt to evolving learning mindsets and digital literacy demands. Teachers are encouraged to reduce administrative workloads that are not aligned with the digital age and focus on adopting digital-based learning models and media that suit modern student needs. This study aimed to implement and analyze the impact of case-based projects integrated with Building Information Modeling (BIM) software on civil engineering students’ understanding of interior building design. This mixed-methods research utilized a sequential explanatory design. A sample of 60 civil engineering students was selected through random sampling. Quantitative data were analyzed using path analysis, while qualitative data were examined through data reduction, data display, and conclusion drawing/verification analysis. The findings indicate that the case-based project approach, integrated with BIM software, was effectively applied and positively impacted students’ skills in learning BIM. Students showed improved abilities to analyze, explore, and synthesize information through hands-on experience with real-world case-based projects. This study contributes a novel educational model by integrating technology with learning methods through case-based projects in BIM software. This innovation supports students in enhancing their analytical and problem-solving skills and aligns with the requirements of the Industrial Revolution 4.0
Journal: Data and Metadata
Pages: 477
Volume: 4
Year: 2025
DOI: 10.56294/dm2025477
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:477:id:1056294dm2025477

Template-Type: ReDIF-Article 1.0
Author-Name: Sukardi
Author-Name-Last: Sukardi
Author-Name: Oriza Candra
Author-Name-First: Oriza
Author-Name-Last: Candra
Author-Name: Emilham Mirshad
Author-Name-First: Emilham
Author-Name-Last: Mirshad
Author-Name: Mahesi Agni Zaus
Author-Name-First: Mahesi Agni
Author-Name-Last: Zaus
Author-Name: Syaiful Islami
Author-Name-First: Syaiful
Author-Name-Last: Islami
Title: Leveraging Smart Home Training Kits as an Innovative Educational Tool to Foster Higher-Order Thinking Skills
Abstract: This study aims to establish the impact of employing Smart Home Training Kits as a new approach to developing Higher-Order Thinking Skills (HOTS) in vocational education. Using a quasi-experimental design, the study involved two groups of vocational students: supporting them by an experimental group applying Smart Home Training Kits and a control group using conventional methods of instruction. Standard pre-tests and post-tests were administered among the students to evaluate the enhancement in the level of higher-order thinking skills, for aspects of critical thinking, problem-solving solving, and creativity. The findings also showed a work improvement in the experimental group compared to the group control group. The experimental group of students who were trained using the Smart Home Training Kits performed better when it came to the analysis, evaluation, and Synthesis of possible solutions regarding smart homes. Also, a number of the activity kits characterized the technical thing being taught in a detailed way that allowed the students to gain a more realistic understanding of the principles at work. The findings of this paper suggest that Smart Home Training Kits are one of the ways through which Higher-Order Thinking Skills can be effectively taught within the technical education training regime while closing the gap between theory and practical. This indicates that the assimilation of these kits in curricula could help the effective development of critical thinking and innovation at the expense of students to face the current world workplace challenges.
Journal: Data and Metadata
Pages: 476
Volume: 4
Year: 2025
DOI: 10.56294/dm2025476
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:476:id:1056294dm2025476

Template-Type: ReDIF-Article 1.0
Author-Name: Abdelkarim Ait Brik
Author-Name-First: Abdelkarim
Author-Name-Last: Ait Brik
Author-Name: Ahmed En-nhaili
Author-Name-First: Ahmed
Author-Name-Last: En-nhaili
Author-Name: Anwar Meddaoui
Author-Name-First: Anwar
Author-Name-Last: Meddaoui
Title: Enhancing Operational Performance through Digitalization and Industry 4.0: A Comprehensive Model for Data Reliability and OEE Optimization
Abstract: In today's industrial context, three key elements are guiding the course of small and medium-sized enterprises (SMEs) towards improved productivity, efficient operations, and sustainable growth. The introduction of Industry 4.0 signifies a groundbreaking shift, integrating state-of-the-art technologies into manufacturing processes and propelling industries towards heightened efficiency and competitiveness. This article deals with the crucial role of productivity measurement in SMEs and examines the impact of data reliability on operational performance assessment. It explores the strategic use of Industry 4.0 tools to enhance data reliability in processes like production, quality, and maintenance. The research focuses on designing a comprehensive model for data collection, reliability, and utilization, ultimately aiming to improve Overall Equipment Effectiveness (OEE) within SMEs. By showcasing the synergistic integration of Industry 4.0 advancements, the article provides practical insights for SME stakeholders to optimize operational performance. The proposed model contributes to the understanding and implementation of efficient methodologies for data management, fostering sustainable improvements using calculation of OEE within SMEs. The case study was conducted in a plastics manufacturing SME that produces components for various industries. These findings can be enhanced and improved through additional case studies to refine the proposed model.
Journal: Data and Metadata
Pages: 475
Volume: 4
Year: 2025
DOI: 10.56294/dm2025475
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:475:id:1056294dm2025475

Template-Type: ReDIF-Article 1.0
Author-Name: Abdulaziz A. Alsulami
Author-Name-First: Abdulaziz A.
Author-Name-Last: Alsulami
Author-Name: Badraddin Alturki
Author-Name-First: Badraddin
Author-Name-Last: Alturki
Title: Enhancing Multiclass Network Intrusion Detection Systems Using Continuous Wavelet Transform on Network Traffic
Abstract: Network systems are susceptible to cyberattacks, which motivates attackers to exploit their vulnerabilities. Scanning network traffic to identify malicious activity is becoming a trend in the cybersecurity domain to mitigate the negative effects of intruders. Network intrusion detection systems (NIDS) are widely recognized as essential tools against cyberattacks. However, there is a need to go beyond designing traditional NIDS, which are preferred to be used with binary classification, towards designing multiclass network intrusion detection systems (MNIDS) to predict the cyberattack category. This, indeed, assists in understanding cyberattack behavior, which mitigates their effects quickly. Machine learning models, including conventional and deep learning, have been widely employed in the design of MNIDS. However, MNIDS based on machine learning can face challenges in predicting the category of cyberattack, especially with complex data that has a large number of categories. Thus, this paper proposes an enhanced MNIDS by exploiting the power of integrating continuous wavelet transform (CWT) with machine learning models to increase the accuracy of predicting cyberattacks in network traffic. This is due to the fact that CWT is considered as an effective method for feature extraction. The experimental results emphasize that using CWT with machine learning models improves the classification performance of MNIDS by up to 3.36% in overall accuracy. Additionally, it enhances the F1-score value in up to 40% of the total classes using the proposed model.
Journal: Data and Metadata
Pages: 474
Volume: 4
Year: 2025
DOI: 10.56294/dm2025474
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:474:id:1056294dm2025474

Template-Type: ReDIF-Article 1.0
Author-Name: Hatim Lakhouil
Author-Name-First: Hatim
Author-Name-Last: Lakhouil
Author-Name: Aziz Soulhi
Author-Name-First: Aziz
Author-Name-Last: Soulhi
Title: The fusion of Lean Manufacturing with Industry 4.0 technologies towards a new pillar for improving supply chain performance, the case of the automotive industry in Morocco
Abstract: In response to the COVID-19 pandemic, a Moroccan automotive glass company embarked on a strategic overhaul to improve its supply chain performance and resilience. Faced with excessive working capital tied up in inventory (Working Capital = $200M), the company chose to integrate advanced artificial intelligence (AI) technologies with Lean Manufacturing principles, including Just-In-Time (JIT) production and workload balancing. The goal was to lower inventory levels while maintaining a high service rate to meet customer demands. AI tools have played a crucial role in predicting quality defects and forecasting equipment availability, facilitating streamlined operations and cost reduction. This initiative also aims to enhance the supply chain's ability to withstand and adapt to future disruptions
Journal: Data and Metadata
Pages: 473
Volume: 4
Year: 2025
DOI: 10.56294/dm2025473
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:473:id:1056294dm2025473

Template-Type: ReDIF-Article 1.0
Author-Name: Mohammad Al-Batah
Author-Name-First: Mohammad
Author-Name-Last: Al-Batah
Author-Name: Mowafaq Salem Alzboon
Author-Name-First: Mowafaq
Author-Name-Last: Salem Alzboon
Author-Name: Muhyeeddin Alqaraleh
Author-Name-First: Muhyeeddin
Author-Name-Last: Alqaraleh
Title: Superior Classification of Brain Cancer Types Through Machine Learning Techniques Applied to Magnetic Resonance Imaging
Abstract: Brain cancer remains one of the most challenging medical conditions due to its intricate nature and the critical functions of the brain. Effective diagnostic and treatment strategies are essential, particularly given the high stakes involved in early detection. Magnetic Resonance (MR) imaging has emerged as a crucial modality for the identification and monitoring of brain tumors, offering detailed insights into tumor morphology and behavior. Recent advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized the analysis of medical imaging, significantly enhancing diagnostic precision and efficiency. This study classifies three primary brain tumor types—glioma, meningioma, and general brain tumors—utilizing a comprehensive dataset comprising 15,000 MR images obtained from Kaggle. We evaluated the performance of six distinct machine learning models: K-Nearest Neighbors (KNN), Neural Networks, Logistic Regression, Support Vector Machine (SVM), Decision Trees, and Random Forests. Each model's effectiveness was assessed through multiple metrics, including classification accuracy (CA), Area Under the Curve (AUC), F1 score, precision, and recall. Our findings reveal that KNN and Neural Networks achieved remarkable classification accuracies of 98.5% and 98.4%, respectively, significantly surpassing the performance of other evaluated models. These results underscore the promise of ML algorithms, particularly KNN and Neural Networks, in improving the diagnostic process for brain cancer through MR imaging. Future research will focus on validating these models with real-world clinical data, aiming to refine and enhance diagnostic methodologies, thus contributing to the development of more accurate, efficient, and accessible tools for brain cancer diagnosis and management.
Journal: Data and Metadata
Pages: 472
Volume: 4
Year: 2025
DOI: 10.56294/dm2025472
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:472:id:1056294dm2025472

Template-Type: ReDIF-Article 1.0
Author-Name: Atmane EL HADBI
Author-Name-First: Atmane
Author-Name-Last: EL HADBI
Author-Name: Mohammed Hatim RZIKI
Author-Name-First: Mohammed Hatim
Author-Name-Last: RZIKI
Author-Name: Yassine JAMIL
Author-Name-First: Yassine
Author-Name-Last: JAMIL
Author-Name: Zaynab AMMARI
Author-Name-First: Zaynab
Author-Name-Last: AMMARI
Author-Name: Mohamed Khalifa BOUTAHIR
Author-Name-First: Mohamed
Author-Name-Last: Khalifa BOUTAHIR
Author-Name: Hamid Bourray
Author-Name-First: Hamid
Author-Name-Last: Bourray
Author-Name: Driss EL Ouadghiri
Author-Name-First: Driss
Author-Name-Last: EL Ouadghiri
Title: Design and Implementation of an Adaptive Tutoring System for Enhanced E-Learning
Abstract: The increasing offer of new information and communication technologies has changed the educational field, e-learning emerged as an important complement to traditional face-to-face education and often a good alternative in many contexts. This shift has been emphasized by global challenges such as the COVID-19 pandemic, which highlighted the importance of remote learning platforms and their effectiveness in such situations. However, many challenges such as the costs and the need for personalized and interactive learning environments remain an obstacle. To address these issues, adaptive e-learning systems and Intelligent Tutoring Systems (ITS) are increasingly being developed and given support by education communities and governments. These systems aim to adapt content to the learner’s cognitive abilities and individual learning styles, for better understanding and retention. This paper explores the design and development of an adaptive ITS, which integrates Artificial Intelligence and data analytics to provide better learning experience. This paper puts the light on the role of adaptive hypermedia in educational interactions, analyzing its key features and how they can be leveraged to enhance learning outcomes. By incorporating learning success metrics, our study provides a comprehensive perspective on the potential of ITS to revolutionize adaptive and personalized e-learning systems, driving significant improvements in both learner engagement and achievement.
Journal: Data and Metadata
Pages: 469
Volume: 4
Year: 2025
DOI: 10.56294/dm2025469
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:469:id:1056294dm2025469

Template-Type: ReDIF-Article 1.0
Author-Name: Atmane El Hadbi
Author-Name-First: Atmane
Author-Name-Last: El Hadbi
Author-Name: Mohammed Hatim Rziki
Author-Name-First: Mohammed
Author-Name-Last: Hatim Rziki
Author-Name: Yassine Jamil
Author-Name-First: Yassine
Author-Name-Last: Jamil
Author-Name: Mohamed Khalifa Boutahir
Author-Name-First: Mohamed
Author-Name-Last: Khalifa Boutahir
Author-Name: Hamid Bourray
Author-Name-First: Hamid
Author-Name-Last: Bourray
Author-Name: Driss EL Ouadghiri
Author-Name-First: Driss
Author-Name-Last: EL Ouadghiri
Title: Analyzing University Dropout Rates in E-Learning and the Potential of Artificial Intelligence to Reduce Them: A Case Study of French Universities
Abstract: During the COVID-19 pandemic, students worldwide faced unprecedented disruption, forcing educators to swiftly transition to remote teaching. In French universities, strong political support at both national and institutional levels facilitated the deployment of digital tools such as learning management systems (e.g., Moodle), collaborative platforms (e.g., Google Meet, Microsoft Teams, Zoom), and social networks. While this shift highlighted the importance and critical role of digital technologies in education, it also raised significant concerns about the quality of online learning, the learning process, and the assessment of knowledge and skills. This case study explores the perceptions of students at Sorbonne Paris Cite Universities regarding the effectiveness of e-learning. Results from a Multiple Correspondence Analysis indicate that system usability and its positive impact on learning are key to the perceived success of e-learning. However, university dropout rates in this context stem from a combination of factors influencing student engagement. Addressing these challenges requires comprehensive solutions involving multiple stakeholders, including organizations, educators, and learners.
Journal: Data and Metadata
Pages: 468
Volume: 4
Year: 2025
DOI: 10.56294/dm2025468
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:468:id:1056294dm2025468

Template-Type: ReDIF-Article 1.0
Author-Name: Mohammed Hatim Rziki
Author-Name-First: Mohammed
Author-Name-Last: Hatim Rziki
Author-Name: Abdelaaziz Hessane
Author-Name-First: Abdelaaziz
Author-Name-Last: Hessane
Author-Name: Mohamed Khalifa Boutahir
Author-Name-First: Mohamed
Author-Name-Last: Khalifa Boutahir
Author-Name: Hamid Bourray
Author-Name-First: Hamid
Author-Name-Last: Bourray
Author-Name: Moulay Driss El Ouadghiri
Author-Name-First: Moulay Driss
Author-Name-Last: El Ouadghiri
Author-Name: Ritai Belkadi
Author-Name-First: Ritai
Author-Name-Last: Belkadi
Title: Predictive Energy Demand and Optimization in Metro Systems Using AI and IoT Technologies
Abstract: Introduction: With the rapid urbanization of modern cities, metro systems have become indispensable for efficient mobility. However, the increasing demand for public transportation has led to rising energy consumption, posing significant challenges for operational sustainability. Current energy management strategies in metro networks rely on static models and centralized systems, which often fail to adapt to real-time fluctuations in energy demand, leading to inefficiencies and wasted resources. Methods: This paper proposes an innovative approach to optimizing energy demand in metro systems by integrating Artificial Intelligence (AI) and the Internet of Things (IoT). By leveraging real-time data collected from IoT sensors deployed throughout the metro network, we apply machine learning algorithms such as Random Forests and Neural Networks to dynamically predict energy demand. These predictions enable metro operators to adjust energy consumption in real-time, thus improving overall system efficiency and reducing operational waste. Our approach was validated using data from the Parisian metro system through extensive simulations. Results: The results of simulations demonstrate significant improvements in energy efficiency. Optimized energy demand management led to a reduction in wasted energy during metro operations, particularly through the utilization of regenerative braking systems. Conclusions: Our findings suggest that integrating AI and IoT technologies into metro systems significantly improves energy efficiency by enabling dynamic energy demand prediction and real-time adjustment of energy consumption. The proposed system is scalable and adaptable, making it suitable for application in metro networks globally, thereby enhancing energy efficiency and supporting sustainable transport initiatives.
Journal: Data and Metadata
Pages: 467
Volume: 4
Year: 2025
DOI: 10.56294/dm2025467
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:467:id:1056294dm2025467

Template-Type: ReDIF-Article 1.0
Author-Name: Mohammed Hatim Rziki
Author-Name-First: Mohammed
Author-Name-Last: Hatim Rziki
Author-Name: Atmane El Hadbi
Author-Name-First: Atmane
Author-Name-Last: El Hadbi
Author-Name: Rita Belkadi
Author-Name-First: Rita
Author-Name-Last: Belkadi
Author-Name: Mohamed Khalifa Boutahir
Author-Name-First: Mohamed
Author-Name-Last: Khalifa Boutahir
Author-Name: Hamid Bourray
Author-Name-First: Hamid
Author-Name-Last: Bourray
Author-Name: Moulay Driss El Ouadghiri
Author-Name-First: Moulay Driss
Author-Name-Last: El Ouadghiri
Title: Blockchain-Powered Energy Optimization in Metro Networks: A Case Study on Electric Braking
Abstract: As urban populations continue to expand, the need for efficient and sustainable metro systems has become increasingly pressing. Traditional energy management methods, while somewhat effective, often fall short in fully utilizing the potential of regenerative braking systems within metro networks. These conventional approaches, which rely heavily on centralized control and energy storage systems, encounter scalability, security, and transparency limitations. Additionally, inefficient management of energy recovery data can result in significant energy losses and higher operational costs. In response to these challenges, this study proposes a blockchain-based solution utilizing Proof-of-Work (PoW) algorithms to optimize energy recovery, particularly during electric braking in metro systems. The developed model securely and transparently validates energy recovery events in real-time, eliminating the need for centralized oversight. By customizing the PoW algorithm, we achieved a balance between computational efficiency and strong security, making this solution scalable and practical for large metro networks. Initial simulations demonstrated a 12-15% improvement in energy recovery efficiency and a 10% reduction in operational costs compared to traditional systems. Furthermore, the comparison between net energy gains and the energy expended by the PoW process highlights the transformative potential of blockchain technologies in metro transportation, offering a pathway to more sustainable and environmentally friendly urban mobility solutions.
Journal: Data and Metadata
Pages: 466
Volume: 4
Year: 2025
DOI: 10.56294/dm2025466
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:466:id:1056294dm2025466

Template-Type: ReDIF-Article 1.0
Author-Name: Mohammad Al-Batah
Author-Name-First: Mohammad
Author-Name-Last: Al-Batah
Author-Name: Mowafaq Salem Alzboon
Author-Name-First: Mowafaq
Author-Name-Last: Salem Alzboon
Author-Name: Muhyeeddin Alqaraleh
Author-Name-First: Muhyeeddin
Author-Name-Last: Alqaraleh
Author-Name: Fawaz Ahmad Alzaghoul
Author-Name-First: Fawaz
Author-Name-Last: Ahmad Alzaghoul
Title: Comparative Analysis of Advanced Data Mining Methods for Enhancing Medical Diagnosis and Prognosis
Abstract: Accurate and early diagnosis, coupled with precise prognosis, is critical for improving patient outcomes in various medical conditions. This paper focuses on leveraging advanced data mining techniques to address two key medical challenges: diagnosis and prognosis. Diagnosis involves differentiating between benign and malignant conditions, while prognosis aims to predict the likelihood of recurrence after treatment. Despite significant advances in medical imaging and clinical data collection, achieving high accuracy in both diagnosis and prognosis remains a challenge. This study provides a comprehensive review of state-of-the-art machine learning and data mining techniques used for medical diagnosis and prognosis, including Neural Networks, K-Nearest Neighbors (KNN), Naïve Bayes, Logistic Regression, Decision Trees, and Support Vector Machines (SVM). These methods are evaluated on their ability to process large, complex datasets and produce actionable insights for medical practitioners.We conducted a thorough comparative analysis based on key performance metrics such as accuracy, Area Under the Curve (AUC), precision, recall, and specificity. Our findings reveal that Neural Networks consistently outperform other techniques in terms of diagnostic accuracy and predictive capacity, demonstrating their robustness in handling high-dimensional and nonlinear medical data. This research underscores the potential of advanced machine learning algorithms in revolutionizing early diagnosis and effective prognosis, thus facilitating more personalized treatment plans and improved healthcare outcomes.
Journal: Data and Metadata
Pages: .465
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.465
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.465:id:1056294dm2024465

Template-Type: ReDIF-Article 1.0
Author-Name: Anber Abraheem Shlash Mohammad
Author-Name-First: Anber Abraheem
Author-Name-Last: Shlash Mohammad
Author-Name: Suleiman Ibrahim Shelash
Author-Name-First: Suleiman Ibrahim
Author-Name-Last: Shelash
Author-Name: Ibtihaj Taher Saber
Author-Name-First: Ibtihaj
Author-Name-Last: Taher Saber
Author-Name: Asokan Vasudevan
Author-Name-First: Asokan
Author-Name-Last: Vasudevan
Author-Name: Riyad Neman Darwazeh
Author-Name-First: Riyad
Author-Name-Last: Neman Darwazeh
Author-Name: Rania Almajali
Author-Name-First: Rania
Author-Name-Last: Almajali
Author-Name: Zhou Fei
Author-Name-First: Zhou
Author-Name-Last: Fei
Title: Internal Audit Governance Factors and their effect on the Risk-Based Auditing Adoption of Commercial Banks in Jordan
Abstract: Introduction This paper aims to explore the impact of internal audit governance factors on the risk-based auditing adoption of commercial banks in Jordan. The population targeted in this paper were managers of the accounting and finance departments in the commercial banks listed on the Amman Stock Exchange. Methods Using the purposive sampling method, 339 responses were obtained in the final sample, which constituted a validity rate of 73.7%. Structural equation modeling (SEM) was adopted as a statistical approach for hypotheses assessment. Results The findings of this paper confirmed the positive impact of internal audit governance factors, including internal audit attributes, audit committee attributes, risk management system, and internal control system, on risk-based auditing adoption. Conclusion A set of recommendations was provided to bank managers, such as enhancing the Board's supervision and monitoring of the internal audit function's actions by encouraging Board members to grasp risk-based auditing thoughts and the value it delivers to the Bank.
Journal: Data and Metadata
Pages: 464
Volume: 4
Year: 2025
DOI: 10.56294/dm2025464
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:464:id:1056294dm2025464

Template-Type: ReDIF-Article 1.0
Author-Name: Mohammad Al-batah
Author-Name-First: Mohammad
Author-Name-Last: Al-batah
Author-Name: Mohammad Al-Batah
Author-Name-First: Mohammad
Author-Name-Last: Al-Batah
Author-Name: Mowafaq Salem Alzboon
Author-Name-First: Mowafaq
Author-Name-Last: Salem Alzboon
Author-Name: Esra Alzaghoul
Author-Name-First: Esra
Author-Name-Last: Alzaghoul
Title: Automated Quantification of Vesicoureteral Reflux using Machine Learning with Advancing Diagnostic Precision
Abstract: This article uses machine learning to quantify vesicoureteral reflux (VUR). VCUGs in pediatric urology are used to diagnose VUR. The goal is to increase diagnostic precision. Various machine learning models categorize VUR grades (Grade 1 to Grade 5) and are evaluated using performance metrics and confusion matrices. Study datasets come from internet repositories with repository names and accession numbers. Machine learning models performed well across several measures. KNN, Random Forest, AdaBoost, and CN2 Rule Induction consistently scored 100% in AUC, CA, F1-score, precision, recall, MCC, and specificity. These models classified grades well individually and collectively. In contrast, the Constant model performed poorly across all criteria, suggesting its inability to categorize VUR grades reliably. With the most excellent average performance ratings, the CN2 Rule Induction model excelled at grade categorization. Confusion matrices demonstrate that machine learning models predict VUR grades. The large diagonal numbers of the matrices show that the models are regularly predicted effectively. However, the Constant model's constant Grade 5 forecast reduced its differentiation. This study shows that most machine learning methods automate VUR measurement. The findings aid objective pediatric urology grading and radiographic evaluation. The CN2 Rule Induction model accurately classifies VUR grades. Machine learning-based diagnostic techniques may increase diagnostic precision, clinical decision-making, and patient outcomes.
Journal: Data and Metadata
Pages: 460
Volume: 4
Year: 2025
DOI: 10.56294/dm2025460
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:460:id:1056294dm2025460

Template-Type: ReDIF-Article 1.0
Author-Name: Denghong Zhang
Author-Name-First: Denghong
Author-Name-Last: Zhang
Author-Name: Benjamin Samraj Prakash Earnest
Author-Name-First: Benjamin Samraj
Author-Name-Last: Prakash Earnest
Author-Name: Ihab Elsayed Mohamed Ali Abdou
Author-Name-First: Ihab Elsayed
Author-Name-Last: Mohamed Ali Abdou
Title: An Innovative algorithm framework for cardiovascular risk assessment based on ECG data
Abstract: Background:Cardiovascular disease (CVD) is a primary universal physical problem, with conventional prediction systems frequently being persistent and expensive. Modern advancements in machine learning (ML)offer a hopeful option for accurate CVD risk assessment by leveraging multifaceted relations among diverse risk factors. Aim:Their search proposes a novel deep learning (DL) system, Dynamic Owl Search algorithm-driven Adaptive Long Short-Term Memory (DOS-ALSTM), to enhance cardiovascular risk prediction utilizing electrocardiogram (ECG) data. Method:The study utilizes ECG data from a diverse population group to train and assess the proposed model. Data is cleaned and normalized employing standard techniques to handle lost values and ensure reliability. Relevant features are extracted using statistical and signal processing technique to detain crucial features from the ECG data. The DOS-ALSTM system integrates a DOS optimization algorithm for optimized parameter regulation and ALSTM networks to detain sequential dependencies in ECG data for accurate risk prediction. The recognized method is evaluated using Python software. Result:The DOS-ALSTM system demonstrates superior performance with superioraccuracy of 99%, recall of 98%, F1-Score of 97.9% and Precision of 98.8% in CVD risk assessment compared to traditional methods
Journal: Data and Metadata
Pages: 457
Volume: 4
Year: 2025
DOI: 10.56294/dm2025457
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:457:id:1056294dm2025457

Template-Type: ReDIF-Article 1.0
Author-Name: Arepalli Gopi
Author-Name-First: Arepalli
Author-Name-Last: Gopi
Author-Name: L.R Sudha
Author-Name-First: L.R
Author-Name-Last: Sudha
Author-Name: Joseph S Iwin Thanakumar
Author-Name-First: Joseph S
Author-Name-Last: Iwin Thanakumar
Title: Novel KNN with Differentiable Augmentation for Feature-Based Detection of Cassava Leaf Disease and Mitigation of Overfitting: An Innovative Memetic Algorithm
Abstract: Many tropical countries depend on cassava, which is susceptible to deadly illnesses. These abnormalities can be diagnosed accurately and quickly to ensure food security. This study compares healthy and sick cassava leaves for four diseases: bacterial blight, brown streak, green mottle, and mosaic. Leaf images were systematically feature extracted to reveal color patterns, morphology, and textural qualities. Model learning methods use this extracted feature dataset. A new KNN+DA method may improve disease identification. Differentiable Augmentation uses data unpredictability to create alternative training samples to increase KNN performance. KNN+DA was compared to SVM, KNN, LR, and a memetic-tuned KNN to comprehend it better. We reached calculation speed, accuracy, recall, precision, and F1-score. KNN+DA outperformed older approaches in accuracy and resilience. KNN with differentiable augmentation improved classification accuracy and reduced overfitting, improving model generalizability for real-world use. Memetic algorithm-tuned KNN is another potential hybrid technique for disease diagnosis. Integrating current machine learning algorithms with cassava leaf photos can provide reliable early disease detection. More environmentally friendly agriculture would result
Journal: Data and Metadata
Pages: .455
Volume: 3
Year: 2025
DOI: 10.56294/dm2024.455
Handle: RePEc:dbk:datame:v:3:y:2025:i::p:.455:id:1056294dm2024455

Template-Type: ReDIF-Article 1.0
Author-Name: Óscar Javier Vásquez-Casallas
Author-Name-First: Óscar Javier
Author-Name-Last: Vásquez-Casallas
Author-Name: Betty Jazmín Gutiérrez-Rodríguez
Author-Name-First: Betty Jazmín
Author-Name-Last: Gutiérrez-Rodríguez
Author-Name: Carlos Arturo Bedoya Sánchez
Author-Name-First: Carlos Arturo
Author-Name-Last: Bedoya Sánchez
Author-Name: Diego Hernando Flórez Martínez
Author-Name-First: Diego Hernando
Author-Name-Last: Flórez Martínez
Title: Design of an information system for the management, visibility, and scientific positioning in research centers: CRIS-AGROSAVIA System study case
Abstract: This research focused on the use of technology to facilitate the management of resources, products, and knowledge services in research, development, and innovation (R+D+i) organizations. Specifically, it highlighted how Current Research Information Systems (CRIS) could be employed for this purpose. The study aimed to develop and implement a CRIS information system at the AGROSAVIA Research Center, with an emphasis on integrating the system with institutional repositories and external/internal systems to manage scientific and technological knowledge assets effectively. Methods: The process of creating the CRIS involved several stages: planning, requirements analysis, system design, development, and implementation. Key elements included the deployment of system interfaces for the target audience (stakeholders of the National System of Science, Technology, and Innovation) and the use of a recognized data model (CERIF standard) to enhance metadata generation, ensure standardization, and enable interoperability with external and internal systems. Results: The system was designed with two primary interfaces: a public version for the external scientific community and a corporate version for internal users of the research center. The CERIF-based data model facilitated repository structuring and the loading of an initial data baseline, supporting effective data management and decision-making processes. Conclusion: This study provides a valuable case for those looking to build information systems for knowledge management. The CRIS developed at AGROSAVIA acted as a tool for process evaluation, scientific communication, and dissemination, offering key insights into the technological architecture, data management model, and technological deployment required for such systems
Journal: Data and Metadata
Pages: 451
Volume: 4
Year: 2025
DOI: 10.56294/dm2025451
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:451:id:1056294dm2025451

Template-Type: ReDIF-Article 1.0
Author-Name: Hendra Sahputra Batubara
Author-Name-First: Hendra
Author-Name-Last: Sahputra Batubara
Author-Name: Nizwardi Jalinus
Author-Name-First: Nizwardi
Author-Name-Last: Jalinus
Author-Name: Fahmi Rizal
Author-Name-First: Fahmi
Author-Name-Last: Rizal
Title: Developing an integrated internship application for vocational schools: aligning user-centered design with technological innovation to enhance internship experiences
Abstract: This research intends to present an integrated vocational school internship application that will improve vocational school internships through a user-cantered design approach and the use of technology. The application is also expected to help meet the demands of vocational students and industry market demands where students are expected to have practical experience in alignment with their academic and career interests. The research design of the study involves both qualitative and quantitative data collection techniques such as interviews with key informants of the industry and self-developed questionnaires administered to students and educators. This approach helps the application provide information regarding the functionality to cater to the needs of all users; this includes information regarding the matching of job vacancies, feedback, and the progress made in the process. The study also focuses on the possibilities of utilizing recent technologies like artificial intelligence and data analysis to improve the strategies of internship delivery so that it becomes more responsive to individual student needs. Consequently, the study demonstrates that applying the user-centred design strategy with technology improvement increases students’ and employers’ satisfaction with the application besides improving the usability of the application. The application ensures better matching of the skills imparted in vocational schools with the workforce needs hence improving the competitiveness of the graduates.
Journal: Data and Metadata
Pages: 447
Volume: 4
Year: 2025
DOI: 10.56294/dm2025447
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:447:id:1056294dm2025447

Template-Type: ReDIF-Article 1.0
Author-Name: Esteban Castillo
Author-Name-First: Esteban
Author-Name-Last: Castillo
Author-Name: Lidia Castro
Author-Name-First: Lidia
Author-Name-Last: Castro
Title: Predictive Model for School Dropout in Chimborazo Province, Ecuador
Abstract: Introduction: School dropout is a complex problem influenced by various factors, including disparities in educational quality, inadequate infrastructure, and adverse socio-cultural conditions. This phenomenon negatively impacts the social and economic development of the country. Despite the recent decrease in dropout rates in Ecuador, the problem remains significant. Objective: To develop predictive models, including linear regression and generalized linear models in R-studio, to forecast dropout rates and identify significant institutional and demographic factors. Method: A quantitative approach was adopted to analyze data from the Ecuadorian Ministry of Education for the periods 2009-2010 to 2023-2024. Data on enrollments, approvals, non-approvals, and dropouts were reviewed using descriptive statistics and correlation analysis. Results: The results showed a decrease in dropout rates starting from the 2013-2014 academic year, although with significant fluctuations. Higher dropout rates were identified in public institutions and rural areas in the Sierra region, specifically in public institutions in Chimborazo province, accounting for 97.47% of the total dropouts, in contrast to students from the Coastal región. Additionally, a p-value of 0.073 was obtained in the linear models, so the null hypothesis was not rejected, suggesting that the residuals are approximately normal. Conclusions: The predictive models (LM and GLM) effectively estimated dropout rates in Chimborazo, with the GLM showing a slightly better fit. The type of institution and geographic location were significantly associated with dropout rates, highlighting the need for interventions targeting public institutions and rural areas. Strategies to reduce dropout rates should focus on improving conditions in these specific areas
Journal: Data and Metadata
Pages: .450
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.450
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.450:id:1056294dm2024450

Template-Type: ReDIF-Article 1.0
Author-Name: Jamal M. M. Joudeh
Author-Name-First: Jamal M.
Author-Name-Last: M. Joudeh
Author-Name: Fandi Omeish
Author-Name-First: Fandi
Author-Name-Last: Omeish
Author-Name: Sager Alharthi
Author-Name-First: Sager
Author-Name-Last: Alharthi
Author-Name: Nabil A. Abu-Loghod
Author-Name-First: Nabil A.
Author-Name-Last: Abu-Loghod
Author-Name: Ahmad M. Zamil
Author-Name-First: Ahmad M.
Author-Name-Last: Zamil
Author-Name: Abdul Hakim M. Joudeh
Author-Name-First: Abdul Hakim
Author-Name-Last: M. Joudeh
Title: Exploring the Impact of E-WOM Information via Social Media on Customer Purchasing Decision: A Mediating Role of Customer Satisfaction
Abstract: The study investigates the impact of E-WOM information on purchasing decisions, using customer satisfaction as a mediator. It examines E-WOM information as independent variables, such as quality, quantity, and credibility, and as dependent variables, such as consumer satisfaction and purchasing decisions, with customer satisfaction serving as a mediator to investigate the relationship between E-WOM information and purchasing decisions. A questionnaire was issued to 307 social media-active clients, and the hypotheses were tested using quantitative methods. Data analysis comprised descriptive statistics, Cronbach's alpha, skewness and kurtosis, and Pearson correlation coefficient, as well as a fit model for measuring questionnaire reliability and validity, regression for sub-hypotheses, and a path model for evaluating main hypotheses. The findings revealed that all three dimensions of E-WOM information had a positive impact on customer satisfaction and purchase decisions, both individually and jointly. Customer satisfaction has a positive influence on purchasing decisions. Furthermore, E-WOM information has been shown to positively impact purchasing decisions via consumer satisfaction. The study suggests that organizations should understand the dimensions that impact customer satisfaction and purchasing decisions in order to fulfill their goals, remain ahead of the competition, and obtain a competitive advantage. Proper tracking of social media reviews, comments, and recommendations may help organizations deliver answers, increase customer satisfaction, and aid in making purchasing decisions
Journal: Data and Metadata
Pages: .449
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.449
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.449:id:1056294dm2024449

Template-Type: ReDIF-Article 1.0
Author-Name: Ahmed Mahdi Abdulkadium
Author-Name-First: Ahmed
Author-Name-Last: Mahdi Abdulkadium
Author-Name: Asaad Sabah Hadi
Author-Name-First: Asaad Sabah
Author-Name-Last: Hadi
Title: Optimizing Query Using the FOAF Relation and Graph Neural Networks to Enhance Information Gathering and Retrieval
Abstract: A lot of students suffer expressing their desired enquiry about to a search engine (SE), and this, in turn, can lead to ambiguit and insufficient results. A poor expression requires expanding a previous user query and refining it by adding more vocabularies that make a query more understandable through the searching process. This research aims at adding vocabulary to an enquiry by embedding features related to each keyword, and representing a feature of each query keyword as graphs and node visualization based on graph convolution network (GCN). This is achieved following two approaches. The first is by mapping between vertices, adding a negative link, and training a graph after embedding. This can help check whether new information reach-es for retrieving data from the predicted link. Another approach is based on adding link and node embedding that can create the shortest path to reaching a specific (target) node, . Particularly, poor data retrieval can lead to a new concept named graph expansion network (GEN). Query expansion (QE) techniques can obtain all documents related to expanding and refining query. On the other hand, such documents are represented as knowledge graphs for mapping and checking the similarity between the connection of a graph based on two authors who have similar interst in a particular field, or who collaborate in a research publications. This can create paths or edges between them as link embedding, thereby increasing the accuracy of document or pa-per retrieval based on user typing
Journal: Data and Metadata
Pages: 443
Volume: 4
Year: 2025
DOI: 10.56294/dm2025443
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:443:id:1056294dm2025443

Template-Type: ReDIF-Article 1.0
Author-Name: Imane Moustati
Author-Name-First: Imane
Author-Name-Last: Moustati
Author-Name: Noreddine Gherabi
Author-Name-First: Noreddine
Author-Name-Last: Gherabi
Author-Name: Mostafa Saadi
Author-Name-First: Mostafa
Author-Name-Last: Saadi
Title: Building an IoB ecosystem for influencing energy consumption in smart cities
Abstract: Introduction: The Internet of Behaviors (IoB) represents a paradigm shift in integrating digital technologies with human behaviors, offering unprecedented insights and opportunities across various domains. This research paper explores the transformative potential of IoB and presents an innovative IoB framework applied to an energy consumption scenario. Objective: We offer an innovative IoB ecosystem aimed at heightening citizens' responsibility and awareness regarding home energy consumption in smart cities. Methodology: We propose a framework that elicits behavioral insights by leveraging smart meter data, clusters citizens based on similar energy consumption patterns using K-Means into groups, applies an LSTM-based prediction model to forecast their future energy consumption, and influences their behavior through a continuous personal reflection loop. Moreover, to foster trust, XAI principles are also integrated into our framework to ensure citizens comprehend and trust the IoB model's results. Results: Our proposed LSTM-based prediction model achieved, on the smart meters’ dataset, high-performance results, an R² value equal to 0.986, a root mean squared error of 0.492 and a mean squared error equal to 0.242. Conclusions: This paper presents how we can leverage the IoB and XAI into the energy sector. However, the IoB's potential is not restricted to a certain domain. It has a revolutionary influence across sectors, with the power sector standing out as one of the domains where the IoB has the potential to alter social practices
Journal: Data and Metadata
Pages: .441
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.441
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.441:id:1056294dm2024441

Template-Type: ReDIF-Article 1.0
Author-Name: Michelle Bernabe
Author-Name-First: Michelle
Author-Name-Last: Bernabe
Author-Name: Ryan Ebardo
Author-Name-First: Ryan
Author-Name-Last: Ebardo
Title: Barriers Leading to the Discontinuance of Telemedicine among Healthcare Providers: A Systematic Review
Abstract: Introduction: Telemedicine, once considered a groundbreaking innovation in healthcare, has seen a marked decline in usage, highlighting numerous barriers to its continued adoption. This systematic review aims to identify and analyze the socio-technological, individual, institutional, and behavioral factors that contribute to the discontinuance of telemedicine among healthcare providers. Methods: A comprehensive search of PubMed and Scopus databases was conducted, identifying 1,070 peer-reviewed articles published between 2020 and 2024. After applying inclusion and exclusion criteria, 22 studies were selected for detailed analysis. Results: Several socio-technological barriers were identified, including issues with system usability, unreliable infrastructure, and a lack of interoperability, all of which hinder the seamless integration of telemedicine into clinical workflows. Additionally, individual-level factors such as low technological self-efficacy, anxiety, and concerns about the depersonalization of care emerged as significant challenges. Institutional barriers, such as insufficient training, inadequate resource allocation, and high workloads, further complicate the adoption of telemedicine. Behavioral resistance, including reluctance to change and fears related to compliance and professional identity, also exacerbated the challenges faced by healthcare providers. Conclusions: Addressing the identified barriers requires a multifaceted approach. Technological improvements, enhanced usability, and targeted interventions aimed at reducing psychological resistance and improving institutional support are essential to promoting the sustained use of telemedicine in healthcare.
Journal: Data and Metadata
Pages: 440
Volume: 4
Year: 2025
DOI: 10.56294/dm2025440
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:440:id:1056294dm2025440

Template-Type: ReDIF-Article 1.0
Author-Name: Brahim Ouzaka
Author-Name-First: Brahim
Author-Name-Last: Ouzaka
Author-Name: Zakia Ait Oufkir
Author-Name-First: Zakia Ait
Author-Name-Last: Oufkir
Author-Name: El Hossain Outougane
Author-Name-First: El Hossain
Author-Name-Last: Outougane
Author-Name: Said Ouhadi
Author-Name-First: Said
Author-Name-Last: Ouhadi
Title: Family entrepreneurship: a bibliometric analysis and future research agenda
Abstract: The family entrepreneurship regroups family members, family business and the entrepreneurship activities. This makes it a fertile and rich research field, which needs to be explored and analyzed to understand the specific behaviors and orientations of the family entrepreneurial initiatives. The main purpose of this paper is to present a bibliometric analysis and research agenda of scientific publications dealing mainly with the family entrepreneurship field. The bibliometric process is the methodological design adopted to review the previous studies about our problematic. The scope of our study is limited to the scientific articles have been published between 2000 and 2022 (September), in the three data bases: Web of sciences, Scopus and Jstor. 73 out of 181 articles selected have been retained and analyzed after the assessment process taking into consideration different inclusion and exclusion criteria. In addition, the Excel’s tools and the VOSviewer software version 1.6.18 are the main technological devices used to carry out this research. Our study shows that family entrepreneurship is a legitimate area of research, despite the fact that it is still in its pre-paradigmatic and launching stages. Thus, further academic studies dealing with the family entrepreneurship research clusters generated through the thematic and bibliometric analysis (as presented by the figure 5) need to be deepened
Journal: Data and Metadata
Pages: .439
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.439
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.439:id:1056294dm2024439

Template-Type: ReDIF-Article 1.0
Author-Name: Izzah Muyassaroh
Author-Name-First: Izzah
Author-Name-Last: Muyassaroh
Author-Name: Aprilia Eki Saputri
Author-Name-First: Aprilia Eki
Author-Name-Last: Saputri
Author-Name: Asep Saefudin
Author-Name-First: Asep
Author-Name-Last: Saefudin
Author-Name: Mela Darmayanti
Author-Name-First: Mela
Author-Name-Last: Darmayanti
Author-Name: Rosiana Mufliva
Author-Name-First: Rosiana
Author-Name-Last: Mufliva
Author-Name: Lea Christina Br. Ginting
Author-Name-First: Lea Christina Br.
Author-Name-Last: Ginting
Author-Name: Faisal Sadam Murron
Author-Name-First: Faisal Sadam
Author-Name-Last: Murron
Author-Name: Ari Arasy Magistra
Author-Name-First: Ari Arasy
Author-Name-Last: Magistra
Title: Bridging Cultures in the Classroom: A Systematic Literature Review of Ethnoscience Research in Indonesian Elementary Science Education
Abstract: Ethnoscience plays a crucial role in integrating cultural knowledge into science education, especially in multicultural contexts like Indonesia. This study aims to provide a comprehensive examination of the current state of ethnoscience research within the realm of elementary science education in Indonesia. By employing a systematic literature review, this research analyzes a corpus of 70 articles published between 2014 and 2023, sourced from prominent databases such as Scopus and Indonesia's Ministry of Education and Culture's accredited national journal database (SINTA). Through content analysis, the study delves into the thematic content, methodologies, and findings of the reviewed literature. The synthesis of these diverse sources offers a nuanced understanding of the landscape of ethnoscience research in Indonesian elementary science education. Additionally, this review identifies existing gaps and provides insights into potential directions for future research, contributing to the ongoing discourse on integrating cultural knowledge in science education within diverse educational settings. This research is of significant value to educators, policymakers, and researchers aiming to enhance the cultural relevance and effectiveness of science education in elementary schools
Journal: Data and Metadata
Pages: 434
Volume: 4
Year: 2025
DOI: 10.56294/dm2025434
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:434:id:1056294dm2025434

Template-Type: ReDIF-Article 1.0
Author-Name: Alberto Bustillos
Author-Name-First: Alberto
Author-Name-Last: Bustillos
Author-Name: Fernanda Marizande
Author-Name-First: Fernanda
Author-Name-Last: Marizande
Author-Name: Andrea Cevallos
Author-Name-First: Andrea
Author-Name-Last: Cevallos
Author-Name: Diana Bustillos
Author-Name-First: Diana
Author-Name-Last: Bustillos
Author-Name: Cristina Arteaga
Author-Name-First: Cristina
Author-Name-Last: Arteaga
Author-Name: Fabricio Vásquez de la Bandera
Author-Name-First: Fabricio
Author-Name-Last: Vásquez de la Bandera
Title: Evaluation of the efficacy of ChatGPT versus medical students in clinical case resolution
Abstract: Introduction: The use of artificial intelligence (AI) in medical education has gained relevance, and tools like ChatGPT offer support in solving clinical cases. This study compared the average performance of ChatGPT against medical students to evaluate its potential as an educational tool. Methods: A cross-sectional quantitative study was conducted with 110 sixth-semester medical students from the Technical University of Ambato. Four clinical cases were designed, covering cardiology, endocrinology, gastroenterology, and neurology scenarios. Multiple-choice questions were used to assess both the participants and ChatGPT. Data were analyzed using the Student's t-test for independent samples. Results: ChatGPT outperformed the students in all cases, with an average score of 8.25 compared to 7.35 for the students. A statistically significant difference was found between the two groups (p = 0.0293). Conclusions: ChatGPT demonstrated superior performance in solving clinical cases compared to medical students. However, limitations such as potential inaccuracies in information highlight the need for further studies and supervision when integrating AI into medical education.
Journal: Data and Metadata
Pages: .433
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.433
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.433:id:1056294dm2024433

Template-Type: ReDIF-Article 1.0
Author-Name: Cesar Augusto Hernández Suárez
Author-Name-First: Cesar Augusto
Author-Name-Last: Hernández Suárez
Author-Name: Juan Diego Hernández Albarracín
Author-Name-First: Juan Diego
Author-Name-Last: Hernández Albarracín
Author-Name: Javier Rodríguez Moreno
Author-Name-First: Javier
Author-Name-Last: Rodríguez Moreno
Title: Digital competences in primary and secondary education: a trend visualisation analysis through VOSviewer
Abstract: Introduction: In an increasingly interconnected society, digital skills are increasingly important in the education of the next generations. Therefore, it is necessary to know the main trends in this field of studies. Methods: This study conducts a trend visualisation analysis on digital competences in basic and secondary education, using the VOSviewer tool to map and visualise the relationships between authors, institutions, and research topics. Results: The visualisation reveals a significant increase in scholarly output related to digital competences, especially from 2020 onwards. Co-authorship networks and keyword co-occurrence highlight central nodes of collaboration and emerging themes in this field of study. Conclusions: The results underline the importance of an interdisciplinary approach and the need to strengthen digital competences in teacher education.
Journal: Data and Metadata
Pages: .432
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.432
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.432:id:1056294dm2024432

Template-Type: ReDIF-Article 1.0
Author-Name: Saleh Alqatan
Author-Name-First: Saleh
Author-Name-Last: Alqatan
Author-Name: Mohammad Alshirah
Author-Name-First: Mohammad
Author-Name-Last: Alshirah
Author-Name: Mohammad Bany Baker
Author-Name-First: Mohammad
Author-Name-Last: Bany Baker
Author-Name: Hayel Khafajeh
Author-Name-First: Hayel
Author-Name-Last: Khafajeh
Author-Name: Suhaila Abuowaida
Author-Name-First: Suhaila
Author-Name-Last: Abuowaida
Title: A Conceptual Framework for the Adoption of Cloud Computing in a Higher Education Institutions
Abstract: Today, with significant improvements of computing capacities, the world is witnessing significant technological advancements. Cloud computing especially, is increasingly becoming an advantageous tool, in developed countries especially, and these countries have invested substantially in cloud computing systems to enable the implementation of hi-tech advancements in many of their industries. On the other hand, in developing and underdeveloped countries, the adoption of cloud computing is still in the early stages; as can be observed, there has been some form of digital transformation of data systems in these countries. In Jordan, The factors affecting cloud computing adoption among higher education institutions were still underexplored, especially on the issues pertaining to cloud computing adoption. Therefore. Major factors with potential impact on user adoption of cloud computing were hence reviewed in this study. A conceptual framework of cloud computing adoption based on Extended Technology-Organizational-Environmental (TOE) framework by Tornatzky and Fleischer was proposed. The framework includes individual factors as the theoretical base for cloud computing adoption, to provide an inclusive comprehension on the factors that could affect behavioral intention and use of cloud computing.
Journal: Data and Metadata
Pages: 431
Volume: 4
Year: 2025
DOI: 10.56294/dm2025431
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:431:id:1056294dm2025431

Template-Type: ReDIF-Article 1.0
Author-Name: R H Samseer
Author-Name-First: R H
Author-Name-Last: Samseer
Author-Name: J Bamini
Author-Name-First: J
Author-Name-Last: Bamini
Author-Name: Khaleel Ibrahim Al- Daoud
Author-Name-First: Khaleel Ibrahim
Author-Name-Last: Al- Daoud
Author-Name: Asokan Vasudevan
Author-Name-First: Asokan
Author-Name-Last: Vasudevan
Author-Name: Suleiman Ibrahim Shelash Mohammad
Author-Name-First: Suleiman Ibrahim
Author-Name-Last: Shelash Mohammad
Author-Name: A. Vasumathi
Author-Name-First: A.
Author-Name-Last: Vasumathi
Title: Optimizing Sequential Decisions: Enhancements to the Brickman Principle with Cumulative Punishment and Probability Adjustments
Abstract: Introduction Determining the optimal stopping point in sequential decision-making scenarios is crucial for maximizing rewards and minimizing costs. Traditional models like the original Brickman Principle often simplify this process by assuming fixed critical values and equal probabilities at each decision stage. These assumptions may not accurately reflect real-world complexities, where costs can be cumulative and probabilities variable. Objective This work seeks to enhance the Brickman Principle by including cumulative punishment elements and non-uniform probability distributions, therefore improving its capacity to accurately represent the intricacies of real-world decision-making. Methods Through a rigorous experimental study, we evaluate the impact of these modifications on optimal stopping rules and expected profits. Results In line with Prospect Theory's emphasis on loss aversion, the results reveal a distinct pattern of risk-averse behavior, with most participants choosing to stop sooner in the sequence to avoid growing fines. Furthermore, we saw substantial variation in both the termination points and anticipated earnings across participants, suggesting that individual disparities in risk tolerance and decision-making approaches are crucial in influencing results
Journal: Data and Metadata
Pages: .429
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.429
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.429:id:1056294dm2024429

Template-Type: ReDIF-Article 1.0
Author-Name: Carlos Henríquez Miranda
Author-Name-First: Carlos
Author-Name-Last: Henríquez Miranda
Author-Name: German Sánchez-Torres
Author-Name-First: German
Author-Name-Last: Sánchez-Torres
Title: Implementation and Evaluation of a Hybrid Recommendation System for the Real Estate Market
Abstract: Introduction: The real estate market has been transformed by digital technologies, especially Industry 4.0, which has made property searching and evaluation more efficient, improving its accuracy with the use of advanced algorithms. Traditional methods have been replaced by online platforms using modern machine learning (ML) algorithms, leading to the need for personalized recommendation systems to improve user experiences. Methodology: This study designed and implemented a hybrid recommendation system that combines collaborative and content-based filtering techniques. The development process involved four phases: literature review, technology selection, prototype implementation, and system deployment. Findings: The proposed hybrid model effectively addressed challenges such as data sparsity and the cold start problem, improving recommendation accuracy. In the evaluation, users indicated high satisfaction with the system’s ability to offer personalized property recommendations. Conclusion: Thus, hybrid recommendation systems can significantly improve the property search experience by offering personalized recommendations. However, further research into the applicability of the system in different domains remains a need for further exploration.
Journal: Data and Metadata
Pages: .426
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.426
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.426:id:1056294dm2024426

Template-Type: ReDIF-Article 1.0
Author-Name: Raúl Prada Núñez
Author-Name-First: Raúl
Author-Name-Last: Prada Núñez
Author-Name: Mariana Elena Peñaloza Tarazona
Author-Name-First: Mariana Elena
Author-Name-Last: Peñaloza Tarazona
Author-Name: Javier Rodríguez Moreno
Author-Name-First: Javier
Author-Name-Last: Rodríguez Moreno
Title: Trends and challenges of integrating the STEAM approach in education: A scopus literature review
Abstract: Introduction: Scopus is a bibliographic database recognized globally for its breadth and depth, and plays a vital role in identifying research trends in education, particularly in the STEAM (Science, Technology, Engineering, Arts and Mathematics) approach. Its vast collection of scientific articles, journals, conferences and patents provides researchers with access to a wide and diverse range of relevant studies. Scopus' ability to provide a comprehensive and up-to-date view of the development and implementation of the STEAM approach is crucial. Methodology: Using advanced bibliometric search and analysis tools, researchers can identify emerging patterns, evaluate pedagogical methodologies, and recognize areas that require further attention. In addition, Scopus facilitates the detection of international collaborations and the identification of leaders in STEAM research, allowing educators and policymakers to make decisions based on solid evidence and current trends. Results: In this bibliometric analysis of publications in Scopus between 2010 and 2024, the terms Educational approach STEAM, STEAM approach and STEAM were used as search metadata, yielding 254,303 results. After applying filters, 263 documents directly related to STEAM were selected. The results, analyzed using Vosviewer, revealed six affinity clusters based on keyword recurrence. Terms associated with STEAM include transdisciplinarity, instructional design, pedagogy, and technology. STEAM education is linked to teacher education, computational thinking, and art education. STEM relates to science and educational robotics. Primary education encompasses preschool and secondary education, comprehensive education, and inquiry-based design. Less frequently, the nodes education and teaching are associated with curriculum, science education, motivation, and pedagogical methods. Conclusion: This keyword analysis demonstrates the wide range of competencies and terms associated with STEAM, providing valuable information for identifying key competencies in this educational approach.
Journal: Data and Metadata
Pages: .424
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.424
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.424:id:1056294dm2024424

Template-Type: ReDIF-Article 1.0
Author-Name: Carlos Arturo Parra Ortega
Author-Name-First: Carlos Arturo
Author-Name-Last: Parra Ortega
Author-Name: Javier Mauricio García Mogollón
Author-Name-First: Javier Mauricio
Author-Name-Last: García Mogollón
Author-Name: Jarol Darley Ramón Valencia
Author-Name-First: Jarol Darley
Author-Name-Last: Ramón Valencia
Title: Agent technology to detect failures in continuous processes
Abstract: Introduction: To control a continuous production system whose components are exposed to failures, it is necessary to provide intelligence to the monitoring mechanism, due to the need to identify the type of failure, its source, and anticipate the consequences that arise from its occurrence. Methodology: In this paper, it is proposed to extend Sanz's multi-resolution model and apply it to a supervision scheme with event detection based on fuzzy logic and implemented using agent technology. Results: A mechanism to validate the implementation using discrete event simulation is also presented. Conclusions: It was concluded that discrete event simulation constitutes an appropriate way to validate a supervisory control system at a high level.
Journal: Data and Metadata
Pages: .423
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.423
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.423:id:1056294dm2024423

Template-Type: ReDIF-Article 1.0
Author-Name: Agus Tri Susilo
Author-Name-First: Agus
Author-Name-Last: Tri Susilo
Author-Name: Muhammad Nur Wangid
Author-Name-First: Muhammad
Author-Name-Last: Nur Wangid
Author-Name: Edi Purwanta
Author-Name-First: Edi
Author-Name-Last: Purwanta
Author-Name: Moh. Salimi
Author-Name-First: Moh.
Author-Name-Last: Salimi
Title: What Influences the Success of Career Exploration in School?
Abstract: Introduction. Career exploration is an individual's efforts to gain a better understanding of career-related information, alternatives, and choices. Through career exploration, individuals develop self-awareness and knowledge about future work, which may contribute to forming a commitment to a career choice. Career exploration behavior encompasses self-assessment and external search activities that provide information to support career choice and adjustment. Aim. The aim of this study was to determine the factors that influence the success of career exploration in schools. Career exploration is one of the important stages in the process of making informed career decision, as it can be linked to difficulties in making career decisions due to a lack of maturity in career exploration. Methodology and research methods. This article presents a systematic literature review on the factors influencing career exploration in schools, using the Scopus and Web of Science databases. The research examines career exploration in schools between 2018 and 2023. A total of 137 articles were reviewed, and 36 were selected based on inclusion criteria. A bibliometric review was then conducted, involving empirical and theoretical analysis of the available data related to the phenomenon of career exploration in schools and the factors that influence it. Result. The bibliometric results and the influential factors related to career exploration in schools are presented. The largest number of affiliated journals and authors studying this phenomenon originate from the United States of America. The research findings indicate that the influential factors of career exploration can be categorized into four groups: in-depth self-exploration, extensive self-exploration, in-depth environmental exploration, and extensive environmental exploration. Scientific novelty. The scientific novelty of this research lies in the discovery of factors that influence the success of career exploration in schools. This includes the initial grand theory of career exploration trends, the distribution of best practices for successful career exploration across various countries representing different continents, and up-to-date literature from the last five years. Practical significance. This systematic literature review has addressed how career exploration activities are conducted in schools, highlighting the influencing factors and their correlations with other fields. The results of this research have implications for future studies, particularly regarding career exploration as an individual decision-making preference. For educators, the findings suggest a need for individualized planning services to enhance students' career exploration, making it more focused and measurable in the context of career decision-making.
Journal: Data and Metadata
Pages: .421
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.421
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.421:id:1056294dm2024421

Template-Type: ReDIF-Article 1.0
Author-Name: Vijaya Lakshmi Alluri
Author-Name-First: Vijaya Lakshmi
Author-Name-Last: Alluri
Author-Name: Karteeka Pavan Kanadam
Author-Name-First: Karteeka Pavan
Author-Name-Last: Kanadam
Author-Name: Helen Josephine Vincent Lawrence
Author-Name-First: Helen Josephine
Author-Name-Last: Vincent Lawrence
Title: Novel HGDBO: A Hybrid Genetic and Dung Beetle Optimization Algorithm for Microarray Gene Selection and Efficient Cancer Classification
Abstract: Introduction: Ovarian cancer ranks as the seventh most frequently diagnosed cancer and stands as the eighth leading cause of cancer-related mortality among women globally. Early detection significantly improves survival rates and outcomes, highlighting the need for enhanced screening methods and increased awareness to facilitate early diagnosis and treatment. Microarray gene data, characterized by its high dimensionality, includes the expression levels of thousands of genes across numerous samples, posing both opportunities and challenges in the analysis of gene functions and disease mechanisms. Method: This paper presents a novel hybrid gene feature selection method called HGDBO, which combines the Dung Beetle Optimization (DBO) algorithm with the Genetic Algorithm (GA) to increase the effectiveness of microarray data analysis. The proposed HGDBO method utilizes the exploratory capabilities of DBO and the exploitative strengths of GA to identify the most relevant genes for disease classification. Experimental results on multiple microarray datasets demonstrate that the hybrid approach offers superior classification performance, stability, and computational efficiency compared to traditional and state-of-the-art methods. To classify ovarian cancer, Naïve-Bayes (NB) and Random-Forest (RF) classification algorithms were employed. Results and Discussion: The proposed Random Forest model outperforms the Naive Bayes model across all metrics, achieving better accuracy (0.96 vs. 0.91), precision (0.95 vs. 0.91), recall (0.97 vs. 0.90), F-1 score (0.95 vs. 0.91), and specificity (0.97 vs. 0.86). Conclusion: These results underscore the effectiveness of the HGDBO method and the Random Forest classifier in enhancing the analysis and classification of ovarian cancer using microarray gene data.
Journal: Data and Metadata
Pages: .420
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.420
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.420:id:1056294dm2024420

Template-Type: ReDIF-Article 1.0
Author-Name: Mohammad Subhi Al-Batah
Author-Name-First: Mohammad Subhi
Author-Name-Last: Al-Batah
Author-Name: Mowafaq Salem Alzboon
Author-Name-First: Mowafaq
Author-Name-Last: Salem Alzboon
Author-Name: Hatim Solayman Migdadi
Author-Name-First: Hatim
Author-Name-Last: Solayman Migdadi
Author-Name: Mutasem Alkhasawneh
Author-Name-First: Mutasem
Author-Name-Last: Alkhasawneh
Author-Name: Muhyeeddin Alqaraleh
Author-Name-First: Muhyeeddin
Author-Name-Last: Alqaraleh
Title: Advanced Landslide Detection Using Machine Learning and Remote Sensing Data
Abstract: Landslides can cause severe damage to infrastructure and human life, making early detection and warning systems critical for mitigating their impact. In this study, we propose a machine learning approach for landslide detection using remote sensing data and topographical features. We evaluate the performance of several machine learning algorithms, including Tree, Random Forest, Gradient Boosting, Logistic Regression, Naïve Bayes, AdaBoost, Neural Network, SGD, kNN, and SVM, on a dataset of remote sensing images and topographical features from the Sikkim region in Malaysia. The results show that the SVM algorithm outperforms the other algorithms with an accuracy of 96.7% and a F1 score of 0.97. The study demonstrates the potential of machine learning algorithms for landslide detection, which can help improve early warning systems and reduce the impact of landslides.
Journal: Data and Metadata
Pages: .419
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.419
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.419:id:1056294dm2024419

Template-Type: ReDIF-Article 1.0
Author-Name: Salem Alzboon Mowafaq
Author-Name-First: Salem Alzboon
Author-Name-Last: Mowafaq
Author-Name: Alqaraleh Muhyeeddin
Author-Name-First: Alqaraleh
Author-Name-Last: Muhyeeddin
Author-Name: Mohammad Subhi Al-Batah
Author-Name-First: Mohammad Subhi
Author-Name-Last: Al-Batah
Title: AI in the Sky: Developing Real-Time UAV Recognition Systems to Enhance Military Security
Abstract: In an era where Unmanned Aerial Vehicles (UAVs) have become crucial in military surveillance and operations, the need for real-time and accurate UAV recognition is increasingly critical. The widespread use of UAVs presents various security threats, requiring systems that can differentiate between UAVs and benign objects, such as birds. This study conducts a comparative analysis of advanced machine learning models to address the challenge of aerial classification in diverse environmental conditions without system redesign. Large datasets were used to train and validate models, including Neural Networks, Support Vector Machines, ensemble methods, and Random Forest Gradient Boosting Machines. These models were evaluated based on accuracy and computational efficiency, key factors for real-time application. The results indicate that Neural Networks provide the best performance, demonstrating high accuracy in distinguishing UAVs from birds. The findings emphasize that Neural Networks have significant potential to enhance operational security and improve the allocation of defense resources. Overall, this research highlights the effectiveness of machine learning in real-time UAV recognition and advocates for the integration of Neural Networks into military defense systems to strengthen decision-making and security operations. Regular updates to these models are recommended to keep pace with advancements in UAV technology, including more agile and stealthier designs
Journal: Data and Metadata
Pages: .417
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.417
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.417:id:1056294dm2024417

Template-Type: ReDIF-Article 1.0
Author-Name: Anthony Fasanando
Author-Name-First: Anthony
Author-Name-Last: Fasanando
Author-Name: Lloy Pinedo
Author-Name-First: Lloy
Author-Name-Last: Pinedo
Author-Name: Andy Rucoba
Author-Name-First: Andy
Author-Name-Last: Rucoba
Author-Name: Segundo Ramírez-Shupingahua
Author-Name-First: Segundo
Author-Name-Last: Ramírez-Shupingahua
Author-Name: John Ruiz-Cueva
Author-Name-First: John
Author-Name-Last: Ruiz-Cueva
Author-Name: Alberto Alva-Arévalo
Author-Name-First: Alberto
Author-Name-Last: Alva-Arévalo
Title: Development of a microchip-based web service for the control of pet information in veterinary clinics
Abstract: Owning a pet entails the responsibility of identifying and registering it, which helps prevent abandonment, avoid theft, and combat the illegal trade of animals. However, pet owners and veterinarians often neglect this process due to lack of awareness, fear, or the associated costs. To address this issue, a web-based service utilizing microchip technology was developed to manage pet information in veterinary clinics. The software was developed using the Web Services Distributed Management methodology, employing tools such as PHP, MySQL, CodeIgniter, HTML, and CSS, among others. As a case study, 30 pets treated at a private veterinary clinic were included, with their owners providing informed consent. The microchip used was of FDX-B BioGlass 8625 technology, and an ISO-certified radio frequency identification reader was employed. The results demonstrate the effectiveness of designing and implementing a comprehensive web service that manages pet information through microchips, including data on name, age, owner, vaccinations, and more. The system is also responsive, functional, and secure. It stands out for its economic accessibility, with an average cost of $ 16, making it affordable for both veterinary clinics and pet owners. The study concludes that the web service facilitates the registration and identification of pets; however, it is crucial to raise awareness among owners about the importance of using microchips and ensuring proper control of their pets' information
Journal: Data and Metadata
Pages: .412
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.412
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.412:id:1056294dm2024412

Template-Type: ReDIF-Article 1.0
Author-Name: William Castillo-González
Author-Name-First: William
Author-Name-Last: Castillo-González
Author-Name: Andrew Alberto López Sánchez
Author-Name-First: Andrew Alberto
Author-Name-Last: López Sánchez
Author-Name: Javier González-Argote
Author-Name-First: Javier
Author-Name-Last: González-Argote
Title: Bibliometrics in health sciences. A methodological proposal
Abstract: This paper addresses the growing importance of bibliometric analysis in the field of health, highlighting its usefulness in mapping and evaluating scientific production and its impact. Based on a review of the literature, the authors identify the need for a standardized methodology to guide the elaboration of bibliometric studies. A detailed guide is proposed that covers from the definition of the objective and scope to the interpretation of results, using tools such as VOSviewer, SciMAT, CiteSpace, SciVal, inCites. This methodology seeks to facilitate the performance of more rigorous and reproducible studies, thus optimizing informed decision-making in scientific research. Furthermore, the article stresses that this proposal should not be seen as a limitation, but as a flexible basis that can be adapted to different contexts and needs.
Journal: Data and Metadata
Pages: .410
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.410
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.410:id:1056294dm2024410

Template-Type: ReDIF-Article 1.0
Author-Name: Salah Turki Alrawashdeh
Author-Name-First: Salah
Author-Name-Last: Turki Alrawashdeh
Author-Name: Khaleel Ibrahim Al Daoud
Author-Name-First: Khaleel Ibrahim
Author-Name-Last: Al Daoud
Author-Name: Badrea Al Oraini
Author-Name-First: Badrea
Author-Name-Last: Al Oraini
Author-Name: Ibrahim Mohammad Suleiman
Author-Name-First: Ibrahim Mohammad
Author-Name-Last: Suleiman
Author-Name: Asokan Vasudevan
Author-Name-First: Asokan
Author-Name-Last: Vasudevan
Author-Name: Lian Xiao
Author-Name-First: Lian
Author-Name-Last: Xiao
Author-Name: Rakan Alshbiel
Author-Name-First: Rakan
Author-Name-Last: Alshbiel
Title: Individual and Technological Factors Affecting the Adoption of AI-Powered Remote Auditing in the Jordanian Banking Sector
Abstract: Introduction Artificial intelligence technologies have recently contributed to the field of remote auditing and have led to significant improvements in the efficiency and outcomes of the audit process. However, this professional technological integration remains unexplored in the Jordanian banking sector. Accordingly, understanding the mechanism of integration between these factors is essential to keep pace with the evolving work environment. This study aims to examine how these factors affect the adoption of remote auditing supported by artificial intelligence in Jordanian banks. Methods A quantitative approach consistent with a cross-sectional design was used to collect primary research data. A structured questionnaire was distributed to 158 decision-makers in various commercial banks in Jordan. The questionnaire measured individual factors (e.g., skill level of users and Attitude towards technology) and technological factors (e.g., technology readiness, data security and privacy, and integration capabilities). Structural equation modeling (SEM) was used to test the relationships between these factors and intention to adopt AI-powered remote auditing using SMART PLS. Results The results depicted that all factors, including individual and technological factors, significantly influenced the adoption of AI-powered remote auditing. Attitude towards technology and integration capabilities were the strongest predictors. Additionally, technology readiness, data security and privacy, and skill level of users had moderate but significant, effects on adoption intention. Conclusion The findings emphasize that both individual perceptions and technological robustness are crucial for adopting AI-powered remote auditing in Jordanian banks. Improving system reliability and showcasing the benefits of AI tools can significantly boost adoption rates
Journal: Data and Metadata
Pages: .408
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.408
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.408:id:1056294dm2024408

Template-Type: ReDIF-Article 1.0
Author-Name: Yuhelmi
Author-Name-Last: Yuhelmi
Author-Name: Mawardi Effendy
Author-Name-First: Mawardi
Author-Name-Last: Effendy
Author-Name: Ridwan
Author-Name-Last: Ridwan
Author-Name: Walhidayat
Author-Name-Last: Walhidayat
Author-Name: Syamsidar Raja
Author-Name-First: Syamsidar
Author-Name-Last: Raja
Author-Name: Adi Fitra Andikos
Author-Name-First: Adi Fitra
Author-Name-Last: Andikos
Author-Name: Ambiyar
Author-Name-Last: Ambiyar
Title: Practicality of syntax soft skill-based learning (Ss-BL): a new model in web-based entrepreneurship learning
Abstract: The soft skills-based learning (Ss-BL) learning the methodology was proposed as an innovative approach to facilitate experiential learning in authentic real-world contexts. This was created to address the shortcomings of the Experiential Learning (EL) approach in enhancing student communication proficiency. Thus, this study's goal is to evaluate the viability of the Ss-BL learning paradigm. In this work, confirmatory factor analysis (CFA) and the Mile and Huberman methodology were employed in an exploratory sequential mixed methods approach. The paradigm for Ss-BL learning is created by conceptualization, theorization, hypothesization, and finalization phases. The research data was acquired via the process of document analysis and conducting a Focus Group Discussion (FGD). A focus group discussion (FGD) was carried out with a sample of 5 experts for research purposes. A non-test was the tool used in the FGD. The Ss-BL model consists of the Motivation, Observation, Real Experience, Implementation, Evaluation, Reflection phases. For all phases, a CFA value of 0.000 was obtained, that is, if the number is less than 2, it means that the Ss-BL model meets the goodness-of-fit model criterion. Among the innovative learning models suggested for entrepreneurial courses in vocational education, the Ss-BL model stands out as both a contribution to the advancement of knowledge and a viable option
Journal: Data and Metadata
Pages: .407
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.407
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.407:id:1056294dm2024407

Template-Type: ReDIF-Article 1.0
Author-Name: Ariss Anass
Author-Name-First: Ariss
Author-Name-Last: Anass
Author-Name: Ennejjai Imane
Author-Name-First: Ennejjai
Author-Name-Last: Imane
Author-Name: Jamal Mabrouki
Author-Name-First: Jamal
Author-Name-Last: Mabrouki
Author-Name: Soumia Ziti
Author-Name-First: Soumia
Author-Name-Last: Ziti
Title: Optimizing a Novel Tracking System for Living Beings and Objects through Advanced Mathematical Modeling and Graph Theory
Abstract: This study extends the formulation of a tracking system for both live items and living persons, and gives a thorough theoretical framework for an advanced tracking system. A large number of tracking systems in use today were created inside certain frameworks and designed to monitor in either infinite or restricted spatial contexts. The latter typically makes use of specialized technological instruments designed with tracking objects or living things in mind. Our contribution to this topic is the formulation of a system theory that both formulates and innovates the challenge of monitoring objects and living things. Graphical modeling is widely used in tracking, which is interesting because it makes it easier to create precise relationships between the objects that need to be tracked and other parts of the system. But our study argues that the best way to achieve a high-performing, contextually relevant, and flexible system in a range of scenarios is still to build a tracking system around graphs, both theoretically and practically. We provide a unique tracking method to further the discipline, based on the ideas of hypergraphs and graph learning. This method carefully examines the order between various linkages inside the system, allowing the system to fully use both direct and indirect relations. The way we formulate tracking is as a complex search problem on graphs and hypergraphs. In this case, the system's components—living things or objects—are represented by vertices, and the kinds of relationships that exist between them are indicated by edges. We present a governing law that facilitates different processing tasks, manages shared data across system parts, and defines the connections between vertices. Additionally, we provide illustrated examples covering single and multi-context tracking scenarios to support our work. These illustrations highlight how, in comparison to current tracking technologies, the suggested approach performs better theoretically. In addition to adding to the theoretical conversation, this discovery has potential applicability in a variety of tracking contexts
Journal: Data and Metadata
Pages: .406
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.406
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.406:id:1056294dm2024406

Template-Type: ReDIF-Article 1.0
Author-Name: Mohammed AFTATAH
Author-Name-First: Mohammed
Author-Name-Last: AFTATAH
Author-Name: Khalid ZEBBARA
Author-Name-First: Khalid
Author-Name-Last: ZEBBARA
Title: Robust ConvNet-Kalman Filter Integration for Mitigating GPS Jamming and Spoofing Attacks Basing on Inertial Navigation System Data
Abstract: GPS (Global Positioning System) is the most accurate system for various applications, especially in transportation. However, GPS is critically vulnerable due to its reliance on radio signals, which can be exploited by hackers through intentional attacks like spoofing and jamming, leading to potentially dangerous disruptions for both humans and services. Moreover, GPS systems can also experience accidental disruptions in urban environments, where signals from multiple satellites may be blocked by buildings, severely affecting the receiver's accuracy. This paper presents a robust method designed to mitigate GPS outages caused by both jamming and spoofing by integrating inertial data. The proposed method leverages two key components: convolutional neural networks (ConvNet) and the Kalman filter (KF). A carefully optimized deep layer in the ConvNet is employed to correct errors in the inertial navigation system (INS). The findings indicate a considerable enhancement in accuracy, with the proposed method reducing the RMSE by 77.68% compared to standalone GPS and by 98.34% compared to standalone INS. This significant improvement underscores the proposed approach's performance in maintaining reliable navigation in environments where GPS signals are compromised
Journal: Data and Metadata
Pages: .405
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.405
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.405:id:1056294dm2024405

Template-Type: ReDIF-Article 1.0
Author-Name: Satya Prakash
Author-Name-First: Satya
Author-Name-Last: Prakash
Author-Name: Anand Singh Jalal
Author-Name-First: Anand
Author-Name-Last: Singh Jalal
Author-Name: Pooja Pathak
Author-Name-First: Pooja
Author-Name-Last: Pathak
Title: Forecasting COVID-19 Pandemic – A scientometric Review of Methodologies Based on Mathematics, Statistics, and Machine Learning
Abstract: Introduction: The COVID-19 pandemic is being regarded as a worldwide public health issue. The virus has disseminated to 228 nations, resulting in a staggering 772 million global infections and a significant death toll of 6.9 million. Since its initial occurrence in late 2019, many approaches have been employed to anticipate and project the future spread of COVID-19. This study provides a concentrated examination and concise evaluation of the forecasting methods utilised for predicting COVID-19. To begin with, A comprehensive scientometric analysis has been conducted using COVID-19 data obtained from the Scopus and Web of Science databases, utilising bibliometric research. Subsequently, a thorough examination and classification of the existing literature and utilised approaches has been conducted. First of its kind, this review paper analyses all kinds of methodologies used for COVID-19 forecasting including Mathematical, Statistical, Artificial Intelligence - Machine Learning, Ensembles, Transfer Learning and hybrid methods. Data has been collected regarding different COVID-19 characteristics that are being taken into account for prediction purposes, as well as the methodology used to develop the model. Additional statistical analysis has been conducted using existing literature to determine the patterns of COVID-19 forecasting in relation to the prevalence of methodologies, programming languages, and data sources. This review study may be valuable for researchers, specialists, and decision-makers concerned in administration of the Corona Virus pandemic. It can assist in developing enhanced forecasting models and strategies for pandemic management.
Journal: Data and Metadata
Pages: .404
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.404
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.404:id:1056294dm2024404

Template-Type: ReDIF-Article 1.0
Author-Name: Miguel Valencia-Contrera
Author-Name-First: Miguel
Author-Name-Last: Valencia-Contrera
Author-Name: Vivia Vilchez-Barboza
Author-Name-First: Vivia
Author-Name-Last: Vilchez-Barboza
Author-Name: Maria Lucia Do Carmo Cruz Robazzi
Author-Name-First: Maria Lucia
Author-Name-Last: Do Carmo Cruz Robazzi
Author-Name: María Quintana-Zavala
Author-Name-First: María
Author-Name-Last: Quintana-Zavala
Author-Name: José Castro-Bastidas
Author-Name-First: José
Author-Name-Last: Castro-Bastidas
Author-Name: Rodrigo-Alejandro Ardiles-Irarrazabal
Author-Name-First: Rodrigo-Alejandro
Author-Name-Last: Ardiles-Irarrazabal
Author-Name: Alba Lozano-Romero
Author-Name-First: Alba
Author-Name-Last: Lozano-Romero
Author-Name: Solange Vallejos Vergara
Author-Name-First: Solange
Author-Name-Last: Vallejos Vergara
Author-Name: Jenifer Villa-Velasquez
Author-Name-First: Jenifer
Author-Name-Last: Villa-Velasquez
Author-Name: Flérida Rivera-Rojas
Author-Name-First: Flérida
Author-Name-Last: Rivera-Rojas
Author-Name: Daniella Cancino Jiménez
Author-Name-First: Daniella
Author-Name-Last: Cancino Jiménez
Author-Name: Naldy Febré
Author-Name-First: Naldy
Author-Name-Last: Febré
Author-Name: Sandra Valenzuela-Suazo
Author-Name-First: Sandra
Author-Name-Last: Valenzuela-Suazo
Title: INTEGRA methodology for the development of integrative reviews: origins, guidelines, and recommendations
Abstract: Introduction: The "INTEGRA" methodology represents an updated approach for integrative reviews, emphasizing the quality of outcomes in response to a need expressed by the scientific community. Objective: To present the INTEGRA methodology and provide guidelines and recommendations for its application. Methods: This methodological study was conducted in two stages: a) Development of the guideline and b) Analysis. The study was carried out by a team of experts from Chile, Colombia, Mexico, Costa Rica, and Brazil, who met at least one of the following criteria: a) holding a doctoral degree or being a doctoral candidate with experience in conducting literature reviews; b) having experience in developing reviews for or with professionals in clinical-care settings, policymakers, government agencies, or other decision-makers. Results: The "INTEGRA" methodology consists of seven stages: 1. (I) Idea or study problem; 2. (N) Narrowing down the inquiry or objective; 3. (T) Targeting the search strategy; 4. (E) Execution or implementation of the search; 5. (G) Grading and quality control of the results; 6. (R) Reviewing the results; 7. (A) Analysis and discussion. Conclusions: The application of the "INTEGRA" methodology will provide authors with guidelines for developing integrative reviews and improving the quality of contributions in this field.
Journal: Data and Metadata
Pages: .401
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.401
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.401:id:1056294dm2024401

Template-Type: ReDIF-Article 1.0
Author-Name: Marcela Hechenleitner-Carvallo
Author-Name-First: Marcela
Author-Name-Last: Hechenleitner-Carvallo
Author-Name: Jacqueline Ibarra-Peso
Author-Name-First: Jacqueline
Author-Name-Last: Ibarra-Peso
Author-Name: Carlos Zúñiga-San Martín
Author-Name-First: Carlos
Author-Name-Last: Zúñiga-San Martín
Author-Name: Angélica Avendaño-Veloso
Author-Name-First: Angélica
Author-Name-Last: Avendaño-Veloso
Author-Name: Eileen Sepúlveda-Valenzuela
Author-Name-First: Eileen
Author-Name-Last: Sepúlveda-Valenzuela
Title: Proposal of Competencies in Telehealth: A Mixed-Methods Study in the Biobío Region, Chile
Abstract: Introduction: Telehealth has become essential in the delivery of healthcare services, especially during the COVID-19 pandemic. Objective: This study aims to identify the specific competencies needed for the effective implementation of telehealth in Biobío, Chile. Methods: A qualitative and quantitative validation of competencies was conducted. The qualitative phase included a focus group with professionals from various health areas to discuss and refine the competencies. The quantitative phase used the Telehealth Competency and Preparedness Perception Scale (EPPCT), employing a non-probabilistic convenience sampling (n=48) among health professionals in the Biobío region, Chile. Results: The qualitative validation highlighted the importance of system efficiency and waitlist management, confidence, and education in the use of technologies, and continuous training. The quantitative validation identified two main dimensions: "Professional Excellence" and "Remote Clinical Approach," encompassing communication, ethical, legal, and technological aspects. Conclusions: The proposed competencies are suitable for telehealth, emphasizing the need for continuous evaluation and training. An additional quantitative study is recommended to confirm and adjust the model, ensuring that healthcare professionals are prepared to face the challenges of remote care
Journal: Data and Metadata
Pages: .399
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.399
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.399:id:1056294dm2024399

Template-Type: ReDIF-Article 1.0
Author-Name: Agusti Tamrin
Author-Name-First: Agusti
Author-Name-Last: Tamrin
Author-Name: Cucuk Wawan Budiyanto
Author-Name-First: Cucuk Wawan
Author-Name-Last: Budiyanto
Author-Name: Ahya’ Alimuddin
Author-Name-First: Ahya’
Author-Name-Last: Alimuddin
Author-Name: Asnul Dahar Minghat
Author-Name-First: Asnul Dahar
Author-Name-Last: Minghat
Title: The effectiveness of the use of Google Sites-Based mobile learning to improve 21st-Century Skills of vocational high school students
Abstract: Introduction: Google Sites was chosen because it can provide real-time services when there are updates to learning materials. Objective: This research aims to develop mobile learning based on Google Sites and determine its effect in improving the skills of 21st-century vocational students. Methods: The development model is based on the Research and Development models by Gall, Gall, and Borg. Product validation by experts is getting a very proper predicate. The application of the media was conducted for students in the 11th class of the Machining Engineering Competency. The product was tested in three Vocational High Schools (VHS) with 78 respondents in three practical and three control classes. The data collection techniques utilized in this study encompassed a multiple-choice test of 4C abilities; an observation sheet completed by teachers, and structured interview instruments for both students and teachers. Data analysis techniques include: (1) qualitative data analysis employing an interactive approach involving data reduction, data display, and conclusion drawing; and (2) quantitative data analysis comprising tests for normality, homogeneity, and hypothesis testing using the independent sample t-test. Results: Findings of the research indicated that: (1) observations conducted by teachers revealed a significant enhancement in skills within both experimental and control groups; (2) within the experimental group, critical thinking skills increased by 11, 85%, communication skills by 5.6%, collaboration skills by 11, 72%, and creative skills by 8%; and (3) the results of the t-test can be inferred that there exist notable disparities between the practical and control classes across the three schools. Conclusions: The conclusion drawn from this research is that Google Sites-based mobile learning effectively enhances the 21st-century skills of students in Vocational High Schools
Journal: Data and Metadata
Pages: .398
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.398
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.398:id:1056294dm2024398

Template-Type: ReDIF-Article 1.0
Author-Name: Rong Zhang
Author-Name-First: Rong
Author-Name-Last: Zhang
Author-Name: Jeffrey Sarmientor
Author-Name-First: Jeffrey
Author-Name-Last: Sarmientor
Author-Name: Anton Louise De Ocampo
Author-Name-First: Anton Louise
Author-Name-Last: De Ocampo
Author-Name: Rowell Hernandez
Author-Name-First: Rowell
Author-Name-Last: Hernandez
Title: Fruit and vegetable self-billing system based on image recognition
Abstract: Introduction: Shopping centers have become a necessary aspect of living, especially for city dwellers. To realize the identification and settlement of fruits and vegetables lacking bar codes is a major problem in supermarket self-service settlement. Methods: In this study, we proposed a novel Garra Rufa fish-optimized multi-objective convolutional neural network (GRFO-MCNN) for fruit and vegetable detection and freshness recognition. To improve feature identification performance, the GRFO-MCNN integrates the CBAM, which consists of the CAM and the SAM. Freshness recognition and fruit and vegetable detection are greatly enhanced by the CBAM by focusing on pertinent regions of images. Results: The proposed model integrate with the automated settlement system which transform the fruits and vegetable purchases by streamline identification and payment process. The Raspberry Pi, a microcontroller with a camera unit, makes up the suggested model to automate the billing system. For this study, we used a Raspberry Pi module to automatically acquire image data of fruits and vegetables. Conclusions: The suggested approach is contrasted with the other traditional approaches. The result shows the suggested approaches outperformed in accuracy (0.93), MAE (0.11), and RMSE (0.53). The fruits and vegetables that are arranged for automatic weighing are captured by the camera module. The microprocessor receives as an input the cost of various products per kilogram automatically. Consequently, the Raspberry Pi automatically calculates and shows the overall lprice of the products on the monitor
Journal: Data and Metadata
Pages: .397
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.397
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.397:id:1056294dm2024397

Template-Type: ReDIF-Article 1.0
Author-Name: ALI MANSOURI
Author-Name-First: ALI
Author-Name-Last: MANSOURI
Author-Name: Ismail BELHAJ
Author-Name-First: Ismail
Author-Name-Last: BELHAJ
Title: Exploring the influence of e-governance on family business strategy execution
Abstract: In a context marked by increasing digital transformation, this study sets out to examine the influence of e-governance on strategy execution within family businesses in Morocco. Adopting a qualitative approach, this research looks specifically at the effects of three dimensions of e-governance - e-participation, e-transparency, and e-accountability - on the effectiveness of strategic execution. Based on data collected from 31 family businesses, the analysis reveals that e-participation and e-accountability contribute significantly to improving strategic alignment, thus fostering more efficient strategic execution. On the other hand, the impact of e-transparency, while relevant, remains less pronounced. This study enriches the literature by highlighting the transformative potential of e-governance in the strategic management of family businesses, while taking into account the particularities of family culture, resistance to change and technological capabilities specific to these organizations.
Journal: Data and Metadata
Pages: .396
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.396
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.396:id:1056294dm2024396

Template-Type: ReDIF-Article 1.0
Author-Name: Hanae Amrani
Author-Name-First: Hanae
Author-Name-Last: Amrani
Author-Name: Zouheir Boussouf
Author-Name-First: Zouheir
Author-Name-Last: Boussouf
Author-Name: AHMED AFTISS
Author-Name-First: AHMED
Author-Name-Last: AFTISS
Title: Exploring Managerial Innovation in the University Context: an In-Depth Look through a Systematic Literature Review
Abstract: This article explores the evolution and growing importance of managerial innovation in the university context, based on an in-depth analysis of existing literature. In the face of globalization and higher education challenges, managerial innovation is crucial for modernizing pedagogical practices, enhancing teaching quality, and addressing students' needs. This systematic literature review evaluates key studies, including those by Xue & Wang (2024) and Chung & Espinoza (2023), to assess the impact of innovation on university management. The analysis focuses on trends from 2014 to 2024, drawing on works from the Scopus database. The study addresses four main questions: the relationship between managerial innovation and university education, their reciprocal interaction, the impact of innovation on higher education, and the gaps in current research. The article highlights key findings while stressing the importance of an ethical approach to implementing managerial innovation in universities. The structure includes the methodology, synthesis, analysis, and conclusions on future research directions
Journal: Data and Metadata
Pages: 394
Volume: 4
Year: 2025
DOI: 10.56294/dm2025394
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:394:id:1056294dm2025394

Template-Type: ReDIF-Article 1.0
Author-Name: John Byron Tuazon
Author-Name-First: John Byron
Author-Name-Last: Tuazon
Author-Name: Ryan Ebardo
Author-Name-First: Ryan
Author-Name-Last: Ebardo
Title: Filipino Meme Culture in Reddit: A Social Exchange Theory Application in an Anonymous Online Community
Abstract: The study explores the dynamics of social exchanges within the r/Philippines subreddit, focusing on how meme culture influence user interactions and engagement. It highlights the significant role of social media in Filipino culture, noting the increasing time spent on platforms like Reddit, which serves as a unique space for community building and cultural expression. Social Exchange Theory (SET) is applied to understand participation behaviors in online communities, emphasizing perceived reciprocity and social capital as critical factors. SET provides a framework for analyzing social interactions, focusing on the perceived costs and benefits that influence user engagement in online communities. The research focuses on posts categorized under the MemePH flair in the r/Philippines subreddit, utilizing publicly accessible data without requiring ethical clearance. A content analysis was conducted to analyze the posted memes, while a thematic analysis was conducted to examine social interactions in the comments section, with classifications established before data collection. The content analysis reveals that memes in the subreddit are primarily categorized as political, propaganda, current events, and dank memes, with humor being a significant driver of engagement. The thematic analysis indicates that perceived community support, exchange ideologies, social ties, and perceived benefits are crucial in motivating users to post and interact within the subreddit.
Journal: Data and Metadata
Pages: 392
Volume: 4
Year: 2025
DOI: 10.56294/dm2025392
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:392:id:1056294dm2025392

Template-Type: ReDIF-Article 1.0
Author-Name: Yasna Sandoval
Author-Name-First: Yasna
Author-Name-Last: Sandoval
Author-Name: Virginia García
Author-Name-First: Virginia
Author-Name-Last: García
Author-Name: Angel Roco-Videla
Author-Name-First: Angel
Author-Name-Last: Roco-Videla
Author-Name: Carlos Rojas
Author-Name-First: Carlos
Author-Name-Last: Rojas
Title: Construction and validation of an instrument for early detection of stuttering in children between 2 and 2 years 11 months based on speech motor control and linguistic skills
Abstract: Introduction: Stuttering is a speech disorder that affects a significant percentage of children in early childhood, characterized by interruptions in the verbal flow. Approximately 3% to 8% of children aged 2 to 6 years have this disorder, and although a high percentage show spontaneous recovery, 20% do not. Early detection is crucial to facilitate intervention and improve recovery prognoses. This study aims to develop and validate an instrument for early detection of stuttering in children aged 2 to 2 years and 11 months, based on speech motor control and linguistic skills. Methods: A quantitative approach was adopted with a descriptive and cross-sectional design. An instrument was constructed that included questions about background and diagnosis, validated by experts using Aiken's V index. It was applied to a sample of 34 caregivers, analyzing internal consistency with Cronbach's Alpha. Results: The instrument showed a Cronbach's Alpha of 0.9360, indicating high reliability. Factor analysis revealed that the instrument measures a single dimension related to stuttering risk. Bartlett's test of sphericity was significant, and all items had saturations greater than 0.55. Conclusions: The developed instrument is consistent and reliable, allowing for early detection of stuttering. Its application will help caregivers identify the need for professional intervention, contributing to improving recovery prognoses in children at risk of stuttering
Journal: Data and Metadata
Pages: .391
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.391
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.391:id:1056294dm2024391

Template-Type: ReDIF-Article 1.0
Author-Name: Asmaa Lamjid
Author-Name-First: Asmaa
Author-Name-Last: Lamjid
Author-Name: Ariss Anass
Author-Name-First: Ariss
Author-Name-Last: Anass
Author-Name: Imane Ennejjai
Author-Name-First: Imane
Author-Name-Last: Ennejjai
Author-Name: Jamal Mabrouki
Author-Name-First: Jamal
Author-Name-Last: Mabrouki
Author-Name: Ziti Soumia
Author-Name-First: Ziti
Author-Name-Last: Soumia
Title: Enhancing the hiring process: A predictive system for soft skills assessment
Abstract: Human Resource Management faces the ongoing challenge of identifying top-performing candidates to enhance organizational success. Traditional recruitment methods heavily rely on assessing hard skills alone, overlooking the importance of soft skills in identifying individuals who excel in their roles. To address this, our paper introduces a novel predictive model that leverages Artificial Intelligence in the hiring process. By analyzing soft skills extracted from CVs, cover letters, websites, professional social media, and psychometric tests, the model accurately predicts potential candidates suitable for specific job roles. This system effectively eliminates poor hiring decisions, reduces time and effort, minimizes recruitment costs, and mitigates turnover risks. The implementation of our proposed model employs various predictive machine learning classifiers, with key input soft skills including creativity, collaboration, empathy, curiosity, and critical thinking. Notably, the Support Vector Machine classifier emerges as the top-performing model in terms of predictive accuracy
Journal: Data and Metadata
Pages: .387
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.387
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.387:id:1056294dm2024387

Template-Type: ReDIF-Article 1.0
Author-Name: Diego Cajamarca Carrazco
Author-Name-First: Diego
Author-Name-Last: Cajamarca Carrazco
Author-Name: Edwin Rogelio Guanga Casco
Author-Name-First: Edwin Rogelio
Author-Name-Last: Guanga Casco
Author-Name: Santiago Mauricio Salazar-Torres
Author-Name-First: Santiago Mauricio
Author-Name-Last: Salazar-Torres
Author-Name: Danny Josue Montalvo Zambrano
Author-Name-First: Danny Josue
Author-Name-Last: Montalvo Zambrano
Author-Name: Eleonora-Melissa Layana-Bajana
Author-Name-First: Eleonora-Melissa
Author-Name-Last: Layana-Bajana
Author-Name: Winston Fernando Zamora Burbano
Author-Name-First: Winston Fernando
Author-Name-Last: Zamora Burbano
Author-Name: María Magdalena Paredes Godoy
Author-Name-First: María Magdalena
Author-Name-Last: Paredes Godoy
Title: Systematic review on sustainable management of natural resources with smart technologies for food production
Abstract: Introduction: The environmental problems related to global warming, climate change, and alterations in natural resources deepen the food supply worldwide, so the applicability of cutting-edge digital technology raises viable alternatives for the transformation of the agricultural sector with generative, resilient, sustainable and adaptive practices to meet the challenges of food insecurity and malnutrition. Based on the applicability of intelligent technologies in production processes, processing, conservation, monitoring, simulation, modeling, and management of natural resources to ensure the goal of sustainable development and zero hunger. Therefore, the object of analysis of the bibliometric review on the sustainable management of natural resources with smart technologies for food production was raised, for which the main databases Scopus, IEEE Eplore, MDPI, and Springer were explored, during a period of six years, with the use of the methodology (PRISMA, 2020). To conclude, it is concluded that the incorporation of smart technologies such as industry 4.0, 5.0, IoT, AI, robotics, smart irrigation, satellite imagery, simulation, autonomous learning Big Data, blockchain that allow ensuring healthy, nutritious and sustainable food
Journal: Data and Metadata
Pages: 384
Volume: 4
Year: 2025
DOI: 10.56294/dm2025384
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:384:id:1056294dm2025384

Template-Type: ReDIF-Article 1.0
Author-Name: Adi Fitra Andikos
Author-Name-First: Adi
Author-Name-Last: Fitra Andikos
Author-Name: M Giatman
Author-Name-First: M
Author-Name-Last: Giatman
Author-Name: Sukardi
Author-Name-First: Sukardi
Author-Name-Last: Sukardi
Title: Work-Based Learning Independent Learning (WBL-MB): Optimizing Learning Models Based on the Industrial World
Abstract: The selection of learning models can have a significant influence on the quality of the learning process. A new learning paradigm called Work Base Learning Merdeka Belajar (WBLMB) was created to increase the effectiveness of integrating learning into the workplace. The main purpose of this study is to evaluate the effectiveness of the WBLMB learning paradigm. In the January-June 2024 semester, the research was carried out at the Multimedia Department of SMK Negeri 1 Koto Baru, Indonesia. Samples from the experimental and control groups were obtained because this study used a pseudo-experimental design. The experimental group used the Work-Based Learning (WBL) model, while the control group used the WBLMB model. In this study, primary and quantitative data were used. Different test equipment is used to perform before and after testing to obtain these results. The N-Gain method was used to create this data to evaluate the efficacy of the WBLMB model. The N-Gain technique is based on the criteria of homogeneity test, normality test, and t-test. The experimental group scored 35.22 out of 40, while the control group scored 38.17. In the follow-up test, the experimental group scored 85.52, while the control group scored 67.12. Based on the post-test findings in the experimental group, the results were 62.44% to 90.76%, with an average score of 79.02%. On the N-Gain value spectrum, a score of 79.02% is classified as very high. The improvement of learning outcomes occurs if the WBL-MB learning paradigm is prioritized in the world of work.
Journal: Data and Metadata
Pages: .415
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.415
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.415:id:1056294dm2024415

Template-Type: ReDIF-Article 1.0
Author-Name: Fanny Elcira Barrantes-Santos
Author-Name-First: Fanny Elcira
Author-Name-Last: Barrantes-Santos
Author-Name: Juan Raú Egoavil-Vera
Author-Name-First: Juan Raú
Author-Name-Last: Egoavil-Vera
Title: Digital Tools and Education in Corporate Social Responsibility: Perspectives from Doctoral Students
Abstract: Introduction: The current demands of globalization and accreditation require universities to align immediately with the Corporate Social Responsibility (CSR) approach, making their best efforts to comply with its standards. Technological advancements play a crucial role in this process, including the use of advanced statistical software for data analysis, digital platforms for survey distribution and data collection, and online educational tools to enhance CSR knowledge and awareness among students. Methods: The study is of a basic type and relational level, with a perception sample applied to 70 students. The data were processed using SPSS statistical software. For the validation of the measurement instrument, confirmatory factor analysis (CFA) was performed using the partial least squares method (PLS-SEM) in SMARTPLS version 3, and exploratory factor analysis (EFA) with principal axis factorization and varimax rotation when CFA results were unsatisfactory. Hypotheses were tested using the Kruskall-Wallis and Mann-Whitney U tests. The significance level used for all hypotheses was 0.05. Results: The results indicate a positive perception of CSR across its dimensions, highlighting the need to strengthen knowledge through the institutionalization of a social responsibility policy in universities. Conclusion: The integration of technology in various forms, such as e-learning modules, virtual workshops, and interactive CSR simulations, underscores the importance of technology in academic research, education, and the effective implementation of CSR policies.
Journal: Data and Metadata
Pages: .411
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.411
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.411:id:1056294dm2024411

Template-Type: ReDIF-Article 1.0
Author-Name: Bahar Asgarova
Author-Name-First: Bahar
Author-Name-Last: Asgarova
Author-Name: Elvin Jafarov
Author-Name-First: Elvin
Author-Name-Last: Jafarov
Author-Name: Nicat Babayev
Author-Name-First: Nicat
Author-Name-Last: Babayev
Author-Name: Allahshukur Ahmadzada
Author-Name-First: Allahshukur
Author-Name-Last: Ahmadzada
Author-Name: Vugar Abdullayev
Author-Name-First: Vugar
Author-Name-Last: Abdullayev
Author-Name: Yitong Niu
Author-Name-First: Yitong
Author-Name-Last: Niu
Title: A decision-making system for the entire life cycle industry chain based on data mining technology optimization
Abstract: When developing a biomass production plan, the factors that influence decision makers include not only the different parts of the biomass supply chain itself, but also the social, environmental and economic impacts of the biomass system and the degree of difficulty in developing it within a particular country. In order to take these factors into account, this paper proposes a two-tier generalised decision-making system (gBEDS) for biomass, with a database at its core, including basic biomass information and detailed decision-making information, in addition to a database of scenarios and a library of case studies that provide demonstrations for new users. On the basis of the database, the decision-making system includes a simulation module for the unit process (uP) and a genetic algorithm for optimising the decisions. With the help of a graphical interface, users can define their own biomass supply chain and evaluate it environmentally, economically, socially or otherwise; on the basis of a simulation and optimisation model of the whole life cycle of biomass production, the system uses data mining methods (fuzzy c-mean clustering and decision trees) to determine the optimal geographic location of the biomass raw material collection and storage and conversion plants. Madab was used to develop a computational model for biomass planning parameters (e.g. costs and c02 emissions) for the biomass supply chain. At the same time, a visual representation of the bioenergy conversion plant and storage data is made using Geographic Information Systems (GIs) to support users in making decisions based on intelligent outputs. Thus, gBEDS supports biomass national planners in developing an effective biomass production plan with comprehensive evaluation, and local designers and implementers in defining optimised, detailed unit processes to implement said plan.
Journal: Data and Metadata
Pages: .381
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.381
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.381:id:1056294dm2024381

Template-Type: ReDIF-Article 1.0
Author-Name: Bahar Asgarova
Author-Name-First: Bahar
Author-Name-Last: Asgarova
Author-Name: Elvin Jafarov
Author-Name-First: Elvin
Author-Name-Last: Jafarov
Author-Name: Nicat Babayev
Author-Name-First: Nicat
Author-Name-Last: Babayev
Author-Name: Allahshukur Ahmadzada
Author-Name-First: Allahshukur
Author-Name-Last: Ahmadzada
Title: Using Data Mining Principles in Implementing Predictive Analytics to Different Areas
Abstract: This study delves into the realm of information-based knowledge discovery technologies and underscores the growing necessity for extensive data representation to enhance the management of care and mitigate the financial costs associated with promoting long-term care. The proliferation of information collected and disseminated through the Internet has reached unprecedented levels in the context of long-term financial health statistics, posing a challenge for businesses to effectively leverage this wealth of data for research purposes. The explicit specification of costs becomes paramount when dealing with substantial volumes of data. Consequently, the literature on the application of big data in logistics is categorized based on the nature of methods employed, such as explanatory, predictive, regulatory, strategic, and operational approaches. This includes a comprehensive examination of how big data analysis is applied within large corporations. In the healthcare domain, the study contributes to the evaluation of usability by providing a framework to analyze the maturity of structures at four distinct levels. The emphasis is particularly on the pivotal role played by predictive analytics in the healthcare industry through big data methodologies. Furthermore, the study advocates for a paradigm shift in management's perception of large business data sets, urging them to view these as strategic resources that must be seamlessly integrated into the company. This integration is seen as imperative for achieving comprehensive business analysis and staying competitive in the ever-evolving landscape of healthcare. The study concludes by shedding light on the limitations inherent in the research and delineating the specific focus areas that have been addressed.
Journal: Data and Metadata
Pages: .380
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.380
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.380:id:1056294dm2024380

Template-Type: ReDIF-Article 1.0
Author-Name: Sergio V. Flores
Author-Name-First: Sergio V.
Author-Name-Last: Flores
Author-Name: Angel Roco-Videla
Author-Name-First: Angel
Author-Name-Last: Roco-Videla
Author-Name: Román M. Montaña
Author-Name-First: Román M.
Author-Name-Last: Montaña
Author-Name: Marcela Caviedes-Olmos
Author-Name-First: Marcela
Author-Name-Last: Caviedes-Olmos
Author-Name: Sofia Pérez-Jiménez
Author-Name-First: Sofia
Author-Name-Last: Pérez-Jiménez
Author-Name: Raúl Aguilera Eguía
Author-Name-First: Raúl
Author-Name-Last: Aguilera Eguía
Title: Genetic Native American ancestry is associated with a low likelihood of VDR rs7975232 risk genotypes for vitamin D levels
Abstract: Introduction: Obesity is associated with chronic diseases, with women being more prone, possibly due to the relationship between the α-estrogen receptor and vitamin D receptors. Objective: The objective of this research is to analyze the distribution of VDR rs7975232 (ApaI) genotypes in Latin American populations and its relationship with genetic ancestry. Methods: 446 SNPs from an AIMs panel were used to estimate genetic ancestry proportions in individuals from Peru, Mexico, Colombia, and Puerto Rico using STRUCTURE software. Kruskal-Wallis, Mann-Whitney tests and logistic regression were applied for analyses. Results: Risk genotypes AA and CA show a low proportion of Native American ancestry and a high proportion of European and African ancestry. Logistic regression indicated an inverse effect of Native American ancestry on risk genotypes. Conclusion: The results suggest that Native American ancestry decreases the likelihood of carrying VDR rs7975232 risk genotypes. These findings contribute to a better understanding of genetic variability and its relationship with health conditions in these populations.
Journal: Data and Metadata
Pages: .379
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.379
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.379:id:1056294dm2024379

Template-Type: ReDIF-Article 1.0
Author-Name: Tran Thi Lan Anh
Author-Name-First: Tran Thi
Author-Name-Last: Lan Anh
Author-Name: Nguyen Thi Nguyet Dung
Author-Name-First: Nguyen Thi Nguyet
Author-Name-Last: Dung
Author-Name: Bui Thi Thu Loan
Author-Name-First: Bui Thi Thu
Author-Name-Last: Loan
Author-Name: Tran Van Hai
Author-Name-First: Tran Van
Author-Name-Last: Hai
Title: Factors affecting the disclosure of ESG information: an experimental study at Vietnamese commercial banks
Abstract: The disclosure of information and the enforcement of ESG policies have become a trend in responsible investment not only for non-financial companies but also for financial institutions - commercial banks. However, in emerging countries like Vietnam, the level of ESG information disclosure is still in its infancy. Therefore, this research aims to identify the influence of factors on the level of ESG information disclosure of Vietnamese commercial banks. The article uses the GMM regression model to assess the impact of factors on the level of ESG information disclosure of 21 Vietnamese commercial banks in the period from 2018 to 2022. In which, the dependent variable is the level of information disclosure of Vietnamese commercial banks measured according to the International Fair Finance Guidelines Method. The results show that the disclosure level of Vietnamese commercial banks is greatly influenced by factors related to the characteristics of the banks. Among these, bank size, financial efficiency, and the level of competition among banks are factors driving banks to disclose ESG information. Conversely, banks with higher leverage tend to restrict ESG information disclosure. Additionally, among environmental factors such as corruption control, legal compliance, economic and social development, only corruption control and legal compliance have an impact on the level of ESG information disclosure by banks.
Journal: Data and Metadata
Pages: .378
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.378
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.378:id:1056294dm2024378

Template-Type: ReDIF-Article 1.0
Author-Name: Imane Ennejjai
Author-Name-First: Imane
Author-Name-Last: Ennejjai
Author-Name: Anass Ariss
Author-Name-First: Anass
Author-Name-Last: Ariss
Author-Name: Jamal Mabrouki
Author-Name-First: Jamal
Author-Name-Last: Mabrouki
Author-Name: Yasser Fouad
Author-Name-First: Yasser
Author-Name-Last: Fouad
Author-Name: Abdulatif Alabdultif
Author-Name-First: Abdulatif
Author-Name-Last: Alabdultif
Author-Name: Rajasekhar Chaganti
Author-Name-First: Rajasekhar
Author-Name-Last: Chaganti
Author-Name: Karima Salah Eddine
Author-Name-First: Karima
Author-Name-Last: Salah Eddine
Author-Name: Asmaa Lamjid
Author-Name-First: Asmaa
Author-Name-Last: Lamjid
Author-Name: Soumia Ziti
Author-Name-First: Soumia
Author-Name-Last: Ziti
Title: An Artificial intelligence Approach to Fake News Detection in the Context of the Morocco Earthquake
Abstract: The catastrophic earthquake that struck Morocco on Septem- ber 8, 2023, garnered significant media coverage, leading to the swift dissemination of information across various social media and online plat- forms. However, the heightened visibility also gave rise to a surge in fake news, presenting formidable challenges to the efficient distribution of ac- curate information crucial for effective crisis management. This paper introduces an innovative approach to detection by integrating Natural language processing, bidirectional long-term memory (Bi-LSTM), con- volutional neural network (CNN), and hierarchical attention network (HAN) models within the context of this seismic event. Leveraging ad- vanced machine learning,deep learning, and data analysis techniques, we have devised a sophisticated fake news detection model capable of precisely identifying and categorizing misleading information. The amal- gamation of these models enhances the accuracy and efficiency of our system, addressing the pressing need for reliable information amidst the chaos of a crisis.
Journal: Data and Metadata
Pages: .377
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.377
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.377:id:1056294dm2024377

Template-Type: ReDIF-Article 1.0
Author-Name: Anass Ariss
Author-Name-First: Anass
Author-Name-Last: Ariss
Author-Name: Imane Ennejjai
Author-Name-First: Imane
Author-Name-Last: Ennejjai
Author-Name: Jamal Mabrouki
Author-Name-First: Jamal
Author-Name-Last: Mabrouki
Author-Name: Asmaa Lamjid
Author-Name-First: Asmaa
Author-Name-Last: Lamjid
Author-Name: Nassim Kharmoum
Author-Name-First: Nassim
Author-Name-Last: Kharmoum
Author-Name: Soumia Ziti
Author-Name-First: Soumia
Author-Name-Last: Ziti
Title: Tracking System for Living Beings and Objects: Integration of Accessible Mathematical Contributions and Graph Theory in Tracking System Design
Abstract: This paper presents a theoretical framework for a tracking system, wherein we generalize the formulation of a tracking system de- signed for living beings and objects. Many tracking systems are typically developed within specific frameworks, either for tracking in limited or unlimited space. The latter often relies on technical tools dedicated to tracking living beings or objects. In this study, we propose a system theory that formulates the task of tracking both living beings and ob- jects. Graphical modeling is widely employed in tracking to establish correct connections between the elements to be tracked and other com- ponents in the system. However, basing a tracking system on graphs in both its theoretical and practical aspects remains the optimal method for achieving a high-performing, relevant, and adaptable system in vari- ous situations. This paper introduces a tracking system based on graph learning and hypergraphs, fully leveraging direct and indirect relations while considering the order between multiple system links. Tracking is thus formulated as a search problem on graphs and hypergraphs, with vertices representing the elements of the system (living beings or ob- jects), and edges representing the types of connections between these elements. We define a law governing the relationships between the ver- tices, managing the shared data between the elements of the system and other processes. Furthermore, examples of single and multi-context track- ing situations demonstrate that the proposed system, in its theoretical foundation, outperforms existing systems.
Journal: Data and Metadata
Pages: .376
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.376
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.376:id:1056294dm2024376

Template-Type: ReDIF-Article 1.0
Author-Name: Diego Cajamarca Carrazco
Author-Name-First: Diego
Author-Name-Last: Cajamarca Carrazco
Author-Name: María Gabriela Tobar-Ruiz
Author-Name-First: María Gabriela
Author-Name-Last: Tobar-Ruiz
Author-Name: Diego Marcelo Almeida López
Author-Name-First: Diego Marcelo
Author-Name-Last: Almeida López
Author-Name: Carlos Eduardo Cevallos Hermida
Author-Name-First: Carlos Eduardo
Author-Name-Last: Cevallos Hermida
Author-Name: Verónica Magdalena Llangarí Arellano
Author-Name-First: Verónica Magdalena
Author-Name-Last: Llangarí Arellano
Author-Name: Mateo Augusto Zavala Tobar
Author-Name-First: Mateo Augusto
Author-Name-Last: Zavala Tobar
Author-Name: María Magdalena Paredes Godoy
Author-Name-First: María Magdalena
Author-Name-Last: Paredes Godoy
Title: Bibliometric analysis of the applicability of artificial intelligence in the integrated management of medical waste
Abstract: The integrated management of medical waste (MD) is a crucial challenge for public health and the environment, aggravated in recent times by population growth and the emergence of pandemics. In this context, the implementation of innovative technologies such as Artificial Intelligence (AI) presents itself as a promising solution. These technological tools can facilitate the identification, classification and tracking of DMs, thus optimizing their collection, treatment and final disposal in an efficient and sustainable manner. For this purpose, it was established to analyze the scientific production related to the integrated management of medical waste and the applicability of Artificial Intelligence. The Scopus database was used during the period 2017 - 2024 based on the PRISMA 2020 methodology. The behavior of the studies presented 9 nodes representing 116 publications. For the co-occurrence of keywords, five clusters and 56 selected keywords were found, which corroborates the importance of the study. However, the application of emerging technologies in combination with comprehensive approaches can significantly contribute to improve DM management, from an adaptive, resilient, and inclusive approach.
Journal: Data and Metadata
Pages: .375
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.375
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.375:id:1056294dm2024375

Template-Type: ReDIF-Article 1.0
Author-Name: Suhaila Abuowaida
Author-Name-First: Suhaila
Author-Name-Last: Abuowaida
Author-Name: Yazan Alnsour
Author-Name-First: Yazan
Author-Name-Last: Alnsour
Author-Name: Zaher Salah
Author-Name-First: Zaher
Author-Name-Last: Salah
Author-Name: Raed Alazaidah
Author-Name-First: Raed
Author-Name-Last: Alazaidah
Author-Name: Mohammad Subhi Al-Batah
Author-Name-First: Mohammad Subhi
Author-Name-Last: Al-Batah
Author-Name: Mowafaq Salem Alzboon
Author-Name-First: Mowafaq Salem
Author-Name-Last: Alzboon
Author-Name: Nawaf Alshdaifat
Author-Name-First: Nawaf
Author-Name-Last: Alshdaifat
Author-Name: Bashar Al-haj Moh’d
Author-Name-First: Bashar
Author-Name-Last: Al-haj Moh’d
Title: Hybrid Ensemble Architecture for Brain Tumor Segmentation Using EfficientNetB4-MobileNetV3 with Multi-Path Decoders
Abstract: Brain tumor segmentation based on multi-modal magnetic resonance imaging is a challenging medical problem due to tumors heterogeneity, irregular boundaries, and inconsistent appearances. For this purpose, we propose a hybrid primal and dual ensemble architecture leveraging EfficientNetB4 and MobileNetV3 through a cross-network novel feature interaction mechanism and an adaptive ensemble learning approach. The proposed method enables segmentation by leveraging recent attention mechanisms, dedicated decoders, and uncertainty estimation techniques. The proposed model was extensively evaluated using the BraTS2019-2021 datasets, achieving an outstanding performance with mean Dice scores of 0.91, 0.87, and 0.83 on whole tumor, tumor core and enhancing tumor regions respectively. The proposed architecture achieves stable performance over a range of tumor types and sizes, with low relative computational cost.
Journal: Data and Metadata
Pages: 374
Volume: 4
Year: 2025
DOI: 10.56294/dm2025374
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:374:id:1056294dm2025374

Template-Type: ReDIF-Article 1.0
Author-Name: Nguyen Thi Mo
Author-Name-First: Nguyen
Author-Name-Last: Thi Mo
Author-Name: Hoang Le Huyen
Author-Name-First: Hoang
Author-Name-Last: Le Huyen
Author-Name: Hoang Van Hue
Author-Name-First: Hoang
Author-Name-Last: Van Hue
Author-Name: Dinh Thi Hang
Author-Name-First: Dinh
Author-Name-Last: Thi Hang
Title: Intention to use eva in financial analysis of securities companies
Abstract: Economic value added abbreviated as (EVA) is a quantitative technique is a quantitative technique measuring the value generated by a team of experts at Stern Stewart & Co. EVA provides managers with optimal financial decision-making tools, yet the adoption of EVA among companies in Vietnam remains limited. This paper aims to examine the factors influencing the intention to use EVA in financial analysis among securities companies. A survey was conducted on 30 securities companies, totaling 85 observations, targeting managerial positions, through selective sampling from January 2024 to March 2024. The article uses the SEM structural model on SPSS and AMOS 20 software to clarify factors affecting the intention to use EVA in financial analysis of securities companies in Vietnam. The results indicate that corporate strategy positively influences the adoption of EVA in financial analysis activities of securities companies. Recommendations include encouraging securities companies to incorporate EVA in their strategic planning and enhancing the financial expertise of CEOs and CPOs.
Journal: Data and Metadata
Pages: 419
Volume: 3
Year: 2024
DOI: 10.56294/dm2024419
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:419:id:1056294dm2024419

Template-Type: ReDIF-Article 1.0
Author-Name: Selpa Dewi
Author-Name-First: Selpa
Author-Name-Last: Dewi
Author-Name: M Giatman
Author-Name-First: M
Author-Name-Last: Giatman
Author-Name: Rusnardi Rahmat Putra
Author-Name-First: Rusnardi
Author-Name-Last: Rahmat Putra
Author-Name: Ambiyar
Author-Name-First: Ambiyar
Author-Name-Last: Ambiyar
Title: Distribution of Rain Intensity: Daily Maximum Rainfall Data in The Province of South Sumatera and West Sumatera
Abstract: The high intensity of rain in May 2024 caused flooding in the Sumatra region, especially Padang City and Palembang City. The region of West Sumatra is also known as the ethalae of disasters with frequent earthquakes, floods, and landslides. In Indonesia, many frequency analyses are conducted using the Gumbel distribution without testing data and clear hydrological reasons. It is feared that this method is considered as a routine way, thus causing the risk of unwanted deviations, resulting in many disasters and errors in the planning of water buildings. This research aims to determine and know the type of distribution that is representative for the maximum daily rainfall frequency in West Sumatra and South Sumatra Provinces as a reference for development and disaster mitigation in the region. The research location is in West Sumatra and South Sumatra. The data used for this study were taken from daily maximum rainfall data from rain stations for 35 to 40 years from 24 rain stations in West Sumatra Province, and 16 rain stations for South Sumatra region. This type of research is development research with the 4D development model (Define, Design, Develop, Disseminate). The development method consists of four different phases: Initial Investigation, Design, Realisation and Development. The data of each station was then organised into two types of data series, namely the annual maximum data series and the annual minimum data series. The results of this data series test are expected to follow one or more types of distributions commonly used in Hydrology and Drainage. The distribution results obtained for the South Sumatra region are Gama-III and LP-III, while for the West Sumatra region the distribution types are Gama-III, LP-III Normal Log and Gembel. The conclusion obtained is that West Sumatra does not have only one type of distribution. For the type of test using the SPSS.22 application, the results obtained are goodness of fit test, parametric test, Chi-Square test, Kolmogorov Smirnov test and Anderson-Darling test and Histrogram (visual) method.
Journal: Data and Metadata
Pages: .371
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.371
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.371:id:1056294dm2024371

Template-Type: ReDIF-Article 1.0
Author-Name: Ahmad Abdullah Aljabr
Author-Name-First: Ahmad
Author-Name-Last: Abdullah Aljabr
Author-Name: Kailash Kailash Kumar
Author-Name-First: Kailash
Author-Name-Last: Kailash Kumar
Title: Recommender System for E-Health
Abstract: Introduction; E-healthcare management services can be significantly enhanced through the implementation of recommender systems, as highlighted in various research papers. These systems, such as Healthcare Recommender Systems (HRS) and Health Care Recommender Systems (HCRS), utilize advanced algorithms and machine learning techniques to provide personalized health recommendations based on user input and medical data. Objective; Recommend healthcare services based on patient's state. Model healthcare information network for efficient service recommendation. Methodology; Recommends healthcare services based on patient's critical situation and requirements. Offers re-configurable healthcare workflows to medical staff. Machine learning method classification is applied using decision tree and its result is presented which reflects 70 to 75% accuracy in predictive models which ensure that health recommender system is a full proof system. Result; Hospital recommender systems represent a significant advancement in healthcare, providing personalized and data-driven recommendations to patients. Conclusion; The integration of recommender systems in e-healthcare management services holds great potential in improving personalized patient care, promoting health awareness, and optimizing the quality of healthcare recommendations. In this paper author analyzed and estimated the level of accuracy of recommendation systems in healthcare for personalized medical treatment. It surveys current applications, challenges, and future directions in this field.
Journal: Data and Metadata
Pages: .370
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.370
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.370:id:1056294dm2024370

Template-Type: ReDIF-Article 1.0
Author-Name: Rohan Sawant
Author-Name-First: Rohan
Author-Name-Last: Sawant
Author-Name: Joshi Deepa
Author-Name-First: Joshi
Author-Name-Last: Deepa
Author-Name: Radhika Menon
Author-Name-First: Radhika
Author-Name-Last: Menon
Author-Name: Shruti Wadalkar
Author-Name-First: Shruti
Author-Name-Last: Wadalkar
Title: Research Trends and Impacts of Blockchain Technology in Construction Sector: Scientistometric Study
Abstract: Construction is a critical business that contributes greatly to a country's economic development. There is an increasing need for greater quality, more safety, and project completion on schedule. The world's fast shift from manual to digital processes is boosting the industry. This article outlines research into where and how this technology may be used in the construction sector. In this study, a literature review was conducted to identify the potential of blockchain applications in the construction industry. Examples of such technologies include smart contracts, BIM, Smart City, supply chain management, real estate management, precast construction, equipment leasing, document file sharing, asset management, construction management, payment management, and trash management. A scientometric study was carried out to better understand the present level of blockchain application in the construction business. Documents published in the Scopus and Web of Science databases between 2011 and 2024 were considered for the study. Scientistometric analysis identifies the most significant and prolific authors, articles, and the development of blockchain in the construction industry. More in-depth study is needed in the near future to develop real-world, on-the-spot solutions for the construction industry. The research reviewed 889 articles published between 2011 and 2024 and conducted a qualitative content analysis. The study's purpose is to look at how this technology may be used in the building industry. Future studies might concentrate on conducting case studies of real-world blockchain uses in building projects. This paper identifies and analyzes research gaps concerning the use of blockchain technology in civil engineering
Journal: Data and Metadata
Pages: .369
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.369
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.369:id:1056294dm2024369

Template-Type: ReDIF-Article 1.0
Author-Name: Said LAKHAL
Author-Name-First: Said
Author-Name-Last: LAKHAL
Title: Forecasting the EUR/USD Exchange Rate Using ARIMA and Machine Learning Models
Abstract: The present paper compared ARIMA with two machine learning algorithms, for forecasting USD/EUR exchange rate data. The experimental results indicated that the performance of ARIMA fell between that of recurrent neural networks and long short-term memory machine learning algorithms.
Journal: Data and Metadata
Pages: .368
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.368
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.368:id:1056294dm2024368

Template-Type: ReDIF-Article 1.0
Author-Name: Gayatri Joshi
Author-Name-First: Gayatri
Author-Name-Last: Joshi
Author-Name: Punal M Arabi
Author-Name-First: Punal M
Author-Name-Last: Arabi
Title: Automated Analysis Of Diabetic Vasculopathy Using Semantic Segmentation Of Thermal Images Of Peroneal Vessel
Abstract: Introduction: Diabetic vascular disease is one of most serious health problems in diabetic patients, it causes the development of severe complications including delayed wound healing and increased susceptibility to infections. Methods: To provide accurate and are non-invasive diagnosis, current work emphasizes on Diabetic Vasculopathy (DV) that is analysed with thermoregulation images through Semantic Segmentation (SS). A novel methodology was adapted, combining thermoregulation imaging with SS using the U-Net++ model to investigate temperature distributions at the skin level. This work introduces a novel method that utilizes MobileNetV2 as the encoder for fast Feature Extraction (FE). Results: The results from the suggested model, achieves a segmentation accuracy of 95%, which is significantly more compared to that of DeepLabV3+ and PSPNet models. A mean and Intersection over Union (IoU) of 85% and 87% was reported by the suggested frameworks throughout the training and validation phases. Conclusion: Classifying normal and abnormal regions can be done via the outcomes, as it offers the great visibility in the thermal image for clinicians by detecting the non-thermal regions
Journal: Data and Metadata
Pages: .367
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.367
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.367:id:1056294dm2024367

Template-Type: ReDIF-Article 1.0
Author-Name: Yuvaraja M.
Author-Name-First: Yuvaraja
Author-Name-Last: M.
Author-Name: Sumathi D.
Author-Name-First: Sumathi
Author-Name-Last: D.
Author-Name: M. Rajeshkumar
Author-Name-First: M.
Author-Name-Last: Rajeshkumar
Author-Name: Mohamed Uvaze Ahamed Ayoobkhan
Author-Name-First: Mohamed Uvaze
Author-Name-Last: Ahamed Ayoobkhan
Title: Hybrid Elephant Herding Optimization Approach for Cluster Head Selection And Secure Data Transmission In Wsn Using Hybrid Approach Cryptography Techniques
Abstract: Introduction: The wireless nature of sensor networks makes safe transfer of data from one node to another a major challenge in communications. Sensing tasks connect these sensor nodes which have limitations of memories and energies. Cryptography techniques are utilised to handle critical issues of security in these networks. The performance of large-scale networks is enhanced in this case by optimisation algorithm mimicking natural behaviours. Methods: This work uses H-EHO (Hybrid Elephant Herding Optimisation technique based on Individual strategies to enhance cluster head selections in WSNs (Wireless Sensor Networks) and thus extend networks’ lifetime. WSNs complete cluster head selection processes, and proposed optimisation approach which selects cluster heads based on tracking of sensor nodes for enhancements. The clan operators of optimisation algorithms are adjusted to handle random walk scale factors of elephants. Clusters of WSNs elect updated sensor nodes in principle. Hybrid algorithm HSR19, a novel security symmetric technique offers greater security during data transfers. It offers integrity, confidentiality, and authentication for cryptographic primary keys. Results: The output of the simulation demonstrates the energy consumption, network longevity, end to end delay, and secure data transfer metrics. The results for choosing an effective and time-efficient cluster head selection process for WSNs are improved by contrasting the two approaches. Conclusion: This comparison also shows the efficiency of communication devices in terms of calculation times for encoding, decoding and energies consumed for various file sizes
Journal: Data and Metadata
Pages: .366
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.366
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.366:id:1056294dm2024366

Template-Type: ReDIF-Article 1.0
Author-Name: Yuvaraja M
Author-Name-First: Yuvaraja
Author-Name-Last: M
Author-Name: Priya R
Author-Name-First: Priya
Author-Name-Last: R
Author-Name: Uma Maheswari S
Author-Name-First: Uma Maheswari
Author-Name-Last: S
Author-Name: Dhanasekar J
Author-Name-First: Dhanasekar
Author-Name-Last: J
Title: Cluster Heat Selection Optimization in Wsn Via Genetic Based Evolutionary Algorithm and Secure Data Transmission Using Paillier Homomorphic Cryptosystem
Abstract: Introduction: Wireless Sensor Networks (WSNs) consist of sensor nodes requiring energy-saving measures to extend their lifespan. Traditional solutions often lead to premature node failure due to non-adaptive network setups. Differential Evolution (DE) and Genetic Algorithms (GA) are two key evolutionary algorithms used for optimizing cluster head (CH) selection in WSNs to enhance energy efficiency and prolong network lifetime. Methods: This study compares DE and GA for CH selection optimization, focusing on energy efficiency and network lifespan. It also introduces an improved decryption method for the Paillier homomorphic encryption system to reduce decryption time and computational cost. Results: Experiments show GA outperforms DE in the number of rounds for the first node to die (FND) and achieves a longer network lifespan, despite fewer rounds for the last node to die (LND). GA has slower fitness convergence but higher population fitness values and significantly faster decoding speeds. Conclusion: GA is more effective than DE for CH selection in WSNs, leading to an extended network lifespan and better energy efficiency. Despite slower fitness convergence, GA's higher fitness values and improved decoding speeds make it a superior choice. The enhancements to the Paillier encryption system further increase its efficiency, offering a robust solution for secure and efficient WSN operation
Journal: Data and Metadata
Pages: .365
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.365
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.365:id:1056294dm2024365

Template-Type: ReDIF-Article 1.0
Author-Name: Ayman Yafoz
Author-Name-First: Ayman
Author-Name-Last: Yafoz
Title: Drones in Action: A Comprehensive Analysis of Drone-Based Monitoring Technologies
Abstract: Unmanned aerial vehicles (UAVs), commonly referred to as drones, are extensively employed in various real-time applications, including remote sensing, disaster management and recovery, logistics, military operations, search and rescue, law enforcement, and crowd monitoring and control, owing to their affordability, rapid processing capabilities, and high-resolution imagery. Additionally, drones mitigate risks associated with terrorism, disease spread, temperature fluctuations, crop pests, and criminal activities. Consequently, this paper thoroughly analyzes UAV-based surveillance systems, exploring the opportunities, challenges, techniques, and future trends of drone technology. It covers common image preprocessing methods for drones and highlights notable one- and two-stage deep learning algorithms used for object detection in drone-captured images. The paper also offers a valuable compilation of online datasets containing drone-acquired photographs for researchers. Furthermore, it compares recent UAV-based imaging applications, detailing their purposes, descriptions, findings, and limitations. Lastly, the paper addresses potential future research directions and challenges related to drone usage
Journal: Data and Metadata
Pages: .364
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.364
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.364:id:1056294dm2024364

Template-Type: ReDIF-Article 1.0
Author-Name: Najah Al-shanableh
Author-Name-First: Najah
Author-Name-Last: Al-shanableh
Author-Name: Mazen Alzyoud
Author-Name-First: Mazen
Author-Name-Last: Alzyoud
Author-Name: Raya Yousef Al-husban
Author-Name-First: Raya Yousef
Author-Name-Last: Al-husban
Author-Name: Nail M. Alshanableh
Author-Name-First: Nail M.
Author-Name-Last: Alshanableh
Author-Name: Ashraf Al-Oun
Author-Name-First: Ashraf
Author-Name-Last: Al-Oun
Author-Name: Mohammad Subhi Al-Batah
Author-Name-First: Mohammad Subhi
Author-Name-Last: Al-Batah
Author-Name: Salem Alzboon Mowafaq
Author-Name-First: Salem Alzboon
Author-Name-Last: Mowafaq
Title: Advanced Ensemble Machine Learning Techniques for Optimizing Diabetes Mellitus Prognostication: A Detailed Examination of Hospital Data
Abstract: Diabetes is a chronic disease that affects millions of people worldwide. Early diagnosis and effective management are crucial for reducing its complications. Diabetes is the fourth-highest cause of mortality due to its association with various comorbidities, including heart disease, nerve damage, blood vessel damage, and blindness. The potential of machine learning algorithms in predicting Diabetes and related conditions is significant, and mining diabetes data is an efficient method for extracting new insights. The primary objective of this study is to develop an enhanced ensemble model to predict Diabetes with improved accuracy by leveraging various machine learning algorithms. This study tested several popular machine learning algorithms commonly used in diabetes prediction, including Naive Bayes (NB), Generalized Linear Model (GLM), Logistic Regression (LR), Fast Large Margin (FLM), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), Gradient Boosted Trees (GBT), and Support Vector Machine (SVM). The performance of these algorithms was compared, and two different ensemble techniques—stacking and voting—were used to build a more accurate predictive model. The top three algorithms based on accuracy were Deep Learning, Naive Bayes, and Gradient Boosted Trees. The machine learning algorithms revealed that individuals with Diabetes are significantly affected by the number of chronic conditions they have, as well as their gender and age. The ensemble models, particularly the stacking method, provided higher accuracy than individual algorithms. The stacking ensemble model achieved a slightly better accuracy of 99.94% compared to 99.34% for the voting method. Building an ensemble model significantly increased the accuracy of predicting Diabetes and related conditions. The stacking ensemble model, in particular, demonstrated superior performance, highlighting the importance of combining multiple machine learning approaches to enhance predictive accuracy
Journal: Data and Metadata
Pages: .363
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.363
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.363:id:1056294dm2024363

Template-Type: ReDIF-Article 1.0
Author-Name: Al Qaysi Hamid Hazim Majid
Author-Name-First: Al Qaysi Hamid
Author-Name-Last: Hazim Majid
Author-Name: Noor Fareen Abdul Rahim
Author-Name-First: Noor Fareen
Author-Name-Last: Abdul Rahim
Author-Name: Ai Ping Teoh
Author-Name-First: Ai
Author-Name-Last: Ping Teoh
Author-Name: Alhamzah Alnoor
Author-Name-First: Alhamzah
Author-Name-Last: Alnoor
Title: Factors Influencing the Intention to Use Human Resource Information Systems Among Employees of SMEs in Iraq
Abstract: Introduction: In light of technological development and digital transformation, today's Small and Medium-Sized Enterprises (SMEs) rely heavily on their ability to use technology to succeed. Employees' acceptance or rejection of modern technology and the factors affecting it are crucial topics for SMEs. Methods: This study investigates the moderating roles of Technology Readiness (TR), Experience (EX), Trust, and Voluntariness of Use (VU) on the relationship between Effort Expectancy (EE), Performance Expectancy (PE), Social Influence (SI), Task-Technology Fit (TTF), Facilitating Condition (FC), and the Intention to Use (ITO) Human Resources Information Systems (HRIS) among employees of SMEs in Iraq. Data from 304 employees of Iraqi SMEs will be collected. Statistical analysis will be performed using SPSS and Partial Least Squares (PLS). Results: This research provides insight into the reasons behind employees' resistance to adopting HRIS, supporting the organization's policy of developing employee skills and training them in information technology systems. Discussion: Additionally, evaluating the acceptance of information technology systems can develop the framework for technical services in companies, including human resource units. Furthermore, defining the model's architecture will update stakeholder knowledge and enhance human resource management services in Iraq
Journal: Data and Metadata
Pages: .362
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.362
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.362:id:1056294dm2024362

Template-Type: ReDIF-Article 1.0
Author-Name: Yassine Zouhair
Author-Name-First: Yassine
Author-Name-Last: Zouhair
Author-Name: Younous El Mrini
Author-Name-First: Younous
Author-Name-Last: El Mrini
Author-Name: Mustapha Belaissaoui
Author-Name-First: Mustapha
Author-Name-Last: Belaissaoui
Author-Name: Abdelhadi Ifleh
Author-Name-First: Abdelhadi
Author-Name-Last: Ifleh
Title: Optimizing the client-consultant relationship to maximize ERP project benefits for Moroccan SMEs
Abstract: The adoption of Enterprise Resource Planning (ERP) has become a common option for Small and Medium-sized Enterprises (SMEs) looking to optimize and integrate their Information Systems (IS). However, ERP Implementation (ERPI) remains a complex process and represents a major challenge for many SMEs, surpassing even that of large companies. Consultants have experience in understanding the client's special needs, enabling them to put in place the right processes to meet those requirements, while ensuring that the client fully exploits the potential benefits offered by the ERP System (ERPS). Client-consultant conflict in ERPS is a major factor in the non-realization of benefits, which makes client-consultant agency management essential to realizing the benefits of ERP after implementation. There is currently no research examining how client-consultant relationship management can impact on the benefits of ERPI within SMEs. The aim of this research is to find out how the management of the client-consultant relationship affects the benefits of ERPS in Moroccan SMEs. This article applies the action research method in two companies, as well as a quantitative research approach using the partial least squares structural equation model (PLS-SEM) to examine data collected from 93 observations. The results are interpreted using IS success model and agency theory. This article presents four paths through which contracts agreements and strategies for conflict resolution contribute to the realization of benefits in ERP Projects (ERPP). Our research has contributed to both research and practice, and the results could help Moroccan consultants and SMEs when implementing ERP
Journal: Data and Metadata
Pages: .361
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.361
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.361:id:1056294dm2024361

Template-Type: ReDIF-Article 1.0
Author-Name: Cao Minh Tien
Author-Name-First: Cao Minh
Author-Name-Last: Tien
Author-Name: Dao Duy Thuan
Author-Name-First: Dao Duy
Author-Name-Last: Thuan
Author-Name: Tran Thi Phuong Lien
Author-Name-First: Tran Thi
Author-Name-Last: Phuong Lien
Title: The relationship between risk and profitability of securities companies
Abstract: Serving as the most important intermediary in the stock market, securities companies in Vietnam are exhibiting unsustainable development and low profitability. Meanwhile, research on this type of business remains limited in both quantity and systematic aspects. The aim of this paper is to clarify the relationship between company strategy, optimal capital structure, and the ability to generate profits for securities companies. Primary data was collected through interviews with 155 experts and managers (directors, deputy directors) from November 2023 to April 2024, and secondary data was sourced from the financial reports of securities companies from 2010 to 2023. The data was cleaned before being processed using SPSS 20 and AMOS 20. The test and analysis results indicate that company strategy has a significant impact on profitability, while the impact of capital structure is negligible. This study adds to the theory of capital structure and provides managerial policy decisions for securities company managers to enhance profitability
Journal: Data and Metadata
Pages: .360
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.360
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.360:id:1056294dm2024360

Template-Type: ReDIF-Article 1.0
Author-Name: Manuel Ayala-Chauvin
Author-Name-First: Manuel
Author-Name-Last: Ayala-Chauvin
Author-Name: Fátima Avilés-Castillo
Author-Name-First: Fátima
Author-Name-Last: Avilés-Castillo
Title: Optimizing Natural Language Processing: A Comparative Analysis of GPT-3.5, GPT-4, and GPT-4o
Abstract: In the last decade, the advancement of artificial intelligence has transformed multiple sectors, with natural language processing standing out as one of the most dynamic and promising areas. This study focused on comparing the GPT-3.5, GPT-4 and GPT-4o language models, evaluating their efficiency and performance in Natural Language Processing tasks such as text generation, machine translation and sentiment analysis. Using a controlled experimental design, the response speed and quality of the outputs generated by each model were measured. The results showed that GPT-4o significantly outperforms GPT-4 in terms of speed, completing tasks 25% faster in text generation and 20% faster in translation. In sentiment analysis, GPT-4o was 30% faster than GPT-4. Additionally, analysis of response quality, assessed using human reviews, showed that while GPT-3.5 delivers fast and consistent responses, GPT-4 and GPT-4o produce higher quality and more de-tailed content. The findings suggest that GPT-4o is ideal for applications that require speed and consistency, while GPT-4, although slower, might be preferred in contexts where text accuracy and quality are important. This study highlights the need to balance efficiency and quality in the selection of language models and suggests implementing additional automatic evaluations in future research to complement the current findings
Journal: Data and Metadata
Pages: .359
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.359
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.359:id:1056294dm2024359

Template-Type: ReDIF-Article 1.0
Author-Name: Meshal Alharbi
Author-Name-First: Meshal
Author-Name-Last: Alharbi
Author-Name: Ahmad Sultan
Author-Name-First: Ahmad
Author-Name-Last: Sultan
Title: Deep Revamped Quantum Convolutional Neural Network on Fashion MNIST Dataset
Abstract: Introduction: Image classification stands as a pivotal undertaking within the domain of computer vision technology. Primarily, this task entails the processes of image augmentation and segmentation, which are executed by various neural network architectures, including multi-layer neural networks, artificial neural networks, and perceptron networks. These image classifiers employ distinct hyperparameters for the prediction and identification of objects. Nevertheless, these neural networks exhibit susceptibility to issues such as overfitting and a lack of interpretability when confronted with low-quality images. Objective: These limitations can be mitigated through the adoption of Quantum Computing (QC) methodologies, which offer advantages such as rapid execution speed, inherent parallelism, and superior resource utilization. Method: This approach aims to ameliorate the challenges posed by conventional Machine Learning (ML) methods. Convolutional Neural Networks (CNNs) are instrumental in reducing the number of parameters while preserving the quality of dataset images. They also possess the capability to automatically discern salient features and maintain robustness in noisy environments. Consequently, a novel approach known as Deep Revamped Quantum CNN (DRQCNN) has been developed and implemented for the purpose of categorizing images contained within the Fashion MNIST dataset, with a particular emphasis on achieving heightened accuracy rates. Results: In order to assess its efficacy, this proposed method is systematically compared with the traditional Artificial Neural Network (ANN). DRQCNN leverages quantum circuits as convolutional filters with a weight adjustment mechanism for multi-dimensional vectors. Conclusions: This innovative approach is designed to enhance image classification accuracy and overall system effectiveness. The efficacy of the proposed system is evaluated through the analysis of key performance metrics, including F1-score, precision, accuracy, and recall
Journal: Data and Metadata
Pages: .358
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.358
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.358:id:1056294dm2024358

Template-Type: ReDIF-Article 1.0
Author-Name: Josue Pilco-Pezo
Author-Name-First: Josue
Author-Name-Last: Pilco-Pezo
Author-Name: Maribel Paredes-Saavedra
Author-Name-First: Maribel
Author-Name-Last: Paredes-Saavedra
Author-Name: Alcides Flores-Paredes
Author-Name-First: Alcides
Author-Name-Last: Flores-Paredes
Author-Name: Mardel Morales-García
Author-Name-First: Mardel
Author-Name-Last: Morales-García
Title: Translation and Validation of a Transformational Leadership Scale in Peruvian Public Servants
Abstract: Background Transformational leadership has been identified as an essential component for success and innovation within the public sector, especially in the digital age and in the face of global challenges. This form of leadership, which seeks to change and inspire people, has been shown to be crucial for improving organizational performance and the quality of public services. However, the application of these principles in Peru faces specific challenges, and there is a notable lack of empirical research on this phenomenon in the Peruvian public sector, particularly in the evaluation of the tools used for its measurement. Objective This study aimed to examine the psychometric properties of the Global Transformational Leadership (GTL) scale in a sample of Peruvian public servants. Methods An instrumental research design was adopted, using non-probabilistic sampling. The sample included 290 Peruvian public servants (M = 34.61, SD = 9.2), with an analysis that encompassed confirmatory factor analysis (CFA) and reliability estimates. Results Descriptive analysis results indicated a high tendency to respond positively on the scale. The CFA confirmed the proposed unidimensional structure of the scale, with acceptable fit according to various indices (χ2 = 39.130, CFI = 0.97, TLI = 0.95, RMSEA = 0.08, SRMR = 0.03), and all factor loadings were significant and greater than 0.50, indicating a strong association with the transformational leadership dimension and exceptionally high internal consistency (α = 0.94). Conclusions The study confirmed that the GTL transformational leadership scale is a psychometrically robust tool and applicable to the Peruvian context. The unidimensional structure and high reliability of the scale suggest that it is suitable for measuring transformational leadership among public servants in Peru.
Journal: Data and Metadata
Pages: .357
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.357
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.357:id:1056294dm2024357

Template-Type: ReDIF-Article 1.0
Author-Name: P Subba Rao
Author-Name-First: P
Author-Name-Last: Subba Rao
Author-Name: Venubabu Rachapudi
Author-Name-First: Venubabu
Author-Name-Last: Rachapudi
Title: Harnessing machine learning technique for improved detection and classification of heart failure
Abstract: Artificial Intelligence (AI) performs exercises recently performed by people utilizing AI and profound learning, Right now simulated intelligence is changing cardiovascular medication identifying problems, therapeutics, risk appraisals, clinical consideration, and medication advancement. The death rates in medical clinics for patients with cardiovascular breakdown display a scope of 10.6% at 30 days, 23.0% at 1 year, and 43.3% at 5 years. Cardiovascular breakdown (HF) patients need customized restorative and careful treatment, in this way early finding is pivotal. The 85% precise Brain Organization (NN) archetypal made this conceivable. By applying our calculation, simulated intelligence can assist with examining crude cardiovascular imaging information from echocardiography, processed tomography, and heart attractive reverberation imaging and EKG accounts. Unpleasant Sets (RS) and strategic relapse (LR) choice trees to analyze congestive cardiovascular breakdown and computerized reasoning to identify future impermanence and destabilization incidents have further developed cardiac illness results. This examination inspects how computer- based intelligence has changed pretty much every area of HF determination, avoidance, and the executives
Journal: Data and Metadata
Pages: .356
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.356
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.356:id:1056294dm2024356

Template-Type: ReDIF-Article 1.0
Author-Name: Kailash Kumar
Author-Name-First: Kailash
Author-Name-Last: Kumar
Author-Name: Abdullah Faisal Al-Fadi Al-Sharif
Author-Name-First: Abdullah Faisal
Author-Name-Last: Al-Fadi Al-Sharif
Title: E-waste Management Using Blockchain Technology
Abstract: Introduction; Bridging the digital divide requires the provision of affordable, fair and quality ICT. With nearly two-thirds of the world’s population still offline, there is a need to provide affordable web access for everyone. For developing countries, increasing the popularity of information and communication technology has become the most important factor in reducing poverty. The danger of electrical and electronic waste disposal contains hazardous substances, but most of the electrical and electronic equipment is still disposed of in an unhealthy environment in the field development area, affecting the level of contamination in Water, Air and Soil ultimately affecting people’s health. Eliminating E-waste responsibility and protecting the environment is a challenge for countries. Smart cities can solve environmental problems through proper waste management for improving human health, protecting water resources, and reducing pollution. Objective; In this paper, we explore how blockchain technology can help smart cities to manage E-waste by providing consistency, immutability, transparency, and accountability control in a distributed, reliable, and secure manner. We discussed the advantages of blockchain technology in various aspects of E-waste management, such as instant tracking and monitoring, E-waste disposal and E-waste management regulation compliance, proper disposal management, E-waste management, and material handling, etc. All examples of disposal services, but in our study we have found that there is no fool proof system to check the disposal of E-waste whether it has been disposed off Fully or Partially. We mainly focused on the tracking of E-waste management system for 100% safe and eco-friendly disposal from the originating point of E-waste to end disposal point of total disposal. Methods; For this, we have used machine-learning model to find the existing percentage of disposal of E-waste at the end-point which reveals that it is never 100%. And partial disposal of E-waste means we have still partial E-waste around us in different forms, which will be a threat for the society to be indulged in hazardous after effects of randomly dumping E-waste. Results; After this we have modelled a Disposal Tracking System(DTS) using blockchain technology to create an E-waste data storage as Decentralized Shareable Ledger (DSL) which records the quantity and state of E-waste data from its originating point to a different level of disposal unit and finally reflect the balance of E-waste data as NIL at the end of last disposal point. Conclusion; This system will helpful for safe and ecofriendly E-waste management and it provides complete transparency and traceability of E-waste during the life cycle of complete disposal. After implementation of this system any district or Block or Village authority can ensure to its citizens for E-waste hazards free environment and safety of natural resources
Journal: Data and Metadata
Pages: .355
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.355
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.355:id:1056294dm2024355

Template-Type: ReDIF-Article 1.0
Author-Name: Sergio V. Flores
Author-Name-First: Sergio V.
Author-Name-Last: Flores
Author-Name: Román M. Montaña
Author-Name-First: Román M.
Author-Name-Last: Montaña
Author-Name: Angel Roco-Videla
Author-Name-First: Angel
Author-Name-Last: Roco-Videla
Author-Name: Marcela Caviedes-Olmos
Author-Name-First: Marcela
Author-Name-Last: Caviedes-Olmos
Title: Association of the rs4988235(C) Polymorphism, a Determinant of Lactose Intolerance, with Genetic Ancestry in Latin American Populations
Abstract: Introduction: the rs4988235(C) polymorphism is associated with lactose intolerance and exhibits heterogeneity among populations. In Europe, the T allele (lactose tolerance) is prevalent in the north, while the C allele (lactose intolerance) is common in Asia and Africa. Methods: genotypes for rs4988235 were obtained from the 1000 Genomes Project database, selecting Latin American samples (Colombians, Mexican Americans, Peruvians, and Puerto Ricans). A total of 446 ancestry-informative markers (AIMs) were used to estimate genetic ancestry proportions. Shapiro-Wilks tests were conducted, and due to non-normality, non-parametric Kruskal-Wallis and post hoc Wilcoxon tests were applied. Results: the Shapiro-Wilks test indicated significant deviations from normality for Native-American (statistic=0.8787, p<0.05) and European ancestry proportions (statistic=0.9653, p<0.05). Kruskal-Wallis analysis showed significant differences in European (statistic=26.6696, p=1.62×10−6) and Native-American (statistic=13.4306, p=0.0012) ancestry proportions among genotypes. Post hoc Wilcoxon tests indicated significant differences between Intolerant (GG) and Heterozygous (GA) genotypes for both ancestries. Conclusions: the proportions of European and Native-American ancestry vary among genotypes of the rs4988235(C) polymorphism, suggesting the effect of admixture on the distribution of lactose intolerance in Latin American populations
Journal: Data and Metadata
Pages: .354
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.354
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.354:id:1056294dm2024354

Template-Type: ReDIF-Article 1.0
Author-Name: Sergio V. Flores
Author-Name-First: Sergio V.
Author-Name-Last: Flores
Author-Name: Román M. Montaña
Author-Name-First: Román M.
Author-Name-Last: Montaña
Author-Name: Angel Roco-Videla
Author-Name-First: Angel
Author-Name-Last: Roco-Videla
Author-Name: Marcela Caviedes-Olmos
Author-Name-First: Marcela
Author-Name-Last: Caviedes-Olmos
Author-Name: Raúl Aguilera Eguía
Author-Name-First: Raúl
Author-Name-Last: Aguilera Eguía
Title: Variability of the SNP rs9939609 in the FTO Gene and Ancestry in Latin American Populations
Abstract: Introduction: Obesity is a complex condition influenced by genetic and environmental factors. The FTO gene has been associated with obesity through several single nucleotide polymorphisms (SNPs), particularly rs9939609, related to higher body mass index (BMI) and risk of obesity. FTO variants influence the regulation of appetite and energy metabolism by affecting RNA methylation and the expression of key genes in adipogenesis. Objective: To investigate the association between the FTO rs9939609 SNP and genetic ancestry proportions in Latin American populations. Methods: Genotypes for rs9939609 were obtained using VcfTools and the 1000 Genomes Project database. Samples from Latin America were selected, covering four mixed populations: Colombians (n=94), Mexicans (n=64), Peruvians (n=85) and Puerto Ricans (n=104), totaling 347 individuals. To estimate genetic ancestry proportions, 446 SNPs from a panel of ancestry informative markers (AIMs) were used. Results: Individuals with the AA genotype of SNP rs9939609 have a higher proportion of Native American ancestry and a lower proportion of European ancestry compared to TT and AT genotypes. The variability in the proportions of ancestry according to the genotype of the SNP rs9939609 suggests a possible genetic stratification in the Latin American populations studied. Conclusions: These findings highlight the importance of considering ancestral composition in genetic studies related to obesity. More research is needed to understand how gene-environment interactions contribute to obesity in various populations, which could lead to more effective and targeted intervention strategies
Journal: Data and Metadata
Pages: .353
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.353
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.353:id:1056294dm2024353

Template-Type: ReDIF-Article 1.0
Author-Name: Petar Halachev
Author-Name-First: Petar
Author-Name-Last: Halachev
Title: The Influence of Artificial Intelligence on the Automation of Processes in Electronic Commerce
Abstract: This study explores the transformative impact of Artificial Intelligence (AI) on automating business processes in electronic commerce (e-commerce), with a focus on enhancing efficiency and customer experience. The research employs Deep Learning (DL) and Machine Learning (ML) as primary analytical tools to process and analyze data from e-commerce transaction records and customers’ browsing histories. Techniques such as data preprocessing, normalization, sentiment analysis, and advanced predictive models using Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Support Vector Machines (SVMs) are utilized. Data collection was conducted using web scraping tools like Beautiful Soup and Scrapy, along with APIs from Amazon and Google. The application of AI in e-commerce has led to significant improvements in inventory control, fraud prevention, and customer relations. ML algorithms have enhanced the estimation of product demand and personalized customer interactions, while DL has strengthened product recommendation systems and fraud detection mechanisms. The findings indicate that AI contributes to a more secure, faster, and smarter operational environment in e-commerce. This research highlights the substantial benefits and broad potential of AI in optimizing e-commerce operations, demonstrating that the integration of advanced AI technologies not only streamlines transactions but also reinforces platforms against fraudulent activities.
Journal: Data and Metadata
Pages: .352
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.352
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.352:id:1056294dm2024352

Template-Type: ReDIF-Article 1.0
Author-Name: Tamara Crystina Valencia Jama
Author-Name-First: Tamara Crystina
Author-Name-Last: Valencia Jama
Author-Name: Yilena Montero Reyes
Author-Name-First: Yilena
Author-Name-Last: Montero Reyes
Author-Name: Lourdes Guadalupe Álvarez Proaño
Author-Name-First: Lourdes Guadalupe
Author-Name-Last: Álvarez Proaño
Author-Name: Klever Washington Moreno Parra
Author-Name-First: Klever Washington
Author-Name-Last: Moreno Parra
Title: Educational technological innovation on social networks facebook and twitter for the area of social studies
Abstract: This Project is based on a project of educational technological innovation in the social networks Facebook and Twitter for the area of Social Studies, due to its level of applicability to classes and the advantages it represents for students and teachers, which can be maintained in communication and interaction outside the classroom; In the case of adolescents, it is more interesting and motivating to learn through networks, exchange knowledge, opinions, criteria for the construction of knowledge and, above all, work as a team. In this sense, a diagnosis was made in students and teachers to identify the level of use of social networks and their incorporation into the teaching-learning process, being able to verify that the educational use of said networks is still insufficient. According to the results obtained, a system of activities was proposed that facilitate work in social networks, for the development of critical thinking and the acquisition of the skills of the Curriculum. A Manual was designed that allows the student to autonomously manage social networks and learn to learn
Journal: Data and Metadata
Pages: .351
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.351
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.351:id:1056294dm2024351

Template-Type: ReDIF-Article 1.0
Author-Name: Diego Isaías Granja Peñaherrera
Author-Name-First: Diego Isaías
Author-Name-Last: Granja Peñaherrera
Author-Name: Yilena Montero Reyes
Author-Name-First: Yilena
Author-Name-Last: Montero Reyes
Author-Name: Maritza Jacqueline Villacis Lizano
Author-Name-First: Maritza Jacqueline
Author-Name-Last: Villacis Lizano
Author-Name: Mayra Alejandra Moreno Genovés
Author-Name-First: Mayra Alejandra
Author-Name-Last: Moreno Genovés
Title: Facebook as a didactic tool for the development of writing in the english language
Abstract: The research focuses on the analysis of the use of social networks such as Facebook in the teaching-learning process of the English language, mainly in the development of writing skills in English. Learning with the use of technological resources favors the development, from an innovative perspective, of the practice of the English language, motivating students to learn to write from new technologies, which is why the research emphasizes the need for teachers of the Technology Management Unit of the University of the Armed Forces ESPE, learn about and use social networks that can facilitate the teaching of writing in the English language, through collaborative learning and through a social network that the majority of young people currently used, enriching the teaching-learning process in addition to generating greater motivation in the student. The chapter structure addresses the research topic from a conceptual theoretical approach to the fundamental categories as characteristics of social networks, social networks on the Internet and the social network Facebook. Likewise, the analysis and interpretation of results is carried out in Chapter II, from the application of the instruments in the selected population and sample, based on descriptive statistics. Finally, a set of strategies are proposed for the development of writing skills in English from the social network Facebook as a teaching tool for the development of writing skills in English. This work supports the importance of using ICTs as innovative teaching resources that respond to the needs in the field of knowledge and practice of students
Journal: Data and Metadata
Pages: .350
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.350
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.350:id:1056294dm2024350

Template-Type: ReDIF-Article 1.0
Author-Name: Suresh Subramanian
Author-Name-First: Suresh
Author-Name-Last: Subramanian
Title: An Effective Topic Modeling Strategies for Recommender Systems in Crowdfunding Platforms
Abstract: Capitalists come up with creative and innovative concepts, but a lack of finance limits their untapped economic potential. There are several channels that new entrepreneurs may use and take advantage of to attract money and other financial resources when beginning a firm thanks to current technology, which has drastically altered the way business is done on a broad scale. An entrepreneur uses the Internet to promote his concept to potential backers through crowdfunding. Online crowdfunding has labored to develop several advanced platforms that may serve as an interface to the fundraising process for a certain concept or project. Typically, the owner of the concept explores the market and does extensive research through a variety of channels, with the Internet assisting in moving ahead and making the idea actual. In truth, the owner of the concept frequently suffers obstacles and financial issues, therefore crowdsourcing helps to alleviate these issues. In this study, machine learning methods were used to train the system on the given data, beginning with the theme, followed by the blurb, which is the topic description, and finally by the topic category. Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) were employed as machine learning approaches to accomplish the goal. This study employs a variety of text classification algorithms, including Support Vector Machine (SVM), EXtreme Gradient Boosting (XG), K-Nearest Neighbours (KNN), and Random Forest (RF), to propose and forecast subject categories. Each algorithm performed differently in terms of precision, predictability, positive rate, and model correctness. SVM was the highest performance measuremen.
Journal: Data and Metadata
Pages: .349
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.349
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.349:id:1056294dm2024349

Template-Type: ReDIF-Article 1.0
Author-Name: María José Parada Carreño
Author-Name-First: María José
Author-Name-Last: Parada Carreño
Author-Name: Antonio José Bravo Valero
Author-Name-First: Antonio José
Author-Name-Last: Bravo Valero
Author-Name: Juan Diego Hernández Albarracín
Author-Name-First: Juan Diego
Author-Name-Last: Hernández Albarracín
Title: Sociocognitive configuration: meanings and creations in the mathematical learning of middle school students
Abstract: The study explores the socio-cognitive configuration in the mathematical learning of middle school students, relating cognitive processes to the social interactions that shape perceptions and performance in mathematics. A qualitative approach was adopted to delve into students' subjective experiences. Meaningful interactions revealed in the classroom enhance mathematical understanding and foster critical and problem-solving skills. The socio-cognitive configuration achieved evidence that mathematical cognition is deeply affected and defined by its social context and educational practices. This study underscores the importance of integrating students' personal and collective meanings in mathematical learning to foster a holistic pedagogical approach to both the transmission of knowledge and its social and personal relevance, which transforms mathematics teaching by making it more applicable to students' real-life challenges and contexts
Journal: Data and Metadata
Pages: .348
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.348
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.348:id:1056294dm2024348

Template-Type: ReDIF-Article 1.0
Author-Name: Mounia Amazian
Author-Name-First: Mounia
Author-Name-Last: Amazian
Author-Name: Zakia Nouira
Author-Name-First: Zakia
Author-Name-Last: Nouira
Author-Name: Mariam Filali
Author-Name-First: Mariam
Author-Name-Last: Filali
Title: Human resources management in the age of artificial intelligence
Abstract: Today's businesses are operating in a complex environment, marked by the emergence of a digital culture that is transforming space and time, as well as relationships at work. These various transformations have given rise to a new concept: artificial intelligence. With the introduction of artificial intelligence, work is being transformed, creating new challenges for organizations and leading to new HR practices in terms of recruitment, training, compensation, talent management and so on. For all these reasons, the mobilization of collective intelligence is ultimately becoming a priority for HR, insofar as it makes it possible to support transformation within organizations by means of agile methods. Against this backdrop of changing organizational practices, a number of questions arise: -What is artificial intelligence and how does it impact organizations? -What role can collective intelligence and artificial intelligence play in the evolution of the HR function? The aim of this theoretical research is to underpin the various concepts linked to these notions. We will also try to understand what is at stake in collective intelligence for HR, and how artificial intelligence can impact organizational practices.
Journal: Data and Metadata
Pages: .347
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.347
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.347:id:1056294dm2024347

Template-Type: ReDIF-Article 1.0
Author-Name: Gufran Ahmad Ansari
Author-Name-First: Gufran Ahmad
Author-Name-Last: Ansari
Author-Name: Salliah Shafi Bhat
Author-Name-First: Salliah
Author-Name-Last: Shafi Bhat
Author-Name: Mohd Dilshad Ansari
Author-Name-First: Mohd
Author-Name-Last: Dilshad Ansari
Author-Name: Sultan Ahmad
Author-Name-First: Sultan
Author-Name-Last: Ahmad
Author-Name: Hikmat A. M. Abdeljaber
Author-Name-First: Hikmat
Author-Name-Last: A. M. Abdeljaber
Title: Prediction and Diagnosis of Breast Cancer using Machine Learning Techniques
Abstract: Introduction: One of the most common types of cancer and a significant contributor to the high death rates among women is breast cancer. It usually occurs in women. It is crucial to acquire a diagnosis early in order to kill cancer from becoming worse. Objective: The traditional diagnosing procedure takes more time. A fast and useful option can apply Machine Learning Technique (MLT) to identify illnesses. However new technology creates a variety of high-dimensional data kinds particularly when it comes to health or cancer data. Methods: Data classification techniques like Machine Learning are efficient. Particularly in the medical field where such techniques are often utilised to make decisions via diagnosis and analysis. Using Wisconsin Breast Cancer Dataset, the proposed research was carried out (WBCD). Some of these issues may be solved using the feature selection approach. Results: This research analyses the classification accuracy of different MLT: Logistic Regression, Support Vector Machine, and K-Nearest Neighbour. According to experiment results, SVM has the best accuracy of all algorithms, at 97.12%. Conclusion: The mentioned prediction models are based on several supervised MLT. Tenfold cross validation is applied. Additionally, author also proposed a Flow chart of breast Cancer using MLT.
Journal: Data and Metadata
Pages: .346
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.346
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.346:id:1056294dm2024346

Template-Type: ReDIF-Article 1.0
Author-Name: Oleksandr Galushchenko
Author-Name-First: Oleksandr
Author-Name-Last: Galushchenko
Author-Name: Inna Pidbereznykh
Author-Name-First: Inna
Author-Name-Last: Pidbereznykh
Author-Name: Oleksandr Piroh
Author-Name-First: Oleksandr
Author-Name-Last: Piroh
Author-Name: Dmytro Khrapach
Author-Name-First: Dmytro
Author-Name-Last: Khrapach
Author-Name: Oleksii Tolmachov
Author-Name-First: Oleksii
Author-Name-Last: Tolmachov
Title: Cybersecurity and geopolitical dimensions of external information interventions in Ukraine: Analysis of current trends
Abstract: Introduction: From the year 2019, the country has experienced a series of cyber incidents affecting its key areas of infrastructure such as energy, finance, communication, government, healthcare, and technology. Purpose: This work aims to define and explain the contemporary threats in the sphere of cyber security, as well as the geopolitical aspect of external interference in Ukraine’s information space for the years 2019-2024. Method: Sources used to gather data for this research were government and cybersecurity firm reports, academic articles, news articles from reliable media houses, and social media listening tools. Quantitative data collected by statistical tools analyzed the trend as well as the intensity of cyber threats. They also included specific case exhibits as well as interviews with the experts to get more information. Results: The study indicated that there will be improved and more frequent attacks and that these will be more complex from 2019 to 2024 in Ukraine. It caused moderate disruption to the key sectors but the disruptions were especially noticed in sectors such as energy, finance, communication, government, healthcare, and technology. Conflict events in Donbas, the COVID-19 outbreak, the enhancement of military tensions, and the start of the Russian invasion of Ukraine in February 2022 were associated with increases in the level of cyber efforts. Conclusion: The conclusion points out that Ukraine needs effective international cooperation, the highest level of technological protection, and profound geopolitical analysis to safeguard cyberspace and prevent conflicts in Eastern Europe
Journal: Data and Metadata
Pages: .345
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.345
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.345:id:1056294dm2024345

Template-Type: ReDIF-Article 1.0
Author-Name: Jessica Paulina Guerrero Rodríguez
Author-Name-First: Jessica Paulina
Author-Name-Last: Guerrero Rodríguez
Author-Name: Angélica Salomé Herrera Molina
Author-Name-First: Angélica Salomé
Author-Name-Last: Herrera Molina
Author-Name: Paola Maricela Machado Herrera
Author-Name-First: Paola Maricela
Author-Name-Last: Machado Herrera
Author-Name: Verónica Rocío Tierra Tierra
Author-Name-First: Verónica Rocío
Author-Name-Last: Tierra Tierra
Author-Name: Tatiana Alexandra González Verdezoto
Author-Name-First: Tatiana Alexandra
Author-Name-Last: González Verdezoto
Author-Name: Edison Fernando Bonifaz Aranda
Author-Name-First: Edison Fernando
Author-Name-Last: Bonifaz Aranda
Author-Name: Verónica Sofía Quenorán Almeida
Author-Name-First: Verónica Sofía
Author-Name-Last: Quenorán Almeida
Author-Name: María Belén Espíndola Lara
Author-Name-First: María Belén
Author-Name-Last: Espíndola Lara
Title: Photoeducation strategy through emerging technologies
Abstract: Excessive exposure to ultraviolet radiation, added to the large amount of time that children spend exposed to the sun, causes lesions on their skin, significantly increasing the risk of suffering from skin cancer or melanoma in adulthood; Emerging technologies, such as mobile apps and UV-sensitive bracelets, offer practical and effective solutions to address the lack of knowledge and awareness about the risks of sun exposure, transforming the way we learn and engage with the world. particularly preschoolers. Objective: propose an educational strategy through emerging technology with the integration of AI in health education programs, aimed at children under 5 years of age. Methodology: a qualitative, ethnographic study was carried out, with the participation of 13 caregivers, 1 teacher, considering inclusion and exclusion criteria. The data were processed manually for exhaustive knowledge and meticulous review that can guide a complete and accurate interpretation of the participants' experiences. Results and conclusion: the study highlights the need for comprehensive and preventive education in photoprotection, using innovative technological tools to reach both children and their parents and teachers. Emerging technologies, such as mobile apps and UV-sensitive bracelets, offer practical and effective solutions to the risks of sun exposure. However, for this strategy to be effective, it is essential that it be implemented in an educational environment that supports and reinforces its use.
Journal: Data and Metadata
Pages: .344
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.344
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.344:id:1056294dm2024344

Template-Type: ReDIF-Article 1.0
Author-Name: John Alok
Author-Name-First: John
Author-Name-Last: Alok
Author-Name: Manish Tiwari
Author-Name-First: Manish
Author-Name-Last: Tiwari
Title: HR Aspects of Corporate Social Responsibility: A Comprehensive Review
Abstract: Introduction: The paper emphasizes the growing significance of Corporate Social Responsibility (CSR) in the business world, particularly how it intersects with Human Resources (HR) practices. It highlights the necessity for organizations to align their CSR initiatives with HR functions to achieve better outcomes. Objective: The review explores how CSR initiatives influence various HR functions, including employee engagement, recruitment, training and development, and overall employee well-being. This indicates that CSR is not just a peripheral concern but is integral to HR strategies. The research synthesizes and analyzes relevant literature on the topic, providing insights into the relationship between CSR and HR. This comprehensive approach aims to clarify the role of HR in embedding CSR values within the organizational culture. Method: The methods used in this paper combine quantitative analysis of a comprehensive HR dataset with qualitative literature review and theoretical frameworks to explore the critical relationship between HR practices and CSR initiatives. Result: By applying these data-driven findings, the organization can better align its workforce planning and development strategies, ultimately enhancing organizational performance and employee satisfaction. Conclusion: This multifaceted approach allows for a deeper understanding of how organizations can effectively integrate CSR into their HR strategies for sustainable success
Journal: Data and Metadata
Pages: 343
Volume: 4
Year: 2025
DOI: 10.56294/dm2025343
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:343:id:1056294dm2025343

Template-Type: ReDIF-Article 1.0
Author-Name: Siham Ammari
Author-Name-First: Siham
Author-Name-Last: Ammari
Author-Name: Amina Guennoun
Author-Name-First: Amina
Author-Name-Last: Guennoun
Author-Name: Ouafae Ammari
Author-Name-First: Ouafae
Author-Name-Last: Ammari
Author-Name: Mohamed Jallal El Adnani
Author-Name-First: Mohamed
Author-Name-Last: Jallal El Adnani
Author-Name: Souad Habbani
Author-Name-First: Souad
Author-Name-Last: Habbani
Title: The impact of entrepreneurship support programs on the survival of young agricultural enterprises: A Cox model approach
Abstract: This study examines the factors influencing the survival of new agricultural enterprises created by young entrepreneurs, using a sample of 184 businesses over a three-year period after their creation. The analysis begins with a description of the sample’s characteristics and then employs two quantitative approaches: the non-parametric Kaplan-Meier method and the semi-parametric Cox model. Empirical results reveal several key elements that significantly impact business survival. Entrepreneurial training is crucial as it enhances the skills needed to address challenges. Prior experience in the agricultural sector also strengthens entrepreneurs' resilience. Sufficient startup capital is essential for supporting initial operations and handling unforeseen issues. Innovation plays a vital role by enabling businesses to differentiate themselves and adapt to market changes. Finally, activity diversification helps mitigate risks and stabilize income. The study highlights the need for more diverse and adaptive post-creation support to effectively assist these businesses in their long-term development. Better-targeted and tailored support for young entrepreneurs could significantly improve survival rates and foster the sustainable growth of new agricultural enterprises
Journal: Data and Metadata
Pages: 342
Volume: 4
Year: 2025
DOI: 10.56294/dm2025342
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:342:id:1056294dm2025342

Template-Type: ReDIF-Article 1.0
Author-Name: FATIH Rabia
Author-Name-First: FATIH
Author-Name-Last: Rabia
Author-Name: AREZKI Sara
Author-Name-First: AREZKI
Author-Name-Last: Sara
Author-Name: GADI Taoufiq
Author-Name-First: GADI
Author-Name-Last: Taoufiq
Title: ZkSNARKs and Ticket-Based E-Voting: A Blockchain System Proof of Concept
Abstract: Most existing electronic voting systems and the traditional centralized ballot management do not meet the requirements for e-voting trustworthiness today since the rate of development in science and technology is ever-increasing. Despite the blockchain-based providers designing systems that guarantee the transparency of the election, the new systems are not exempted from threats that hackers can leverage to influence the votes. This further supports the evidence presented that Blockchain based systems have progressed but there is always more that can be done especially in terms of further strengthening the transparency, security and authentications to minimize the existing risks. In order to support these vulnerabilities, we are proposing in this paper using zK-SNARK a scheme that meets the basic requirements of electronic voting and ensures the reliability and security of voting. In this scheme, a Merkle tree is used to store each voter’s ticket where the ticket hash is created and registered into the tree’s leaf. The voter then proves that they possess a valid ticket and are eligible to vote through zk-SNARK proof, which is very secure and efficient in verifying the voter’s authenticity. This approach keeps the voting process anonymous yet allows for a fast and secure method of authenticating the voters.
Journal: Data and Metadata
Pages: .341
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.341
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.341:id:1056294dm2024341

Template-Type: ReDIF-Article 1.0
Author-Name: Alirio Antonio Mejía Marín
Author-Name-First: Alirio Antonio
Author-Name-Last: Mejía Marín
Author-Name: Jesús Orlando Gómez Rivero
Author-Name-First: Jesús Orlando
Author-Name-Last: Gómez Rivero
Title: Implementation of artificial intelligence in the educational processes of university teachers
Abstract: Introduction: Higher education is clarity an unprecedented transformation due to the growing incorporation of artificial intelligence (AI) tools in university teaching. The promise of AI in this context is clear: improve the quality of education, personalize learning, and prepare students for an ever-changing world. However, its use raises fundamental questions about the traditional role of the teacher and the student learning experience. Objective: Describe the implementation of artificial intelligence in the educational processes developed by university teachers of a private Ecuadorian institution. Method: Based on the positivist paradigm with a quantitative approach, a non-experimental cross-sectional design at a descriptive level is supported by field research. The population comprised 56 teachers, who answered a 29-item questionnaire validated by expert judgment and with a reliability level of 0.91. Results: The main findings demonstrate that teachers at higher education institutions implement AI in their educational processes in an incipient manner, which could be due to a lack of knowledge about the subject. Likewise, it was found that they do not consider the use of AI to be a good practice. AI in the tasks and evaluations that students develop, and they do not perceive the use of this tool in education as essential. However, in a contradictory way, the majority of teachers agree that they need training in AI applied to education and that this will permeate the future of universities
Journal: Data and Metadata
Pages: .338
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.338
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.338:id:1056294dm2024338

Template-Type: ReDIF-Article 1.0
Author-Name: Saad Saadouni
Author-Name-First: Saad
Author-Name-Last: Saadouni
Author-Name: Amina Guennoun
Author-Name-First: Amina
Author-Name-Last: Guennoun
Author-Name: Souad Habbani
Author-Name-First: Souad
Author-Name-Last: Habbani
Title: The impact of financial structure on performance: The case of ISO-certified, listed Moroccan companies
Abstract: This study analyzes the financial performance of 17 ISO-certified, listed Moroccan companies for the year 2022. The primary goal is to assess the impact of various financial variables on these companies' profitability. The financial variables examined include the degree of financial indebtedness, self-financing, financial leverage, liquidity, guarantees, company size, and speed of capital turnover. The analysis focuses on the relationship between these variables and profitability models (ROE and ROA). The findings indicate that the profitability models have respective risk levels of 5% and less than 10%. Significant variables influencing the financial performance of the studied companies were identified. The results emphasize the importance of financial management and strategic decision-making based on these models to enhance company performance
Journal: Data and Metadata
Pages: 337
Volume: 4
Year: 2025
DOI: 10.56294/dm2025337
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:337:id:1056294dm2025337

Template-Type: ReDIF-Article 1.0
Author-Name: Olha Voron
Author-Name-First: Olha
Author-Name-Last: Voron
Author-Name: Yaroslav Kichuk
Author-Name-First: Yaroslav
Author-Name-Last: Kichuk
Author-Name: Olena Yemelianova
Author-Name-First: Olena
Author-Name-Last: Yemelianova
Author-Name: Sergii Stepanov
Author-Name-First: Sergii
Author-Name-Last: Stepanov
Author-Name: Nataliia Shevtsova
Author-Name-First: Nataliia
Author-Name-Last: Shevtsova
Title: The use of Google web applications to create a learning environment in war conditions
Abstract: Introduction: In the modern world the digitalization of educational trends offers many mechanisms for implementation. At the same time their use depends on many conditions. The purpose of this article is to study the peculiarities of using Google web applications to create an accessible and interactive learning environment in wartime. The main objective are using Google tools to implement education in wartime, describe the results of their use, finding its efficiency. Method: The realization of this goal involves the use of certain scientific methods. In particular, we are talking about the use of a survey and the method of content analysis of scientific literature, which made it possible to compare the Ukrainian experience with the existing paradigms of perception of Google tools in education. Results: The study results show that Google web applications are an important part of the modern educational process. All applications have a simple and user-friendly interface. The measurements showed that Google Classroom (33.33%) and Google Meet (26.67%) are the most used applications. This proves their important role in organizing online classes both at the organizational and direct learning levels. Teachers also use Google Drive, Google Docs, and Google Forms, which fulfill their function in organizing the modern educational process. Conclusion: The conclusions note that the importance of further research is dictated by the need to take into account new changes and capabilities of the tools.
Journal: Data and Metadata
Pages: 336
Volume: 4
Year: 2025
DOI: 10.56294/dm2025336
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:336:id:1056294dm2025336

Template-Type: ReDIF-Article 1.0
Author-Name: Julio Rodrigo Morillo Cano
Author-Name-First: Julio Rodrigo
Author-Name-Last: Morillo Cano
Author-Name: Mely Anahí Castro Galárraga
Author-Name-First: Mely Anahí
Author-Name-Last: Castro Galárraga
Author-Name: Fátima Fernanda Orbe Cerón
Author-Name-First: Fátima Fernanda
Author-Name-Last: Orbe Cerón
Title: Evolution in the diagnosis and treatment of breast cancer: a PRISMA 2020 systematic review
Abstract: Breast cancer is the most common neoplasm and one of the leading causes of cancer-related mortality in women worldwide, presenting significant challenges in diagnosis, treatment, and prevention. The objective of the study was to systematically and critically review the scientific literature published between 2020 and 2024 on breast cancer, with an emphasis on advances in diagnostics, therapeutics, and the understanding of its risk factors and biomarkers, to identify trends and gaps in current knowledge. A systematic review was conducted following PRISMA 2020 guidelines, analyzing 19,038 articles identified in PubMed, from which 10 key studies were selected based on their relevance, methodological quality, and significant contributions. The results show that the integration of genomic, immunohistochemical, and immunological biomarkers has improved diagnosis and treatment personalization, especially in aggressive subtypes such as triple-negative breast cancer. Prevention is progressing with models integrating factors like mammographic density and polygenic risk, although barriers to implementation persist. Moreover, global disparities in diagnosis and treatment reflect structural inequities, particularly in low- and middle-income countries. Innovations in targeted therapies are expanding options for advanced and metastatic cases. In conclusion, diagnostic and therapeutic strategies for breast cancer have evolved significantly during the studied period, contributing to a more personalized and effective approach, yet challenges related to equity, accessibility, and the global implementation of advancements remain
Journal: Data and Metadata
Pages: .334
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.334
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.334:id:1056294dm2024334

Template-Type: ReDIF-Article 1.0
Author-Name: K. Prathap Kumar
Author-Name-First: K.
Author-Name-Last: Prathap Kumar
Author-Name: R. Rohini
Author-Name-First: R.
Author-Name-Last: Rohini
Title: Resource allocation on periotity based schuduling and improve the security using DSSHA-256
Abstract: Cloud computing has gained popularity with advancements in virtualization technology and the deployment of 5G. However, scheduling workload in a heterogeneous multi-cloud environment is a complicated process. Users of cloud services want to ensure that their data is secure and private, especially sensitive or proprietary information. Several research works have been proposed to solve the challenges associated with cloud computing. The proposed Adaptive Priority based scheduling (PBS) focuses on reducing data access completion time and computation expense for task scheduling in cloud computing. PBS assigns tasks depending on its size and selects the minimum cost path for data access. It contains a task register, scheduler, and task execution components for efficient task execution. The proposed system also executes a double signature mechanism for data privacy and security in data storage. This study correlates the performance of three algorithms, PBS, (Task Requirement Degree) TRD and (recommended a Risk adaptive Access Control) RADAC, in terms of task execution time and makespan time. The experimental results demonstrate that PBS outperforms TRD and RADAC in both metrics, as the number of tasks increases. PBS has a minimum task execution time and a lower makespan time than the other two algorithms
Journal: Data and Metadata
Pages: 193
Volume: 3
Year: 2024
DOI: 10.56294/dm2024193
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:193:id:1056294dm2024193

Template-Type: ReDIF-Article 1.0
Author-Name: G. Meenalochini
Author-Name-First: G.
Author-Name-Last: Meenalochini
Author-Name: D. Amutha Guka
Author-Name-First: D.
Author-Name-Last: Amutha Guka
Author-Name: Ramkumar Sivasakthivel
Author-Name-First: Ramkumar
Author-Name-Last: Sivasakthivel
Author-Name: Manikandan Rajagopal
Author-Name-First: Manikandan
Author-Name-Last: Rajagopal
Title: A Progressive UNDML Framework Model for Breast Cancer Diagnosis and Classification
Abstract: According to recent research, it is studied that the second most common cause of death for women worldwide is breast cancer. Since it can be incredibly difficult to determine the true cause of breast cancer, early diagnosis is crucial to lowering the disease's fatality rate. Early cancer detection raises the chance of survival by up to 8 %. Radiologists look for irregularities in breast images collected from mammograms, X-rays, or MRI scans. Radiologists of all levels struggle to identify features like lumps, masses, and micro-calcifications, which leads to high false-positive and false-negative rates. Recent developments in deep learning and image processing give rise to some optimism for the creation of improved applications for the early diagnosis of breast cancer. A methodological study was carried out in which a new Deep U-Net Segmentation based Convolutional Neural Network, named UNDML framework is developed for identifying and categorizing breast anomalies. This framework involves the operations of preprocessing, quality enhancement, feature extraction, segmentation, and classification. Preprocessing is carried out in this case to enhance the quality of the breast picture input. Consequently, the Deep U-net segmentation methodology is applied to accurately segment the breast image for improving the cancer detection rate. Finally, the CNN mechanism is utilized to categorize the class of breast cancer. To validate the performance of this method, an extensive simulation and comparative analysis have been performed in this work. The obtained results demonstrate that the UNDML mechanism outperforms the other models with increased tumor detection rate and accuracy
Journal: Data and Metadata
Pages: 198
Volume: 3
Year: 2024
DOI: 10.56294/dm2024198
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:198:id:1056294dm2024198

Template-Type: ReDIF-Article 1.0
Author-Name: El Houssaine Fathi
Author-Name-First: El Houssaine
Author-Name-Last: Fathi
Author-Name: Ahlam Qafas
Author-Name-First: Ahlam
Author-Name-Last: Qafas
Author-Name: Jouilil Youness
Author-Name-First: Jouilil
Author-Name-Last: Youness
Title: Economic Growth Unleashed: The Power of Institutional Quality
Abstract: This paper examines the relationship between economic growth and institutional quality in the context of the Moroccan economy. Using annual data from 1970 to 2020 and an Autoregressive Distributed Lag (ARDL) cointegration approach, we analyze the long-run and short-run nexus between these two variables. The statistical tests performed, including the ADF and Phillips Perron tests, indicate integration at different orders, and the bounds cointegration test proposed by Pesaran was also conducted. The study finds that institutional quality has a positive short-term impact on economic growth. Furthermore, in the long term, the study reveals that institutional quality continues to positively influence economic growth in Morocco (P-value=0,01<5 %). These results contribute valuable insights to the existing empirical literature and can guide policymakers and stakeholders in implementing institutional reforms to promote economic development
Journal: Data and Metadata
Pages: 208
Volume: 3
Year: 2024
DOI: 10.56294/dm2024208
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:208:id:1056294dm2024208

Template-Type: ReDIF-Article 1.0
Author-Name: Lida Vásquez-Pajuelo
Author-Name-First: Lida
Author-Name-Last: Vásquez-Pajuelo
Author-Name: Jhonny Richard Rodriguez-Barboza
Author-Name-First: Jhonny Richard
Author-Name-Last: Rodriguez-Barboza
Author-Name: Karina Raquel Bartra-Rivero
Author-Name-First: Karina Raquel
Author-Name-Last: Bartra-Rivero
Author-Name: Edgar Antonio Quintanilla-Alarcón
Author-Name-First: Edgar Antonio
Author-Name-Last: Quintanilla-Alarcón
Author-Name: Wilfredo Vega-Jaime
Author-Name-First: Wilfredo
Author-Name-Last: Vega-Jaime
Author-Name: Eduardo Francisco Chavarri-Joo
Author-Name-First: Eduardo Francisco
Author-Name-Last: Chavarri-Joo
Title: Digital Challenges: The Need to Improve the Use of Information Technologies in Teaching
Abstract: In the post-pandemic scenario, a study was conducted at I.E. 50499 Justo Barrionuevo Álvarez in Cusco, Peru, to investigate the relationship between the use of information technologies and digital competencies among teachers. With a sample of 54 teachers, a structured questionnaire was administered to assess their competencies. The results revealed a direct positive correlation between the use of technologies and digital competencies, with a Spearman's Rho coefficient of 0,877, indicating a significant relationship. Correlations between the use of information technologies and the dimensions of digital competencies ranged from moderate to high. Significant correlations were observed in areas such as problem-solving (Rho=0,457), information and digital literacy (Rho=0,633), and security (Rho=0,743), among others. These findings suggest that, despite limited experience and limited knowledge of digital technologies among teachers in the institution, there is a notable relationship between the use of these technologies and their digital competencies. This study underscores the need for further training in information technologies for teachers in non-modernized urban contexts and for those who are older adults with limited prior experience in the digital domain. Enhancing digital competencies is crucial for adapting to the educational challenges in this new era of education
Journal: Data and Metadata
Pages: 216
Volume: 3
Year: 2024
DOI: 10.56294/dm2024216
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:216:id:1056294dm2024216

Template-Type: ReDIF-Article 1.0
Author-Name: Ismail Ezzerrifi Amrani
Author-Name-First: Ismail Ezzerrifi
Author-Name-Last: Amrani
Author-Name: Ahmed Lahjouji El Idrissi
Author-Name-First: Ahmed Lahjouji
Author-Name-Last: El Idrissi
Author-Name: Bahri Abdelkhalek
Author-Name-First: Bahri
Author-Name-Last: Abdelkhalek
Author-Name: Ahmad El Allaoui
Author-Name-First: Ahmad
Author-Name-Last: El Allaoui
Title: A dragonfly algorithm for solving the Fixed Charge Transportation Problem FCTP
Abstract: The primary focus of this article is dedicated to a thorough investigation of the Fixed Load Transportation Problem (FCTP) and the proposition of an exceedingly efficient resolution method, with a specific emphasis on the achievement of optimal transportation plans within practical time constraints. The FCTP, recognized for its intricate nature, falls into the NP-complete category, notorious for its exponential growth in solution time as the problem's size escalates. Within the realm of combinatorial optimization, metaheuristic techniques like the Dragonfly algorithm and genetic algorithms have garnered substantial acclaim due to their remarkable capacity to deliver high-quality solutions to the challenging FCTP. These techniques demonstrate substantial potential in accelerating the resolution of this formidable problem. The central goal revolves around the exploration of groundbreaking solutions for the Fixed Load Transportation Problem, all while concurrently minimizing the time investment required to attain these optimal solutions. This undertaking necessitates the adept utilization of the Dragonfly algorithm, an algorithm inspired by natural processes, known for its adaptability and robustness in solving complex problems. The FCTP, functioning as an optimization problem, grapples with the multifaceted task of formulating distribution plans for products originating from multiple sources and destined for various endpoints. The overarching aspiration is to minimize overall transportation costs, a challenge that mandates meticulous considerations, including product availability at source locations and demand projections at destination points. The proposed methodology introduces an innovative approach tailored explicitly for addressing the Fixed Charge Transport Problem (FCTP) by harnessing the inherent capabilities of the Dragonfly algorithm. This adaptation of the algorithm's underlying processes is precisely engineered to handle large-scale FCTP instances, with the ultimate objective of unveiling solutions that have hitherto remained elusive. The numerical results stemming from our rigorous experiments unequivocally underscore the remarkable prowess of the Dragonfly algorithm in discovering novel and exceptionally efficient solutions. This demonstration unequivocally reaffirms its effectiveness in overcoming the inherent challenges posed by substantial FCTP instances. In summary, the research represents a significant leap forward in the domain of FCTP solution methodologies by seamlessly integrating the formidable capabilities of the Dragonfly algorithm into the problem-solving process. The insights and solutions presented in this article hold immense promise for significantly enhancing the efficiency and effectiveness of FCTP resolution, ultimately benefiting a broad spectrum of industries and logistics systems, and promising advancements in the optimization of transportation processes
Journal: Data and Metadata
Pages: 218
Volume: 3
Year: 2024
DOI: 10.56294/dm2024218
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:218:id:1056294dm2024218

Template-Type: ReDIF-Article 1.0
Author-Name: Oumaima El Haddadi
Author-Name-First: Oumaima
Author-Name-Last: El Haddadi
Author-Name: Max Chevalier
Author-Name-First: Max
Author-Name-Last: Chevalier
Author-Name: Bernard Dousset
Author-Name-First: Bernard
Author-Name-Last: Dousset
Author-Name: Ahmad El Allaoui
Author-Name-First: Ahmad
Author-Name-Last: El Allaoui
Author-Name: Anass El Haddadi
Author-Name-First: Anass
Author-Name-Last: El Haddadi
Author-Name: Olivier Teste
Author-Name-First: Olivier
Author-Name-Last: Teste
Title: Overview on Data Ingestion and Schema Matching
Abstract: This overview traced the evolution of data management, transitioning from traditional ETL processes to addressing contemporary challenges in Big Data, with a particular emphasis on data ingestion and schema matching. It explored the classification of data ingestion into batch, real-time, and hybrid processing, underscoring the challenges associated with data quality and heterogeneity. Central to the discussion was the role of schema mapping in data alignment, proving indispensable for linking diverse data sources. Recent advancements, notably the adoption of machine learning techniques, were significantly reshaping the landscape. The paper also addressed current challenges, including the integration of new technologies and the necessity for effective schema matching solutions, highlighting the continuously evolving nature of schema matching in the context of Big Data
Journal: Data and Metadata
Pages: 219
Volume: 3
Year: 2024
DOI: 10.56294/dm2024219
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:219:id:1056294dm2024219

Template-Type: ReDIF-Article 1.0
Author-Name: Lucía Asencios-Trujillo
Author-Name-First: Lucía
Author-Name-Last: Asencios-Trujillo
Author-Name: Djamila Gallegos-Espinoza
Author-Name-First: Djamila
Author-Name-Last: Gallegos-Espinoza
Author-Name: Lida Asencios-Trujillo
Author-Name-First: Lida
Author-Name-Last: Asencios-Trujillo
Author-Name: Livia Piñas-Rivera
Author-Name-First: Livia
Author-Name-Last: Piñas-Rivera
Author-Name: Carlos LaRosa-Longobardi
Author-Name-First: Carlos
Author-Name-Last: LaRosa-Longobardi
Author-Name: Rosa Perez-Siguas
Author-Name-First: Rosa
Author-Name-Last: Perez-Siguas
Title: Automatic Mobile Learning System for the Constant Preparation of the Student Community
Abstract: Introduction: the events that occurred with the pandemic caused a drastic change in all activities with direct contact due to the high risk of contagion, with educational centers being affected by the closure measures and the imposition of virtual classes to continue with student preparation, leading many students to see the need to have a computer to take their classes, eventually showing boredom due to the lack of desire to be in front of a computer, This to a certain extent weakens their interest in learning and affects their learning because mobile devices have become more important due to the various applications that provide students with information. For this reason, we propose mobile learning that allows students to have more information, as well as interaction with different students so that they have the opportunity to learn on a constant basis. Objective: the objective is to create an automatic mobile learning system for the constant preparation of the student community. Method: a methodology based on a client-server model to take advantage of the various educational resources accompanied by the good support it provides the subjects for students with the interaction of a mobile application. Results: through the operation of the system, it was visualized that the tests carried out with the students were presented with an efficiency of 96,70 %, Conclusions: this system presents a high efficiency that allows to reinforce the subjects that need more prominence in the student's learning and progress of level through the teacher's evaluations
Journal: Data and Metadata
Pages: 221
Volume: 3
Year: 2024
DOI: 10.56294/dm2024221
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:221:id:1056294dm2024221

Template-Type: ReDIF-Article 1.0
Author-Name: Edith Georgina Surdez Pérez
Author-Name-First: Edith Georgina
Author-Name-Last: Surdez Pérez
Author-Name: María del Carmen Sandoval Caraveo
Author-Name-First: María del Carmen
Author-Name-Last: Sandoval Caraveo
Author-Name: Maribel Flores Galicia
Author-Name-First: Maribel
Author-Name-Last: Flores Galicia
Title: Social capital in small industrial firms and its link with innovation
Abstract: Introduction: social Capital in organizations is an intangible asset that represents the favourable relationships that exist between work teams, within an organization and externally, to different interest groups. Objective: this study examined the link between internal relational social capital (RSC) and external RSC with innovation in small industrial firms in Tabasco, Mexico. There was also an inquiry into how much internal RSC and external RSC explain innovation. Methods: the design was nonexperimental, cross-sectional, descriptive, correlational, and explanatory. Linear regression analysis was used. Results: significant positive relationships was identified between internal RSC and external RSC and innovation. The internal RSC and external RSC contributed significantly to the explaining of innovation. Areas of opportunity were identified for these firms in process design and formal research activities for new raw materials, production procedures and patent generation. Conclusion: to promote innovation, managers of small industrial companies must continue to establish strategies and practices to strengthen RSC
Journal: Data and Metadata
Pages: 227
Volume: 3
Year: 2024
DOI: 10.56294/dm2024227
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:227:id:1056294dm2024227

Template-Type: ReDIF-Article 1.0
Author-Name: Anali Alvarado-Acosta
Author-Name-First: Anali
Author-Name-Last: Alvarado-Acosta
Author-Name: Jesús Fernández-Saavedra
Author-Name-First: Jesús
Author-Name-Last: Fernández-Saavedra
Author-Name: Brian Meneses-Claudio
Author-Name-First: Brian
Author-Name-Last: Meneses-Claudio
Title: Transformation and digital challenges in Peru during the COVID-19 pandemic, in the educational sector between 2020 and 2023: Systematic Review
Abstract: Introduction: digital transformation in the Peruvian educational sector has experienced a significant boost after facing the COVID-19 pandemic. During the period between 2020 and 2023, various innovative methods have been implemented to ensure the continuity of the academic year. Objective: explain how the digital transformation was carried out in the Peruvian educational sector after facing the COVID-19 pandemic to the present (2020 – 2023). Method: examples from many institutions, statistical studies and scientific and technological references were taken into account to achieve the objective. Throughout this work we are analyzing the different and innovative methods used by teachers to provide continuity to the academic year and how digital challenges were overcome. Results: 78 documents from Scopus and Scielo were reviewed, leaving 62 after filtering. These cover 8 categories on the impact of the pandemic on education, the transition to online teaching, job skills, challenges and advantages of virtual education, innovation in higher education, educational evaluation in virtual environments, educational internationalization and challenges for teachers during the COVID-19 pandemic. Conclusions: in conclusion, the digital transformation in the Peruvian educational sector after the COVID-19 pandemic has been fundamental to guarantee the continuity of the teaching-learning process
Journal: Data and Metadata
Pages: 232
Volume: 3
Year: 2024
DOI: 10.56294/dm2024232
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:232:id:1056294dm2024232

Template-Type: ReDIF-Article 1.0
Author-Name: Ji-Hyun Jang
Author-Name-First: Ji-Hyun
Author-Name-Last: Jang
Author-Name: Masatsugu Nemoto
Author-Name-First: Masatsugu
Author-Name-Last: Nemoto
Title: A Study of Factors Influencing Happiness in Korea: Topic Modelling and Neural Network Analysis
Abstract: The aim of this study is to derive the important factors that influence levels of happiness in Korea, and to identify which factors are particularly important among these influencing factors. To achieve this goal, topic modelling analysis, machine learning analysis and neural network analysis methods were utilized. The Netminer 4.5 program was used for topic modelling analysis and machine learning analysis, and SPSS MODELER 18 was used to perform neural network analysis. Two types of analysis data were used in this study. The first consisted of 1 000 papers relating to happiness published in academic journals managed by the Springer publishing company, which were used to derive happiness-influencing factors. The second consisted of a survey conducted in 2020 by the Community Well-being Center of the Graduate School of Public Administration at Seoul National University in Korea. A total of 16 655 people responded to this survey. The analysis results of the study are as follows. Important variables that affect the level of happiness of Korean residents are: family life, social status, income, health, and perceptions of inequality. Analysis using neural network analysis of the most important factors influencing happiness showed that satisfaction with family life had the most important influence. This suggests that policies that can improve the quality of family life, such as family-friendly work environments, childcare support, and domestic violence prevention and response programmes, will become important in the future
Journal: Data and Metadata
Pages: 238
Volume: 3
Year: 2024
DOI: 10.56294/dm2024238
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:238:id:1056294dm2024238

Template-Type: ReDIF-Article 1.0
Author-Name: Rafael Emiliano Sulca Quispe
Author-Name-First: Rafael Emiliano
Author-Name-Last: Sulca Quispe
Author-Name: Víctor Enrique Lizama Mendoza
Author-Name-First: Víctor Enrique
Author-Name-Last: Lizama Mendoza
Author-Name: Luisa Margarita Díaz Ricalde de Arenas
Author-Name-First: Luisa Margarita
Author-Name-Last: Díaz Ricalde de Arenas
Author-Name: Carlos Heraclides Pajuelo Camones
Author-Name-First: Carlos Heraclides
Author-Name-Last: Pajuelo Camones
Author-Name: Juan Pablo Trujillo Soncco
Author-Name-First: Juan Pablo
Author-Name-Last: Trujillo Soncco
Title: Academic self-efficacy and anxiety about English learning in university students
Abstract: In the university context, it is a concern to improve English learning in students, given that much scientific information is found in this language, hence the interest in examining the related factors. The objective was to determine the relationship between academic self-efficacy (AA) and linguistic anxiety about learning English (ALAI). The research was quantitative, basic and descriptive correlational design, with a non-probabilistic sample of 246 students from a state university. The validity of the instruments was evaluated by means of expert judgment and factor analysis. Cronbach's alpha reliability of the AA was 0,938 and of the ALAI 0,908. The results showed a significant inverse correlation between the variables (r = -0,403, p = 0,001). It is concluded that the higher the AA, the lower the ALAI, which merits improving students' self-efficacy
Journal: Data and Metadata
Pages: 239
Volume: 3
Year: 2024
DOI: 10.56294/dm2024239
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:239:id:1056294dm2024239

Template-Type: ReDIF-Article 1.0
Author-Name: Mhammed El Bakkali
Author-Name-First: Mhammed
Author-Name-Last: El Bakkali
Author-Name: Redouane Messnaoui
Author-Name-First: Redouane
Author-Name-Last: Messnaoui
Author-Name: Mustapha Elkhaoudi
Author-Name-First: Mustapha
Author-Name-Last: Elkhaoudi
Author-Name: Omar Cherkaoui
Author-Name-First: Omar
Author-Name-Last: Cherkaoui
Author-Name: Aziz Soulhi
Author-Name-First: Aziz
Author-Name-Last: Soulhi
Title: Predicting saturation for a new fabric using artificial intelligence (fuzzy logic): experimental part
Abstract: Weaving saturation can have harmful consequences, such as problems with loom performance, accelerated wear of mechanical parts and loss of raw materials. To avoid these problems, when designing and creating new fabrics, the densities and yarn qualities must be carefully matched with the weaves to ensure successful testing. To facilitate this task, this study focuses on the development of a practical fuzzy logic model for predicting the saturation of new fabrics. An experimental part was carried out to validate this fuzzy model. The fabric samples used in this study came from three different types of weaves, namely plain, twill and satin. These samples also included five weft counts (Nm) and eight different densities. The results obtained using the fuzzy logic model developed were compared with experimental values. The prediction results were satisfactory and precise, demonstrating the effectiveness of the fuzzy logic model developed. The mean absolute error of the calculated fuzzy model was 1,97 %. It was therefore confirmed that this fuzzy model was both fast and reliable for predicting the saturation of new fabric
Journal: Data and Metadata
Pages: 251
Volume: 3
Year: 2024
DOI: 10.56294/dm2024251
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:251:id:1056294dm2024251

Template-Type: ReDIF-Article 1.0
Author-Name: Ana Karen Romero
Author-Name-First: Ana Karen
Author-Name-Last: Romero
Author-Name: Deyanira Bernal
Author-Name-First: Deyanira
Author-Name-Last: Bernal
Author-Name: Reyna Christian Sánchez
Author-Name-First: Reyna Christian
Author-Name-Last: Sánchez
Title: Analysis of scientific information from a bibliometric approach between Chat GPT and Scopus: A comparative study
Abstract: One of the main challenges faced by teachers, researchers, and students today is efficiently filtering reliable and useful information available on the internet, as well as in scientific academic databases. To address this phenomenon, the bibliometrics tool is used, which involves understanding the number of publications, analyzing them, and determining their trend based on the application of filters and relationships of scientific concepts in specialized topics. There are other technological tools that allow finding bibliographic information on the internet, such as artificial intelligence (AI) specifically through the ChatGPT chatbot (Generative Pre-trained transformer). The objective of this article is to identify the differences between the results of a bibliometric analysis from Scopus and ChatGPT; the research type is documentary; the search strategy for the bibliometric analysis was "Dynamic Capabilities." Findings show that there are differences between the data obtained from the two bibliometric analyses, including authors, subject areas, affiliations, and keywords; it should be noted that the use of ChatGPT is a basic and simple tool that complements the bibliometric analysis provided by an academic database like Scopus; it is suggested to compare the results analytically and manually at all times, which is of interest to academia and the development of theoretical frameworks in research work
Journal: Data and Metadata
Pages: 252
Volume: 3
Year: 2024
DOI: 10.56294/dm2024252
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:252:id:1056294dm2024252

Template-Type: ReDIF-Article 1.0
Author-Name: Edisson Vladimir Maldonado Mariño
Author-Name-First: Edisson Vladimir
Author-Name-Last: Maldonado Mariño
Author-Name: Dario Orlando Siza Saquinga
Author-Name-First: Dario Orlando
Author-Name-Last: Siza Saquinga
Author-Name: Diego Eduardo Guato Canchinia
Author-Name-First: Diego Eduardo
Author-Name-Last: Guato Canchinia
Author-Name: Alexander Javier Ramos Velastegui
Author-Name-First: Alexander Javier
Author-Name-Last: Ramos Velastegui
Title: Systematization of research on the incidence of pesticides in people, use of biomarkers
Abstract: Currently the use of pesticides in agriculture has expanded in the search for greater productivity. These products can harm people's health in various ways. These effects can be captured through the use of genotoxicity biomarkers. The objective of this research is to systematize studies on biomarkers of genotoxicity of people exposed to pesticides in South America. The PRISMA method was applied to determine the studies to be analyzed. 15 documents met the inclusion criteria. Among the adverse health effects perceived in studies are neurological, respiratory, dermatological and endocrine disorders, as well as an increased risk of cancer. The main biomarkers identified are the comet assay, the cytokinesis blockade micronucleus assay, and the buccal cytoma micronucleus assay. Polymerase chain reaction, chromosomal aberrations, flow cytometry, and fluorescence in situ hybridization were also taken into account. Limitations were determined by biomarker. The usefulness of using multiple biomarkers is highlighted for a more complete and precise evaluation of pesticide exposure and genotoxic damage in agricultural workers in South America. The establishment of protective measures for workers against the use of pesticides and opting for the use of pesticides of biological origin will contribute to the preservation of people's health
Journal: Data and Metadata
Pages: 253
Volume: 3
Year: 2024
DOI: 10.56294/dm2024253
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:253:id:1056294dm2024253

Template-Type: ReDIF-Article 1.0
Author-Name: Allison Ramirez-Cruz
Author-Name-First: Allison
Author-Name-Last: Ramirez-Cruz
Author-Name: Caleb Sucapuca
Author-Name-First: Caleb
Author-Name-Last: Sucapuca
Author-Name: Mardel Morales-García
Author-Name-First: Mardel
Author-Name-Last: Morales-García
Author-Name: Víctor D. Álvarez-Manrique
Author-Name-First: Víctor D.
Author-Name-Last: Álvarez-Manrique
Author-Name: Liset Z. Sairitupa-Sanchez
Author-Name-First: Liset Z.
Author-Name-Last: Sairitupa-Sanchez
Author-Name: Alcides A Flores-Saenz
Author-Name-First: Alcides A
Author-Name-Last: Flores-Saenz
Author-Name: Wilter C. Morales-García
Author-Name-First: Wilter C.
Author-Name-Last: Morales-García
Title: Validation of a Job Satisfaction Scale among Health Workers
Abstract: Background: job satisfaction is a key focus in organizational behavior studies, particularly relevant in the healthcare sector and nursing. It influences patient care quality and staff retention and is shaped by the work environment, working conditions, managerial support, and interactions among colleagues. However, there is limited research specifically addressing the job satisfaction of nurses in Peru, a critical area in health administration. Objective: this study aimed to evaluate the metric properties of the S20/23 job satisfaction scale among Peruvian nurses. Methods: an instrumental research design was employed using a non-probabilistic sample of 325 nurses from two hospitals in Lima, Peru. The Chilean version of the S20/23 scale was used, comprising four dimensions of job satisfaction (relationship with supervision, physical work space, professional fulfillment, and training and decision-making opportunities). Data analysis included descriptive statistics, confirmatory factor analysis (CFA), and reliability tests using Cronbach's Alpha and McDonald's Omega. Results: the CFA revealed a satisfactory fit for the four-dimensional structure with 18 items (χ2 = 387,290, df = 124, p < ,001, CFI = 0,92, TLI = 0,90, RMSEA = 0,08, SRMR = 0,05). The scale also demonstrated high reliability for each dimension: relationship with supervision (α = 0,90, ꞷ = 0,87), physical work space (α, ꞷ = 0,92), professional fulfillment (α, ꞷ = 0,88), and training and decision-making opportunities (α = 0,88, ꞷ = 0,84), with acceptable factor loadings (>0,70). Conclusions: the adapted 18-item S20/23 scale is a valid and reliable tool for assessing job satisfaction among Peruvian nurses. The study highlights the importance of specific job satisfaction dimensions, such as relationships with supervisors and professional development opportunities, in the Peruvian nursing context
Journal: Data and Metadata
Pages: 260
Volume: 3
Year: 2024
DOI: 10.56294/dm2024260
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:260:id:1056294dm2024260

Template-Type: ReDIF-Article 1.0
Author-Name: M. Munir
Author-Name-First: M.
Author-Name-Last: Munir
Author-Name: Dwi Fitria Al Husaeni
Author-Name-First: Dwi Fitria
Author-Name-Last: Al Husaeni
Author-Name: R. Rasim
Author-Name-First: R.
Author-Name-Last: Rasim
Author-Name: Laksmi Dewi
Author-Name-First: Laksmi
Author-Name-Last: Dewi
Author-Name: Azizah Nurul Khoirunnisa
Author-Name-First: Azizah Nurul
Author-Name-Last: Khoirunnisa
Title: Bibliometric Mapping of Trends of Project-Based Learning with Augmented Reality on Communication Ability of Children with Special Needs (Autism)
Abstract: Autistic children have the right to education. Education helps them develop their communication skills. Using a project-based learning model can improve communication skills. Currently, there is a lot of research discussing the use of project-based learning for children with special needs. Therefore, this study aims to analyze the use of project-based learning with augmented reality on the communication skills of autistic children. The method used is a systematic literature review and theoretical bibliometric analysis of research on communication skills of children with autism sourced from Scopus from 2013 to 2022. The research stages are determining i) research questions; ii) inclusion criteria; iii) quality assessment; iv) data collection; and v) bibliometric analysis. The results of this research note that research on the communication skills of autistic children is still a research trend that is of great interest to researchers with an increase in research occurring from 2015 to 2022. Countries in the Americas and Asia contributed the most to research on this research theme. There is a relationship between project requirements (P), communication skills (CS), and autism spectrum disorder (ASD). This relationship is indicated by the strength of the P→CS link of 2 and the strength of the CS→ASD link of 4. This review shows that the characteristics of project-based learning can help train the level of communication skills of autistic children and will be better if assisted by the use of AR
Journal: Data and Metadata
Pages: 261
Volume: 3
Year: 2024
DOI: 10.56294/dm2024261
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:261:id:1056294dm2024261

Template-Type: ReDIF-Article 1.0
Author-Name: D Sasirega
Author-Name-First: D
Author-Name-Last: Sasirega
Author-Name: V. Krishnapriya
Author-Name-First: V.
Author-Name-Last: Krishnapriya
Title: Hybrid Feature Selection with Chaotic Rat Swarm Optimization-Based Convolutional Neural
Abstract: Introduction: Early diagnosis of Cardiovascular Disease (CVD) is vital in reducing mortality rates. Artificial intelligence and machine learning algorithms have increased the CVD prediction capability of clinical decision support systems. However, the shallow feature learning in machine learning and incompetent feature selection methods still pose a greater challenge. Consequently, deep learning algorithms are needed to improvise the CVD prediction frameworks. Methods: This paper proposes an advanced CDSS for CVD detection using a hybrid DL method. Initially, the Improved Hierarchical Density-based Spatial Clustering of Applications with Noise (IHDBSCAN), Adaptive Class Median-based Missing Value Imputation (ACMMVI) and Clustering Using Representatives-Adaptive Synthetic Sampling (CURE-ADASYN) approaches are introduced in the pre-processing stage for enhancing the input quality by solving the problems of outliers, missing values and class imbalance, respectively. Then, the features are extracted, and optimal feature subsets are selected using the hybrid model of Information gain with Improved Owl Optimization algorithm (IG-IOOA), where OOA is improved by enhancing the search functions of the local search process. These selected features are fed to the proposed Chaotic Rat Swarm Optimization-based Convolutional Neural Networks (CRSO-CNN) classifier model for detecting heart disease. Results: Four UCI datasets are used to validate the proposed framework, and the results showed that the OOA-DLSO-ELM-based approach provides better heart disease prediction with high accuracy of 97,57 %, 97,32 %, 96,254 % and 97,37 % for the four datasets. Conclusions: Therefore, this proposed CRSO-CNN model improves the heart disease classification with reduced time complexity for all four UCI datasets
Journal: Data and Metadata
Pages: 262
Volume: 3
Year: 2024
DOI: 10.56294/dm2024262
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:262:id:1056294dm2024262

Template-Type: ReDIF-Article 1.0
Author-Name: Edwin Gustavo Estrada-Araoz
Author-Name-First: Edwin Gustavo
Author-Name-Last: Estrada-Araoz
Author-Name: Yolanda Paredes-Valverde
Author-Name-First: Yolanda
Author-Name-Last: Paredes-Valverde
Author-Name: Rosel Quispe-Herrera
Author-Name-First: Rosel
Author-Name-Last: Quispe-Herrera
Author-Name: Néstor Antonio Gallegos-Ramos
Author-Name-First: Néstor Antonio
Author-Name-Last: Gallegos-Ramos
Author-Name: Freddy Abel Rivera-Mamani
Author-Name-First: Freddy Abel
Author-Name-Last: Rivera-Mamani
Author-Name: Alfonso Romaní-Claros
Author-Name-First: Alfonso
Author-Name-Last: Romaní-Claros
Title: Investigating the attitude of university students towards the use of ChatGPT as a learning resource
Abstract: Introduction: currently, the integration of innovative technologies plays a crucial role in students' academic formation. In this context, ChatGPT emerges as a cutting-edge tool with the potential to transform the educational experience. Objective: to assess the attitude of university students towards the use of ChatGPT as a learning resource. Methods: a quantitative study, non-experimental design and observational and descriptive type. The sample was determined through simple random sampling and consisted of 269 university students of both genders who were administered the Attitudes towards the Use of ChatGPT Scale, an instrument with adequate metric properties. Results: the attitude towards the use of ChatGPT as a learning resource was predominantly rated at a medium level, as were the affective, cognitive, and behavioral dimensions. This suggests that students enjoy using ChatGPT as a tool in their learning process and consider it facilitates and improves their educational experience. However, they expressed concern about the possibility of this tool generating inaccurate results. Conclusions: the attitude of university students towards the use of ChatGPT as a learning resource was rated at a medium level. Likewise, it was determined that as students progressed in their academic training, they developed a more favorable attitude towards the use of ChatGPT
Journal: Data and Metadata
Pages: 268
Volume: 3
Year: 2024
DOI: 10.56294/dm2024268
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:268:id:1056294dm2024268

Template-Type: ReDIF-Article 1.0
Author-Name: Sudhamalla Mallesh
Author-Name-First: Sudhamalla
Author-Name-Last: Mallesh
Author-Name: D. Haripriya
Author-Name-First: D.
Author-Name-Last: Haripriya
Title: GAN-based E-D Network to Dehaze Satellite Images
Abstract: The intricate nature of remote sensing image dehazing poses a formidable challenge due to its multifaceted characteristics. Considered as a preliminary step for advanced remote sensing image tasks, haze removal becomes crucial. A novel approach is introduced with the objective of dehazing an image employing an encoder-decoder architecture embedded in a generative adversarial network (GAN). This innovative model systematically captures low-frequency information in the initial phase and subsequently assimilates high-frequency details from the remote sensing image. Incorporating a skip connection within the network serves the purpose of preventing information loss. To enhance the learning capability and assimilate more valuable insights, an additional component, the multi-scale attention module, is introduced. Drawing inspiration from multi-scale networks, an enhanced module is meticulously designed and incorporated at the network's conclusion. This augmentation methodology aims to further enhance the dehazing capabilities by assimilating context information across various scales. The material for fine-tuning the dehazing algorithm has been obtained from the RICE-I dataset that serves as the testing ground for a comprehensive comparison between our proposed method and other two alternative approaches. The experimental results distinctly showcase the superior efficacy of our method, both in qualitative and quantitative terms. Our proposed methodology performed better with respect to contemporary dehazing techniques in terms of PSNR and SSIM although it requires longer simulation times. So, it could be concluded that we contributed a more comprehensive RS picture dehazing methodology to the existing dehazing methodology literature
Journal: Data and Metadata
Pages: 276
Volume: 3
Year: 2024
DOI: 10.56294/dm2024276
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:276:id:1056294dm2024276

Template-Type: ReDIF-Article 1.0
Author-Name: Vijaya Saradhi Thommandru
Author-Name-First: Vijaya
Author-Name-Last: Saradhi Thommandru
Author-Name: T. Suma
Author-Name-First: T.
Author-Name-Last: Suma
Author-Name: Mary Odilya Teena
Author-Name-First: Mary
Author-Name-Last: Odilya Teena
Author-Name: Muthukrishnan
Author-Name-First: Muthukrishnan
Author-Name-Last: Muthukrishnan
Author-Name: P Thamaraikannan
Author-Name-First: P
Author-Name-Last: Thamaraikannan
Author-Name: S. Manikandan
Author-Name-First: S.
Author-Name-Last: Manikandan
Title: Intelligent Optimization Framework for Future Communication Networks using Machine Learning
Abstract: Confronting the undeniably complicated versatile correspondence organization, knowledge is the advancement heading of organization versatile improvement innovation later on. Portable correspondence information is a significant part representing things to come data society. AI calculation is embraced in the versatile improvement plot, which can facilitate different enhancement goals as per the progressions of climate and state and understand the ideal boundary arrangement. Canny portable terminal hardware is turning out to be increasingly well known. The combination and advancement of social, portable and area administrations make the conventional informal organization easily change to versatile correspondence organization. AI is a part of man-made consciousness. Its examination objective is to construct a framework which can advance a few guidelines from information and apply them to the resulting information handling. In light of chart hypothesis, this paper tackles the issue of correspondence network information really, and concentrates on the calculation of huge information examination in view of AI
Journal: Data and Metadata
Pages: 277
Volume: 3
Year: 2024
DOI: 10.56294/dm2024277
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:277:id:1056294dm2024277

Template-Type: ReDIF-Article 1.0
Author-Name: Mohamed Cherradi
Author-Name-First: Mohamed
Author-Name-Last: Cherradi
Author-Name: Anass El Haddadi
Author-Name-First: Anass
Author-Name-Last: El Haddadi
Title: Data lake management using topic modeling techniques
Abstract: With the rapid rise of information technology, the amount of unstructured data from the data lake is rapidly growing and has become a great challenge in analyzing, organizing and automatically classifying in order to derive the meaningful information for a data-driven business. The scientific document has unlabeled text, so it's difficult to properly link it to a topic model. However, crafting a topic perception for a heterogeneous dataset within the domain of big data lakes presents a complex issue. The manual classification of text documents requires significant financial and human resources. Yet, employing topic modeling techniques could streamline this process, enhancing our understanding of word meanings and potentially reducing the resource burden. This paper presents a comparative study on metadata-based classification of scientific documents dataset, applying the two well-known machine learning-based topic modelling approaches, Latent Dirichlet Analysis (LDA) and Latent Semantic Allocation (LSA). To assess the effectiveness of our proposals, we conducted a thorough examination primarily centred on crucial assessment metrics, including coherence scores, perplexity, and log-likelihood. This evaluation was carried out on a scientific publications corpus, according to information from the title, abstract, keywords, authors, affiliation, and other metadata aspects. Results of these experiments highlight the superior performance of LDA over LSA, evidenced by a remarkable coherence value of (0,884) in contrast to LSA's (0,768)
Journal: Data and Metadata
Pages: 282
Volume: 3
Year: 2024
DOI: 10.56294/dm2024282
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:282:id:1056294dm2024282

Template-Type: ReDIF-Article 1.0
Author-Name: Edwin Gustavo Estrada-Araoz
Author-Name-First: Edwin Gustavo
Author-Name-Last: Estrada-Araoz
Author-Name: Yesenia Veronica Manrique-Jaramillo
Author-Name-First: Yesenia Veronica
Author-Name-Last: Manrique-Jaramillo
Author-Name: Víctor Hugo Díaz-Pereira
Author-Name-First: Víctor Hugo
Author-Name-Last: Díaz-Pereira
Author-Name: Jenny Marleny Rucoba-Frisancho
Author-Name-First: Jenny Marleny
Author-Name-Last: Rucoba-Frisancho
Author-Name: Yolanda Paredes-Valverde
Author-Name-First: Yolanda
Author-Name-Last: Paredes-Valverde
Author-Name: Rosel Quispe-Herrera
Author-Name-First: Rosel
Author-Name-Last: Quispe-Herrera
Author-Name: Darwin Rosell Quispe-Paredes
Author-Name-First: Darwin Rosell
Author-Name-Last: Quispe-Paredes
Title: Assessment of the level of knowledge on artificial intelligence in a sample of university professors: A descriptive study
Abstract: Introduction: The knowledge of artificial intelligence (AI) by university professors provides them with the ability to effectively integrate these innovative technological tools, resulting in a significant improvement in the quality of the teaching and learning process. Objective: To assess the level of knowledge about AI in a sample of Peruvian university professors. Methods: Quantitative study, non-experimental design and descriptive cross-sectional type. The sample consisted of 55 university professors of both sexes who were administered a questionnaire to assess their level of knowledge about AI, which had adequate metric properties. Results: The level of knowledge about AI was low for 41.8% of professors, regular for 40%, and high for 18.2%. This indicates that there is a significant gap in the knowledge of university professors about AI and its application in education, which could limit their ability to fully leverage AI tools and applications in the educational environment and could affect the quality and effectiveness of teaching. Likewise, it was determined that age and self-perception of digital competencies of professors were significantly associated with their level of knowledge about AI (p<0.05). Conclusions: Peruvian university professors are characterized by presenting a low level of knowledge about AI. Therefore, it is recommended to implement training and professional development programs focused on artificial intelligence, in order to update and improve their skills in this field
Journal: Data and Metadata
Pages: 285
Volume: 3
Year: 2024
DOI: 10.56294/dm2024285
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:285:id:1056294dm2024285

Template-Type: ReDIF-Article 1.0
Author-Name: Sreemoyee Biswas
Author-Name-First: Sreemoyee
Author-Name-Last: Biswas
Author-Name: Vrashti Nagar
Author-Name-First: Vrashti Nagar
Author-Name-Last: Vrashti Nagar
Author-Name: Nilay Khare
Author-Name-First: Nilay
Author-Name-Last: Khare
Author-Name: Priyank Jain
Author-Name-First: Priyank
Author-Name-Last: Jain
Author-Name: Pragati Agrawal
Author-Name-First: Pragati
Author-Name-Last: Agrawal
Title: LDCML: A Novel AI-Driven Approach form Privacy-Preserving Anonymization of Quasi-Identifiers
Abstract: Introduction: the exponential growth of data generation has led to an escalating concern for data privacy on a global scale. This work introduces a pioneering approach to address the often overlooked data privacy leakages associated with quasi-identifiers, leveraging artificial intelligence, machine learning and data correlation analysis as foundational tools. Traditional data privacy measures predominantly focus on anonymizing sensitive attributes and exact identifiers, leaving quasi-identifiers in their raw form, potentially exposing privacy vulnerabilities. Objective: the primary objective of the presented work, is to anonymise the quasi-identifiers to enhance the overall data privacy preservation with minimal data utility degradation. Methods: In this study, the authors propose the integration of ℓ-diversity data privacy algorithms with the OPTICS clustering technique and data correlation analysis to anonymize the quasi-identifiers. Results: to assess its efficacy, the proposed approach is rigorously compared against benchmark algorithms. The datasets used are - Adult dataset and Heart Disease Dataset from the UCI machine learning repository. The comparative metrics are - Relative Distance, Information Loss, KL Divergence and Execution Time. Conclusion: the comparative performance evaluation of the proposed methodology demonstrates its superiority over established benchmark techniques, positioning it as a promising solution for the requisite data privacy-preserving model. Moreover, this analysis underscores the imperative of integrating artificial intelligence (AI) methodologies into data privacy paradigms, emphasizing the necessity of such approaches in contemporary research and application domains
Journal: Data and Metadata
Pages: 287
Volume: 3
Year: 2024
DOI: 10.56294/dm2024287
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:287:id:1056294dm2024287

Template-Type: ReDIF-Article 1.0
Author-Name: Zouheir Boussouf
Author-Name-First: Zouheir
Author-Name-Last: Boussouf
Author-Name: Hanae Amrani
Author-Name-First: Hanae
Author-Name-Last: Amrani
Author-Name: Mouna Zerhouni Khal
Author-Name-First: Mouna
Author-Name-Last: Zerhouni Khal
Author-Name: Fouad Daidai
Author-Name-First: Fouad
Author-Name-Last: Daidai
Title: Artificial Intelligence in Education: a Systematic Literature Review
Abstract: The article explores the increasing influence of artificial intelligence (AI) in education, addressing contemporary challenges and highlighting its significance in refining teaching methods and enhancing learning efficiency. It is a structured literature review that systematically analyzes existing literature on AI in education, drawing insights from prominent researchers to understand current and future trends. Four key questions guide the analysis: the relationship between education and AI, their interaction, AI's contribution to educational evolution, and research challenges. The study employs a systematic review of literature, focusing on works by eminent scholars such as Lee, Memarian, and Yuan, selected from the Scopus database spanning from 1986 to 2024. It follows a structured approach to gather and analyze data from selected studies. The article progresses by presenting an introduction to the topic, outlining the methodology, and summarizing and analyzing key findings from selected literature. It explores the intrinsic relationship between education and AI, their interaction, and AI's role in evolving the educational process. Major findings underscore the importance of a cautious and ethical approach to integrating AI in education. Despite its potential benefits, challenges and shortcomings in current research are acknowledged, urging for further exploration and consideration of ethical implications
Journal: Data and Metadata
Pages: 288
Volume: 3
Year: 2024
DOI: 10.56294/dm2024288
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:288:id:1056294dm2024288

Template-Type: ReDIF-Article 1.0
Author-Name: Berrami Hind
Author-Name-First: Berrami
Author-Name-Last: Hind
Author-Name: Manar Jallal
Author-Name-First: Manar
Author-Name-Last: Jallal
Author-Name: Zineb Serhier
Author-Name-First: Zineb
Author-Name-Last: Serhier
Author-Name: Mohammed Bennani Othmani
Author-Name-First: Mohammed
Author-Name-Last: Bennani Othmani
Title: Exploring the Horizon: The Impact of AI Tools on Scientific Research
Abstract: The rise of artificial intelligence (AI) and natural language processing (NLP) has revolutionized many aspects of daily life, particularly in the field of development of medical research articles. the use of AI in scientific writing has both advantages and disadvantages. As AI tools gain in popularity and their application becomes more ubiquitous, it's essential to consider how they may affect the future of medical literature. This work aims to describe a number of IT-based tools that contribute to scientific research and writing as ChatGPT, Gemini, Elicit, SCISPACE... Each tool has its own advantages and applications, not to mention shortcomings that can affect the quality of medical research. To conclude artificial intelligence tools have emerged as catalysts for innovation in healthcare research, providing motivation and driving progress even amidst challenges. Therefore, it's crucial to confront the obstacles related to AI and to tackle ethical and regulatory issues to enhance research quality and scientific output
Journal: Data and Metadata
Pages: 289
Volume: 3
Year: 2024
DOI: 10.56294/dm2024289
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:289:id:1056294dm2024289

Template-Type: ReDIF-Article 1.0
Author-Name: Jackie Frank Chang Saldaña
Author-Name-First: Jackie Frank
Author-Name-Last: Chang Saldaña
Author-Name: Lincoln Fritz Cachay Reyes
Author-Name-First: Lincoln Fritz
Author-Name-Last: Cachay Reyes
Author-Name: Julio Cesar Pastor Segura
Author-Name-First: Julio Cesar
Author-Name-Last: Pastor Segura
Author-Name: Liz Sobeida Salirrosas Navarro
Author-Name-First: Liz Sobeida
Author-Name-Last: Salirrosas Navarro
Author-Name: Janet Yvone Castagne Vasquez
Author-Name-First: Janet Yvone
Author-Name-Last: Castagne Vasquez
Title: Document processing system with digital signatures and administrative management in public universities. A review of the literature
Abstract: Introduction: the concern about the limited progress in public institutions in Peru in the field of digitization of processes, despite the existence of legislation in force with coordinated actions from the State, to advance the digital development of the country. Objective: analyze the current situation of the system of document processing through digital signatures and administrative management in public universities. Methods: bibliographic research developed through a systematic review of repositories of Peruvian universities dated since 2019 and with the support of Google Scholar. Results: the findings showed that the existing advances continue to be scarce despite having demonstrated the benefits they bring to these entities in the use of human resources, materials, and time costs, as well as in the streamlining of their administrative processes, in line with the global trend of zero paper. Conclusions: un effort should be made to convey the benefits achieved with the application of this system, to overcome the doubts expressed by the respondents and to achieve an adequate implementation of the system
Journal: Data and Metadata
Pages: 292
Volume: 3
Year: 2024
DOI: 10.56294/dm2024292
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:292:id:1056294dm2024292

Template-Type: ReDIF-Article 1.0
Author-Name: Jackie Frank Chang Saldaña
Author-Name-First: Jackie Frank
Author-Name-Last: Chang Saldaña
Author-Name: Lincoln Fritz Cachay Reyes
Author-Name-First: Lincoln Fritz
Author-Name-Last: Cachay Reyes
Author-Name: Julio Cesar Pastor Segura
Author-Name-First: Julio Cesar
Author-Name-Last: Pastor Segura
Author-Name: Liz Sobeida Salirrosas Navarro
Author-Name-First: Liz Sobeida
Author-Name-Last: Salirrosas Navarro
Title: Vehicle license plate recognition system with artificial intelligence for the detection of alerted vehicles at the National University of Ucayali
Abstract: Introduction: technological advances have led to the creation of artificial intelligence, implementing it in tasks until recently developed directly by man, as in the case of parking lot surveillance. Objective: to learn about the application of a vehicle license plate recognition system with artificial intelligence for the detection of alerted vehicles at the National University of Ucayali during the period 2022-2023. Methods: qualitative approach study, inductive method and descriptive research level; the population consisted of university personnel over 19 years of age, regardless of gender and whose employment status was by appointment or contract, among whom a non-probabilistic sampling was applied, established in thirteen people, to whom an interview composed of twelve items was applied and who filled out an informed consent form, guaranteeing confidentiality, to have reliable data and scientific integrity of the same. Results: there are favorable and unfavorable opinions; the former are contributed by people who understand the process and agree with its implementation, while the latter respond to doubts generated by the lack of information and institutional communication. Conclusions: it is necessary to improve the communication system to avoid misinterpretations, doubts, and confusions in the use of private data, giving the users of the campus the certainty that the advances, in cooperation with the competent authorities, result in an adequate progress for the organization and control of their assets
Journal: Data and Metadata
Pages: 293
Volume: 3
Year: 2024
DOI: 10.56294/dm2024293
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:293:id:1056294dm2024293

Template-Type: ReDIF-Article 1.0
Author-Name: Anouar Bachar
Author-Name-First: Anouar Bachar
Author-Name-Last: Anouar Bachar
Author-Name: Omar EL Bannay
Author-Name-First: Omar EL Bannay
Author-Name-Last: Omar EL Bannay
Title: A proposed method for detecting network intrusion using an ensemble learning (stacking -voting) approach with unbalanced data
Abstract: The use of computer networks has become necessary in most human activities. However, these networks are exposed to potential threats affecting the confidentiality, integrity, and availability of data. Nowadays, the security of computer networks is based on tools and software such as antivirus software. Among the techniques used for machine protection, firewalls, data encryption, etc., were mentioned. These techniques constitute the first phase of computer network security. However, they remain limited and do not allow for full network protection. In this paper, a Network Intrusion Detection System (NIDS) was proposed for binary classification. This model was based on ensemble learning techniques, where the base models were carefully selected in a first layer. Several machine learning algorithms were individually studied to choose the best ones based on multiple metrics, including calculation speed. The SMOTE technique was used to balance the data, and cross-validation was employed to mitigate overfitting issues. Regarding the approaches used in this research, a stacking and voting model was employed, trained, and tested on a UNSW-NB15 dataset. The stacking classifier achieved a higher accuracy of 96 %, while the voting approach attained 95,6 %
Journal: Data and Metadata
Pages: 297
Volume: 3
Year: 2024
DOI: 10.56294/dm2024297
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:297:id:1056294dm2024297

Template-Type: ReDIF-Article 1.0
Author-Name: Milagros Maria Erazo-Moreno
Author-Name-First: Milagros Maria
Author-Name-Last: Erazo-Moreno
Author-Name: Gloria María Villa-Córdova
Author-Name-First: Gloria María
Author-Name-Last: Villa-Córdova
Author-Name: Geraldine Amelia Avila-Sánchez
Author-Name-First: Geraldine Amelia
Author-Name-Last: Avila-Sánchez
Author-Name: Fabiola Kruscaya Quispe-Anccasi
Author-Name-First: Fabiola Kruscaya
Author-Name-Last: Quispe-Anccasi
Author-Name: Segundo Sigifredo Pérez-Saavedra
Author-Name-First: Segundo Sigifredo
Author-Name-Last: Pérez-Saavedra
Title: Social media and education: perspectives on digital inclusion in the university setting
Abstract: Social networks have become pivotal in education, offering opportunities for inclusive learning experiences. This study seeks to understand the role of social networks in educational inclusion by analyzing students' usage, motivations, and perceived benefits. It focuses on identifying usage patterns, main activities, and perceptions regarding the impact of social networks on communication, interpersonal relationships, and access to educational information. A quantitative approach was employed, gathering data through a questionnaire from 355 university students of the specialty of secondary education in Lima during the 2023-2 semester. Statistics on social media usage, predominant activities, and perceived benefits associated with their use were analyzed. Findings revealed high social media usage, with WhatsApp (96,7 %) being the most used platform, followed by Facebook (63,6 %) and Instagram (40,5 %). Main activities were entertainment (66,4 %), family communication (60,9 %), and education (58,1 %). Students also valued improved interpersonal relationships (32,6 %) and access to information (68,7 %). Social networks play a crucial role in educational inclusion, providing opportunities for communication, collaboration, and information access. The need to balance their use and address challenges like digital dependency, prioritizing student well-being in the digital age, is emphasized
Journal: Data and Metadata
Pages: 299
Volume: 3
Year: 2024
DOI: 10.56294/dm2024299
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:299:id:1056294dm2024299

Template-Type: ReDIF-Article 1.0
Author-Name: Karina Raquel Bartra-Rivero
Author-Name-First: Karina Raquel
Author-Name-Last: Bartra-Rivero
Author-Name: Lida Vásquez-Pajuelo
Author-Name-First: Lida
Author-Name-Last: Vásquez-Pajuelo
Author-Name: Geraldine Amelia Avila-Sánchez
Author-Name-First: Geraldine Amelia
Author-Name-Last: Avila-Sánchez
Author-Name: Elba María Andrade-Díaz
Author-Name-First: Elba María
Author-Name-Last: Andrade-Díaz
Author-Name: Gliria Susana Méndez-Ilizarbe
Author-Name-First: Gliria Susana
Author-Name-Last: Méndez-Ilizarbe
Author-Name: Jhonny Richard Rodriguez-Barboza
Author-Name-First: Jhonny Richard
Author-Name-Last: Rodriguez-Barboza
Author-Name: Yvonne Jacqueline Alarcón-Villalobos
Author-Name-First: Yvonne Jacqueline
Author-Name-Last: Alarcón-Villalobos
Title: How Digital Competence Reduces Technostress
Abstract: This research examined the link between digital competencies and technostress among university instructors in remote settings in Peru, with the goal of identifying if improving digital skills could help mitigate technostress. A non-experimental, quantitative methodology was employed, gathering data via standardized surveys such as the DigCompEdu Check-In and RED TIC. The participant group comprised 120 teachers, whose responses were analyzed using logistic regression in SPSS v27. Descriptive findings indicated that 55,6 % of the teachers demonstrated a high level of professional commitment, and 58,9 % showed proficient digital pedagogical skills. Inferential analysis showed a significant correlation between digital competencies and technostress, with a Nagelkerke index of 0,622, suggesting that about 62,2 % of the variation in technostress could be explained by differences in digital competencies. The study concludes that enhancing digital competencies among teachers could substantially reduce their technostress, emphasizing the need to effectively integrate these skills into teaching practices to improve the educational experience in virtual settings
Journal: Data and Metadata
Pages: 303
Volume: 3
Year: 2024
DOI: 10.56294/dm2024303
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:303:id:1056294dm2024303

Template-Type: ReDIF-Article 1.0
Author-Name: Eduardo Rafael Jauregui Romero
Author-Name-First: Eduardo Rafael Jauregui Romero
Author-Name-Last: Eduardo Rafael Jauregui Romero
Author-Name: Javier Alca Gomez
Author-Name-First: Javier
Author-Name-Last: Alca Gomez
Author-Name: Manuel Eduardo Vilca Tantapoma
Author-Name-First: Manuel Eduardo
Author-Name-Last: Vilca Tantapoma
Author-Name: Orlando Tito Llanos Gonzales
Author-Name-First: Orlando Tito Llanos Gonzales
Author-Name-Last: Orlando Tito Llanos Gonzales
Title: Artificial intelligence in potential customer segmentation: machine learning approach
Abstract: Integrating artificial intelligence (AI) into sales processes at a business level, specifically, in the segmentation of potential customers, is currently a very important issue for the promotion of your products and services. The present study focused on the analysis of the effectiveness of the machine learning approach used in mass consumption companies for the segmentation of potential customers. To achieve this objective, a systematic review of the literature will be carried out with a qualitative approach and supported by the PRISMA methodology. The results achieved in the review carried out showed that machine learning algorithms present better results compared to other approaches; Furthermore, regarding customer segmentation, this can be done through grouping, which is one of the most recognized machine learning techniques. It is concluded that it is necessary to expand the methods provided by this approach, using them to extract knowledge from unstructured, monitoring, and network data to achieve descriptive, causal, and prescriptive analyses; In addition, to outline the journey that customers take when purchasing and deploy decision support capabilities. All these benefits, at a business level, are provided by machine learning, reason enough for the proposed marketing strategies to be based on the information it offers
Journal: Data and Metadata
Pages: 305
Volume: 3
Year: 2024
DOI: 10.56294/dm2024305
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:305:id:1056294dm2024305

Template-Type: ReDIF-Article 1.0
Author-Name: Miriam Viviana Ñañez-Silva
Author-Name-First: Miriam Viviana
Author-Name-Last: Ñañez-Silva
Author-Name: Julio Cesar Quispe-Calderón
Author-Name-First: Julio Cesar
Author-Name-Last: Quispe-Calderón
Author-Name: Patricia Matilde Huallpa-Quispe
Author-Name-First: Patricia Matilde
Author-Name-Last: Huallpa-Quispe
Author-Name: Bertha Nancy Larico-Quispe
Author-Name-First: Bertha Nancy
Author-Name-Last: Larico-Quispe
Title: Analysis of academic research data with the use of ATLAS.ti. Experiences of use in the area of Tourism and Hospitality Administration
Abstract: Qualitative data analysis in academic research is a challenge. In this context, the use of tools such as ATLAS.ti has emerged as a potential solution to improve the understanding and management of data in the analysis of in-depth interviews. The main objective of the research was to analyze the perspectives of Tourism and Hospitality Management students on the use of ATLAS.ti in the analysis of interviews in qualitative research. The methodology employs a qualitative approach and a descriptive-interpretative design. Data were collected through in-depth interviews and focus groups directed to 40 students of the X cycle who conducted this approach in their research to opt for the bachelor’s degree in Tourism and Hospitality Administration during the years 2022 and 2023. The findings reveal that the use of ATLAS.ti in qualitative data analysis is highly beneficial, facilitating the coding, organization, and identification of emerging patterns in in-depth interviews. The relevance of its effective use in qualitative analysis is highlighted, improving data management, and understanding of participants' perspectives. It is concluded that it is a valuable and effective tool in this context, although the need for researchers to acquire a deep understanding of the tool and receive adequate training is emphasized. It is suggested that they focus on continuous training in its use and constant practice of its advanced functionalities, especially in areas such as coding and code creation, to achieve a deeper interpretation of qualitative data
Journal: Data and Metadata
Pages: 306
Volume: 3
Year: 2024
DOI: 10.56294/dm2024306
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:306:id:1056294dm2024306

Template-Type: ReDIF-Article 1.0
Author-Name: Ihsan Fathoni Amri
Author-Name-First: Ihsan
Author-Name-Last: Fathoni Amri
Author-Name: Nur Chamidah
Author-Name-First: Nur
Author-Name-Last: Chamidah
Author-Name: Toha Saifudin
Author-Name-First: Toha
Author-Name-Last: Saifudin
Author-Name: Dannu Purwanto
Author-Name-First: Dannu
Author-Name-Last: Purwanto
Author-Name: Alwan Fadlurohman
Author-Name-First: Alwan
Author-Name-Last: Fadlurohman
Author-Name: Ariska Fitriyana Ningrum
Author-Name-First: Ariska
Author-Name-Last: Fitriyana Ningrum
Author-Name: Saeful Amri
Author-Name-First: Saeful
Author-Name-Last: Amri
Title: Prediction of extreme weather using nonparametric regression approach with Fourier series estimators
Abstract: In Jepara, Central Java, Indonesia, significant correlations between high rainfall and wind speed impact multiple sectors including health, agriculture, and infrastructure. This study aims to predict the effects of extreme weather by employing nonparametric regression based on Fourier series estimators. Data from December 2023 to March 2024, sourced from NASA, were analyzed using sinus, cosinus, and combined Fourier functions to model the dynamic and seasonal fluctuations of weather variables. This approach allows for a flexible modeling of these previously undefined functional relationships. The analysis revealed that the combined function model was superior, achieving an optimal Generalized Cross-Validation (GCV) score of 0,236498 with a Fourier coefficient K=3, indicating a well-fitted model. Moreover, this model exhibited a low Mean Absolute Percentage Error (MAPE) of 1,887, demonstrating high predictive accuracy. These findings not only affirm the efficacy of Fourier series in nonparametric regression for weather forecasting but also underscore its potential in informing public policy and bolstering disaster preparedness in Jepara and similar regions vulnerable to extreme weather conditions
Journal: Data and Metadata
Pages: 319
Volume: 3
Year: 2024
DOI: 10.56294/dm2024319
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:319:id:1056294dm2024319

Template-Type: ReDIF-Article 1.0
Author-Name: Carlos Alberto Gómez-Cano
Author-Name-First: Carlos Alberto
Author-Name-Last: Gómez-Cano
Author-Name: Verenice Sánchez-Castillo
Author-Name-First: Verenice
Author-Name-Last: Sánchez-Castillo
Author-Name: Rolando Eslava-Zapata
Author-Name-First: Rolando
Author-Name-Last: Eslava-Zapata
Title: Bibliometric analysis of the main applications of digital technologies to business management
Abstract: In today's digital age, information technologies have revolutionized how companies manage their business operations and strategies. The application of these technologies in business management has demonstrated significant impacts in various sectors. The main objective was to analyze the scientific production related to the main applications of digital technologies to business management. The research paradigm was mixed through developing a bibliometric study and a thematic analysis of relevant sources. The SCOPUS database was used during the period 2000 – 2024. A total of 85 investigations were obtained. The behavior of investigations behaved heterogeneously while starting in 2019; it experienced notable growth with a maximum peak in 2023 of 24 investigations. The thematic analysis corroborated the importance of digital transformation for business management and the critical role played by the designed introduction of digital technologies. The findings allow us to affirm that it is a heterogeneous field, influenced by various disciplines and in the process of consolidation, due to the range of potentialities it offers
Journal: Data and Metadata
Pages: 321
Volume: 3
Year: 2024
DOI: 10.56294/dm2024321
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:321:id:1056294dm2024321

Template-Type: ReDIF-Article 1.0
Author-Name: Víctor Joselito Linares-Cabrera
Author-Name-First: Víctor Joselito
Author-Name-Last: Linares-Cabrera
Author-Name: María Amelia Díaz-Nicho de Linares
Author-Name-First: María Amelia
Author-Name-Last: Díaz-Nicho de Linares
Author-Name: Abrahán Cesar Neri-Ayala
Author-Name-First: Abrahán Cesar
Author-Name-Last: Neri-Ayala
Author-Name: Cesar Armando Díaz-Valladares
Author-Name-First: Cesar Armando
Author-Name-Last: Díaz-Valladares
Author-Name: Pablo Cesar Cadenas-Calderón
Author-Name-First: Pablo Cesar
Author-Name-Last: Cadenas-Calderón
Author-Name: Gladys Magdalena Aguinaga-Mendoza
Author-Name-First: Gladys Magdalena
Author-Name-Last: Aguinaga-Mendoza
Title: E-government and administrative management at the Provincial Municipality of Huaura, Peru
Abstract: By using digital technologies to streamline procedures and increase the productivity of public services, e-government modernizes administrative management and makes government more accessible and responsive to citizens' requests for assistance. The purpose of this study was to determine the relationship between e-government and administrative management in the Provincial Municipality of Huaura, Peru. Using a sample of 129 administrative workers and a population of 194 administrative workers, a quantitative, non-experimental, cross-sectional and correlational methodology was developed. Participants completed a survey-questionnaire. The results showed a substantial relationship between administrative management and e-government in the Provincial Municipality of Huaura, with a sig. of less than 5 % and a Rho value of 0,596. This allowed us to deduce that the planning, organization, management and control of the entity's public resources will improve to the extent that a more solid electronic infrastructure is implemented, political will and institutional architecture, governance through transformations and organizational redesign, and whether or not its citizens have the necessary tools or knowledge to access online information and services
Journal: Data and Metadata
Pages: 322
Volume: 3
Year: 2024
DOI: 10.56294/dm2024322
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:322:id:1056294dm2024322

Template-Type: ReDIF-Article 1.0
Author-Name: Merly Enith Mego Torres
Author-Name-First: Merly Enith
Author-Name-Last: Mego Torres
Author-Name: Lindon Vela Meléndez
Author-Name-First: Lindon
Author-Name-Last: Vela Meléndez
Author-Name: Juan Diego Dávila Cisneros
Author-Name-First: Juan Diego
Author-Name-Last: Dávila Cisneros
Author-Name: Roibert Pepito Mendoza Reyna
Author-Name-First: Roibert Pepito
Author-Name-Last: Mendoza Reyna
Title: Digital modernization and public management: A bibliometric review
Abstract: Introduction: the article examines the issue of digital modernization in Latin America, where, despite over a decade of efforts, progress has been slow. It focuses on the importance of e-government for modern public administration, highlighting the limited digitization of activities. Objective: to evaluate the theoretical-conceptual development of the relationship between digital modernization and public administration. Methodology: the bibliometric technique was used, drawing from Scopus documents and employing a specific search protocol, resulting in 1,602 records with metadata. Results: there is shown growth in research since 2003, with studies primarily concentrated in the United States, the United Kingdom, and the Netherlands. Original articles in social sciences are highlighted, emphasizing the role of digital modernization in transparency and democratization of public administration. Conclusion: while there have been advancements in research since 2003, Latin American countries face significant challenges compared to other regions. The need for greater collaboration and research in this area in Latin America is emphasized to leverage the benefits of digital modernization. It is suggested to establish specific policies and strategies to drive governmental digitization and enhance the efficiency of public services, closing the existing gap
Journal: Data and Metadata
Pages: 323
Volume: 3
Year: 2024
DOI: 10.56294/dm2024323
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:323:id:1056294dm2024323

Template-Type: ReDIF-Article 1.0
Author-Name: Julio Jesús Vargas Peña
Author-Name-First: Julio Jesús
Author-Name-Last: Vargas Peña
Author-Name: Karla Stefani Solís Castillo
Author-Name-First: Karla Stefani
Author-Name-Last: Solís Castillo
Author-Name: Deysi Viviana Bonilla Ledesma
Author-Name-First: Deysi Viviana
Author-Name-Last: Bonilla Ledesma
Title: Study of prevalence and evolution of uterine fibroids during pregnancy in patients of the Medical Center and Medical Specialties FOB of Guayaquil
Abstract: Fibroids or leiomyomas are the most common benign tumors of the upper portion of the female genital tract; They can reach large sizes and generally do not require surgical treatment during pregnancy. Most of these masses present asymptomatically and are discovered as an incidental finding in ultrasounds during pregnancy or at termination in the case of a cesarean section. Knowing the prevalence of fibroids in the pregnant population is of utmost importance to understand its possible impact on the health of the mother and fetus. Determining the prevalence and evolution of fibroids during pregnancy allows us to identify the number of women with this condition that may be affected by this pathology. The objective of this study is to evaluate the prevalence and evolution of uterine fibroids during pregnancy and determine their complications in women between 20 and 45 years old at the Medical Center and Medical FOB Specialties of Guayaquil. From the results of this study it can be concluded that fibroids during pregnancy are related to maternal age and their size does not increase, but rather tends to decrease. Its association with pregnancy increases the risk of complications. Pregnant patients with fibroids have a higher risk of uterine atony and bleeding after cesarean section than patients who terminated their pregnancy by delivery. Understanding the prevalence of fibroids during pregnancy is crucial to improve knowledge, clinical management, and prevention and care strategies in this population.
Journal: Data and Metadata
Pages: 324
Volume: 2
Year: 2023
DOI: 10.56294/dm2023324
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:324:id:1056294dm2023324

Template-Type: ReDIF-Article 1.0
Author-Name: Edwin Gustavo Estrada-Araoz
Author-Name-First: Edwin Gustavo
Author-Name-Last: Estrada-Araoz
Author-Name: Jhemy Quispe-Aquise
Author-Name-First: Jhemy
Author-Name-Last: Quispe-Aquise
Author-Name: Yasser Malaga-Yllpa
Author-Name-First: Yasser
Author-Name-Last: Malaga-Yllpa
Author-Name: Guido Raúl Larico-Uchamaco
Author-Name-First: Guido Raúl
Author-Name-Last: Larico-Uchamaco
Author-Name: Giovanna Rocio Pizarro-Osorio
Author-Name-First: Giovanna Rocio
Author-Name-Last: Pizarro-Osorio
Author-Name: Marleni Mendoza-Zuñiga
Author-Name-First: Marleni
Author-Name-Last: Mendoza-Zuñiga
Author-Name: Alex Camilo Velasquez-Bernal
Author-Name-First: Alex Camilo
Author-Name-Last: Velasquez-Bernal
Author-Name: Cesar Elias Roque-Guizada
Author-Name-First: Cesar Elias
Author-Name-Last: Roque-Guizada
Author-Name: María Isabel Huamaní-Pérez
Author-Name-First: María Isabel
Author-Name-Last: Huamaní-Pérez
Title: Role of artificial intelligence in education: Perspectives of Peruvian basic education teachers
Abstract: Introduction: in the educational context, the integration of artificial intelligence is transforming the way teachers teach and students learn. However, there are challenges that teachers must face when incorporating artificial intelligence into their pedagogical practice. Objective: to evaluate the perspectives of Peruvian basic education teachers on the implementation of artificial intelligence in the educational context. Methods: a quantitative, non-experimental, cross-sectional descriptive study was conducted. The sample consisted of 125 basic education teachers selected through probabilistic sampling. These participants were administered a scale designed to evaluate their perspectives on artificial intelligence, which demonstrated adequate metric properties. Results: it was found that teachers had a partial knowledge of what artificial intelligence is and its scope. Among the advantages of artificial intelligence, it stands out that it was an effective teaching resource and a necessary tool to provide personalized education. However, among the disadvantages highlighted are concerns that it could foster academic dishonesty, doubts about its reliability, and a lack of confidence in its ability to guarantee the confidentiality of information. Conclusions: the perspective of basic education teachers on the implementation of artificial intelligence in the educational context is heterogeneous. Although they recognize the disadvantages and have a partial knowledge of what artificial intelligence is and its scope, they show willingness to explore and take advantage of its possibilities in the educational field
Journal: Data and Metadata
Pages: 325
Volume: 3
Year: 2024
DOI: 10.56294/dm2024325
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:325:id:1056294dm2024325

Template-Type: ReDIF-Article 1.0
Author-Name: Edith Mariela Quispe Sanabria
Author-Name-First: Edith Mariela
Author-Name-Last: Quispe Sanabria
Author-Name: Julio Cesar Pizarro Avellaneda
Author-Name-First: Julio Cesar
Author-Name-Last: Pizarro Avellaneda
Author-Name: Edward Eddie Bustinza Zuasnabar
Author-Name-First: Edward Eddie
Author-Name-Last: Bustinza Zuasnabar
Author-Name: Ana Mónica Huaraca García
Author-Name-First: Ana Mónica
Author-Name-Last: Huaraca García
Author-Name: Lizet Doriela Mantari Mincami
Author-Name-First: Lizet Doriela
Author-Name-Last: Mantari Mincami
Author-Name: Hilario Romero Giron
Author-Name-First: Hilario Romero Giron
Author-Name-Last: Hilario Romero Giron
Author-Name: Yesser Soriano Quispe
Author-Name-First: Yesser
Author-Name-Last: Soriano Quispe
Title: Blockchain Technology in Digital Identity Management and Verification
Abstract: This study analyzes the potential of Blockchain technology to improve security and privacy in the management and verification of digital identities, aspects that currently face challenges. Through a literature review, it was found that Blockchain offers a decentralized approach that provides greater control to users over their data through cryptographic mechanisms. The cases examined demonstrate benefits such as efficiency and automation in identity processes. However, further research is required to address pending challenges and achieve widespread application considering the particularities of each context. The objective is to analyze how this technology can positively transform the way digital identity is managed in an inclusive and privacy-respecting manner
Journal: Data and Metadata
Pages: 326
Volume: 3
Year: 2024
DOI: 10.56294/dm2024326
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:326:id:1056294dm2024326

Template-Type: ReDIF-Article 1.0
Author-Name: Edwin Gustavo Estrada-Araoz
Author-Name-First: Edwin Gustavo
Author-Name-Last: Estrada-Araoz
Author-Name: Marilú Farfán-Latorre
Author-Name-First: Marilú
Author-Name-Last: Farfán-Latorre
Author-Name: Willian Gerardo Lavilla-Condori
Author-Name-First: Willian Gerardo
Author-Name-Last: Lavilla-Condori
Author-Name: Luis Iván Yancachajlla-Quispe
Author-Name-First: Luis Iván
Author-Name-Last: Yancachajlla-Quispe
Author-Name: Dominga Asunción Calcina-Álvarez
Author-Name-First: Dominga Asunción
Author-Name-Last: Calcina-Álvarez
Title: Variables associated with the development of research competencies in university students from Southern Peru: A cross-sectional study
Abstract: Introduction: the development of research competencies among university students is a crucial aspect of contemporary academic education. These competencies have not only become indispensable for professional advancement but are also essential for societal progress. However, their development is not always uniform, and their acquisition is associated with various variables. Objective: to determine the variables associated with research competencies in university students from Southern Peru. Methods: a quantitative, non-experimental, cross-sectional descriptive study was conducted. The sample consisted of 302 university students selected through probabilistic sampling. Data collection was done using the Research Competencies Questionnaire, which had adequate metric properties. Results: research competencies of 72,8 % of students were moderately developed, 17,5 % were not developed, while 9,6 % were fully developed. Furthermore, upon evaluating dimensions, it was found that organizational, communicational, and collaborative skills were also moderately developed. Additionally, it was determined that research competencies were significantly associated with membership in research groups and the number of weekly hours students dedicated to research activities (p<0,05). Conclusions: membership in a research group and greater dedication of hours were associated with a higher level of development of research competencies. Moreover, overall, it was determined that the majority of students had a moderate level of development of these competencies
Journal: Data and Metadata
Pages: 327
Volume: 3
Year: 2024
DOI: 10.56294/dm2024327
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:327:id:1056294dm2024327

Template-Type: ReDIF-Article 1.0
Author-Name: María Verónica Aveiga Hidalgo
Author-Name-First: María Verónica
Author-Name-Last: Aveiga Hidalgo
Author-Name: Leidy Daniela Escobar Bastidas
Author-Name-First: Leidy Daniela
Author-Name-Last: Escobar Bastidas
Author-Name: Justin Josué Montenegro Rodríguez
Author-Name-First: Justin Josué
Author-Name-Last: Montenegro Rodríguez
Author-Name: Leticia Mercedes Enríquez López
Author-Name-First: Leticia Mercedes
Author-Name-Last: Enríquez López
Title: Preventive measures for the care of floriculture workers
Abstract: People's health can be affected by the use of chemical products, especially in agricultural work. Pregnant women are more sensitive and prone to suffering from problems. The objective of this research is to develop preventive measures for the health care of the workers of the “Florsani LTDA” plantation in the San Isidro parish. The research was both qualitative and quantitative with a non-experimental design and having descriptive scope. A questionnaire was applied to 30 women who work on the plantation. It was determined that there is little perception of risk by women, there were difficulties in previous births and effects on their health were evident, with burning in the throat being more represented. A proposal of measures was made to contribute to the preservation of the health of all women. Prevention, risk assessment, training and continuous monitoring are essential to ensure safe and healthy work environments for everyone.
Journal: Data and Metadata
Pages: 328
Volume: 2
Year: 2023
DOI: 10.56294/dm2023328
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:328:id:1056294dm2023328

Template-Type: ReDIF-Article 1.0
Author-Name: Ekta Dalal
Author-Name-First: Ekta
Author-Name-Last: Dalal
Author-Name: Parvinder Singh
Author-Name-First: Parvinder
Author-Name-Last: Singh
Title: TextRefine: A Novel approach to improve the accuracy of LLM Models
Abstract: Natural Language Processing (NLP) is an interdisciplinary field that investigates the fascinating world of human language with the goal of creating computational models and algorithms that can comprehend, produce, and analyze natural language in a way that is similar to humans. LLMs still encounter issues with loud and unpolished input material despite their outstanding performance in natural language processing tasks. TextRefine offers a thorough pretreatment pipeline that refines and cleans the text data before using it in LLMs to overcome this problem . The pipeline includes a number of actions, such as removing social tags, normalizing whitespace, changing all lowercase letters to uppercase, removing stopwords, fixing Unicode issues, contraction unpacking, removing punctuation and accents, and text cleanup. These procedures work together to strengthen the integrity and quality of the input data, which will ultimately improve the efficiency and precision of LLMs. Extensive testing and comparisons with standard techniques show TextRefine's effectiveness with 99 % of the accuracy
Journal: Data and Metadata
Pages: 331
Volume: 3
Year: 2024
DOI: 10.56294/dm2024331
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:331:id:1056294dm2024331

Template-Type: ReDIF-Article 1.0
Author-Name: Ángel Emiro Páez Moreno
Author-Name-First: Ángel Emiro
Author-Name-Last: Páez Moreno
Author-Name: Carolina Parra Fonseca
Author-Name-First: Carolina
Author-Name-Last: Parra Fonseca
Title: Design and validation of an instrument to measure e-governance through factor analysis
Abstract: E-governance combines the use of electronic means in interaction between government and citizens, government and business, and within government operations to enhance democratic, governmental, and business aspects of governance. Thus, e-governance is built on a paradigmatic dimension such as e-democracy (relationship between government and citizens) and an operational dimension such as e-governance. The objective was to design and validate an instrument to measure e-governance based on three factors: a) e-administration, b) e-services, and c) e-democracy. Method: based on the level of importance given to each factor (sample of 2042 Latin American citizens), as well as the relationships between them, an analysis of the importance of each factor is carried out. Results: after the confirmatory analysis, the definitive instrument with which e-governance can be measured by other researchers and future research is obtained, considering the three selection factors, namely: e-administration, e-services and e-democracy. Conclusions: this research contributes to political science through the design and validation of an instrument consisting of 39 items that can be used to measure e-governance according to the dimensions proposed by the United Nations Educational, Scientific and Cultural Organization
Journal: Data and Metadata
Pages: 332
Volume: 3
Year: 2024
DOI: 10.56294/dm2024332
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:332:id:1056294dm2024332

Template-Type: ReDIF-Article 1.0
Author-Name: Macoumba Fall
Author-Name-First: Macoumba
Author-Name-Last: Fall
Author-Name: Mohammed Fattah
Author-Name-First: Mohammed
Author-Name-Last: Fattah
Author-Name: Mohammed Mahfoudi
Author-Name-First: Mohammed
Author-Name-Last: Mahfoudi
Author-Name: Younes Balboul
Author-Name-First: Younes
Author-Name-Last: Balboul
Author-Name: Said Mazer
Author-Name-First: Said
Author-Name-Last: Mazer
Author-Name: Moulhime El Bekkali
Author-Name-First: Moulhime
Author-Name-Last: El Bekkali
Author-Name: Ahmed D. Kora
Author-Name-First: Ahmed D.
Author-Name-Last: Kora
Title: Optimizing Energy Consumption in 5G HetNets: A Coordinated Approach for Multi-Level Picocell Sleep Mode with Q-Learning
Abstract: Cell standby, particularly picocell sleep mode (SM), is a prominent strategy for reducing energy consumption in 5G networks. The emergence of multi-state sleep states necessitates new optimization approaches. This paper proposes a novel energy optimization strategy for 5G heterogeneous networks (HetNets) that leverages macrocell-picocell coordination and machine learning. The proposed strategy focuses on managing the four available picocell sleep states. The picocell manages the first three states using the Q-learning algorithm, an efficient reinforcement learning technique. The associated macrocell based on picocell energy efficiency controls the final, deeper sleep state. This hierarchical approach leverages localized and network-wide control strengths for optimal energy savings. By capitalizing on macrocell-picocell coordination and machine learning, this work presents a promising solution for achieving significant energy reduction in 5G HetNets while maintaining network performance
Journal: Data and Metadata
Pages: 333
Volume: 3
Year: 2024
DOI: 10.56294/dm2024333
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:333:id:1056294dm2024333

Template-Type: ReDIF-Article 1.0
Author-Name: Alexandra Marisol Barcia Maridueña
Author-Name-First: Alexandra Marisol
Author-Name-Last: Barcia Maridueña
Author-Name: Iván Andrés Muñoz Mata
Author-Name-First: Iván Andrés
Author-Name-Last: Muñoz Mata
Author-Name: Marcia Lisbeth Verdugo Arcos
Author-Name-First: Marcia Lisbeth
Author-Name-Last: Verdugo Arcos
Author-Name: Thalía Lilibeth Figueroa Suárez
Author-Name-First: Thalía Lilibeth
Author-Name-Last: Figueroa Suárez
Title: Public policies in Ecuador to mitigate violence against children and adolescents
Abstract: The article focuses on examining public policies in Ecuador to mitigate violence against children and adolescents, this in the context that the rates of violence in the country have increased over the years and the ways in which they are produces are diverse, as well as the aggressors are no longer only found in the family environment but also in the school environment and in other areas where the minor has participation. In this sense, a review of the regulations in force in the country is carried out to assess their coverage and effectiveness based on international instruments on which they are based. The result of this review has made it possible to identify that despite the diversity of legal instruments, children's rights continue to be violated, which infers the need for actions to reinforce the guarantees of their compliance
Journal: Data and Metadata
Pages: 334
Volume: 3
Year: 2024
DOI: 10.56294/dm2024334
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:334:id:1056294dm2024334

Template-Type: ReDIF-Article 1.0
Author-Name: Adrián Isaac Toala Tapia
Author-Name-First: Adrián Isaac
Author-Name-Last: Toala Tapia
Author-Name: Bryan Gonzalo Eras Jumbo
Author-Name-First: Bryan Gonzalo
Author-Name-Last: Eras Jumbo
Author-Name: Gina Ivanoba Cadena Rosero
Author-Name-First: Gina Ivanoba
Author-Name-Last: Cadena Rosero
Author-Name: Cristian Gerald De Pablo Chapiliquin
Author-Name-First: Cristian Gerald
Author-Name-Last: De Pablo Chapiliquin
Title: New orthodontic treatment alternative in a teenager: a clinical case
Abstract: Dentistry is essential to maintain good general health, since dental problems can affect the health of the rest of the body. Orthodontics is a specialty of dentistry that is responsible for preventing, diagnosing and correcting dental malpositions and irregularities in the jaw. The dental world is constantly changing, so the use of alternatives in orthodontic treatment presents greater susceptibility to innovation and implementation of new techniques. The objective of this research is to found a new alternative to complete orthodontic treatment in the final stage. A clinical case of a teenager was shown, whose treatment was prolonged due to the COVID-19 pandemic. The use of fluid resin pins was used on the teeth and elastic therapy was performed. Correct settlement was evident after two months of treatment. Measures are proposed to maintain people's oral health after orthodontic treatment with fixed or removable techniques. Furthermore, the importance of treatment in adolescents is noted.
Journal: Data and Metadata
Pages: 336
Volume: 2
Year: 2023
DOI: 10.56294/dm2023336
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:336:id:1056294dm2023336

Template-Type: ReDIF-Article 1.0
Author-Name: Balusamy Nachiappan
Author-Name-First: Balusamy Nachiappan
Author-Name-Last: Balusamy Nachiappan
Author-Name: N Rajkumar
Author-Name-First: N
Author-Name-Last: Rajkumar
Author-Name: C Kalpana
Author-Name-First: C
Author-Name-Last: Kalpana
Author-Name: A Mohanraj
Author-Name-First: A
Author-Name-Last: Mohanraj
Author-Name: B Prabhu Shankar
Author-Name-First: B
Author-Name-Last: Prabhu Shankar
Author-Name: C Viji
Author-Name-First: C
Author-Name-Last: Viji
Title: Machine Learning-Based System for Automated Presentation Generation from CSV Data
Abstract: Effective presentation slides are crucial for conveying information efficiently, yet existing tools lack content analysis capabilities. This paper introduces a content-based PowerPoint presentation generator, aiming to address this gap. By leveraging automated techniques, slides are generated from text documents, ensuring original concepts are effectively communicated. Unstructured data poses challenges for organizations, impacting productivity and profitability. While traditional methods fall short, AI-based approaches offer promise. This systematic literature review (SLR) explores AI methods for extracting data from unstructured details. Findings reveal limitations in existing methods, particularly in handling complex document layouts. Moreover, publicly available datasets are task-specific and of low quality, highlighting the need for comprehensive datasets reflecting real-world scenarios. The SLR underscores the potential of Artificial-based approaches for information extraction but emphasizes the challenges in processing diverse document layouts. The proposed is a framework for constructing high-quality datasets and advocating for closer collaboration between businesses and researchers to address unstructured data challenges effectively
Journal: Data and Metadata
Pages: 359
Volume: 3
Year: 2024
DOI: 10.56294/dm2024359
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:359:id:1056294dm2024359

Template-Type: ReDIF-Article 1.0
Author-Name: Mostafa El Khaoudi
Author-Name-First: Mostafa
Author-Name-Last: El Khaoudi
Author-Name: Mhammed El Bakkali
Author-Name-First: Mhammed
Author-Name-Last: El Bakkali
Author-Name: Redouane Messnaoui
Author-Name-First: Redouane
Author-Name-Last: Messnaoui
Author-Name: Omar Cherkaoui
Author-Name-First: Omar
Author-Name-Last: Cherkaoui
Author-Name: Aziz Soulhi
Author-Name-First: Aziz Soulhi
Author-Name-Last: Aziz Soulhi
Title: Literature review on artificial intelligence in dyeing and finishing processes
Abstract: The finishing process in the textile sector is recognized as one of the most complex. This complexity arises from the diversity of structures, the multiple steps involved, the use of complex machinery, the variety of materials, chemicals and dyes, and the need to combine creativity and precision. Therefore, it is crucial to have tools that can improve efficiency, flexibility, and decision-making in this complex area. This literature review aims to provide relevant information on the use of digital engineering in the field of textile finishing. In this review, we used a systematic literature review methodology to examine how digital engineering is applied in the dyeing and finishing process. The data for this study was collected from reputed databases such as Science Direct, IEEE Xplore, Textile Research Journal and Google Scholar. We used the Prisma framework to select relevant articles, which led to the exclusive inclusion of journal articles in our literature review. A comprehensive framework has been developed to understand the impacts of using digital engineering. The approach presented in this framework provides a comprehensive and highly effective approach to addressing the complex challenges associated with ambiguity, modifications and subtleties frequently observed in the ennobling process. The results of various studies explored different aspects, such as properties of textile materials, chemicals and dyes, performance of finishing machines, organizational performance of finishing companies, as well as health concerns and safety at work. Although these studies have provided valuable solutions, they unfortunately remain insufficient to meet the requirements of the finishing process, which remains a complex area characterized by uncertainties, variations, and subtleties inherent to the practice. This particularity of each dyed and finished product promotes an environment conducive to experimentation and continued research
Journal: Data and Metadata
Pages: 360
Volume: 3
Year: 2024
DOI: 10.56294/dm2024360
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:360:id:1056294dm2024360

Template-Type: ReDIF-Article 1.0
Author-Name: Cesar Alvino Poemape Alfaro
Author-Name-First: Cesar Alvino
Author-Name-Last: Poemape Alfaro
Author-Name: Miguel Fernando Ramos Romero
Author-Name-First: Miguel Fernando
Author-Name-Last: Ramos Romero
Author-Name: Flor de María Lioo Jordan
Author-Name-First: Flor de María
Author-Name-Last: Lioo Jordan
Author-Name: Viviana Inés Vellón Flores
Author-Name-First: Viviana Inés
Author-Name-Last: Vellón Flores
Author-Name: Jesús Jacobo Coronado Espinoza
Author-Name-First: Jesús Jacobo
Author-Name-Last: Coronado Espinoza
Author-Name: Abraham César Neri Ayala
Author-Name-First: Abraham César
Author-Name-Last: Neri Ayala
Title: Non-performing loans and their impact on the profitability of Peruvian Municipal Savings and Loan Banks
Abstract: Efficiently managing loans granted can have an immediate effect on the profitability and viability of a financial institution. Considering this, this study determined the impact of past-due loans on the profitability of the Peruvian Municipal Savings and Loan Banks during the period 2022. A quantitative, non-experimental, cross-sectional, correlational-causal methodology was used, which employed documentary analysis and the design of a data sheet. The population and sample consisted of 11 Municipal Savings and Loan Associations, which have been approved and are inspected by the Superintendence of Banking, Insurance and Private Pension Fund Administrators. A positive and moderate correlation of Rho = 460 and a significance level greater than 0,05 (0,154 > 0,05) was found, that is, overdue loans have a positive, but not significant, impact on the profitability of these financial institutions. The behaviors of these variables allow us to conclude that having more past-due loans will not always result in lower profitability, since there may be other factors that help mitigate this negative impact
Journal: Data and Metadata
Pages: 362
Volume: 3
Year: 2024
DOI: 10.56294/dm2024362
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:362:id:1056294dm2024362

Template-Type: ReDIF-Article 1.0
Author-Name: Salma Benchikh
Author-Name-First: Salma
Author-Name-Last: Benchikh
Author-Name: Tarik Jarou
Author-Name-First: Tarik
Author-Name-Last: Jarou
Author-Name: Lamrani Roa
Author-Name-First: Lamrani
Author-Name-Last: Roa
Author-Name: Nasri Elmehdi
Author-Name-First: Nasri
Author-Name-Last: Elmehdi
Title: Impact of feature selection on the prediction of global horizontal irradiation under ouarzazate city climate
Abstract: Ensuring accurate forecasts of Global Horizontal Irradiance (GHI) stands as a pivotal aspect in optimizing the efficient utilization of solar energy resources. Machine learning techniques offer promising prospects for predicting global horizontal irradiance. However, within the realm of machine learning, the importance of feature selection cannot be overestimated, as it is crucial in determining performance and reliability of predictive models. To address this, a comprehensive machine learning algorithm has been developed, leveraging advanced feature importance techniques to forecast GHI data with precision. The proposed models draw upon historical data encompassing solar irradiance characteristics and environmental variables within the Ouarzazate region, Morocco, spanning from 1st January 2018, to 31 December 2018, with readings taken at 60-minute intervals. The findings underscore the profound impact of feature selection on enhancing the predictive capabilities of machine learning models for GHI forecasting. By identifying and prioritizing the most informative features, the models exhibit significantly enhanced accuracy metrics, thereby bolstering the reliability, efficiency, and practical applicability of GHI forecasts. This advancement not only holds promise for optimizing solar energy utilization but also contributes to the broader discourse on leveraging machine learning for renewable energy forecasting and sustainability initiatives
Journal: Data and Metadata
Pages: 363
Volume: 3
Year: 2024
DOI: 10.56294/dm2024363
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:363:id:1056294dm2024363

Template-Type: ReDIF-Article 1.0
Author-Name: Mohammed Amraoui
Author-Name-First: Mohammed
Author-Name-Last: Amraoui
Author-Name: Imane Lasri
Author-Name-First: Imane
Author-Name-Last: Lasri
Author-Name: Fouzia Omary
Author-Name-First: Fouzia
Author-Name-Last: Omary
Author-Name: Mohamed Khalifa Boutahir
Author-Name-First: Mohamed Khalifa
Author-Name-Last: Boutahir
Title: Securing Biomedical Audio Data in IoT Healthcare Systems: An Evaluation of Encryption Methods for Enhanced Privacy
Abstract: Communication technology have advanced quickly since the COVID-19 epidemic started, providing consumers with additional benefits and conveniences. Concerns over the privacy and confidentiality of this data have grown in importance as initiatives that promote the use of audio and video to enhance interpersonal interactions become more common. In the context of the Internet of Things (IoT), audio communications security is essential in the biomedical domain. Sensitive medical data may be compromised in these connections, which include exchanges between patients and doctors and broadcasts of vital signs. To protect patient privacy and reduce cybersecurity threats, strong security measures such as data encryption must be put in place. Our study attempts to address these issues in this environment. Comparative examination of the Chacha20, Salsa20, and Camellia encryption algorithms enabled us to ascertain that Chacha20 performs exceptionally well when it comes to audio file decryption and encryption speed. The results of our trials attest to this encryption method's astounding effectiveness and efficacy. We have also used the noise reduction technique, which is frequently used in audio security to enhance the quality of recordings and make it easier to identify significant information in audio signals. Then, Fourier transform technique, which is also used to analyze audio files and can be used to identify changes, extract hidden information, and authenticate audio files. By doing this, the audio files security and integrity are strengthened
Journal: Data and Metadata
Pages: 365
Volume: 3
Year: 2024
DOI: 10.56294/dm2024365
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:365:id:1056294dm2024365

Template-Type: ReDIF-Article 1.0
Author-Name: Veera V Rama Rao M
Author-Name-First: Veera V
Author-Name-Last: Rama Rao M
Author-Name: Kiran Sree Pokkuluri
Author-Name-First: Kiran Sree
Author-Name-Last: Pokkuluri
Author-Name: N. Raghava Rao
Author-Name-First: N.
Author-Name-Last: Raghava Rao
Author-Name: S Sureshkumar
Author-Name-First: S
Author-Name-Last: Sureshkumar
Author-Name: S Balakrishnan
Author-Name-First: S
Author-Name-Last: Balakrishnan
Author-Name: A Shankar
Author-Name-First: A
Author-Name-Last: Shankar
Title: A secured and energy-efficient system for patient e-healthcare monitoring using the Internet of Medical Things (IoMT)
Abstract: Introduction: the Internet of Things (IoT) is gaining popularity in several industries owing to the autonomous and low-cost functioning of its sensors. In medical and healthcare usage, IoT gadgets provide an environment to detect patients' medical problems, such as blood volume, oxygen concentration, pulse, temperatures, etc. and take emergency action as necessary. The problem of imbalanced energy usage across biosensor nodes slows down the transmission of patient data to distant centres and has a detrimental effect on the health industry. In addition, the patient's sensitive information is sent through the insecure Internet and is exposed to potential threats. For clinical uses, information privacy and stability against hostile traffic constitute a further research challenge. Methods: this article proposes a Secured and Energy-Efficient System (SEES-IoMT) e-healthcare utilizing the Internet of Medical Things (IoMT) monitoring, the main goal of which is to reduce the connectivity cost and energy usage between sensing devices while feasibly forwarding the medical data. SEES-IoMT also guarantees the clinical data of the patients against unverified and malevolent nodes to enhance the privacy and security of the system. Result and Discussion: in consideration of the memory and power limitations of healthcare IoT gadgets, this approach is designed to be very lightweight. A thorough examination of this system's safety is performed to demonstrate its reliability. Conclusion: in terms of computing speed and security, the research compares SEES-IoMT to relevant methods in the IoT medical environment to demonstrate its applicability and resilience
Journal: Data and Metadata
Pages: 368
Volume: 3
Year: 2024
DOI: 10.56294/dm2024368
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:368:id:1056294dm2024368

Template-Type: ReDIF-Article 1.0
Author-Name: Safia Nasih
Author-Name-First: Safia
Author-Name-Last: Nasih
Author-Name: Sara Arezki
Author-Name-First: Sara Arezki
Author-Name-Last: Sara Arezki
Author-Name: Taoufiq Gadi
Author-Name-First: Taoufiq
Author-Name-Last: Gadi
Title: Blockchain Technology for tracking and tracing containers: model and conception
Abstract: The maritime industry has increasingly integrated advanced technologies such as AI, Blockchain, Big Data, and IoT, transforming traditional port operations into smart facilities aimed at enhancing global trade competitiveness. A particular focus has been on improving tracking and tracing services, with Blockchain technology emerging as pivotal for ensuring data integrity, transparency, and traceability across supply chains. This article proposes a blockchain-based tracking and tracing system model tailored for monitoring containers in Moroccan ports. Utilizing the Unified Modeling Language (UML), the model seeks to optimize resource allocation and boost stakeholder satisfaction through detailed diagrams and functional data requirements depiction. Despite challenges such as IoT terminal platform connectivity and operator resitance, successful implementation was achieved, establishing a foundational framework for a comprehensive container monitoring system. This model provides valuable insights for supply chain professionals and scholars interested in item tracking, aiming to integrate Blockchain with technologies like RFID, GPS, RTLS, QR Codes, BLE, and IoT sensors to enhance port operation efficiency and container management effectiveness. By leveraging these integrated technologies, ports can further improve operational efficiency and ensure accurate traceability of containers throughout the supply chain, contributing to overall trade facilitation and economic growth
Journal: Data and Metadata
Pages: 373
Volume: 3
Year: 2024
DOI: 10.56294/dm2024373
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:373:id:1056294dm2024373

Template-Type: ReDIF-Article 1.0
Author-Name: Syed Aleem Uddin Gilani
Author-Name-First: Syed Aleem
Author-Name-Last: Uddin Gilani
Author-Name: Murad Al-Rajab
Author-Name-First: Murad
Author-Name-Last: Al-Rajab
Author-Name: Mahmoud Bakka
Author-Name-First: Mahmoud
Author-Name-Last: Bakka
Title: Challenges and opportunities in traffic flow prediction: review of machine learning and deep learning perspectives
Abstract: In recent days, traffic prediction has been essential for modern transportation networks. Smart cities rely on traffic management and prediction systems. This study utilizes state-of-the-art deep learning and machine learning techniques to adjust to changing traffic conditions. Modern DL models, such as LSTM and GRU, are examined here to see whether they may enhance prediction accuracy and provide valuable insights. Repairing problems and errors connected to weather requires hybrid models that integrate deep learning with machine learning. These models need top-notch training data to be precise, flexible, and able to generalize. Researchers are continuously exploring new approaches, such as hybrid models, deep learning, and machine learning, to discover traffic flow data patterns that span several places and time periods. Our current traffic flow estimates need improvement. Some expected benefits are fewer pollutants, higher-quality air, and more straightforward urban transportation. With machine learning and deep learning, this study aims to improve traffic management in urban areas. Long Short-Term Memory (LSTM) models may reliably forecast traffic patterns
Journal: Data and Metadata
Pages: 378
Volume: 3
Year: 2024
DOI: 10.56294/dm2024378
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:378:id:1056294dm2024378

Template-Type: ReDIF-Article 1.0
Author-Name: Saif Al-Deen H. Hassan
Author-Name-First: Saif Al-Deen
Author-Name-Last: H. Hassan
Author-Name: Hasan Al-Furiji
Author-Name-First: Hasan
Author-Name-Last: Al-Furiji
Author-Name: Mohammed Kareem Rashid
Author-Name-First: Mohammed
Author-Name-Last: Kareem Rashid
Author-Name: Zahraa Abed Hussein
Author-Name-First: Zahraa
Author-Name-Last: Abed Hussein
Author-Name: Bhavna Ambudkar
Author-Name-First: Bhavna
Author-Name-Last: Ambudkar
Title: Trending Algorithm on Twitter through 2023
Abstract: Introduction: by doing so, Twitter's trending algorithm sets the benchmark for what online discussion and information flow look like. It must be clearly understood by the researchers and the users as to how it developed and impacted. Objective: this paper discusses the Twitter trending algorithm discussion until 2023, highlighting its aspects and ethical considerations. Method: to demonstrate trend identification, we adopted a cross-sectional approach that involved data mining of trends defined by the Twitter platform from January 2020 to October 2023, applying machine learning techniques. In total, 1,984,544 unique trends were identified in the two cities over the 1584 days of Twitter API research. Results: this research identified that there are many changes in the current trending algorithm regarding Twitter, and current real-time content and users’ participation are the major concerns. The assessed model, known as TrendDetector, predicts the trend of commercials to be 80 %, while the non-commercial trend is assessed to be 60 %. Trend selection was guided by the traffic of tweets, the number of users, and the extent of new content. Conclusions: user-generated activities, content, and spread, as well as the structure and design of the platform, are thus an intricate mix in the case of the trending algorithm on Twitter. It enhances the timely acquisition of information while being associated with preconditions of bias and manipulation. Future research must look at aspects such as the algorithm's transparency and the ethicality of the trends it selects
Journal: Data and Metadata
Pages: 384
Volume: 3
Year: 2024
DOI: 10.56294/dm2024384
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:384:id:1056294dm2024384

Template-Type: ReDIF-Article 1.0
Author-Name: Gissela Yajaira Hinojosa Barreto
Author-Name-First: Gissela Yajaira
Author-Name-Last: Hinojosa Barreto
Author-Name: Nathaly Beatriz Chávez García
Author-Name-First: Nathaly Beatriz
Author-Name-Last: Chávez García
Author-Name: Jaime Mesías Cajas
Author-Name-First: Jaime Mesías Cajas
Author-Name-Last: Jaime Mesías Cajas
Title: Implementation of a sales information management system applying business intelligence in SMEs in the canton of La Maná
Abstract: Small and Medium Enterprises (SMEs) are essential to the global economy, promoting employment, innovation and sustainable development. Effective sales information management is critical to your success, involving the collection, storage, analysis and application of data about customers, products, distribution channels, prices and market trends. Proper management of this data allows SMEs to understand customer demands, identify market opportunities and optimize their sales strategies. However, SMEs face significant challenges in this area, such as technological limitations, budget constraints and data complexity, which can lead to manual processes, lack of visibility in the supply chain and loss of competitiveness. The research carried out in the La Maná canton, in Cotopaxi, Ecuador, supports the implementation of a sales information management system due to its intense commercial activity and the presence of SMEs in sectors such as agriculture and commerce. The study adopted a mixed methodology, which combined literature review and field research, using inductive and deductive approaches. Managers, administrators and workers from two companies were surveyed. The results indicate that this methodology is effective in achieving the research objectives, underscoring the importance of integrating various methodologies to obtain a complete understanding of the topic
Journal: Data and Metadata
Pages: 385
Volume: 3
Year: 2024
DOI: 10.56294/dm2024385
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:385:id:1056294dm2024385

Template-Type: ReDIF-Article 1.0
Author-Name: Elizabeth Magdalena Recalde Drouet
Author-Name-First: Elizabeth Magdalena
Author-Name-Last: Recalde Drouet
Author-Name: David Mauricio Tello Salazar
Author-Name-First: David Mauricio
Author-Name-Last: Tello Salazar
Author-Name: Tatiana Lizbeth Charro Domínguez
Author-Name-First: Tatiana Lizbeth
Author-Name-Last: Charro Domínguez
Author-Name: Pablo Jordán Catota Pinthsa
Author-Name-First: Pablo Jordán
Author-Name-Last: Catota Pinthsa
Title: Analysis of the repercussions of Artificial Intelligence in the Personalization of the Virtual Educational Process in Higher Education Programs
Abstract: This study examined how artificial intelligence (AI) has transformed the personalization of the virtual educational process in higher education programs. A systematic review of literature published between 2012 and 2023 was carried out, evaluating empirical studies, reports and review articles available in academic databases such as IEEE Xplore, SpringerLink and Google Scholar. Methods discussed include intelligent tutoring systems, learning analytics, and recommendation systems. The results showed that AI significantly improved the personalization of learning. Intelligent tutoring systems provide real-time adaptive feedback, adjusting content and pacing based on students' individual needs, improving their understanding and retention. Learning analytics helps identify student behavior patterns and predict academic issues, thereby facilitating timely interventions that help improve performance. Additionally, recommender systems personalize study materials based on student preferences and progress, thereby optimizing the educational experience. However, significant challenges have been identified, such as the need to protect data privacy and mitigate algorithmic biases that can affect the fairness and efficiency of these systems. In conclusion, the integration of AI into virtual higher education has enhanced the personalization of learning, improving both student satisfaction and academic performance. However, there is a need to continue to focus on developing ethical and equitable AI systems to address identified issues and maximize educational benefits
Journal: Data and Metadata
Pages: 386
Volume: 3
Year: 2024
DOI: 10.56294/dm2024386
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:386:id:1056294dm2024386

Template-Type: ReDIF-Article 1.0
Author-Name: Hong Xiang
Author-Name-First: Hong
Author-Name-Last: Xiang
Author-Name: Anrong Wang
Author-Name-First: Anrong
Author-Name-Last: Wang
Author-Name: Wenxi Tan
Author-Name-First: Wenxi
Author-Name-Last: Tan
Author-Name: Xiaoju Dai
Author-Name-First: Xiaoju
Author-Name-Last: Dai
Author-Name: Le Zhang
Author-Name-First: Le
Author-Name-Last: Zhang
Title: Employment cognition and occupational contradictions among college graduates under the new employment form – based on data analysis
Abstract: Total employment among college grads is now under significant pressure, and structural conflicts are quite visible. People are starting to take notice of the serious job crisis that college students face. According to the research study, college students' employment cognition is a key factor in this problem. The importance of enhancing students' employment cognition cannot be overstated. According to the article, knowing the important aspects that could affect one's job cognition is the first step in improving students' employment cognition. SEM analysis shows these characteristics were positively and substantially associated with employment cognition. This article aims to use big data technologies to conduct extensive studies and analyses on AI employment cognition and occupational contradictions. The first step is implementing a scientific approach to building a multi-level linked big data management platform for Employment Cognition. The platform will be used during the development of Employment career advancement. The subsequent objective is to build an employment team by including information resources. Finally, the results show a huge variation in self-ability cognition and a remarkable difference in how college students think about their job capacity due to new work. Their physiological features and societal expectations are related to this. However, the most critical factor in enhancing the quality of college graduates' jobs is for the relevant department to improve the nurturing of their prior abilities in this area
Journal: Data and Metadata
Pages: 389
Volume: 3
Year: 2024
DOI: 10.56294/dm2024389
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:389:id:1056294dm2024389

Template-Type: ReDIF-Article 1.0
Author-Name: Hala Mellouli
Author-Name-First: Hala
Author-Name-Last: Mellouli
Author-Name: Anwar Meddaoui
Author-Name-First: Anwar
Author-Name-Last: Meddaoui
Author-Name: Abdelhamid Zaki
Author-Name-First: Abdelhamid
Author-Name-Last: Zaki
Title: Enhancing industrial decision-making through Multi-Criteria Decision-Making approaches and ML-Integrated Frameworks
Abstract: Decision-making in current industrial contexts has shifted from intuition to a data-driven approach, requiring prompt processing of huge datasets. However, conventional Multi-Criteria Decision Making (MCDM) methodologies fall short of navigating the intricacy of large datasets. This paper introduces an innovative decision-support system integrating multi-criteria methods with machine learning techniques such as artificial neural networks. The proposed six-step framework aims to optimize operational decisions by analyzing real-time performance data. The research contributes to the advancement of decision-making methodologies in the industrial field, offering dynamic responsiveness and improved recommendations compared to traditional MCDM methods. While results are promising, future work should focus on robustness testing particularly in terms of its dependence on real-time data, to ensure sustained efficacy and mitigate potential biases in recommendations over time.
Journal: Data and Metadata
Pages: 391
Volume: 3
Year: 2024
DOI: 10.56294/dm2024391
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:391:id:1056294dm2024391

Template-Type: ReDIF-Article 1.0
Author-Name: Anber Abraheem Shlash Mohammad
Author-Name-First: Anber Abraheem Shlash
Author-Name-Last: Mohammad
Author-Name: Iyad A.A Khanfar
Author-Name-First: Iyad A.A
Author-Name-Last: Khanfar
Author-Name: Badrea Al Oraini
Author-Name-First: Badrea
Author-Name-Last: Al Oraini
Author-Name: Asokan Vasudevan
Author-Name-First: Asokan
Author-Name-Last: Vasudevan
Author-Name: Ibrahim Mohammad Suleiman
Author-Name-First: Ibrahim Mohammad
Author-Name-Last: Suleiman
Author-Name: Ala'a M. Al-Momani
Author-Name-First: Ala'a M.
Author-Name-Last: Al-Momani
Title: User acceptance of health information technologies (HIT): an application of the theory of planned behavior
Abstract: Health Information Technologies (HIT) has a significant chance of enhancing the standard of medical treatment, but their acceptance faces major obstacles including low adoption rates and professional hesitancy. Limited research on HIT adoption, especially in poor nations, adds to this problem and clearly challenges health care managers and researchers. It emphasizes the need of knowing the elements influencing acceptance, choice, and usage of healthcare technology to improve user adoption willingness. Using past studies from several nations, this paper investigates the elements driving HIT adoption within the prism of the Theory of Planned Behavior (TPB). Using a Systematic Literature Review (SLR) under direction from the PRISMA framework guaranteed an open and exhaustive study. With eight publications compared to six from wealthy countries, the results expose a notable trend: emerging countries help more to promote HIT adoption research. Furthermore, the combination of TPB with other theories like the Technology Acceptance Model (TAM) provides a whole framework for grasp the elements influencing HIT uptake. Core TPB components include subjective norms, attitude, and perceived behavioral control are well known in industrialized nations and supported by TAM's perceived utility and simplicity of use, along with demographic elements, therefore stressing a user-centric approach. Research on emerging nations, particularly China, shows, on the other hand, a wide spectrum of variables on HIT adoption including personal, technical, social, and institutional ones. The results greatly improve our knowledge of HIT adoption seen from the TPB perspective and provide insightful analysis for legislators developing sensible plans for HIT implementation
Journal: Data and Metadata
Pages: 394
Volume: 3
Year: 2024
DOI: 10.56294/dm2024394
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:394:id:1056294dm2024394

Template-Type: ReDIF-Article 1.0
Author-Name: Anber Abraheem Shlash Mohammad
Author-Name-First: Anber Abraheem
Author-Name-Last: Shlash Mohammad
Author-Name: Iyad A.A Khanfar
Author-Name-First: Iyad A.A
Author-Name-Last: Khanfar
Author-Name: Badrea Al Oraini
Author-Name-First: Badrea
Author-Name-Last: Al Oraini
Author-Name: Asokan Vasudevan
Author-Name-First: Asokan
Author-Name-Last: Vasudevan
Author-Name: Ibrahim Mohammad Suleiman
Author-Name-First: Ibrahim Mohammad
Author-Name-Last: Suleiman
Author-Name: Zhou Fei
Author-Name-First: Zhou
Author-Name-Last: Fei
Title: Predictive analytics on artificial intelligence in supply chain optimization
Abstract: AI-powered predictive analytics is among the most important ways of optimizing supply chains. This paper on AI-powered predictive analytics will address improving the competitiveness and effectiveness of supply chain operations. Nevertheless, current methods are not always scalable or adaptable to complex supply networks and changing market environments. Therefore, this paper posits that Supply Chain Optimization using Artificial Intelligence (SCO-AI) systems can help with these concerns. SCO-AI employs real-time data analysis and advanced machine learning algorithms which results to reduced response time, enhanced logistics route optimization, improved demand planning as well as real-time inventory control. Thus, the idea herein suggested fits smoothly into existing supply chain frameworks for data-driven decisions that make companies remain agile in ever-changing market dynamics. SCO-AI implementation has seen significant improvements in inventory turnover rate, rates of on-time delivery as well as overall supply chain costs. In this period of high business turbulence, such kind of research builds up the robustness of a given supply chain while at the same time minimizing operational risks by means of simulations and case studies
Journal: Data and Metadata
Pages: 395
Volume: 3
Year: 2024
DOI: 10.56294/dm2024395
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:395:id:1056294dm2024395

Template-Type: ReDIF-Article 1.0
Author-Name: Manuel William Villa Quisphe
Author-Name-First: Manuel William
Author-Name-Last: Villa Quisphe
Author-Name: José Augusto Cadena Moreano
Author-Name-First: José Augusto
Author-Name-Last: Cadena Moreano
Author-Name: Juan Carlos Chancusig Chisag
Author-Name-First: Juan Carlos
Author-Name-Last: Chancusig Chisag
Title: Artificial intelligence: prototype of an automated irrigation system for the cultivation of roses in Cotopaxi
Abstract: Implementing artificial intelligence in agriculture can improve efficiency, reduce pollution, and promote more effective agricultural production. Efficient irrigation management avoids wasting water and ensures that plants receive the right amount of water at the right time. The purpose of this research is to present an intelligent irrigation system based on neural networks and fuzzy logic, to avoid the presence of pests due to excess relative humidity in rose crops in Cotopaxi. A mixed methodology was used. The SCRUM methodology, Android Studio as an integrated development environment, a relational database management system and the Mobile-D method were used as software elements. For the prototype construction, the main hardware element that was used was the Arduino Board. The system for irrigating automated water using fuzzy logic took less time than manual irrigation. Training actions were proposed for employers and employees in the use and maintenance of the automated irrigation system, to maintain continuous improvement in the process
Journal: Data and Metadata
Pages: 398
Volume: 3
Year: 2024
DOI: 10.56294/dm2024398
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:398:id:1056294dm2024398

Template-Type: ReDIF-Article 1.0
Author-Name: Alex Paúl
Author-Name-First: Alex Paúl
Author-Name-Last: Alex Paúl
Author-Name: Xavier Santiago Salazar Defaz
Author-Name-First: Xavier Santiago
Author-Name-Last: Salazar Defaz
Author-Name: Xavier Alfonso Proaño Maldonado
Author-Name-First: Xavier Alfonso
Author-Name-Last: Proaño Maldonado
Author-Name: Franklin Hernán Vásquez Teneda
Author-Name-First: Franklin Hernán
Author-Name-Last: Vásquez Teneda
Title: Proposal for a protection system of an industrial electrical network
Abstract: An electrical protection system in an industry Works by detecting and acting against abnormal conditions in the electrical system with the objective of guaranteeing the safety of people, protecting equipment and ensuring the continuity of industrial process. Taking into account the importance of guaranteeing adequate electrical protection system in an industrial activity in this research a proposal for the protection system for an industrial electrical network is presented. As a previous step proposal, the methodology desingned for the coordination of protections for an industrial electrical network was proposed. The proposal was designed taking into account four operating scenarios required to calibrate the industry’s protection devices. The short circuit analysis maximum in each of the system bars for each of the scenarios allowed determining the maximum phase failure currents it is 44,44 % for emergency 1 and 66,66 % for emergency 2, while the maximum ground fault current was founding emergency scenario2. At the news to the four scenarios of the industrial network in the actuation times of the devices of protection, there is not considerable variation; this is justified by the current time graph because when there is a serious short circuit current, the action time should be shorter. On the contrary, when there is a small current time will be greater
Journal: Data and Metadata
Pages: 399
Volume: 3
Year: 2024
DOI: 10.56294/dm2024399
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:399:id:1056294dm2024399

Template-Type: ReDIF-Article 1.0
Author-Name: Carlos Ivan Quinatoa Caiza
Author-Name-First: Carlos Ivan
Author-Name-Last: Quinatoa Caiza
Author-Name: Alex Ivan Paguay Llamuca
Author-Name-First: Alex Ivan
Author-Name-Last: Paguay Llamuca
Author-Name: Xavier Alfonso Proaño Maldonado
Author-Name-First: Xavier Alfonso
Author-Name-Last: Proaño Maldonado
Title: Integration of electromagnetic and mechanical models for effective lightning protection in buildings
Abstract: The study focused on the design of an advanced algorithm for the optimal sizing of protection systems against atmospheric discharges in architectural structures, applying the rolling sphere method. This technique facilitated the incorporation of user-specified parameters through an advanced graphical interface. The methodology began with the exhaustive accumulation of data relevant to the project. Risk indices were estimated through sophisticated risk analysis software applications. If adjustments were required, the process continued; If not, the building was considered to be adequately secured. The ground resistivity was evaluated according to IEEE Std. 81, and the rolling sphere method was implemented according to IEC 662305-3. The grounding systems were configured in accordance with IEEE Std. 142 and IEEE Std. 80. To analyze the interaction of electrical discharges with the protected building, the electrical equivalents of elements such as meshes, fused copper rods were computed. , and conductors positioned horizontally and vertically. Using these data, a model was built in ATPDraw, interconnected with Python for the generation of graphical representations of the current waves in the different protection subsystems. To conclude and corroborate the effectiveness of the process, the risk indices were reevaluated. The validation of the algorithm was achieved by minimizing the margin of error to insignificant levels by incorporating standardized data proposed by organizations such as IEC and IEEE, thus confirming the precision of the designed algorithm
Journal: Data and Metadata
Pages: 400
Volume: 3
Year: 2024
DOI: 10.56294/dm2024400
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:400:id:1056294dm2024400

Template-Type: ReDIF-Article 1.0
Author-Name: Nehal Ettaloui
Author-Name-First: Nehal
Author-Name-Last: Ettaloui
Author-Name: Sara Arezki
Author-Name-First: Sara
Author-Name-Last: Arezki
Author-Name: Taoufiq Gadi
Author-Name-First: Taoufiq
Author-Name-Last: Gadi
Title: IoT-Blockchain Based Model for Enhancing Diabetes Management and Monitoring
Abstract: The integration of Internet of Things (IoT) and blockchain technology in healthcare, especially for diabetes management, represents a transformative advancement enabling continuous, proactive monitoring of patients' health. This paper aims to present an IoT-blockchain-based model for continuous, secure, and efficient health monitoring in diabetes management. IoT devices like smart glucose monitors and insulin pumps collect and transmit real-time health data, allowing for prompt treatment adjustments and complication prevention. Blockchain ensures data security and integrity through encryption and decentralized storage, safeguarding against unauthorized access and tampering. This secure data transmission is crucial for maintaining patient privacy and complying with regulations such as GDPR and HIPAA. The combination of IoT and blockchain promises enhanced security, transparency, cost reduction, and improved patient outcomes. It enhances patient engagement by enabling seamless communication between patients and healthcare providers, facilitating personalized and timely medical advice. The integration of these technologies holds promise for revolutionizing healthcare delivery, offering sustainable solutions to managing chronic conditions like diabetes
Journal: Data and Metadata
Pages: 406
Volume: 3
Year: 2024
DOI: 10.56294/dm2024406
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:406:id:1056294dm2024406

Template-Type: ReDIF-Article 1.0
Author-Name: Ayesha Agrawal
Author-Name-First: Ayesha
Author-Name-Last: Agrawal
Author-Name: Vinod Maan
Author-Name-First: Vinod
Author-Name-Last: Maan
Title: Enhanced Brain Tumor Segmentation and Size Estimation in MRI Samples using Hybrid Optimization
Abstract: The area of medical imaging specialization, specifically in the context of brain tumor segmentation, has long been challenged by the inherent complexity and variability of brain structures. Traditional segmentation methods often struggle to accurately differentiate between the diverse types of tissues within the brain, such as white matter, grey matter, and cerebrospinal fluid, leading to suboptimal results in tumor identification and delineation. These limitations necessitate the development of more advanced and precise segmentation techniques to enhance diagnostic accuracy and treatment planning. In response to these challenges, the proposed study introduces a novel segmentation approach that combines the Grey Wolf Optimization approach and the Cuckoo Search approach within a Fuzzy C-Means (FCM) framework. The integration of GWO and CS is designed to leverage their respective strengths in optimizing the segmentation of brain tissues. This hybrid approach was rigorously tested across multiple Magnetic Resonance Imaging (MRI) datasets, demonstrating significant enhancements over existing segmentation methods. The study observed a 4,9 % improvement in accuracy, 3,5 % increase in precision, 4,5 % higher recall, 3,2 % less delay, and 2,5 % better specificity in tumor segmentation. The implications of these advancements are profound. By achieving higher precision and accuracy in brain tumor segmentation, the proposed method can substantially aid in early diagnosis and accurate staging of brain tumors, eventually leading to more effective treatment planning and improved patient outcomes. Furthermore, the integration of GWO and CS within the FCM process sets a new benchmark in medical imaging, paving the way for future investigation in the field of study
Journal: Data and Metadata
Pages: 408
Volume: 3
Year: 2024
DOI: 10.56294/dm2024408
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:408:id:1056294dm2024408

Template-Type: ReDIF-Article 1.0
Author-Name: Anber Abraheem Shlash Mohammad
Author-Name-First: Anber Abraheem
Author-Name-Last: Shlash Mohammad
Author-Name: Mai Alshebel
Author-Name-First: Mai
Author-Name-Last: Alshebel
Author-Name: Badrea Al Oraini
Author-Name-First: Badrea
Author-Name-Last: Al Oraini
Author-Name: Asokan Vasudevan
Author-Name-First: Asokan
Author-Name-Last: Vasudevan
Author-Name: Suleiman Ibrahim Shelash Mohammad
Author-Name-First: Suleiman Ibrahim
Author-Name-Last: Shelash Mohammad
Author-Name: Huiying Jiang
Author-Name-First: Huiying
Author-Name-Last: Jiang
Author-Name: Aktham Al Sarayreh
Author-Name-First: Aktham
Author-Name-Last: Al Sarayreh
Title: Research on Multimodal College English Teaching Model Based on Genetic Algorithm
Abstract: Analyzing College English texts is essential for quantitatively evaluating their grammar, phrases, and words to enhance their use in writing, conversation, and other contexts. The precise and clear use of College English words, phrases, and sentences is essential to knowledge-based and foundational learning systems. Text data analytics run into problems with data amount, data diversity, data integration and interoperability. It is challenging to accomplish human-computer interaction in spoken College English communication and to assist students with corrections using the conventional methodology of teaching College English. Therefore, this paper proposed the Genetic Algorithm-based intelligent English course optimization system (GA-IECOS) to handle the scheduling above issue of college English classes and optimize college English teaching courses. The results demonstrate that the conventional BP neural network's local scheduling optimization issue may be resolved using the multidirectional mutation genetic BP neural network method. Subsequently, a mix of formative and summative assessments will be used to establish a couple of groups to evaluate the effectiveness using a control population and a trial group of a GA-IECOS for English language classes using a multidirectional mutation genetic algorithm and an optimization neural network. The results demonstrate that the GA-IECOS algorithm is more effective in the classroom and may greatly improve students' English performance
Journal: Data and Metadata
Pages: 421
Volume: 3
Year: 2024
DOI: 10.56294/dm2024421
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:421:id:1056294dm2024421

Template-Type: ReDIF-Article 1.0
Author-Name: Anber Abraheem Shlash Mohammad
Author-Name-First: Anber Abraheem
Author-Name-Last: Shlash Mohammad
Author-Name: Khaleel Ibrahim Al- Daoud
Author-Name-First: Khaleel Ibrahim
Author-Name-Last: Al- Daoud
Author-Name: Badrea Al Oraini
Author-Name-First: Badrea
Author-Name-Last: Al Oraini
Author-Name: Suleiman Ibrahim Shelash Mohammad
Author-Name-First: Suleiman Ibrahim
Author-Name-Last: Shelash Mohammad
Author-Name: Asokan Vasudevan
Author-Name-First: Asokan
Author-Name-Last: Vasudevan
Author-Name: Jin Zhang
Author-Name-First: Jin
Author-Name-Last: Zhang
Author-Name: Mohammad Faleh Ahmmad Hunitie
Author-Name-First: Mohammad Faleh Ahmmad
Author-Name-Last: Hunitie
Title: Using Digital Twin Technology to Conduct Dynamic Simulation of Industry-Education Integration
Abstract: The high accident rate in the construction industry has a major impact on how well projects turn out. Despite substantial investments in safety planning and supervision, there has been a marked increase in the construction industry's accident rate compared to other sectors. Serious games based on VR have recently been used in the study, suggesting that workers are now more safety conscious. However, these situations need many resources to create and are not always realistic. Hence this paper, Digital Twin-based Construction Safety Training Framework (DT-CSTF) with Artificial Intelligence (AI), has been proposed to monitor employees' emotional, mental, and physical well-being in real-time. The report sheds light on the significance of DT technology and its function in Industry 5.0. Using the Unity game engine, the proposed DT-CSTF creates a virtual reality-based training environment (VRTE) prototype that incorporates BIM, construction timetables, and safety requirements. Following this, the suggested structure enables gathering user data about risks and providing tailored feedback. Automated virtual reality game training scenarios are created using data given by digital twins on project intent, project status, safety requirements, and history. Both improved digital twins and periodic construction safety monitoring are anticipated to reap the benefits of dynamic virtual reality training. The proposed management system offers effectiveness of VR-based security training, cost-benefit analysis, monitoring,employee behaviour, safety education values are obtained by the ratio of 96,90 %, 98,33 %, 99,25 %, 95,91 %, 98,66 % respectively
Journal: Data and Metadata
Pages: 422
Volume: 3
Year: 2024
DOI: 10.56294/dm2024422
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:422:id:1056294dm2024422

Template-Type: ReDIF-Article 1.0
Author-Name: Wilter C. Morales-García
Author-Name-First: Wilter C.
Author-Name-Last: Morales-García
Author-Name: Liset Z. Sairitupa-Sanchez
Author-Name-First: Liset Z.
Author-Name-Last: Sairitupa-Sanchez
Author-Name: Mardel Morales-García
Author-Name-First: Mardel Morales-García
Author-Name-Last: Mardel Morales-García
Title: Adaptation and Validation of a Self-Assessment Work Performance Scale for Nursing Staff
Abstract: Introduction: work performance in nursing is crucial for the effectiveness of health services and the quality of patient care. Factors affecting this performance include work motivation, organizational culture, institutional support, and working conditions. The need for accurate tools to measure work performance in specific contexts, such as nursing in Peru, is imperative, especially given the increased demands and pressures brought about by the COVID-19 pandemic. Objective: this study aims to adapt and validate a Short Version of the Self-Assessment Work Performance Scale for Peruvian nursing staff, ensuring its relevance and psychometric accuracy in this specific context. Method: an instrumental design was used with convenience sampling, selecting 409 Peruvian nurses (M=20,22, SD=2,6). The scale, composed of 10 items, was adapted to Spanish and evaluated through confirmatory factor analysis. Reliability measures such as Cronbach's alpha and McDonald's omega were employed, along with invariance analysis to ensure the scale's consistency across sexes. Results: the factor structure confirmed the construct validity of the scale with a good fit in the unifactorial models (χ² = 139,820, df = 35, p < ,001, CFI = 0,94, TLI = 0,93, RMSEA = 0,07, SRMR = 0,03). Reliability was high, with Cronbach's alpha and McDonald's omega of 0,92 for the general model. The scale demonstrated full measurement invariance across sexes, reinforcing its applicability in gender-divided populations. Conclusions: the scale is a valid and reliable tool for assessing work performance in nursing staff in Peru. Its ability to adequately reflect the specific conditions and challenges of this professional group ensures its utility in the continuous improvement of health service quality and effective management of nursing staff in diverse and demanding contexts
Journal: Data and Metadata
Pages: 423
Volume: 3
Year: 2024
DOI: 10.56294/dm2024423
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:423:id:1056294dm2024423

Template-Type: ReDIF-Article 1.0
Author-Name: Alimul Haque
Author-Name-First: Alimul
Author-Name-Last: Haque
Author-Name: Shams Raza
Author-Name-First: Shams
Author-Name-Last: Raza
Author-Name: Sultan Ahmad
Author-Name-First: Sultan
Author-Name-Last: Ahmad
Author-Name: Alamgir Hossain
Author-Name-First: Alamgir
Author-Name-Last: Hossain
Author-Name: Hikmat A. M. Abdeljaber
Author-Name-First: Hikmat A. M.
Author-Name-Last: Abdeljaber
Author-Name: A. E. M. Eljialy
Author-Name-First: A. E. M.
Author-Name-Last: Eljialy
Author-Name: Sultan Alanazi
Author-Name-First: Sultan
Author-Name-Last: Alanazi
Author-Name: Jabeen Nazeer
Author-Name-First: Jabeen
Author-Name-Last: Nazeer
Title: Implication of Different Data Split Ratio on the Performance of Model in Price Prediction of Used Vehicles Using Regression Analysis
Abstract: Introduction: artificial intelligence (AI) and Machine Learning have become buzzwords lately due to technological changes and data quality testing, especially in shape and finish analysis. Lots of research has been conducted for linear regression algorithms to predict the price in different sectors for share stock, rental properties, prices of used cars etc. This study provides suitable data split ratio for optimum cost estimation based on linear regression model. In present days there is an increasing demand for having own car for every middle-class family therefore this have given opportunity to motor vehicle business to offer wide range of used vehicle for re-sale especially companies like Maruti Suzuki, Tata motors & Mahendra motors in Indian motor vehicle industries. Therefore, it is important to know the current value of your car before spending your hard-earned money on any item. Objective: the objective of this paper is finding appropriate value of cars in Metropolitans or even in state capitals. Features like model, mileage, AC, seating capacities, fuel type automatic will be taken into account when doing this. This estimate is designed to help customers find the right options to suit their needs. Method: we have used a linear regression model to estimate the value of the respective car. Results: for doing this price prediction in this paper using liner regression we have tried to find the optimum accuracy of model by varying data split ratio for training and test data set and concluded with the result that 80/20 ratio is the best ratio with optimum model accuracy for business domain analysis with labelled data set. Conclusions: the findings underscore the importance of careful consideration when selecting a data split ratio for price prediction models in the used vehicle market. The insights gleaned from this study can inform future research and contribute to the development of more accurate and reliable regression models in similar domains
Journal: Data and Metadata
Pages: 425
Volume: 3
Year: 2024
DOI: 10.56294/dm2024425
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:425:id:1056294dm2024425

Template-Type: ReDIF-Article 1.0
Author-Name: Nataliia Yuhan
Author-Name-First: Nataliia
Author-Name-Last: Yuhan
Author-Name: Yuliia Herasymenko
Author-Name-First: Yuliia
Author-Name-Last: Herasymenko
Author-Name: Oleksandra Deichakivska
Author-Name-First: Oleksandra
Author-Name-Last: Deichakivska
Author-Name: Anzhelika Solodka
Author-Name-First: Anzhelika
Author-Name-Last: Solodka
Author-Name: Yevhen Kozlov
Author-Name-First: Yevhen
Author-Name-Last: Kozlov
Title: Translation as a linguistic act in the context of artificial intelligence: the impact of technological changes on traditional approaches
Abstract: The purpose of this article is to study translation as a human speech act in the context of artificial intelligence. Using the method of analysing the related literature, the article focuses on the impact of technological changes on traditional approaches and explores the links between these concepts and their emergence in linguistics and automatic language processing methods. The results show that the main methods include stochastic, rule-based, and methods based on finite automata or expressions. Studies have shown that stochastic methods are used for text labelling and resolving ambiguities in the definition of word categories, while contextual rules are used as auxiliary methods. It is also necessary to consider the various factors affecting automatic language processing and combine statistical and linguistic methods to achieve better translation results. Conclusions - In order to improve the performance and efficiency of translation systems, it is important to use a comprehensive approach that combines various techniques and machine learning methods. The research confirms the importance of automated language processing in the fields of AI and linguistics, where statistical methods play a significant role in achieving better results
Journal: Data and Metadata
Pages: 429
Volume: 3
Year: 2024
DOI: 10.56294/dm2024429
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:429:id:1056294dm2024429

Template-Type: ReDIF-Article 1.0
Author-Name: Olga Ievsieieva
Author-Name-First: Olga
Author-Name-Last: Ievsieieva
Author-Name: Halyna Matskiv
Author-Name-First: Halyna
Author-Name-Last: Matskiv
Author-Name: Nataliia Raiter
Author-Name-First: Nataliia
Author-Name-Last: Raiter
Author-Name: Oleksandr Momot
Author-Name-First: Oleksandr
Author-Name-Last: Momot
Author-Name: Anatolii Shysh
Author-Name-First: Anatolii
Author-Name-Last: Shysh
Title: The Use of Big Data in Corporate Accounting and Data Analysis: Opportunities and Challenges
Abstract: Introduction: the era of Big Data technologies is restructuring corporate accounting, enabling a wide array of dynamic potential. This project explores how Big Data affects financial management, focusing on forecasting, risk management, and technological advances. Method: this work is informed by a large-scale review of scholarly literature, industry reports, and case studies. Databases like Google Scholar, PubMed, IEEE Xplore, Scopus, and Web of Science were used for data collection. Keywords included Big Data, corporate accounting, financial forecasting, risk management, data analytics, AI in accounting, machine learning in finance, and blockchain technology applied to accounting. The review was structured thematically, focusing on financial forecasting, risk management, and ethical considerations affected by Big Data practices in this domain. Results: Big Data improves financial forecasting accuracy using historical data, market trends, and consumer behavior analytics. In risk management, Big Data facilitates effective proactive actions through thorough risk evaluation. Emerging technologies are anticipated to automate complex tasks, enhance predictive analytics, and improve the security and reliability of financial transactions. Conclusions: Big Data holds significant potential for corporate accounting, though challenges such as managerial complexity, data privacy, and expertise requirements for handling large volumes of data remain. The study highlights the importance of flexibility and technological adaptability, as well as specialized skill sets. It calls for continual dialogue and policy development to meet the ethical challenges presented by Big Data/AI, promoting responsible deployment while ensuring fairness. This review contributes to academic discourse and provides strategic guidance for practitioners in the evolving landscape of corporate accounting
Journal: Data and Metadata
Pages: 430
Volume: 3
Year: 2024
DOI: 10.56294/dm2024430
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:430:id:1056294dm2024430

Template-Type: ReDIF-Article 1.0
Author-Name: Hanna Kravchenko
Author-Name-First: Hanna
Author-Name-Last: Kravchenko
Author-Name: Zoya Ryabova
Author-Name-First: Zoya
Author-Name-Last: Ryabova
Author-Name: Halyna Kossova-Silina
Author-Name-First: Halyna
Author-Name-Last: Kossova-Silina
Author-Name: Stepan Zamojskyj
Author-Name-First: Stepan
Author-Name-Last: Zamojskyj
Author-Name: Daria Holovko
Author-Name-First: Daria
Author-Name-Last: Holovko
Title: Integration of information technologies into innovative teaching methods: Improving the quality of professional education in the digital age
Abstract: Introduction: modern possibilities of using digital technologies in vocational education are actively used to improve the training of specialists and adapt them to the requirements of the labour market. The purpose of the article is to analyse the integration of information technology into innovative teaching methods and to study the improvement of the quality of vocational education in the digital age. Method: the type of research is quantitative. The authors used such scientific methods: comparison and content analysis. The materials were processed from 02-09-2023 to 21-12-2023. A survey of teachers of vocational education institutions (140 people) was also conducted, based on which the main opinions on the state and prospects of digitalisation in this area are presented. Results: it was showed how often and effectively digital technologies are used and what innovative tools teachers use. It is also demonstrated that the difficulties in reforming the material base of education are recognised as extremely significant in Ukrainian reality. The importance of continuous professional development is emphasised, as digital technologies are developing rapidly. Conclusions: it was summarised the results of the study, emphasising that the digitalisation of vocational education aims to ensure the proper development of education in line with the current challenges of the labour market
Journal: Data and Metadata
Pages: 431
Volume: 3
Year: 2024
DOI: 10.56294/dm2024431
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:431:id:1056294dm2024431

Template-Type: ReDIF-Article 1.0
Author-Name: Sergio V. Flores
Author-Name-First: Sergio V.
Author-Name-Last: Flores
Author-Name: Alicia Figueroa-Barra
Author-Name-First: Alicia
Author-Name-Last: Figueroa-Barra
Author-Name: María Labraña-Palma
Author-Name-First: María
Author-Name-Last: Labraña-Palma
Author-Name: Angel Roco-Videla
Author-Name-First: Angel
Author-Name-Last: Roco-Videla
Author-Name: Marcela Caviedes-Olmos
Author-Name-First: Marcela
Author-Name-Last: Caviedes-Olmos
Author-Name: Sofía Perez-Jiménez
Author-Name-First: Sofía
Author-Name-Last: Perez-Jiménez
Author-Name: Raúl Aguilera Eguía
Author-Name-First: Raúl
Author-Name-Last: Aguilera Eguía
Title: Variability and positive selection in FOXP2, a gene associated with the development of language, speech, and cognition
Abstract: Introduction: the FOXP2 gene has been identified as a key genetic factor in the development of language and human cognition. Mutations in FOXP2 have been associated with language disorders and speech difficulties. Additionally, this gene has been linked to various neuropsychiatric conditions. The objective of this study is to analyze the genetic differentiation of populations in the FOXP2 gene and in the rs10447760, rs1456031, rs2253478 and rs2396753 polymorphisms. Method: data from the "1000 Genomes" Project were used to analyze genetic variability in FOXP2 in 2504 individuals from 26 populations and 5 macro populations. Linkage disequilibrium, Hardy-Weinberg equilibrium and allele frequencies of the SNPs were evaluated. Genetic differentiation was estimated using the FST statistic. Results: a highly differentiated region was identified in intron 3 of FOXP2 between the African macro population and the rest, with a maximum FST of 0,78. This region contains an epigenetic mark H3K27Ac, suggesting a regulatory role. Hardy-Weinberg imbalances were observed in some populations for the SNPs analyzed. Linkage disequilibrium analysis showed that these SNPs have independent effects. Conclusions: the highly differentiated region in FOXP2 suggests a past natural selection event, supporting an adaptive role of this gene in the evolution of language, speech and cognition. Population differences in Hardy-Weinberg equilibrium and genetic variability highlight the importance of considering genetic variation in future association studies with FOXP2
Journal: Data and Metadata
Pages: 439
Volume: 3
Year: 2024
DOI: 10.56294/dm2024439
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:439:id:1056294dm2024439

Template-Type: ReDIF-Article 1.0
Author-Name: Sergio V. Flores
Author-Name-First: Sergio
Author-Name-Last: V. Flores
Author-Name: Román M. Montaña
Author-Name-First: Román M.
Author-Name-Last: Montaña
Author-Name: Angel Roco-Videla
Author-Name-First: Angel
Author-Name-Last: Roco-Videla
Author-Name: Marcela Caviedes-Olmos
Author-Name-First: Marcela
Author-Name-Last: Caviedes-Olmos
Author-Name: Sofía Pérez-Jiménez
Author-Name-First: Sofía
Author-Name-Last: Pérez-Jiménez
Author-Name: Raúl Aguilera Eguía
Author-Name-First: Raúl
Author-Name-Last: Aguilera Eguía
Title: Genetic Variability of SNP rs7089580 in latin american populations and its impact on Warfarin dosage
Abstract: Introduction: genetic variability in genes that encode drug metabolizing enzymes can influence the response to medications and the doses necessary for an adequate therapeutic effect. In the case of warfarin, a widely used anticoagulant, the enzyme CYP2C9 is responsible for metabolizing its active enantiomer, S-warfarin. Method: the frequencies of the T allele of the SNP rs7089580 were analyzed in Latin American populations using data from the 1000 Genomes Project. Tools such as VCFtools were used to determine the frequency of the T allele and the Hardy-Weinberg equilibrium (HW) and linkage disequilibrium (LD) between the SNP rs7089580 and the promoter SNP rs12251841 of the CYP2C9 gene were evaluated. Results: the frequencies of the T allele vary significantly between populations, with the Puerto Rican population presenting the highest frequency (17 %) and the Peruvian population the lowest (4 %). The results show that Latin American populations are in HW equilibrium, suggesting stability in genetic frequencies. Conclusions: the variability in the frequency of the T allele of the SNP rs7089580 in Latin American populations reflects the complex genetic mix of the region. The balance of HW and the strong linkage disequilibrium between the SNPs suggest that rs7089580 may be a useful marker to predict CYP2C9 expression and response to warfarin
Journal: Data and Metadata
Pages: 440
Volume: 3
Year: 2024
DOI: 10.56294/dm2024440
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:440:id:1056294dm2024440

Template-Type: ReDIF-Article 1.0
Author-Name: Verenice Sánchez-Castillo
Author-Name-First: Verenice
Author-Name-Last: Sánchez-Castillo
Author-Name: Rita Ávila Romero
Author-Name-First: Rita
Author-Name-Last: Ávila Romero
Author-Name: Bernardo Gerardo Juárez Olascoaga
Author-Name-First: Bernardo Gerardo
Author-Name-Last: Juárez Olascoaga
Title: Analysis of research trends on the implementation of information systems in the agricultural sector
Abstract: The process of introducing computer systems in the agricultural sector, also known as Agriculture 4.0, seeks to optimize agricultural production and management at different stages of the agricultural production system. The purpose of the study was to explore research trends on the implementation of computer systems in the agricultural sector. The research approach was quantitative, with a descriptive scope and based on bibliometric procedures. The research was conducted in the SCOPUS database in the period between 1994 and 2023. A total of 73 investigations were obtained. The behavior of the research was heterogeneous, but a stable trend towards the growth of the field could be identified. Regarding the structure of knowledge, research in the area of biological sciences and agriculture predominated with 25 articles. The most productive country is India and the affiliation of the same country was Kumaun University India and the Czech University of Life Sciences Prague, both with four investigations (n=4). The most cited journal with 110 citations was Ecological Informatics, a journal that has an impact factor of 0,92. Four main lines of research were identified from the keyword co-occurrence analysis
Journal: Data and Metadata
Pages: 442
Volume: 3
Year: 2024
DOI: 10.56294/dm2024442
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:442:id:1056294dm2024442

Template-Type: ReDIF-Article 1.0
Author-Name: Volodymyr
Author-Name-First: Volodymyr
Author-Name-Last: Volodymyr
Author-Name: Vadym Kolumbet
Author-Name-First: Vadym
Author-Name-Last: Kolumbet
Author-Name: Petar Halachev
Author-Name-First: Petar
Author-Name-Last: Halachev
Author-Name: Vladyslav Khambir
Author-Name-First: Vladyslav
Author-Name-Last: Khambir
Author-Name: Ruslan Ivanenko
Author-Name-First: Ruslan
Author-Name-Last: Ivanenko
Title: Methods and algorithms of optimization in computer engineering: review and comparative analysis
Abstract: Introduction: the main areas of application of artificial intelligence for algorithmic analysis and optimization of information flows in tasks of multiparametric diagnostics by means of computer engineering are considered. The issues of globalization of all areas of humanitarian, scientific, technical and engineering activities of human society are considered. It is noted that the common denominator of all directions is information flows. The main tools for their management and algorithmic analysis are multi-parametric methods of artificial intelligence. Method: one of its most relevant areas has been highlighted - the use of evolutionary algorithms in combination with modern diagnostic systems based on computer engineering. The possibility of using intelligent analysis of data from biophysical laser systems in assessing the state of “living matter” - the biological media of the human body - is considered. Results: through algorithmic optimization, a set of new cancer detection markers was determined: the statistical parameters of optical anisotropy maps wavelet coefficients linear distributions - the differences between these markers lie in the range from 4 to 20 times; the asymmetry of the wavelet coefficients autocorrelation function - the differences between these markers lie within two orders of magnitude; for normal state, the wavelet coefficients distributions are multifractal; for prostate cancer, the distributions of the wavelet amplitude coefficients are multifractal. Conclusions: a comparative study of the algorithmic optimization of differences of cancer through the use of multiparametric statistical, correlational, fractal and wavelet analysis of polarization tomograms of optical anisotropy of blood layers of donors and prostate cancer sicks is presented
Journal: Data and Metadata
Pages: 443
Volume: 3
Year: 2024
DOI: 10.56294/dm2024443
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:443:id:1056294dm2024443

Template-Type: ReDIF-Article 1.0
Author-Name: Miriam Viviana Ñañez-Silva
Author-Name-First: Miriam Viviana
Author-Name-Last: Ñañez-Silva
Author-Name: Guido Rubén Lucas-Valdez
Author-Name-First: Guido Rubén
Author-Name-Last: Lucas-Valdez
Author-Name: Bertha Nancy Larico-Quispe
Author-Name-First: Bertha Nancy
Author-Name-Last: Larico-Quispe
Author-Name: Yuri Peñafiel-García
Author-Name-First: Yuri
Author-Name-Last: Peñafiel-García
Title: Education for Sustainability: A Data-Driven Methodological Proposal for the Strengthening of Environmental Attitudes in University Students and Their Involvement in Policies and Decision-Making
Abstract: Introduction: in the context of global environmental challenges, university education emerges as a fundamental pillar to cultivate proactive attitudes towards sustainability. This research not only seeks to influence individual perceptions, but also the ability of these students to contribute significantly to policies and decision-making processes related to the environment. Objective: implement a methodological proposal that uses educational events, artistic events and social responsibility projects to strengthen attitudes towards environmental sustainability in university students. Method: a quantitative approach and a pre-experimental design were used, applying pre-test and post-test to students from five majors at a public university. The intervention was based on four thematic axes: classification and selection of solid waste, rational use of water, efficient use of electrical energy and university safety. Results: the results revealed positive changes in student attitudes, with significant increases in solid waste classification (from 28 % to 72 %), university safety (from 32 % to 75 %), rational use of water (from 34 % % to 76 %) and energy efficiency (from 43 % to 82 %). In addition, a strengthening of continuous environmental knowledge was observed by 46 %, representing an increase of 81 %. Conclusions: these findings suggest that universities can play a crucial role in promoting environmental educational policies that train professionals committed to nature and future generations, thus contributing to the construction of a paradigm that integrates ethics and socio-environmental responsibility
Journal: Data and Metadata
Pages: 448
Volume: 3
Year: 2024
DOI: 10.56294/dm2024448
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:448:id:1056294dm2024448

Template-Type: ReDIF-Article 1.0
Author-Name: Sergio V. Flores
Author-Name-First: Sergio V.
Author-Name-Last: Flores
Author-Name: Ángel Roco-Videla
Author-Name-First: Ángel
Author-Name-Last: Roco-Videla
Author-Name: Joel Antonio Herrera-Soto
Author-Name-First: Joel Antonio
Author-Name-Last: Herrera-Soto
Author-Name: Marcela Caviedes-Olmos
Author-Name-First: Marcela
Author-Name-Last: Caviedes-Olmos
Author-Name: Román M. Montaña
Author-Name-First: Román M.
Author-Name-Last: Montaña
Title: Worldwide genetic variability of the rs1861868 SNP in the FTO gene associated with obesity
Abstract: Introduction: genetic predisposition to obesity is linked to an imbalance between food intake and energy expenditure, regulated by the nervous and endocrine systems. The FTO gene variants significantly impact obesity susceptibility in different populations. The objective of the research was to analyze the genetic variability of the SNP rs1861868 in the FTO gene and its association with obesity in various populations. Method: genotype data from 1000 Genomes and allele frequencies from ALFRED were analyzed. Moran's I assessed spatial autocorrelation, Hardy-Weinberg equilibrium was tested using VCFtools, and ANOVA compared risk allele frequencies across continents. Results: Moran's I indicated no significant spatial autocorrelation globally, but higher concentrations of the risk allele were observed in Europe. ANOVA showed significant differences in risk allele frequencies among continents, with Europe having the highest frequency. Hardy-Weinberg equilibrium was observed within macro populations but not globally. Conclusions: regional variations significantly impact the distribution of the rs1861868 (T) risk allele. Evolutionary, historical, and demographic are candidate factors that shaped the genetic landscape of the FTO gene related to obesity
Journal: Data and Metadata
Pages: 453
Volume: 3
Year: 2024
DOI: 10.56294/dm2024453
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:453:id:1056294dm2024453

Template-Type: ReDIF-Article 1.0
Author-Name: Valentyn Bannikov
Author-Name-First: Valentyn
Author-Name-Last: Bannikov
Author-Name: Stanislav Petko
Author-Name-First: Stanislav
Author-Name-Last: Petko
Author-Name: Oleksandr Semenov
Author-Name-First: Oleksandr
Author-Name-Last: Semenov
Author-Name: Oleksandr Zhurba
Author-Name-First: Oleksandr
Author-Name-Last: Zhurba
Author-Name: Kateryna Lohinova
Author-Name-First: Kateryna
Author-Name-Last: Lohinova
Title: Analysis of the use of blockchain technologies and smart contracts to automate management processes and ensure sustainability
Abstract: Introduction: this paper discusses and analyzes how blockchain technologies and smart contracts apply to automate assurance management processes with sustainability using a perspective model. The increase in demand for systems that are clear and secure in the automation of management processes calls for innovations such as blockchain and smart contracts. Objective: the objectives of the article are to identify the status of blockchain and smart contract adoption in many management processes; to consider the effect these technologies have on the efficiency, transparency, and sustainability of management operations. Methodology: we used regression and Markov analysis simulations to analyze the impacts of blockchain technologies on the management processes. The case study data were used to predict the long-term sustainability impacts, and simulations were carried out. Results: the regression established a positive but substantial effect of the adoption of blockchain technologies on the efficiency of management processes. 75 % of the efficiency score varies with the level of blockchain adoption. Simulations done using the Markov chain also showed that under the highest level of blockchain adoption, there is an effectivity of 90 percent where management processes would have improved and be efficient for the remaining ten years. The simulations also attested that partial adoption still offered a 70 % probability of sustained improvements. Conclusions: this paper provides strong evidence through regression analysis and Markov simulations showing the influence of these technologies. The ability of organizations to focus on innovative solutions toward sustainable management results is therefore realized
Journal: Data and Metadata
Pages: 461
Volume: 3
Year: 2024
DOI: 10.56294/dm2024461
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:461:id:1056294dm2024461

Template-Type: ReDIF-Article 1.0
Author-Name: Hatim Lakhouil
Author-Name-First: Hatim
Author-Name-Last: Lakhouil
Author-Name: Aziz Soulhi
Author-Name-First: Aziz
Author-Name-Last: Soulhi
Title: Fuzzy Decision-Making Model for the inventory leveling under uncertainty conditionModelo de toma de decisiones difusa para la nivelación del inventario en condiciones de incertidumbre
Abstract: The Option to create inventory is not always the optimal choice, due to the associated expenses and space requirements. Nevertheless, there are instances where a shortage of materials on customer lines can result in substantial financial penalties. This constant contradiction places supply chain managers in a perplexing predicament, especially when considering the amplification of inventory through the bullwhip effect as it moves across different stages. Moreover, the uncertain backdrop created by unforeseen events intensifies this already critical situation, compelling managers to seek new decision-making approaches. These approaches should enable the simulation of risks and the selection of suitable scenarios, particularly within the intricate domain of stochastic and dynamically evolving supply chains. The purpose of this study is to provide a new decision-making model rooted in the fuzzy logic concept introduced by Loutfi Zadeh in 1965. This model is applied to criteria assessed by experts, representing the most pertinent parameters for guiding inventory optimization. The chosen criteria encompass Lead Time, Equipment Production Reliability, and Warehousing Costs. This model exhibits the potential to unearth intricate patterns and associations among variables that conventional statistical methods struggle to reveal. Notably, the integration of fuzzy logic for inventory prediction yields promising outcomes, extendable to the realm of artificial intelligence, where comprehensive inference rules facilitate effective decision-making
Journal: Data and Metadata
Pages: 142
Volume: 3
Year: 2024
DOI: 10.56294/dm2024142
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:142:id:1056294dm2024142

Template-Type: ReDIF-Article 1.0
Author-Name: Luz Castillo-Cordero
Author-Name-First: Luz
Author-Name-Last: Castillo-Cordero
Author-Name: Milagros Contreras-Chihuán
Author-Name-First: Milagros
Author-Name-Last: Contreras-Chihuán
Author-Name: Brian Meneses-Claudio
Author-Name-First: Brian
Author-Name-Last: Meneses-Claudio
Title: Datamart for the analysis of information in the sales process of the company WC HVAC Engineering
Abstract: Introduction: information has become a crucial asset for companies in decision making and performance evaluation. Information technologies, such as Business Intelligence, allow data to be converted into relevant information. The implementation of a Datamart, a specialized database, stands out as a solution to analyze specific data from a business area. Objective: the main objective is to determine how the implementation of a Datamart affects data analysis in the sales area of the company. Method: a bibliographic review of various sources was carried out using the PICO keywords. In addition, filters were applied to limit the search to relevant articles published in the last 5 years in Spanish or English. Then, 31 relevant documents that highlighted the implementation of Datamarts in the sales area were evaluated. Results: predominant Datamart development methods were identified, such as the Kimball and Hefesto methodologies. Likewise, effectiveness was measured through indicators such as processing time, report generation, user satisfaction and availability of information. Conclusions: in conclusion, a well-implemented Datamart can be a key tool to improve data management and analysis in the sales area of a company
Journal: Data and Metadata
Pages: 184
Volume: 3
Year: 2024
DOI: 10.56294/dm2024184
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:184:id:1056294dm2024184

Template-Type: ReDIF-Article 1.0
Author-Name: Mohamed Bouincha
Author-Name-First: Mohamed
Author-Name-Last: Bouincha
Author-Name: Youness Jouilil
Author-Name-First: Youness
Author-Name-Last: Jouilil
Author-Name: Mustapha Berrouyne
Author-Name-First: Mustapha
Author-Name-Last: Berrouyne
Title: The effectiveness of education assistance programs using AI innovation. Case for tackling school dropout in Morocco
Abstract: Introduction: since 2008, Morocco's Tayssir program has been a key public initiative aimed at combating school dropout rates, by offering conditional cash transfers to households with school-aged children, particularly targeting rural communities with high poverty rates. This initiative seeks to ensure equitable access to education, regardless of socioeconomic status, and boosted school attendance rates. Objective: to assess the impact of the Tayssir program on reducing school dropout rates in rural Morocco and to examine the effectiveness of targeting strategies and incentives provided to families. Methods: the study utilized cross-sectional data from the Household Survey Panel Data. Propensity score matching (PSM) techniques were employed to estimate the program's impact on school dropout rates, comparing beneficiaries with a control group not participating in the program. Various statistical analyses were conducted to explore the characteristics of participants and to validate the logistic model used. Results: the propensity score matching analysis revealed a statistically significant reduction in school dropout rates among beneficiaries of the Tayssir program. The average treatment effect on the treated (ATET) demonstrated a decrease in dropout rates by approximately 43 % using one-to-one matching, 42,7 % with k-nearest neighbor, and 38,6 % via kernel matching methods. Furthermore, no significant gender differences were observed in the program's impact. Conclusions: the Tayssir program has significantly contributed to reducing school dropout rates in rural Morocco, ensuring better access to education for children from disadvantaged backgrounds. The program's effectiveness underscores the importance of targeted interventions and conditional cash transfers in promoting educational attainment. Future recommendations include expanding the beneficiary base, refining targeting mechanisms, and establishing a unified social registry to improve program governance
Journal: Data and Metadata
Pages: 206
Volume: 3
Year: 2024
DOI: 10.56294/dm2024206
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:206:id:1056294dm2024206

Template-Type: ReDIF-Article 1.0
Author-Name: Rajendran Bhojan
Author-Name-First: Rajendran
Author-Name-Last: Bhojan
Author-Name: Manikandan Rajagopal
Author-Name-First: Manikandan
Author-Name-Last: Rajagopal
Author-Name: R Ramesh
Author-Name-First: R
Author-Name-Last: Ramesh
Title: Big Data De-duplication using modified SHA algorithm in cloud servers for optimal capacity utilization and reduced transmission bandwidth
Abstract: Data de-duplication in cloud storage is crucial for optimizing resource utilization and reducing transmission overhead. By eliminating redundant copies of data, it enhances storage efficiency, lowers costs, and minimizes network bandwidth requirements, thereby improving overall performance and scalability of cloud-based systems. The research investigates the critical intersection of data de-duplication (DD) and privacy concerns within cloud storage services. Distributed Data (DD), a widely employed technique in these services and aims to enhance capacity utilization and reduce transmission bandwidth. However, it poses challenges to information privacy, typically addressed through encoding mechanisms. One significant approach to mitigating this conflict is hierarchical approved de-duplication, which empowers cloud users to conduct privilege-based duplicate checks before data upload. This hierarchical structure allows cloud servers to profile users based on their privileges, enabling more nuanced control over data management. In this research, we introduce the SHA method for de-duplication within cloud servers, supplemented by a secure pre-processing assessment. The proposed method accommodates dynamic privilege modifications, providing flexibility and adaptability to evolving user needs and access levels. Extensive theoretical analysis and simulated investigations validate the efficacy and security of the proposed system. By leveraging the SHA algorithm and incorporating robust pre-processing techniques, our approach not only enhances efficiency in data de-duplication but also addresses crucial privacy concerns inherent in cloud storage environments. This research contributes to advancing the understanding and implementation of efficient and secure data management practices within cloud infrastructures, with implications for a wide range of applications and industries
Journal: Data and Metadata
Pages: 245
Volume: 3
Year: 2024
DOI: 10.56294/dm2024245
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:245:id:1056294dm2024245

Template-Type: ReDIF-Article 1.0
Author-Name: Flor Damiano-Aulla
Author-Name-First: Flor Damiano-Aulla
Author-Name-Last: Flor Damiano-Aulla
Author-Name: Jeydi Raqui-Rojas
Author-Name-First: Jeydi
Author-Name-Last: Raqui-Rojas
Author-Name: Víctor D. Álvarez-Manrique
Author-Name-First: Víctor D.
Author-Name-Last: Álvarez-Manrique
Author-Name: Liset Z.
Author-Name-First: Liset Z.
Author-Name-Last: Liset Z.
Author-Name: Wilter C. Morales-García
Author-Name-First: Wilter C.
Author-Name-Last: Morales-García
Title: Validation of an Organizational Climate Scale in health workers
Abstract: Introduction: organizational climate is a key factor in employee performance and satisfaction. In this study, the validity and reliability of an organizational climate scale in agroindustrial companies in Peru was examined. Objective: to analyze the psychometric properties of an organizational climate scale adapted to Peruvian Spanish. Methods: A methodological study was carried out. Demographic data were collected, as well as responses to an organizational climate questionnaire. Results: the data were analyzed using confirmatory factorial analysis (CFA). The reliability of the instrument was high (α = 0,92). However, the factor loadings of several items were not adequate, so a unidimensional model was tested, then a model with adequate factor loadings, and finally an optimal model. In this last 9-item model, the fit was optimal, and the factor loading was adequate for all items. Conclusion: overall, the organizational climate scale demonstrated good reliability and validity in this context of agroindustrial companies in Peru. However, some items needed to be revised to improve the scale's accuracy. These findings provide a valuable tool for measuring the organizational climate in these types of companies and pave the way for future research in this field
Journal: Data and Metadata
Pages: 257
Volume: 3
Year: 2024
DOI: 10.56294/dm2024257
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:257:id:1056294dm2024257

Template-Type: ReDIF-Article 1.0
Author-Name: Irma Chalco-Ccapa
Author-Name-First: Irma Chalco-Ccapa
Author-Name-Last: Irma Chalco-Ccapa
Author-Name: Gaby Torres-Mamani
Author-Name-First: Gaby
Author-Name-Last: Torres-Mamani
Author-Name: Mardel Morales-García
Author-Name-First: Mardel
Author-Name-Last: Morales-García
Author-Name: Alcides A Flores-Saenz
Author-Name-First: Alcides A
Author-Name-Last: Flores-Saenz
Author-Name: Liset Z. Sairitupa-Sanchez
Author-Name-First: Liset Z.
Author-Name-Last: Sairitupa-Sanchez
Author-Name: Maribel Paredes-Saavedra
Author-Name-First: Maribel
Author-Name-Last: Paredes-Saavedra
Author-Name: Wilter C. Morales-García
Author-Name-First: Wilter C.
Author-Name-Last: Morales-García
Title: Validation and invariance of an Individual Work Performance Questionnaire (IWPQ-P) in Peruvian Nurses
Abstract: Background: performance evaluation is essential to ensure quality healthcare services, especially in the field of nursing. Objective: The objective of this study was to analyze the factorial structure, reliability, and invariance by sex and age of the work performance scale in Peruvian nurses. Methods: confirmatory factor analysis (CFA) was conducted to evaluate the internal structure of the scale, and psychometric properties including reliability and convergent validity were determined. Additionally, factorial invariance was evaluated according to participants' sex and age. Results: the CFA supported the structure of three factors (Task Performance, Counterproductive Behaviors, Contextual Performance) and showed adequate and stable psychometric properties for a 12-item version (: χ2 = 231,09, df = 78; CFI = 0,97, TLI = 0,96, RMSEA = 0,06 (90 % CI: 0,05-0,06), and SRMR = 0,03). Strict factorial invariance was demonstrated for both sex and age, and adequate internal consistency was found for each dimension, as well as convergent validity. Conclusions: the work performance scale, in its 12-item version (IWPQ-P), is a valid and reliable measure for evaluating work performance in Peruvian nurses. Its factorial invariance by sex and age makes it a useful tool for future research and practical applications in nursing performance evaluation
Journal: Data and Metadata
Pages: 259
Volume: 3
Year: 2024
DOI: 10.56294/dm2024259
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:259:id:1056294dm2024259

Template-Type: ReDIF-Article 1.0
Author-Name: Svitlana Marushchak
Author-Name-First: Svitlana
Author-Name-Last: Marushchak
Author-Name: Iryna Fadyeyeva
Author-Name-First: Iryna
Author-Name-Last: Fadyeyeva
Author-Name: Petar Halachev
Author-Name-First: Petar
Author-Name-Last: Halachev
Author-Name: Nursultan Zharkenov
Author-Name-First: Nursultan
Author-Name-Last: Zharkenov
Author-Name: Sergii Pakhomov
Author-Name-First: Sergii
Author-Name-Last: Pakhomov
Title: The role of artificial intelligence and machine learning in forecasting economic trends
Abstract: Introduction: The globalisation of the economy, dynamic changes in financial markets, and the advent of big data have spurred the development and implementation of artificial intelligence (AI) and machine learning (ML) tools for forecasting economic trends. The purpose of this study is to evaluate the impact of AI and ML on the accuracy and effectiveness of economic trend forecasting. The authors analyse examples of AI and ML applications in various economic sectors during the period 2019–2023, including regional aspects. Methods: To achieve the objectives of this study, we conducted a comprehensive qualitative and quantitative analysis of the role of artificial intelligence (AI) and machine learning (ML) in predicting economic trends. Results: The findings indicate that the use of AI and ML improves the efficiency of economic trend forecasting and allows for quicker adaptation to market changes, thereby reducing risks and uncertainty. Conclusions: Thus, the integration of artificial intelligence and machine learning in economic analysis not only increases the effectiveness of forecasting but also lays the foundations for the sustainable development of economies in a globalised world.
Journal: Data and Metadata
Pages: .247
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.247
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.247:id:1056294dm2024247

Template-Type: ReDIF-Article 1.0
Author-Name: Patakamudi Swathi
Author-Name-First: Patakamudi
Author-Name-Last: Swathi
Author-Name: Dara Sai Tejaswi
Author-Name-First: Dara Sai
Author-Name-Last: Tejaswi
Author-Name: Mohammad Amanulla Khan
Author-Name-First: Mohammad Amanulla
Author-Name-Last: Khan
Author-Name: Miriyala Saishree
Author-Name-First: Miriyala
Author-Name-Last: Saishree
Author-Name: Venu Babu Rachapudi
Author-Name-First: Venu Babu
Author-Name-Last: Rachapudi
Author-Name: Dinesh Kumar Anguraj
Author-Name-First: Dinesh Kumar
Author-Name-Last: Anguraj
Title: Real-Time Vehicle Detection for Traffic Monitoring: A Deep Learning Approach
Abstract: Vehicle detection is an essential technology for intelligent transportation systems and autonomous vehicles. Reliable real-time detection allows for traffic monitoring, safety enhancements and navigation aids. However, vehicle detection is a challenging computer vision task, especially in complex urban settings. Traditional methods using hand-crafted features like HAAR cascades have limitations. Recent deep learning advances have enabled convolutional neural networks (CNNs) like Faster R-CNN, SSD and YOLO to be applied to vehicle detection with significantly improved accuracy. But each technique has tradeoffs between precision and processing speed. Two-stage detectors like Faster R-CNN are highly accurate but slow at 7 FPS. Single-shot detectors like SSD are faster at 22 FPS but less precise. YOLO is extremely fast at 45 FPS but has lower accuracy. This paper reviews prominent deep learning vehicle detectors. It proposes a new integrated method combining YOLOv3 detection, optical flow tracking and trajectory analysis to enhance both accuracy and speed. Results on highway and urban datasets show improved precision, recall and F1 scores compared to YOLOv3 alone. Optical flow helps filter noise and recover missed detections. Trajectory analysis enables consistent object IDs across frames. Compared to other CNN models, the proposed technique achieves a better balance of real-time performance and accuracy. Occlusion handling and small object detection remain open challenges. In summary, deep learning has enabled major progress but enhancements in model architecture, training data and occlusion handling are needed to realize the full potential for traffic management applications. The integrated method proposed offers improved performance over baseline detectors. We have achieved 99 % accuracy in our project
Journal: Data and Metadata
Pages: 295
Volume: 3
Year: 2024
DOI: 10.56294/dm2024295
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:295:id:1056294dm2024295

Template-Type: ReDIF-Article 1.0
Author-Name: Duverly Joao Incacutipa-Limachi
Author-Name-First: Duverly Joao
Author-Name-Last: Incacutipa-Limachi
Author-Name: Edwin Gustavo Estrada-Araoz
Author-Name-First: Edwin Gustavo
Author-Name-Last: Estrada-Araoz
Author-Name: Yony Abelardo Quispe-Mamani
Author-Name-First: Yony Abelardo
Author-Name-Last: Quispe-Mamani
Author-Name: Euclides Ticona-Chayña
Author-Name-First: Euclides
Author-Name-Last: Ticona-Chayña
Author-Name: Adderly Mamani-Flores
Author-Name-First: Adderly
Author-Name-Last: Mamani-Flores
Title: Assessment of the scientific production of a public university in southern Peru: A bibliometric study
Abstract: Introduction: The scientific production of universities plays a crucial role in the generation and dissemination of knowledge, as well as in strengthening the position of academic institutions on both national and international levels. Objective: To evaluate the scientific production in the Scopus database of a public university in southern Peru. Methods: A bibliometric and retrospective investigation was conducted. Documents indexed in the Scopus database were analyzed by evaluating the quantity of documents, authors, journals where the documents were published, types of documents, language of publication, funding, areas of knowledge to which the documents belong, and co-authorship networks. Results: A total of 763 indexed documents were identified in the Scopus database, showing a trend towards increased production in recent years. The majority of indexed documents were characterized by being original articles, published in foreign journals and in English language, and self-financed. Additionally, it was observed that more documents were published in the areas of Social Sciences and Agricultural and Biological Sciences. Conclusions: In recent years, significant growth has been observed in the scientific production in the Scopus database of a public university in southern Peru. Therefore, it is imperative to promote an institutional research culture, focused on the development of research skills, with the purpose of increasing both the quantity and quality of publications
Journal: Data and Metadata
Pages: 301
Volume: 3
Year: 2024
DOI: 10.56294/dm2024301
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:301:id:1056294dm2024301

Template-Type: ReDIF-Article 1.0
Author-Name: Edwin Gustavo Estrada-Araoz
Author-Name-First: Edwin Gustavo
Author-Name-Last: Estrada-Araoz
Author-Name: Guido Raúl Larico-Uchamaco
Author-Name-First: Guido Raúl
Author-Name-Last: Larico-Uchamaco
Author-Name: José Octavio Ruiz-Tejada
Author-Name-First: José Octavio
Author-Name-Last: Ruiz-Tejada
Author-Name: Jair Emerson Ferreyros-Yucra
Author-Name-First: Jair Emerson
Author-Name-Last: Ferreyros-Yucra
Author-Name: Alex Camilo Velasquez-Bernal
Author-Name-First: Alex Camilo
Author-Name-Last: Velasquez-Bernal
Author-Name: Cesar Elias Roque-Guizada
Author-Name-First: Cesar Elias
Author-Name-Last: Roque-Guizada
Author-Name: María Isabel Huamaní-Pérez
Author-Name-First: María Isabel
Author-Name-Last: Huamaní-Pérez
Author-Name: Yasser Malaga-Yllpa
Author-Name-First: Yasser
Author-Name-Last: Malaga-Yllpa
Title: Scientific production of thesis juries at a Peruvian public university: A bibliometric study
Abstract: Introduction: thesis juries are a group of academics or experts whose purpose is to ensure the integrity and rigor in the processes of evaluation and academic defense of theses, as well as to provide critical and constructive feedback aimed at improving their quality. Objective: to evaluate the scientific production in the Scopus, Web of Science, and Scielo databases of the thesis juries of the Faculty of Education of a public university in Peru. Methods: bibliometric, retrospective, and descriptive research that included 69 teachers who served as thesis juries during the period 2020-2023. The scientific production of the thesis committees was identified through the search of their publications registered in the Scopus, Web of Science, and Scielo databases. Results: 56,5 % of the teachers who served as thesis juries had no scientific production registered in the Scopus, Web of Science, or Scielo databases, while 43,5 % did have some scientific production in these databases. Additionally, it was found that the scientific production of the teachers was mainly based on original articles, published in Spanish, and self-financed. Conclusions: the scientific production in the Scopus, Web of Science, and Scielo databases of the thesis juries of the Faculty of Education of a public university in Peru was low. Therefore, it is imperative to implement policies aimed at strengthening their research and writing skills
Journal: Data and Metadata
Pages: 304
Volume: 3
Year: 2024
DOI: 10.56294/dm2024304
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:304:id:1056294dm2024304

Template-Type: ReDIF-Article 1.0
Author-Name: Anastasiia Danilkova
Author-Name-First: Anastasiia
Author-Name-Last: Danilkova
Author-Name: Volodymyr Bondar
Author-Name-First: Volodymyr
Author-Name-Last: Bondar
Author-Name: Kateryna Bannikova
Author-Name-First: Kateryna
Author-Name-Last: Bannikova
Author-Name: Svitlana Prokhorovska
Author-Name-First: Svitlana
Author-Name-Last: Prokhorovska
Author-Name: Tetiana Vodolazhska
Author-Name-First: Tetiana
Author-Name-Last: Vodolazhska
Title: Using data and analytics to optimise the human resources processes
Abstract: Introduction: Business development and HR management systems based on modern technologies open up significant prospects for companies to actively promote themselves in the market and achieve positive results in the context of HR management. Currently, many companies are implementing modern HR tools aimed at increasing efficiency and reducing ongoing risks at minimal cost. In this regard, HR analytics has become a necessary tool to help find information about employees and make informed decisions based on it. Objective. Given the relevance of the research topic, it becomes possible to determine its purpose, яка полягає в узагальненні та систематизації підходів до застосування інструментів, програмних продуктів та платформ для аналітики процесів управління персоналом з метою покращення загального економічного стану компанії. Methods. To achieve this goal, the general scientific methods of analysis, synthesis, generalisation, induction and deduction were used. Results. To achieve this goal, the following results were obtained: the essence of HR analytics and the possibilities of its application for personnel management were determined; software products and platforms for analysing personnel management processes were generalised; the main analytical tools used in the field of personnel management were systematised. It is proved that one of the most important areas of application of HR analytics is the recruitment process. Conclusions. With the help of data and new existing analytical methods, HR professionals have the opportunity to optimise recruitment procedures, identify suitable candidates, which will ultimately contribute to improving the company's condition, provided that the labour resources and intellectual capital are used in a rational and balanced manner
Journal: Data and Metadata
Pages: .243
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.243
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.243:id:1056294dm2024243

Template-Type: ReDIF-Article 1.0
Author-Name: Natalia Lemesheva
Author-Name-First: Natalia
Author-Name-Last: Lemesheva
Author-Name: Halyna Antonenko
Author-Name-First: Halyna
Author-Name-Last: Antonenko
Author-Name: Petar Halachev
Author-Name-First: Petar
Author-Name-Last: Halachev
Author-Name: Olha Suprun
Author-Name-First: Olha
Author-Name-Last: Suprun
Author-Name: Yevhenii Tytarchuk
Author-Name-First: Yevhenii
Author-Name-Last: Tytarchuk
Title: The impact of quantum computing on the development of algorithms and software
Abstract: Introduction: There is a great potential that the quantum computing can change the way of algorithms and software development more than classical computers. Thus, this article will try to focus on how algorithm design and software development can be affected by quantum computing as well as what possibilities could appear when quantum principles are implemented into traditional paradigms. This paper aims at identifying the impact of quantum computing on algorithm and software advancement, through a discussion of essential quantum algorithms, quantum languages, as well as the opportunities and challenges of quantum technologies. Method: An extensive literature review and theoretical investigation was also performed to investigate the foundational concepts of quantum computing and subsequent effects on algorithm and software engineering. Some of the research questions included exploring the contrast between classical and quantum algorithms, reviewing current literature on quantum programming languages, and delving into examples of real-life deployments of quantum algorithms cross numerous domains. Results: This paper shows that quantum computing brings qualitatively new paradigms in the algorithm design and function while the quantum algorithms such as Shor’s and Grover’s perform exponentially faster certain problems. Software development for quantum has brought the need to devise new frameworks of coding in light of probability in quantum circuit. It is also comforting to note that there is still effort being made although in its most embryonic form to create quantum programming languages like Qiskit and Cirq. Some of challenges include quantum decoherence; limited number of quantum hardware; and need for strong error correction processes. Conclusion: While there are currently relatively few quantum algorithms it is believed that the findings in this field have the ability to revolutionize algorithm and software design and subjects like cryptography, optimization and AI. However, trends in quantum computing show that the constraints to computational capabilities are likely to be lifted to allow creativity to develop the most powerful software solutions
Journal: Data and Metadata
Pages: .242
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.242
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.242:id:1056294dm2024242

Template-Type: ReDIF-Article 1.0
Author-Name: Viktoriya Mykhaylenko
Author-Name-First: Viktoriya
Author-Name-Last: Mykhaylenko
Author-Name: Nadiia Safonova
Author-Name-First: Nadiia
Author-Name-Last: Safonova
Author-Name: Ruslan Ilchenko
Author-Name-First: Ruslan
Author-Name-Last: Ilchenko
Author-Name: Anton Ivashchuk
Author-Name-First: Anton
Author-Name-Last: Ivashchuk
Author-Name: Ivanna Babik
Author-Name-First: Ivanna
Author-Name-Last: Babik
Title: Using artificial intelligence to personalise curricula and increase motivation to learn, taking into account psychological aspects
Abstract: Objectives: This study aimed to assess the effectiveness of artificial intelligence on education, focusing on how it can be leveraged to personalised learning experiences tailored to the specific needs of students. Study Design: A comprehensive literature review was conducted, alongside an analysis of psychological factors that influence student motivation. Place and Duration of the Study: Relevant academic sources and case studies were reviewed over the duration of six months to gather insights on AI applications in education. Sample: The sample consisted of the scientific thought and scientists that have integrated AI technologies into their curricula. Methodology: A qualitative analysis from literature was utilised in this research to evaluate AI tools' effectiveness in enhancing personalised learning outcomes. Results: The findings indicate that ChatGPT is currently the most widely utilised AI tool in educational contexts, demonstrating a significant capacity to personalised learning by adapting it to individual psychological profiles and learning paces. Conclusion: The integration of AI technologies in education presents unprecedented opportunities for curriculum personalisation and student engagement. However, it also necessitates careful consideration of ethical issues, especially related to learner data privacy, to ensure responsible implementation
Journal: Data and Metadata
Pages: .241
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.241
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.241:id:1056294dm2024241

Template-Type: ReDIF-Article 1.0
Author-Name: Julio Rodrigo Morillo Cano
Author-Name-First: Julio Rodrigo
Author-Name-Last: Morillo Cano
Author-Name: Alisson Daniela Navarrete Medina
Author-Name-First: Alisson Daniela
Author-Name-Last: Navarrete Medina
Author-Name: Darwin Raúl Noroña Salcedo
Author-Name-First: Darwin Raúl
Author-Name-Last: Noroña Salcedo
Author-Name: Edmundo Daniel Navarrete Arboleda
Author-Name-First: Edmundo Daniel
Author-Name-Last: Navarrete Arboleda
Title: Influence of exposure to psychosocial risks on occupational stress among telemedicine agents in Quito
Abstract: Psychosocial risks are workplace conditions that impact the physical, mental, and social health of workers. This study analyzes the relationship between psychosocial risks and occupational stress among telemedicine agents in Quito. The objective was to determine the extent to which exposure to psychosocial risks influences occupational stress in this population. A non-experimental, cross-sectional, and correlational design was used, with the participation of 91 telemedicine agents selected based on predefined criteria. Using a survey technique, two instruments were applied: The Psychosocial Risk Factors Questionnaire and the adapted OIT-OMS Occupational Stress Scale. The hypothesis proposed a positive correlation between the variables, and the Tau-b Kendall test was used for inferential analysis. The results showed that 57.2% of participants presented high exposure to psychosocial factors, with job demands, workplace conditions, and workload identified as the most frequent dimensions. Additionally, 42.9% of agents reported some level of stress, mainly related to deficiencies in leadership, lack of group cohesion, and adverse organizational conditions. The Tau-b Kendall correlation coefficient was 0.806, indicating a strong association between the variables. It is concluded that psychosocial risks have a significant relationship with occupational stress and that their mitigation could reduce stress by up to 31.14%, highlighting the need for specific interventions to improve the work environment and the well-being of employees
Journal: Data and Metadata
Pages: .240
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.240
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.240:id:1056294dm2024240

Template-Type: ReDIF-Article 1.0
Author-Name: Sylvia del Pilar Núñez Arroba
Author-Name-First: Sylvia del Pilar
Author-Name-Last: Núñez Arroba
Author-Name: Liliana Katherine Sailema López
Author-Name-First: Liliana Katherine
Author-Name-Last: Sailema López
Author-Name: Génesis Alexandra Zúñiga Cárdenas
Author-Name-First: Génesis Alexandra
Author-Name-Last: Zúñiga Cárdenas
Title: Analysis of polycystic ovary syndrome and infertility using PRISMA 2020 literature review
Abstract: Polycystic ovary syndrome (PCOS) is a complex endocrine and metabolic condition that significantly affects the fertility of women of reproductive age. The aim of the study was to analyze polycystic ovary syndrome and its impact on infertility through a PRISMA 2020 literature review. A systematic search was conducted in PubMed using MeSH terms related to PCOS and infertility, covering publications from 2018 to 2024. Original articles, systematic reviews, and relevant meta-analyses were included, while duplicate studies or those with insufficient methodological quality were excluded. The results showed that hyperandrogenism, insulin resistance, and chronic inflammation are key mechanisms affecting ovulation and endometrial quality, contributing to infertility. Lifestyle modifications, such as diet and exercise, are identified as the first line of treatment, while emerging therapies like resveratrol, probiotics, and traditional Chinese medicine offer promising options. Additionally, relevant relationships between PCOS and comorbidities such as thyroid diseases and osteoporosis are observed, expanding its systemic impact. Despite advances in therapies and pathophysiological understanding, challenges persist due to the lack of classification between genotypes and clinical phenotypes. It is concluded that the management of PCOS should be comprehensive and personalized, integrating innovative and multidisciplinary strategies to improve patients' quality of life and reproductive outcomes. This study provides a solid foundation to guide future research and optimize clinical practice
Journal: Data and Metadata
Pages: .239
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.239
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.239:id:1056294dm2024239

Template-Type: ReDIF-Article 1.0
Author-Name: Genesis Vanesa Montesdeoca Abad
Author-Name-First: Genesis Vanesa
Author-Name-Last: Montesdeoca Abad
Author-Name: Dámaris Micaela Ortiz Sánchez
Author-Name-First: Dámaris Micaela
Author-Name-Last: Ortiz Sánchez
Author-Name: Shirley Lizbeth Hidalgo Jaitia
Author-Name-First: Shirley Lizbeth
Author-Name-Last: Hidalgo Jaitia
Author-Name: Nairovys Gómez
Author-Name-First: Nairovys
Author-Name-Last: Gómez
Title: Use of the VIKOR method in the analysis of the training and function of the surgical nurse
Abstract: The role of the surgical nurse is fundamental to ensure the correct execution of procedures in the operating room, ranging from initial preparation to closure of surgery. In preoperative preparation, the surgical nurse is responsible for maintaining sterility, verifying the operation of equipment, and preparing the operating table. Surgical instrumentation training, which varies by country, generally requires a bachelor's degree followed by a master's specialization to acquire the necessary skills. To optimize the role of surgical nurses, the VIKOR method, designed for multi-criteria decision making, was applied. This method helped select the best option considering various evaluation criteria such as technical competence, time management, problem solving and knowledge of protocols. The evaluation included a detailed analysis of the nurses' competence in different phases of surgery, resulting in a ranking that highlights the importance of continuous training in these criteria. The results indicated that the optimal performance of nurses during surgery is crucial for the success of surgical procedures, and the need to qualify nurses in the areas analyzed to improve their effectiveness in the operating room was identified
Journal: Data and Metadata
Pages: .238
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.238
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.238:id:1056294dm2024238

Template-Type: ReDIF-Article 1.0
Author-Name: Ronald Medardo Gómez Coba
Author-Name-First: Ronald Medardo
Author-Name-Last: Gómez Coba
Author-Name: Jhoseline Melissa Pérez Villacrés
Author-Name-First: Jhoseline Melissa
Author-Name-Last: Pérez Villacrés
Author-Name: Manuel Benites Rolando
Author-Name-First: Manuel
Author-Name-Last: Benites Rolando
Title: Reciproc blue y wave one gold en conductos curvos
Abstract: Introduction: endodontic treatment is performed daily in various health centers by specialists and general dentists, however, the obturation treatment can fail and a deobturation of the materials lodged in the root canals whether straight or curved is required. Objective: to determine which of the two systems Reciproc Blue and WaveOne Gold is more effective in the deobturation of curved root canals by means of a literature review of related studies. Method: qualitative and descriptive literature review with scientific articles obtained from the PubMed online database as the study population. Results: WaveOne Gold presented a minimum time of between 2,03 and 4,9 min in the duration of the cleaning of curved root canals, while Reciproc Blue presented a time between 3,21 and 5,4 min. Conclusions: it was determined that the two systems Reciproc Blue and WaveOne Gold have a good efficacy in the treatment of deobturation of curved root canals, since they eliminate the materials lodged in these canals after an incorrect obturation, with cleaning percentages higher than 2 %.
Journal: Data and Metadata
Pages: .237
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.237
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.237:id:1056294dm2024237

Template-Type: ReDIF-Article 1.0
Author-Name: Mauricio Fernando Enrriquez Grijalva
Author-Name-First: Mauricio Fernando
Author-Name-Last: Enrriquez Grijalva
Author-Name: Melany Yamilex Reascos Chalacán
Author-Name-First: Melany Yamilex
Author-Name-Last: Reascos Chalacán
Author-Name: Alex Javier Criollo Rodriguez
Author-Name-First: Alex Javier
Author-Name-Last: Criollo Rodriguez
Title: Vertical position: What does ESAMyN say? A visionary perspective to reduce maternal mortality
Abstract: Upright labor has proven to be a practice with multiple benefits, supported by both scientific research and clinical experience. This approach uses gravity to facilitate the baby's descent, improves fetal alignment and increases the efficiency of uterine contractions. As a result, it can speed up labor and decrease the need for medical interventions. It also offers women a greater sense of control and comfort during labor, enhancing their physical and emotional experience. From the newborn's perspective, upright delivery can aid in a smoother postnatal transition. Culturally, many indigenous communities have adopted this practice for centuries, based on a deep understanding of female anatomy and physiology. The reintroduction of these practices in the modern context shows a growing recognition of natural methods and a more holistic approach to obstetric care. By combining these techniques with evidence-based medicine, significant advances can be made in reducing maternal mortality and improving the birth experience for women and their newborns.
Journal: Data and Metadata
Pages: .236
Volume: 3
Year: 2024
DOI: 10.56294/dm2024236
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.236:id:1056294dm2024236

Template-Type: ReDIF-Article 1.0
Author-Name: Melany Priscila Velásquez Araujo
Author-Name-First: Melany Priscila
Author-Name-Last: Velásquez Araujo
Author-Name: Heyka Carmen Gámez Cevallos
Author-Name-First: Heyka Carmen
Author-Name-Last: Gámez Cevallos
Author-Name: Doménica Amarilis Villalva Fonseca
Author-Name-First: Doménica Amarilis
Author-Name-Last: Villalva Fonseca
Title: New technologies in pregnancy monitoring
Abstract: The advancement of information and communication technologies (ICT) has transformed prenatal care by providing new tools for pregnancy monitoring and management. This article reviews the impact of ICT, including mobile applications, remote monitoring devices, and online platforms, on pregnancy monitoring. It highlights how these technologies can enhance the knowledge and self-care of pregnant women, empowering them to make informed decisions about their health. The challenges and limitations associated with the use of ICT in prenatal care, such as the digital divide and the need for reliable information, are also discussed. The review suggests that while ICT offers numerous advantages, it is crucial to address access barriers and educate pregnant women and healthcare professionals on the effective use of these technologies to maximize their benefits. Women consider the use of ICT important due to their ease of access and ability to provide relevant information about pregnancy. The conclusions highlight that the use of ICT has allowed for better monitoring of pregnancy, empowering pregnant women and facilitating communication with healthcare experts. However, it is important to address issues such as lack of digital skills and language barriers to ensure equitable access to these technologies for all pregnant women.
Journal: Data and Metadata
Pages: .235
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.235
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.235:id:1056294dm2024235

Template-Type: ReDIF-Article 1.0
Author-Name: Walter Vinicio Culque Topanta
Author-Name-First: Walter Vinicio
Author-Name-Last: Culque Topanta
Author-Name: Luis Antonio Llerena Ocaña
Author-Name-First: Luis Antonio
Author-Name-Last: Llerena Ocaña
Author-Name: Fausto Alberto Viscaino Naranjo
Author-Name-First: Fausto Alberto
Author-Name-Last: Viscaino Naranjo
Title: Web application using data mart to improve decision making in the sales area of ​​the company Autorepuestos Pérez
Abstract: In the business world, software as a service simplifies the storage of important company data and also facilitates the exchange and control of this data, providing the company with a higher level of competitiveness. Autorepuestos Pérez, the teams responsible for locating data from various sources increasingly rely on spreadsheets to share their information. This often leads to human errors, confusion, and complex reconciliations. Data Marts will take a centralized place where the necessary data will be collected and organized before creating reports, dashboards, and visualizations, which can be used efficiently and effectively, supporting decision-making and increasing institutional credibility
Journal: Data and Metadata
Pages: .234
Volume: 3
Year: 2024
DOI: 10.56294/dm2024234
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.234:id:1056294dm2024234

Template-Type: ReDIF-Article 1.0
Author-Name: Edwin Fabricio Lozada Torres
Author-Name-First: Edwin Fabricio
Author-Name-Last: Lozada Torres
Author-Name: Rodrigo Cadena Martínez
Author-Name-First: Rodrigo
Author-Name-Last: Cadena Martínez
Author-Name: María Angélica Pico Pico
Author-Name-First: María Angélica
Author-Name-Last: Pico Pico
Title: Technical debt management in academic environments: perspectives and challenges of a software development team
Abstract: Technical debt are those convenience tasks that developers perform to obtain short-term benefits but that in the future can produce activities that are difficult to perform or more costly. The impact of technical debt is perceived in all project resources, becoming a present and future problem in software development, affecting the quality of the software. This study uses a qualitative analysis, through interviews with a software development team, explores the knowledge and perception of technical debt, the practices used to manage it, the factors that influence its appearance and accumulation, the impact that development suffers, as well as the team and the maintainability of the software, as a result, ideas and solutions are proposed to address it effectively. The study was carried out with a qualitative investigative approach, which was based on the study of a single case with which the experiences and practices in the perception and management of technical debt are investigated. The data obtained reveal that technical debt affects the normal development of the development team and that it is present in the activities carried out by the team. Although the development team has a perception of the debt, it does not address it directly, and it is necessary to implement good practices to identify it and integrate it into the software development process. Addressing technical debt with the implementation of a formal process to manage it will ensure that scheduled time is controlled and resources are optimized, which will result in a culture of good practices that will ensure product quality, improving team performance and guaranteeing software maintainability
Journal: Data and Metadata
Pages: .233
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.233
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.233:id:1056294dm2024233

Template-Type: ReDIF-Article 1.0
Author-Name: Fausto Alberto Viscaino Naranjo
Author-Name-First: Fausto Alberto
Author-Name-Last: Viscaino Naranjo
Author-Name: Walter Vinicio Culque Toapanta
Author-Name-First: Walter Vinicio
Author-Name-Last: Culque Toapanta
Author-Name: Luis Antonio Llerena Ocaña
Author-Name-First: Luis Antonio
Author-Name-Last: Llerena Ocaña
Title: Analysis of the design and usability characteristics of mobile applications preferred by students: perspectives from the unidad educativa González Suárez
Abstract: This study examined the design and usability preferences in mobile applications among adolescent students at the González Suárez Educational Unit in the city of Ambato. The research focused on identifying the features most valued by students in the context of mobile applications. A survey was conducted with 242 upper elementary and high school students, evaluating ten design and usability features using a 5-point Likert scale. The results revealed a strong preference for intuitive interfaces, fast loading times, and ease of navigation. Personalization of appearance and compatibility with different devices were also highly valued. Offline functionality was considered important, reflecting local connectivity realities. Opinions on integration with social networks and the inclusion of tutorials were more divided. The study concluded that the design of educational mobile applications for Ecuadorian adolescents should prioritize efficiency, customization, and adaptability to different conditions of use. These findings provide a solid foundation for the development of more effective and engaging applications for adolescent students in the Ecuadorian context
Journal: Data and Metadata
Pages: .232
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.232
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.232:id:1056294dm2024232

Template-Type: ReDIF-Article 1.0
Author-Name: Awais Azam
Author-Name-First: Awais
Author-Name-Last: Azam
Author-Name: Alimul Haque
Author-Name-First: Alimul
Author-Name-Last: Haque
Author-Name: Sakshi Rai Rai
Author-Name-First: Sakshi Rai
Author-Name-Last: Rai
Title: Predicting Housing Sale Prices Using Machine Learning with Various Data Split Ratios
Abstract: Introduction: Recent advancements in technology and data analytics have propelled the rapid growth of artificial intelligence (AI) and machine learning (ML), which are now central to various industries. These technologies have become essential tools in many sectors, especially in predictive modeling for asset pricing. Objective: From stock markets and rental properties to real estate and second-hand goods, AI and ML algorithms are widely applied to estimate values, optimize pricing strategies, and forecast market trends. Method: By analyzing vast amounts of data, these tools enable more accurate predictions and informed decision-making, revolutionizing traditional approaches to pricing and valuation. In this study, the primary goal is to achieve the most accurate price prediction for houses or apartments by experimenting with different data split ratios. Result: RMSE (House Price) 188965.28 is acceptable as best average price for houses. Conclusions: The value of RMSE of this model are relatively low and also the value Squared Correlation is 64% which is above the threshold of 50%, so the predicted price of this model is seems appropriate, so I have presented this model and its predicted house price as final acceptable value for my research outcome
Journal: Data and Metadata
Pages: .231
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.231
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.231:id:1056294dm2024231

Template-Type: ReDIF-Article 1.0
Author-Name: Katherine Andrea Vargas
Author-Name-First: Katherine Andrea
Author-Name-Last: Vargas
Author-Name: Yenny Carolina Romero
Author-Name-First: Yenny Carolina
Author-Name-Last: Romero
Author-Name: Nicolas Esteban Vega
Author-Name-First: Nicolas Esteban
Author-Name-Last: Vega
Title: Digital marketing strategies focused on social networks. Systematic review
Abstract: Introduction: Social networks have become a very powerful commercial tool for companies. Using it strategically can boost sales, improve online presence, and build customer loyalty. The objective pursued in this scientific article is to analyze digital marketing strategies focused on social networks during the last five years. Methodology: For the study, the authors were based on a systematic review of the literature that addresses the relationship between digital marketing strategies focused on social networks. The methodological guidelines of the PRISMA method, published in the period between 2019 – 2023, were followed. Results: Currently, social networks have acquired a central role in digital marketing, offering companies the ability to deploy creative strategies and attract the attention of their target audience. Effective customer relationship management (CRM) and the building of emotional bonds stand out as essential elements to achieve the successful propagation of advertising campaigns on these platforms. Conclusion: The impact of digital marketing on social networks has been transformative in various aspects. There has been a notable increase in the global reach of brands, allowing them to connect with audiences worldwide. The interaction between brands and users has seen significant growth, driven by the proliferation of multimedia content and consumer engagement. This makes it easier for organizations to more effectively publicize their products or services on a broader scale, thanks to the impact generated on social networks.
Journal: Data and Metadata
Pages: .230
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.230
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.230:id:1056294dm2024230

Template-Type: ReDIF-Article 1.0
Author-Name: María Angélica Pico Pico
Author-Name-First: María Angélica
Author-Name-Last: Pico Pico
Author-Name: Rosa Gabriela Camero Berrones
Author-Name-First: Rosa Gabriela
Author-Name-Last: Camero Berrones
Author-Name: Edwin Fabricio Lozada Torres
Author-Name-First: Edwin Fabricio
Author-Name-Last: Lozada Torres
Author-Name: Luis Rafael Freire Lescano
Author-Name-First: Luis Rafael
Author-Name-Last: Freire Lescano
Title: Proposal for a usability engineering model applicable to the requirements analysis phase of mobile applications
Abstract: Usability is frequently evaluated in the final phases of development or when the application is finished, although it is true that this evaluation provides important information to improve new versions of products, it is much more important to have elements that allow obtaining relevant information that allows incorporating usability attributes in the requirements analysis phase in order to obtain quality software products and even more so when it comes to applications intended for use on mobile devices, we must consider that usability is not only about reducing the size of a website to adapt to mobile devices, but on the contrary, considering this element means thinking about how people use mobile devices and understanding that the mobile experience is so unique. as the user, which is why to obtain quality mobile applications, adequate usability engineering must be carried out in the requirements phase. In this work, the proposal for a Usability Engineering Model applicable to the application requirements analysis phase is presented. This proposal integrates the usability models and criteria in the Software Engineering process
Journal: Data and Metadata
Pages: .229
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.229
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.229:id:1056294dm2024229

Template-Type: ReDIF-Article 1.0
Author-Name: Inna Sokhan
Author-Name-First: Inna
Author-Name-Last: Sokhan
Author-Name: Maksym Bevz
Author-Name-First: Maksym
Author-Name-Last: Bevz
Author-Name: Viktor Zapadenko
Author-Name-First: Viktor
Author-Name-Last: Zapadenko
Author-Name: Svitlana Breus
Author-Name-First: Svitlana
Author-Name-Last: Breus
Author-Name: Yuliia Pereguda
Author-Name-First: Yuliia
Author-Name-Last: Pereguda
Title: Technological Innovation as a Factor in Strengthening Economic Sustainability in the Context of Globalization
Abstract: Introduction: The study explored the impact of global competitiveness and technological progress on enhancing economic sustainability within the framework of globalization. Methods: A comprehensive survey was conducted, gathering data from 210 respondents. Logit regression analysis was used to assess the influence of technological innovation, global competitiveness, and technical progress on sustainability outcomes. Model accuracy was tested using the Receiver Operating Characteristic (ROC) curve. Results: The findings showed that technological innovation increased the likelihood of achieving economic sustainability with an odds ratio of 2.34 (p = 0.003). Global competitiveness also played a significant role, improving sustainability by 1.90 times (p = 0.008). Technical progress positively influenced sustainability, with an odds ratio of 1.62 (p = 0.025). The model's predictive accuracy was validated with an AUC value of 0.82. Conclusions: The study emphasized the importance of fostering technological innovation, strengthening global market participation, and advancing technological capabilities to drive economic sustainability. In line with the Sustainable Development Goals, policymakers and business leaders should prioritize innovation ecosystems and strategic investments in technology to ensure long-term growth and resilience in a globalized economy
Journal: Data and Metadata
Pages: .228
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.228
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.228:id:1056294dm2024228

Template-Type: ReDIF-Article 1.0
Author-Name: Valerii Kononenko
Author-Name-First: Valerii
Author-Name-Last: Kononenko
Author-Name: Oleksandr Yaremenko
Author-Name-First: Oleksandr
Author-Name-Last: Yaremenko
Author-Name: Oleksandr Konotopenko
Author-Name-First: Oleksandr
Author-Name-Last: Konotopenko
Author-Name: Serhii Lapshin
Author-Name-First: Serhii
Author-Name-Last: Lapshin
Author-Name: Andrii Moisiiakha
Author-Name-First: Andrii
Author-Name-Last: Moisiiakha
Title: Implementation of Automation Mechanisms in Public Administration in Ukraine: Analysis of Challenges and Prospects
Abstract: Introduction: This study evaluated the adoption of automation technologies within Ukraine's public administration, focusing on their effectiveness and identifying key challenges and opportunities for future development. Methods: The research employed an in-depth literature review, analysis of the existing regulatory framework, statistical assessments, and empirical investigations. A comparative analysis used international examples from Estonia, Singapore, and South Korea to identify best practices applicable to the Ukrainian context. Results: The results indicate that automation technologies significantly improve administrative efficiency. However, key challenges still need to be addressed, including gaps in the legal and regulatory frameworks and inadequate personnel training. Conclusions: The study concludes with recommendations for enhancing automation in Ukraine's public administration, drawing on international experiences while considering national specifics. A comprehensive digital integration across all levels of public administration is essential for ensuring effective and transparent governance
Journal: Data and Metadata
Pages: .227
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.227
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.227:id:1056294dm2024227

Template-Type: ReDIF-Article 1.0
Author-Name: Olha Prokopenko
Author-Name-First: Olha
Author-Name-Last: Prokopenko
Author-Name: Hanna Mashika
Author-Name-First: Hanna
Author-Name-Last: Mashika
Author-Name: Liudmyla Hryhorieva
Author-Name-First: Liudmyla
Author-Name-Last: Hryhorieva
Author-Name: Olena Khanova
Author-Name-First: Olena
Author-Name-Last: Khanova
Author-Name: Anatoliy Parfinenko
Author-Name-First: Anatoliy
Author-Name-Last: Parfinenko
Author-Name: Almagul Nurgaliyeva
Author-Name-First: Almagul
Author-Name-Last: Nurgaliyeva
Title: Digital Technologies and Innovative Models of Risk Management in International Tourism
Abstract: Introduction: This study examines the readiness for developing and implementing innovative risk management models in international tourism. The research is part of the project "International tourism in the system of economic relations: research of security and sustainability. The purpose of the study is to examine the willingness of managers in the tourism system to make decisions regarding risk prevention and prompt elimination of the consequences of risk occurrence. Methods: A review of recent research and regulatory documents in risk management was conducted, followed by a sociological survey targeting managers from Ukraine, Estonia, and Kazakhstan. Data was collected from 255 managers in 68 companies, using anonymous questionnaires to assess their familiarity and engagement with risk management techniques in international tourism. Results: The study revealed varying levels of risk management expertise among managers: 14% had a low level, 43% had an average level, 19% had sufficient knowledge, and 10% were proficient in managing risks and generating innovative ideas for risk identification, analysis, and management within the tourism sector. Conclusions: Risk management remains an undervalued yet essential aspect in international tourism, where its neglect can jeopardize valuable business ventures. This study highlights the need for enhanced risk management strategies and serves as a potential framework for broader research applications beyond the tourism industry. Keywords: digital tools, tourism business, international tourism, risk management, sociological research
Journal: Data and Metadata
Pages: .226
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.226
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.226:id:1056294dm2024226

Template-Type: ReDIF-Article 1.0
Author-Name: Yevhenii Tytarchuk
Author-Name-First: Yevhenii
Author-Name-Last: Tytarchuk
Author-Name: Sergii Pakhomov
Author-Name-First: Sergii
Author-Name-Last: Pakhomov
Author-Name: Dmytro Beirak
Author-Name-First: Dmytro
Author-Name-Last: Beirak
Author-Name: Vasyl Sydorchuk
Author-Name-First: Vasyl
Author-Name-Last: Sydorchuk
Author-Name: Svitlana Vasylyuk Zaitseva
Author-Name-First: Svitlana
Author-Name-Last: Vasylyuk Zaitseva
Title: The impact of distributed systems on the architecture and design of computer systems: advantages and challenges
Abstract: A distributed system can encompass a variety of configurations, including mainframes, personal computers, workstations, and minicomputers. The varying degrees of software flexibility and the ability to execute tasks in parallel facilitate simultaneous data processing across multiple processors. The higher the resilience of an application, the quicker it can recover after a system failure. Organisations increasingly adopt distributed computing systems as they face increased data generation and demand for enhanced application performance. These systems enable businesses to scale effectively in response to growing data volumes. Integrating additional hardware into a distributed system is generally simpler than upgrading a centralised system reliant on powerful servers. Distributed systems comprise numerous nodes that collaborate towards a common objective. This article aims to provide a comprehensive overview of distributed systems, their architectural frameworks, and essential components. This study examines how distributed systems influence the architecture and design of computer systems. The research methods consist of reviewing existing literature and analysing case studies on implementing distributed systems. Key findings indicate that the evolution of distributed systems is ongoing, driven by emerging technologies and the increasing demand for efficient, scalable, and secure solutions. Innovations such as edge computing, blockchain technology, 5G, and the integration of AI and machine learning are among the notable trends shaping the future landscape of distributed systems. Looking ahead, designers and architects need to stay informed about these advancements to create reliable and adaptable distributed systems that can address the dynamic needs of users and organisations
Journal: Data and Metadata
Pages: .225
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.225
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.225:id:1056294dm2024225

Template-Type: ReDIF-Article 1.0
Author-Name: Olha Chernysh
Author-Name-First: Olha
Author-Name-Last: Chernysh
Author-Name: Oleksandr Smishko
Author-Name-First: Oleksandr
Author-Name-Last: Smishko
Author-Name: Yuliia Koverninska
Author-Name-First: Yuliia
Author-Name-Last: Koverninska
Author-Name: Mykola Prokopenko
Author-Name-First: Mykola
Author-Name-Last: Prokopenko
Author-Name: Ihor Pistunov
Author-Name-First: Ihor
Author-Name-Last: Pistunov
Title: The Role of Artificial Intelligence in Financial Analysis and Forecasting: Using Data and Algorithms
Abstract: Introduction: This study explores the role of Artificial Intelligence (AI) in financial analysis and forecasting, focusing on its application in the banking sector. AI's ability to process large datasets and enhance prediction accuracy is critical for improving financial decision-making, particularly in forecasting stock prices, currency rates, and market trends. Methods: The research employed traditional statistical methods such as ARIMA models and machine learning algorithms like Gradient Boosting Machines and Random Forests. These methods were applied to financial data sets to assess the impact of AI on forecasting accuracy and risk assessment. Data preprocessing and model training were conducted using R statistical software. Results: Integrating AI models improved forecasting accuracy by 30% compared to traditional methods, and risk assessment accuracy increased by 20%. Gradient Boosting Machines outperformed other models in identifying investment portfolio risks, while Random Forests provided robust predictions of trading volumes. Conclusions: AI has the potential to revolutionize financial analysis by increasing the efficiency and accuracy of forecasts. However, data privacy, algorithmic bias, and ethical concerns must be addressed to ensure fair and responsible AI use in finance. Collaboration among researchers, financial experts, and policymakers is essential for maximizing AI's benefits while mitigating risks
Journal: Data and Metadata
Pages: .224
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.224
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.224:id:1056294dm2024224

Template-Type: ReDIF-Article 1.0
Author-Name: Sultan Ahmad
Author-Name-First: Sultan
Author-Name-Last: Ahmad
Author-Name: Md Alimul Haque
Author-Name-First: Md
Author-Name-Last: Alimul Haque
Author-Name: Hikmat A. M. Abdeljaber
Author-Name-First: Hikmat A.
Author-Name-Last: M. Abdeljaber
Author-Name: M. U. Bokhari
Author-Name-First: M. U.
Author-Name-Last: Bokhari
Author-Name: Jabeen Nazeer
Author-Name-First: Jabeen
Author-Name-Last: Nazeer
Author-Name: B. K. Mishra
Author-Name-First: B. K.
Author-Name-Last: Mishra
Title: Phishing Website Detection: A Dataset-Centric Approach for Enhanced Security
Abstract: Introduction; Phishing involves cybercriminals creating fake websites that appear to be real sites with the aim of obtaining personal information. With the increasing sophistication of phishing websites, machine learning today provides a useful approach to scan and counter such attacks. Objective; In this study, we seek to apply machine learning algorithms on the dataset - Phishing_Legitimate_full.csv – which consists of phishing websites and genuine websites that have been labeled. Method; This paper aims to identify the most effective feature selection method for predicting phishing websites. Result; The findings highlight the potential of machine learning in enhancing cybersecurity by automating threat detection and intelligence. Phishing attacks rely on social engineering strategies to present deceptive links as trustworthy sources, deceiving individuals into sharing confidential data. Conclusion; This study explores the utilization of curated datasets and machine learning algorithms to develop adaptive and efficient phishing detection mechanisms, providing a robust defense against such malicious activities
Journal: Data and Metadata
Pages: .223
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.223
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.223:id:1056294dm2024223

Template-Type: ReDIF-Article 1.0
Author-Name: Vuyyuru Lakshmi Lalitha
Author-Name-First: Vuyyuru
Author-Name-Last: Lakshmi Lalitha
Author-Name: Dinesh Kumar Anguraj
Author-Name-First: Dinesh
Author-Name-Last: Kumar Anguraj
Title: Developing a Novel Method for Emotion Detection through Natural Language Processing
Abstract: The analysis of audience emotional responses to textual content is vital across various fields, including politics, entertainment, industry, and research. Sentiment Analysis (SA), a branch of Natural Language Processing (NLP), employs statistical, lexical, and machine learning methods to predict audience emotions—neutral, positive, or negative—in response to diverse social media content. However, a notable research gap persists due to the lack of robust tools capable of quantifying features and independent text essential for assessing primary audience emotions within large-scale social media datasets. This study addresses the gap by introducing a novel approach to analyse the relationships within social media texts and evaluate audience emotions. A Dense Layer Graph (DLG-TF) model is proposed for textual feature analysis, enabling the exploration of intricate interconnections in the media landscape and enhancing emotion prediction capabilities. Social media data is processed using advanced convolutional network models, with emotion predictions derived from analysing textual features. Experimental results reveal that the DLG-TF model outperforms traditional emotion prediction techniques by delivering more accurate predictions across a broader emotional spectrum. Performance metrics, including accuracy, precision, recall, and F-measure, are assessed and compared against existing methodologies, demonstrating the superiority of the proposed model in utilizing social media datasets effectively
Journal: Data and Metadata
Pages: .222
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.222
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.222:id:1056294dm2024222

Template-Type: ReDIF-Article 1.0
Author-Name: Wilter C. Morales-García
Author-Name-First: Wilter C.
Author-Name-Last: Morales-García
Author-Name: Liset Z. Sairitupa-Sanchez
Author-Name-First: Liset Z.
Author-Name-Last: Sairitupa-Sanchez
Author-Name: Alcides Flores-Paredes
Author-Name-First: Alcides
Author-Name-Last: Flores-Paredes
Author-Name: Mardel Morales-García
Author-Name-First: Mardel
Author-Name-Last: Morales-García
Author-Name: Fernando N. Gutierrez-Caballero
Author-Name-First: Fernando N.
Author-Name-Last: Gutierrez-Caballero
Title: Influence of Attitude toward Artificial Intelligence (AI) on Job Performance with AI in Nurses
Abstract: AI has revolutionized the workplace, significantly impacting the nursing profession. Attitudes toward AI, defined as workers’ perceptions and beliefs about its utility and effectiveness, are critical for its adoption and efficient use in clinical settings. Factors such as age, marital status, and education level may influence this relationship, affecting job performance. This study examines the influence of attitude toward AI on job performance with AI among Peruvian nurses, while also assessing how sociodemographic characteristics moderate this relationship. A descriptive cross-sectional design was used with a sample of 249 Peruvian nurses aged 24 to 53 years (M = 35.58, SD = 8.3). Data were collected using two validated scales: the Brief Artificial Intelligence Job Performance Scale (BAIJPS) and the Attitude toward Artificial Intelligence Scale (AIAS-4). Descriptive statistics, Pearson correlations, and multiple linear regression were applied. A significant positive correlation was found between attitude toward AI and job performance with AI (r = 0.43, p < 0.01). Age (β = -0.177, p < 0.05), divorced marital status (β = -8.144, p < 0.01), and having a bachelor’s degree (β = -3.016, p < 0.05) were negatively associated with job performance, while being from the Selva region had a positive effect (β = 4.182, p < 0.05). A favorable attitude toward AI positively influences nurses’ job performance, highlighting the need for interventions that enhance AI perception. Age, marital status, and education moderate this relationship, suggesting AI adoption strategies should be tailored to different demographic groups.
Journal: Data and Metadata
Pages: 221
Volume: 4
Year: 2025
DOI: 10.56294/dm2025221
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:221:id:1056294dm2025221

Template-Type: ReDIF-Article 1.0
Author-Name: Yadira Paola Borja Brazales
Author-Name-First: Yadira Paola
Author-Name-Last: Borja Brazales
Author-Name: Vladimir Marconi Ortiz Bustamante
Author-Name-First: Vladimir Marconi
Author-Name-Last: Ortiz Bustamante
Author-Name: Nelson Wilfrido Guagchinga Chicaiza
Author-Name-First: Nelson Wilfrido
Author-Name-Last: Guagchinga Chicaiza
Author-Name: Margarita Nataly Cadena Castillo
Author-Name-First: Margarita Nataly
Author-Name-Last: Cadena Castillo
Title: Organizational culture and innovative behavior Through ANOVA analysis
Abstract: This study examines the impact of organizational culture on innovative behavior within the financial sector, focusing on the Sumak Samy Credit Union. The objective is to establish a link between these variables through a quantitative methodology encompassing exploratory, descriptive, bibliographic and correlational research. A total of 20 employees of the cooperative participated in the research and completed surveys to ensure the reliability of the data. The research used two questionnaires: to assess organizational culture and to innovative behavior. The study confirmed the reliability of the instruments through Cronbach's alpha coefficients, which were 0.73 for organizational culture and 0.69 for innovative behavior, indicating satisfactory consistency. The findings underline the need to improve the indicators with both aspects, and the importance of improvement for the Cooperative
Journal: Data and Metadata
Pages: .220
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.220
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.220:id:1056294dm2024220

Template-Type: ReDIF-Article 1.0
Author-Name: Tamy Johanna Logro León
Author-Name-First: Tamy Johanna
Author-Name-Last: Logro León
Author-Name: Katherin Vanessa Gallegos Cadena
Author-Name-First: Katherin Vanessa
Author-Name-Last: Gallegos Cadena
Author-Name: Paulina Alexandra Arias Arroyo
Author-Name-First: Paulina Alexandra
Author-Name-Last: Arias Arroyo
Title: EFL Pre-service Teachers' Understanding about Dyslexia
Abstract: Initial teacher preparation plays a crucial role in identifying and supporting students with special educational needs, such as dyslexia, a disorder that affects English language learning. The purpose of this study was to examine the level of knowledge and difficulties of 124 pre-service English teachers at a public university in Ecuador about dyslexia. Data were collected in three different courses from 6th to 8th level from a public university and analyzed using SPSS 27.0 through descriptive statistics, Chi test, and Kruskall Wallis test, taking into account the participant’s scores from the Knowledge and Beliefs about Developmental Dyslexia Scale (KBDDS). A Spanish-adapted version by Betancor (2022) of the original version by Soriano-Ferrer and Echegaray-Bengoa (2014). The results showed that the participants still did not have enough knowledge about dyslexia. Also, a thorough analysis of gender and level variables was conducted and showed that there was no significant effect. These results suggest the need to promote courses and include the topic of dyslexia for future English teachers in their classes to prepare them to provide more inclusive teaching
Journal: Data and Metadata
Pages: .219
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.219
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.219:id:1056294dm2024219

Template-Type: ReDIF-Article 1.0
Author-Name: Rodolfo Matius Mendoza Poma
Author-Name-First: Rodolfo Matius
Author-Name-Last: Mendoza Poma
Author-Name: Milton Alberto Sampedro Arrieta
Author-Name-First: Milton Alberto
Author-Name-Last: Sampedro Arrieta
Author-Name: Freddy Anaximandro Álvarez Lema
Author-Name-First: Freddy Anaximandro
Author-Name-Last: Álvarez Lema
Author-Name: Manuel Antonio Abarca Zaquinaula
Author-Name-First: Manuel Antonio
Author-Name-Last: Abarca Zaquinaula
Title: Analysis of technological tools for tourism in the Pichincha province
Abstract: The purpose of the research was to analyze the technological tools used by tourists visiting the province of Pichincha before and during the pandemic. The research approach is quantitative, relational and comparative with an analysis before and during the confinement. The selection of tourist attractions was based on the MINTUR guide. The population was unknown, so we worked with an infinite population. The survey technique was applied. The Kolmogorov Smirnov Normality test was used to measure the degree of concordance of the information collected. The Wilcoxon test was applied. A non-parametric test was also performed, for which the Speraman correlation coefficient was used. With the results, it was determined that the most used technological tools were the computer with 61.56% and the cell phone with 34.03% of acceptance by tourists during the pandemic. Another factor was the most used platforms, where social networks stood out with 77.92%. It is evident that during the confinement tourists made use of technological tools to search for tourist sites in the province of Pichincha
Journal: Data and Metadata
Pages: .218
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.218
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.218:id:1056294dm2024218

Template-Type: ReDIF-Article 1.0
Author-Name: Marcia Janeth Chiluisa Chiluisa
Author-Name-First: Marcia Janeth
Author-Name-Last: Chiluisa Chiluisa
Author-Name: Olga Lorena González Ortiz
Author-Name-First: Olga Lorena
Author-Name-Last: González Ortiz
Author-Name: Marco Paul Beltran Semblantes
Author-Name-First: Marco Paul
Author-Name-Last: Beltran Semblantes
Title: L1 Interference in English Major Students’ Pronunciation at Technical University of Cotopaxi
Abstract: This study aims to investigate the impact of mother tongue interference (Spanish) on the pronunciation of the phoneme /d/ in different positions among seventh-semester students majoring in English at the Technical University of Cotopaxi. The study employs a mixed approach with a correlational design. The correlation between the variables allows for the analysis of the quantifiable relationship between Spanish as a mother tongue interference and the quality of the pronunciation of the phoneme /d/ in English. The degree and direction of this correlation will help determine whether and to extent the interference has a significant impact on pronunciation. The findings of the study shed light on the challenges faced by students when pronouncing words with the phoneme /d/ in different positions. In the initial position, the interference is characterized by the transference of Spanish tongue positions and aspiration tendencies, leading to the production of [d] instead of the standard English sound, information detailed in the results and discussion section. In the middle position, students exhibit a tendency to apply Spanish flapping and elision patterns, resulting in the articulation of a tap or alveolar flap [ɾ] instead of the expected English sound. In the final position, the influence of Spanish leads some students to voice the English final /d/ even when English requires voicelessness, indicating the persistence of L1 interference. The results emphasize the importance of addressing specific phonetic and phonological aspects that arise due to mother tongue interference in pronunciation training. Understanding the intricate interplay between L1 influence and the articulation of the phoneme /d/ can lead to targeted interventions that enhance students' pronunciation skills and contribute to their overall communicative competence in English
Journal: Data and Metadata
Pages: .217
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.217
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.217:id:1056294dm2024217

Template-Type: ReDIF-Article 1.0
Author-Name: Oscar Bladimir Velasco Panchi
Author-Name-First: Oscar Bladimir
Author-Name-Last: Velasco Panchi
Author-Name: Jirma Elizabeth Veintimilla Ruiz
Author-Name-First: Jirma Elizabeth
Author-Name-Last: Veintimilla Ruiz
Author-Name: Luis David Moreano Martínez
Author-Name-First: Luis David
Author-Name-Last: Moreano Martínez
Author-Name: Isabel Regina Armas Heredia
Author-Name-First: Isabel Regina
Author-Name-Last: Armas Heredia
Title: Analysis of innovation competencies and the creation of entrepreneurship
Abstract: The research explores the role of innovation competencies in the university environment and its influence on the creation of entrepreneurship, in the Ecuadorian context. These competencies, understood as a set of technical, creative, digital and strategic skills, are essential to transform ideas into sustainable initiatives. The relationship between innovation and entrepreneurship is analyzed from a theoretical and empirical perspective, addressing how institutional and cultural factors can influence entrepreneurial development. A mixed-methods approach was adopted. The qualitative phase consisted of interviews with technology startup entrepreneurs to identify key competencies; while the quantitative phase included surveys of 150 entrepreneurs from various sectors, statistically evaluating the relationship between these competencies and business performance. Tools such as the Likert scale and linear regression analysis were used to measure the impact of competencies on performance. The results underscore the relevance of strategic and digital competencies to generate competitive advantages, highlighting their role in uncertainty management and process optimization. Additionally, challenges such as limited access to resources and the stigma associated with failure were identified, which hinder the consolidation of entrepreneurial projects in Ecuador. This study contributes to the design of educational strategies and public policies aimed at strengthening the innovation and entrepreneurship ecosystem, promoting a positive impact on the sustainable development of the country
Journal: Data and Metadata
Pages: .216
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.216
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.216:id:1056294dm2024216

Template-Type: ReDIF-Article 1.0
Author-Name: Milton Marcelo Cárdenas
Author-Name-First: Milton Marcelo
Author-Name-Last: Cárdenas
Title: Associational strategy around a collective brand of Ecuadorian potters within the popular and solidarity economy in the framework of good living
Abstract: Introduction: The artisanal sector within Ecuador's Popular and Solidarity Economy faces challenges such as low levels of associativity, limited financial resources, and difficulties in product commercialization. These obstacles have adversely affected competitiveness and sustainability in La Victoria Parish and the Cotopaxi Province. Therefore, this study focuses on proposing an associative strategy based on a collective brand to strengthen entrepreneurs in the artisanal sector of La Victoria Parish and the Cotopaxi Province. Methods: A qualitative and descriptive approach was adopted, focusing on associativity in artisanal communities within the framework of Ecuador's Popular and Solidarity Economy. Additionally, methods such as Saaty's Analytical Hierarchy Process (AHP) were applied to evaluate the identified challenges, along with the modeling of the Entropy and VIKOR methods to support the implementation of academic projects aimed at enhancing the artisanal sector. Results: The implementation of an associative strategy based on a collective brand demonstrated improvements in product quality, the optimization of commercialization channels, and access to financial inclusion programs, fostering local economic development. Conclusions: Associativity has proven to be a fundamental pillar for enhancing the competitiveness and sustainability of the artisanal sector. Moreover, the proposed strategy can be replicated in other communities, strengthening territorial development and promoting social inclusion
Journal: Data and Metadata
Pages: .215
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.215
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.215:id:1056294dm2024215

Template-Type: ReDIF-Article 1.0
Author-Name: Medardo Ángel Ulloa Enríquez
Author-Name-First: Medardo Ángel
Author-Name-Last: Ulloa Enríquez
Title: Methodology for the execution of work method studies, time standardization for the improvement of production efficiency
Abstract: Productive organizations generally, as a company policy, tend to look for ways to improve their processes and at the same time increase the efficiency of their production lines, an activity that will be developed with the necessary expertise to achieve the desired success; in this article, the methodology for the execution of work method studies and time standardization in a case study is shared. The objective is to show how studies of this nature should be carried out step by step as a guide for industrial engineering professionals dedicated to this work
Journal: Data and Metadata
Pages: .214
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.214
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.214:id:1056294dm2024214

Template-Type: ReDIF-Article 1.0
Author-Name: Bertha Alejandra Paredes Calderón
Author-Name-First: Bertha Alejandra
Author-Name-Last: Paredes Calderón
Author-Name: Belén Anahí Centeno Rubio
Author-Name-First: Belén Anahí
Author-Name-Last: Centeno Rubio
Author-Name: Manuel Enrique Lanas López
Author-Name-First: Manuel Enrique
Author-Name-Last: Lanas López
Title: Impact of audiovisual content on Facebook and its relationship with charitable actions
Abstract: The audiovisual and graphic content published on Facebook by the Segunda Oportunidad Foundation of Quito in 2023 is analyzed. Strategies are derived from the study that make gains or losses in donations visible, which contribute to the subsistence of the animals. The qualitative-quantitative research is developed from the use of the analytical-synthetic method in the description of the object of study, through the separation, decomposition and union of the object by classes, arriving at a correspondence relationship where contents are identified, metrics and interactions. Interviews, content analysis and monitoring indices are used as research techniques. The results demonstrate the importance of managing visuality and its relationship with the results obtained on social networks
Journal: Data and Metadata
Pages: .213
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.213
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.213:id:1056294dm2024213

Template-Type: ReDIF-Article 1.0
Author-Name: Franklin Eduardo Falconí Suarez
Author-Name-First: Franklin Eduardo
Author-Name-Last: Falconí Suarez
Author-Name: Dayanna Yulissa Gallardo Espín
Author-Name-First: Dayanna Yulissa
Author-Name-Last: Gallardo Espín
Title: Choloflix: new consumer market on VOD platforms for Ecuadorian cinema
Abstract: Choloflix has modernized and promoted the film industry in Ecuador. Since its appearance in 2020 during the mandatory confinement in the country due to the Covid-19 virus, it has been presented as an entertainment industry focused on the exhibition of purely Ecuadorian audiovisual content, managing to enter the Netflix consumer market and creating a new consumption square. This article proposes a comparative analysis of consumption between the VOD platforms Netflix and Choloflix, through instruments that allow to qualify and quantify consumption trends
Journal: Data and Metadata
Pages: .212
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.212
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.212:id:1056294dm2024212

Template-Type: ReDIF-Article 1.0
Author-Name: Estela Maribel García Navarro
Author-Name-First: Estela Maribel
Author-Name-Last: García Navarro
Author-Name: Jorge Andrés Bautista Samaniego
Author-Name-First: Jorge Andrés
Author-Name-Last: Bautista Samaniego
Author-Name: Ana Julia Quintero Ordóñez
Author-Name-First: Ana Julia
Author-Name-Last: Quintero Ordóñez
Author-Name: Giselle Lorena Nuñez Nuñez
Author-Name-First: Giselle Lorena
Author-Name-Last: Nuñez Nuñez
Author-Name: Wellington Isaac Maliza Cruz
Author-Name-First: Wellington Isaac
Author-Name-Last: Maliza Cruz
Title: Comprehensive Continuous Education Plan for Remote Learning in Emergency Situations: Proposal Aimed at High School Teachers
Abstract: Remote education faces significant challenges, including the need for adequate technological infrastructure and pedagogical adaptations to maintain educational quality in crisis situations. This study proposes to develop a comprehensive methodology in Moodle to optimize high school education in virtual environments, facilitating teachers' adaptation to remote teaching and improving educational resilience. Using a descriptive and propositional approach, a systematic literature review was conducted in academic databases, and techniques such as Project-Based Learning, Flipped Classroom, Gamification, Microlearning, and Design Thinking were proposed. The results indicate that the methodology improves the implementation of active teaching methods and emphasizes the importance of ongoing teacher training and curricular development. In conclusion, the proposal seeks to transform the educational paradigm towards one that is more resilient and adaptable, ensuring the continuity and quality of education in virtual environments
Journal: Data and Metadata
Pages: .211
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.211
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.211:id:1056294dm2024211

Template-Type: ReDIF-Article 1.0
Author-Name: Wilter C. Morales-García
Author-Name-First: Wilter C.
Author-Name-Last: Morales-García
Author-Name: Liset Z. Sairitupa-Sanchez
Author-Name-First: Liset Z.
Author-Name-Last: Sairitupa-Sanchez
Author-Name: Alcides Flores-Paredes
Author-Name-First: Alcides
Author-Name-Last: Flores-Paredes
Author-Name: Jai Pascual-Mariño
Author-Name-First: Jai
Author-Name-Last: Pascual-Mariño
Author-Name: Mardel Morales-García
Author-Name-First: Mardel
Author-Name-Last: Morales-García
Title: Influence of Self-Efficacy in the Use of Artificial Intelligence (AI) and Anxiety Toward AI Use on AI Dependence Among Peruvian University Students
Abstract: Background: The advancement of artificial intelligence (AI) in education has transformed the way students interact with technological tools, creating new challenges related to self-efficacy, anxiety, and AI dependence. Self-efficacy refers to one's confidence in their ability to use AI, while AI-related anxiety pertains to the fear or concern when interacting with these systems. These variables can influence technological dependence, affecting academic performance and emotional well-being. Objective: This study aims to examine the influence of self-efficacy in AI use and anxiety toward AI on AI dependence among Peruvian university students. Methods: A descriptive cross-sectional study was conducted with 528 Peruvian university students aged 18 to 37 years (M = 19.00, SD = 3.84). Scales were used to measure AI self-efficacy, anxiety toward AI, and AI dependence. Correlation and multiple regression analyses were applied to identify predictors of technological dependence. Results: The results showed that AI self-efficacy was positively correlated with AI anxiety (r = 0.43, p < .01) and AI dependence (r = 0.61, p < .01). Anxiety also significantly correlated with AI dependence (r = 0.71, p < .01). Multiple regression analysis revealed that both AI anxiety (β = 1.131, p < .001) and AI self-efficacy (β = 0.610, p < .001) predicted AI dependence. Additionally, business administration students exhibited greater dependence compared to students from other fields (β = 1.025, p < .05). Conclusions: Students with higher self-efficacy in AI use tend to utilize AI more frequently but also experience greater anxiety and dependence on AI. Educational interventions should focus on reducing AI-related anxiety to prevent excessive dependence, especially among students.
Journal: Data and Metadata
Pages: 210
Volume: 4
Year: 2025
DOI: 10.56294/dm2025210
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:210:id:1056294dm2025210

Template-Type: ReDIF-Article 1.0
Author-Name: Vugar Abdullayev
Author-Name-First: Vugar
Author-Name-Last: Abdullayev
Author-Name: Ajesh Faizal
Author-Name-First: Ajesh
Author-Name-Last: Faizal
Author-Name: Irada Seyidova
Author-Name-First: Irada
Author-Name-Last: Seyidova
Author-Name: Seymur Mikayilov
Author-Name-First: Seymur
Author-Name-Last: Mikayilov
Author-Name: Rubaba Mammadova
Author-Name-First: Rubaba
Author-Name-Last: Mammadova
Author-Name: Lala Pirverdiyeva
Author-Name-First: Lala
Author-Name-Last: Pirverdiyeva
Author-Name: Etibar Guliyev
Author-Name-First: Etibar
Author-Name-Last: Guliyev
Title: Integration of Artificial Intelligence and Robotics into the industrial sector
Abstract: The 4th industrial revolution is driven by the implementation of automated robots and artificial intelligence (AI) to enhance efficiency, accuracy, and safety. This integration encompasses several vital domains like optimizing the supply chain, interaction between human and robots on the shop floor, predictive maintenance, automation of repetitive tasks, customisation, behaviour design, and safety management, data analysis, etc. AI-enabled robots perform repetitive tasks at very high precision, reducing the chances of human error and allowing workers to focus on more complex tasks. Automated upkeep utilizes AI to determine the time machinery will likely fail, which minimizes downtime and maintenance costs. Automated testing and AI-driven vision systems support quality control by ensuring a balanced quality of the product. AI improves supply chain processes, optimizing logistics and inventory management. Collaboration between humans and collaborative robot’s results in safer and more productive environments with people working alongside each other. Artificial Intelligence plays an important role in making smarter decisions, analysing data more effectively, and providing valuable information that can be used to improve operations. Manufacturing customization and flexibility are reliant on adaptive systems and the ability to manufacture personalized products by means of productivity. Safe and Risk Management is consolidated because robots work in dangerous scenarios and artificial intelligence models assess potential dangers. Despite challenges including labour displacement, cybersecurity, ethics, and data integration stemming from this technology, these are all potentially available on your terms. This article reviews the broader impacts that robots and artificial Intelligence have had on the industrial sector, placing emphasis on the revolution it could lead towards as well as the key elements to consider before implementing it.
Journal: Data and Metadata
Pages: 209
Volume: 4
Year: 2025
DOI: 10.56294/dm2025209
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:209:id:1056294dm2025209

Template-Type: ReDIF-Article 1.0
Author-Name: Ximena Morales-Urrutia
Author-Name-First: Ximena
Author-Name-Last: Morales-Urrutia
Author-Name: Valeria Pillajo
Author-Name-First: Valeria
Author-Name-Last: Pillajo
Title: Bitcoin Volatility: A Profitability-Focused Approach
Abstract: This study delved into the complex world of cryptocurrencies, analyzing their behavior, profitability, and volatility. Through a thorough and meticulous analysis of the 2021 – 2023 period, the volatile nature of these digital assets was revealed, where profits could be suddenly affected by external events. Bitcoin, two of the cryptocurrencies with the largest presence in the market, were the subject of a thorough analysis using sound statistical methodologies. Descriptive statistics were employed to characterize the overall behavior of cryptocurrencies, including measures of central tendency, dispersion, and distribution. Additionally, normality and stationarity tests were used to choose the best variant of the GARCH model, which was EGARCH, to estimate conditional volatility, future volatility and price profitability, allowing to identify patterns and dynamics in their variability. The results of the study revealed that cryptocurrencies, while presenting attractive potential returns, also carry a high degree of volatility. However, thanks to the in-depth analysis of the behavior of these assets we can identify opportune moments to make purchases, sales or strategic investments. The main goal of this study is to provide investors with the information needed to make strategic and informed decisions about their cryptocurrency investment
Journal: Data and Metadata
Pages: .208
Volume: 3
Year: 2025
DOI: 10.56294/dm2024.208
Handle: RePEc:dbk:datame:v:3:y:2025:i::p:.208:id:1056294dm2024208

Template-Type: ReDIF-Article 1.0
Author-Name: Silvia Carolina Zambonino Torres
Author-Name-First: Silvia Carolina
Author-Name-Last: Zambonino Torres
Author-Name: Wilson Edmundo Cisneros Basurto
Author-Name-First: Wilson Edmundo
Author-Name-Last: Cisneros Basurto
Author-Name: Flavio Raúl Vega Padilla
Author-Name-First: Flavio Raúl
Author-Name-Last: Vega Padilla
Author-Name: Ingrid Ninoshka Ruiz-Ruiz
Author-Name-First: Ingrid Ninoshka
Author-Name-Last: Ruiz-Ruiz
Author-Name: Paulina Mercedes Erazo Molina
Author-Name-First: Paulina Mercedes
Author-Name-Last: Erazo Molina
Title: Digital Skills And Sustainability In Teacher Training: The Use Of Ai For Continuous Improvement
Abstract: This study analyzes the incidence of the use of artificial intelligence (AI) in the development of digital and sustainable competencies in teachers of higher education institutions in Ecuador. A quantitative and descriptive research was applied to a sample of 200 university teachers, evaluating their levels of digital competencies. To diagnose the teaching competencies in digital knowledge of teachers in higher education, a test was applied during the second semester of the year 2024 to 300 teachers from universities in the Ecuadorian highlands. Digital competencies were analyzed in four dimensions: Information, Communication and collaboration, Use of digital devices and tools, and Content creation. The results showed that professors present a medium to medium-high level of appropriation in all dimensions, the lowest being Content Creation. The conclusions highlight the importance of implementing new measures in the institutional environment for the strengthening of digital competencies and the adaptation to new forms of teaching and learning where the adoption of AI tools and their relationship with sustainable practices in the classroom, constitutes a viable alternative for such purposes. The results revealed a significant positive correlation between the use of AI tools and the strengthening of digital and sustainable competencies. In addition, barriers related to the lack of knowledge were identified.
Journal: Data and Metadata
Pages: 207
Volume: 4
Year: 2025
DOI: 10.56294/dm2025207
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:207:id:1056294dm2025207

Template-Type: ReDIF-Article 1.0
Author-Name: Dipra Mitra
Author-Name-First: Dipra
Author-Name-Last: Mitra
Author-Name: Ankur Goyal
Author-Name-First: Ankur
Author-Name-Last: Goyal
Author-Name: Ganesh Gupta
Author-Name-First: Ganesh
Author-Name-Last: Gupta
Author-Name: Shivkant
Author-Name-Last: Shivkant
Title: Plant Leaf Disease Detection and Recommendation System using Alex Net-Honey Badger Fusion Algorithm
Abstract: Introduction: Plant diseases pose a significant challenge to the agriculture sector, affecting crop yield and quality, and thereby impacting the global economy. This paper discusses the urgent requirement for effective and precise detection and management of plant diseases. Objective: Utilizing the latest developments in machine learning and deep learning, specifically Convolutional Neural Networks (CNNs), we present a streamlined algorithm for identifying plant leaf diseases and providing treatment recommendations. To increase feature selection and classification accuracy, this method combines the strengths of the Honey Badger method (HBA) and antlion optimisation (ALO). Methods: This research thoroughly validates the suggested algorithm on a dataset of 87,000 RGB images that are categorised into 38 distinct plant diseases in order to compare it with state-of-the-art methods already in use. Result: The outcomes demonstrate outstanding performance with respect to accuracy, precision, recall, and F1-score, outperforming traditional models like Random Forest (RF), Support Vector Machine (SVM), and other deep learning models. By adding a recommendation mechanism to the algorithm, this work significantly advances the field by providing useful guidance on the management and prevention of diseases. Conclusion: The study has important ramifications for plant pathology and agricultural technologies. It offers farmers practical ways to successfully fight plant diseases, hence lowering food insecurity and improving crop productivity.
Journal: Data and Metadata
Pages: 206
Volume: 4
Year: 2025
DOI: 10.56294/dm2025206
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:206:id:1056294dm2025206

Template-Type: ReDIF-Article 1.0
Author-Name: Jeannette Mercedes Acosta Nuñez
Author-Name-First: Jeannette Mercedes
Author-Name-Last: Acosta Nuñez
Author-Name: Mónica Guadalupe Paredes Garcés
Author-Name-First: Mónica Guadalupe
Author-Name-Last: Paredes Garcés
Author-Name: Jenny del Rocío Molina Salas
Author-Name-First: Jenny del Rocío
Author-Name-Last: Molina Salas
Author-Name: Carmen Marlene Salguero Fiallos
Author-Name-First: Carmen Marlene
Author-Name-Last: Salguero Fiallos
Author-Name: Elizabeth Giovanna Guerrero Guerrero
Author-Name-First: Elizabeth Giovanna
Author-Name-Last: Guerrero Guerrero
Author-Name: Mery Susana Rodríguez Gamboa
Author-Name-First: Mery Susana
Author-Name-Last: Rodríguez Gamboa
Author-Name: Margarita Genoveva Sánchez Yánez
Author-Name-First: Margarita Genoveva
Author-Name-Last: Sánchez Yánez
Author-Name: Miriam Fernández Nieto
Author-Name-First: Miriam
Author-Name-Last: Fernández Nieto
Title: Teaching Mechanical Ventilation through High Fidelity Simulation
Abstract: Introduction: The study aims to evaluate the use of high-fidelity clinical simulation in teaching mechanical ventilation to students in the master’s program in Nursing with a specialization in Critical Care. The simulation seeks to replicate complex clinical scenarios to improve students' competencies in a controlled and safe environment. Methodology: Simulations based on the HAMILTON-C6 ventilator were used, with students facing mechanical ventilation situations. The High-Fidelity Clinical Simulation Satisfaction Scale (ESSAF) was the instrument employed to measure students' perceptions of the effectiveness of this methodology. Simulation sessions were followed by debriefing to promote critical reflection and practical learning. Results: Students reported high satisfaction with the simulation, highlighting its usefulness in improving clinical assessment and decision-making in critical situations, with an average score of 3.57 for its utility in assessing clinical situations. Additionally, the simulation facilitated self-reflection on performance and the development of technical skills. However, the time allocated to the simulations received a lower rating (average score of 3.13), suggesting the need to extend the sessions for more complete learning. Discussion: The standard deviation showed consistency in the responses regarding the utility of the simulation and its ability to integrate theory and practice. However, there was more variability in perceptions of the difficulty of the cases and simulation time, indicating areas for improvement. Conclusion: Clinical simulation is a valuable tool for teaching mechanical ventilation in critical care, but it is recommended to increase the complexity of the scenarios and adjust the duration of the simulations to optimize learning.
Journal: Data and Metadata
Pages: .205
Volume: 3
Year: 2024
DOI: 10.56294/dm2024.205
Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.205:id:1056294dm2024205

Template-Type: ReDIF-Article 1.0
Author-Name: Carmen Viteri
Author-Name-First: Carmen
Author-Name-Last: Viteri
Author-Name: Cristina Arteaga
Author-Name-First: Cristina
Author-Name-Last: Arteaga
Author-Name: Verónica Robayo
Author-Name-First: Verónica
Author-Name-Last: Robayo
Author-Name: Kattyta Hidalgo
Author-Name-First: Kattyta
Author-Name-Last: Hidalgo
Author-Name: Deysi Guevara
Author-Name-First: Deysi
Author-Name-Last: Guevara
Title: Discriminative ability of a nutritional risk questionnaire applied to patients with celiac disease
Abstract: A questionnaire can be a rapid tool to identify nutritional risk, allowing early intervention, especially in people with diseases such as celiac disease, where poor absorption of nutrients can cause severe deficiencies. This study assessed nutritional risk in 35 patients with prior informed consent, using a validated questionnaire, and analyzing its sensitivity and specificity. The study revealed that 65.7% are malnourished, with 48.6% underweight, especially children (72.7%) and adults (54.5%). In addition, 5.7% of patients, especially young people, are obese (16.7%). The application of the “Nutritional Screening Initiative” questionnaire showed that 66.7% are at nutritional risk, requiring improved eating habits. The correlation analysis indicated a significant association between BMI and nutritional risk. The ROC curve indicated a low discriminatory capacity, although the sensitivity was high (91.7%), correctly identifying cases at nutritional risk. However, at other thresholds, decision-making is almost random, as indicated by the sensitivity and specificity. It is concluded that the ROC curve suggested limitations in the capacity to discriminate nutritional risk, with a high sensitivity but moderate specificity. It is crucial to implement personalized nutritional interventions and improve classification models to more accurately identify risk in this population.
Journal: Data and Metadata
Pages: 204
Volume: 4
Year: 2025
DOI: 10.56294/dm2025204
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:204:id:1056294dm2025204

Template-Type: ReDIF-Article 1.0
Author-Name: Ranta Butarbutar
Author-Name-First: Ranta
Author-Name-Last: Butarbutar
Author-Name: Rubén González Vallejo
Author-Name-First: Rubén
Author-Name-Last: González Vallejo
Title: Factors Influencing AI-Assisted Thesis Writing in University: A Pull-Push-Mooring Theory Narrative Inquiry Study
Abstract: This study aims to examine the factors that motivate, attract, and anchor students to adopt AI tools during the writing process in the context of push-pull-mooring (PPM) theory. Utilizing a narrative inquiry research approach, this study employed observation, in-depth interviews, and document analysis for data collection. The analysis identified the key factors through reflexive thematic methods. Key pull factors include the generation of credit authorship contributions and the integration of AI into academic writing. The pull factors encompass topic selection, dynamic literature review, research questions, proposal conceptualization, designing research methods, data analysis, revising drafts, and managing references. AI integration incorporates active learning, self-regulated learning (SRL), inquiry-based learning, and overcoming linguistic challenges. The push factors identified include reference inaccuracies, confidentiality of research, and overreliance on AI. Three anchoring principles guide the ethical incorporation of AI in thesis writing: institutional academic policies, AI augmentation, and comprehensive contextual learning approach. But the study's limitations include the small sample size of ten students from a single university, which affects the generalizability of the results.
Journal: Data and Metadata
Pages: 203
Volume: 4
Year: 2025
DOI: 10.56294/dm2025203
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:203:id:1056294dm2025203

Template-Type: ReDIF-Article 1.0
Author-Name: Hayder A. Nahi
Author-Name-First: Hayder
Author-Name-Last: A. Nahi
Author-Name: Akmam Majed Mousa
Author-Name-First: Akmam
Author-Name-Last: Majed Mousa
Author-Name: Ebtehal Akeel Hamed
Author-Name-First: Ebtehal
Author-Name-Last: Akeel Hamed
Author-Name: Ali Khalid Ali
Author-Name-First: Ali
Author-Name-Last: Khalid Ali
Author-Name: Sarmad Jawad
Author-Name-First: Sarmad
Author-Name-Last: Jawad
Author-Name: Ahmed Mahdi Abdulkadium
Author-Name-First: Ahmed
Author-Name-Last: Mahdi Abdulkadium
Author-Name: Rusul A. Salman
Author-Name-First: Rusul A.
Author-Name-Last: Salman
Title: Quantum Key Distribution For Enabling Secure Network Function Vitalization Orchestration Over A Network
Abstract: Quantum Key Distribution (QKD) provides an state-of-the-art solution that work toward to enhance security of network and performance contrast to conventional systems. This paper focal point on the utilize of QKD to authorize secure orchestration and authorize network functions virtualization (NFV). The QKD-based solution is contrast with presenting solutions utilizing applying science and security KPIs. The outcomes display that the QKD solution exceed conventional solutions, with throughput stretch out 250 Mbit/s contrast to 150 Mbit/s, and response time of 4 ms versus 10 ms. The bit error rate (BER) registered a notable depletion to 1.2e-10 contrast to 1.8e-9, and an interception rate of 0% against 5% in conventional systems was attained. The work as well appears that the time wanted to distribute quantum keys is at most 4 ms, with a key exchange success rate of 99.8%. The model also give a demonstration of peak attack resistance with 100 successfully blocked hacking attempts registered. in spite of an extra 10ms data encryption processing time and a small 3% throughput effect, the general performance remainder marvelous with a network function deployment time of 150ms and only 0.1% packet loss. These measure reveal the efficacy of QKD in enhancing the security and efficiency of virtual networks. The paper give empirical perceptions to hold up the implementation of quantum security techniques in time ahead network infrastructures.
Journal: Data and Metadata
Pages: 202
Volume: 4
Year: 2025
DOI: 10.56294/dm2025202
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:202:id:1056294dm2025202

Template-Type: ReDIF-Article 1.0
Author-Name: Hayder A. Nahi
Author-Name-First: Hayder A.
Author-Name-Last: Nahi
Author-Name: Ali Khalid Ali
Author-Name-First: Ali
Author-Name-Last: Khalid Ali
Author-Name: Mohamed Ali Alaraji
Author-Name-First: Mohamed
Author-Name-Last: Ali Alaraji
Author-Name: Zahraa Jawad Mohi
Author-Name-First: Zahraa
Author-Name-Last: Jawad Mohi
Author-Name: Noor Thamer Mahmood
Author-Name-First: Noor
Author-Name-Last: Thamer Mahmood
Author-Name: Akmam Majed Mousa
Author-Name-First: Akmam
Author-Name-Last: Majed Mousa
Author-Name: Moatasem Mohammed Saeed
Author-Name-First: Moatasem
Author-Name-Last: Mohammed Saeed
Author-Name: Rusul A.Almansoori
Author-Name-First: Rusul
Author-Name-Last: A.Almansoori
Title: Blockchain Network for Regulation Decentralized E-Government Systems
Abstract: The combination of blockchain network with e-government systems carries convert possible for increasing transparency, trust, in addition to the efficiency in common managements. This paper looks into the implementation of blockchain technology to evolve a decentralized frame for e-governance, holding high difficulties of fraudulence, inefficiency, and loss of liability in conventional systems. The results detect that blockchain has the ability for notably increase transparency and trust over 30-50% via unchangeable and demonstrable data records, decreasing fraud over 75%. Furthermore, the systems that depending on blockchain-use enable authentication procedures to be 60-80% rapidly compared to classic techniques, simplification official paper verification and approval processes. Additionally, this paper emphasizes reducing expenditures of 20-30% resultant procedure automation and lessens reliance on mediators, giving further sustainable governmental functioning. As well, the results of blockchain's scalability permits decentralized e-government platforms to process 50-70% extra transactions without compromising performance, pretending its viability for extensive common services. These outcomes emphasize the possibility of blockchain to revolutionize e-governance via promoting a further transparent, efficient, and reliable system. This paper supplies a foundational framework for policymakers and technologists work toward to extend blockchain solutions in common managements, facilitating the road for a decentralized and national centric governance model.
Journal: Data and Metadata
Pages: 201
Volume: 4
Year: 2025
DOI: 10.56294/dm2025201
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:201:id:1056294dm2025201

Template-Type: ReDIF-Article 1.0
Author-Name: Roberto Moya-Jiménez
Author-Name-First: Roberto
Author-Name-Last: Moya-Jiménez
Author-Name: Elizabeth Morales-Urrutia
Author-Name-First: Elizabeth
Author-Name-Last: Morales-Urrutia
Author-Name: Andrea Lara-Saltos
Author-Name-First: Andrea
Author-Name-Last: Lara-Saltos
Author-Name: Andrea Goyes-Balladares
Author-Name-First: Andrea
Author-Name-Last: Goyes-Balladares
Author-Name: José Miguel Ocaña
Author-Name-First: José Miguel
Author-Name-Last: Ocaña
Author-Name: Juan Paredes-Chicaiza
Author-Name-First: Juan
Author-Name-Last: Paredes-Chicaiza
Author-Name: Wilmer Chaca-Espinoza
Author-Name-First: Wilmer
Author-Name-Last: Chaca-Espinoza
Author-Name: Andres Medina-Moncayo
Author-Name-First: Andres
Author-Name-Last: Medina-Moncayo
Title: Materials in Technological-Wearable Devices for Health: Review and Perspective
Abstract: The convergence between the textile industry and technology has revolutionized material design, enabling the development of smart textiles for wearable technological devices, especially in the healthcare sector. These devices, designed to continuously monitor physiological parameters and provide personalized support, have found in smart textiles an essential solution thanks to their properties of flexibility, comfort and adaptability, key to their prolonged use. This article examines the evolution of smart textiles from passive textiles, capable of responding to environmental stimuli, to ultra-smart textiles, which integrate sensors, actuators, microprocessors, and artificial intelligence algorithms to process information and offer adaptive solutions. The critical properties of smart textile materials are analyzed, such as their conductive, sensory, biocompatible, and energy-harvesting capabilities, as well as their application in areas such as health monitoring, treatment delivery, fall prevention, and rehabilitation. Advances in manufacturing methods are also explored, highlighting associated challenges such as technology integration and sustainability. This study presents a systematic review culminating in an integrative table of the main textile materials used in wearables for health, providing a clear view of their current potential and future areas of research. This approach not only highlights technological advancements, but also opportunities for innovation in smart textile design, positioning them as a key element in the transformation of personalized and technological health.
Journal: Data and Metadata
Pages: 200
Volume: 4
Year: 2025
DOI: 10.56294/dm2025200
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:200:id:1056294dm2025200

Template-Type: ReDIF-Article 1.0
Author-Name: Antonio Quiña-Mera
Author-Name-First: Antonio
Author-Name-Last: Quiña-Mera
Author-Name: Zamia Marlene Guitarra De la Cruz
Author-Name-First: Zamia Marlene
Author-Name-Last: Guitarra De la Cruz
Author-Name: Cathy Guevara-Vega
Author-Name-First: Cathy
Author-Name-Last: Guevara-Vega
Title: Efficiency study of GraphQL and REST Microservices in Docker containers: A computational experiment
Abstract: Introduction: In the constant evolution of technology, implementing new services in computer systems is crucial. However, the integration of these services presents problems and certain challenges in the deployment of applications. Technologies such as Docker and microservices architectures are alternatives to alleviate such integration. The aim was to compare the performance efficiency between microservices architectures implemented with GraphQL and REST, deployed in Docker and localhost environments. Methods: A computational experiment was conducted following the Wholin methodology to compare the performance efficiency of microservices architectures. The experimental design consisted of deploying both a GraphQL API and a REST API with identical functionalities in Docker containers and a localhost environment. Both APIs were consumed under controlled complexity and data volume conditions, ensuring a fair evaluation. Results: The experiment showed that the average response time in the Docker environment was significantly lower compared to the localhost environment. Also, the GraphQL API outperformed the REST API. In addition, a research artifact including all the study materials was published on Zenodo to support the replicability of the experiment. Conclusion: The architecture deployed in Docker is more efficient for microservices execution, particularly when GraphQL is used.
Journal: Data and Metadata
Pages: 199
Volume: 4
Year: 2025
DOI: 10.56294/dm2025199
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:199:id:1056294dm2025199

Template-Type: ReDIF-Article 1.0
Author-Name: Luis Paúl Castro Medina
Author-Name-First: Luis Paúl
Author-Name-Last: Castro Medina
Author-Name: Karen Alejandra Benavides Flores
Author-Name-First: Karen Alejandra
Author-Name-Last: Benavides Flores
Author-Name: Ramiro Saraguro
Author-Name-First: Ramiro
Author-Name-Last: Saraguro
Author-Name: Edgar Vinicio Lema Cáceres
Author-Name-First: Edgar Vinicio
Author-Name-Last: Lema Cáceres
Title: A model to improve the cheese production process through Lean Manufacturing tools. Case study: Lácteos Montúfar
Abstract: More and more companies worldwide have implemented the Lean Manufacturing Methodology to improve Quality by focusing on standardized processes and continuous improvement through the elimination of waste. The Dairy Industry has been one of the most important activities in Ecuador and requires adapting this Methodology in its processes. The objective of this study is to improve the cheese production process, focusing on waste elimination and continuous improvement. For this, the VSM (Value Stream Map) has been carried out to know the flow of materials of the company, as well as the times that add and do not add value, the actual measurement of the times of how long the process takes has been carried out and with this Information was obtained from the indicators takt time, cycle time, real time; The evaluation of the OEE percentage and finally the balance diagram were also made. After having the baseline, the application of tools such as 5S, Kaizen, TPM and the redistribution of facilities was proposed. The results obtained indicated that there are bottlenecks in the Draining and Grinding and Molding subprocesses and the OEE percentage would be oscillating at 76%, however, when the improvements were proposed, it would rise to 82% and there would no longer be bottlenecks. , thus evidencing that the Implementation of Lean Manufacturing Tools is of great importance since it improves the organization even in the work environment.
Journal: Data and Metadata
Pages: 198
Volume: 4
Year: 2025
DOI: 10.56294/dm2025198
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:198:id:1056294dm2025198

Template-Type: ReDIF-Article 1.0
Author-Name: Fernando Ramírez
Author-Name-First: Fernando
Author-Name-Last: Ramírez
Author-Name: Silvia Arciniega
Author-Name-First: Silvia
Author-Name-Last: Arciniega
Author-Name: Stefany Flores
Author-Name-First: Stefany
Author-Name-Last: Flores
Author-Name: José Jácome
Author-Name-First: José
Author-Name-Last: Jácome
Author-Name: Mateo Chancosi
Author-Name-First: Mateo
Author-Name-Last: Chancosi
Title: Learning styles and academic performance in engineering students: A pre- and pos-pandemic bibliometric study
Abstract: Introduction: The relationship between learning styles and academic performance has gained significant attention, particularly in engineering education, as it plays an important role in enhancing the quality of the learning process. This study aims to provide a comprehensive bibliometric analysis of research trends in this field, focusing on pre- and post-pandemic periods. Methods: A total of 1397 articles from the Scopus database were analyzed using VOSviewer software to map the scientific production until 2023. The analysis was divided into two periods: 2016-2019 and 2020-2023, identifying clústers of research focused on learning styles, academic performance, and the growing importance of e-learning post-pandemic. Results: Five main clústers were identified between 2016-2019, including learning styles, the development of evaluation instruments, psychological aspects, curricular development, and general learning. In the post-pandemic period, three dominant clústers emerged, focused on learning styles, academic performance, and e-learning. Co-authorship analysis revealed changes in collaboration patterns, with increased global cooperation, particularly in the United States, China, and Spain during the 2020-2023 period. Conclusions: The study highlights the increasing relevance of research on learning styles and the shift toward remote learning triggered by the pandemic. These findings underscore the need for further exploration of adaptive teaching strategies to diverse learning preferences in the evolving educational landscape.
Journal: Data and Metadata
Pages: 197
Volume: 4
Year: 2025
DOI: 10.56294/dm2025197
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:197:id:1056294dm2025197

Template-Type: ReDIF-Article 1.0
Author-Name: Diego Javier Trejo España
Author-Name-First: Diego Javier
Author-Name-Last: Trejo España
Author-Name: Cosme MacArthur Ortega Bustamanate
Author-Name-First: Cosme MacArthur
Author-Name-Last: Ortega Bustamanate
Title: Characterization of Technological Clusters in the northern region of Ecuador
Abstract: This study examined the existence of technology clusters in northern Ecuador, focusing on the provinces of Imbabura, Esmeraldas, and Carchi. Through qualitative and quantitative data analysis, it was determined that complete technology clusters have not yet been formed, but there are initiatives that are characterized by their development between 2015 and 2023. The results indicated a significant growth in the number of technology companies, an increase in R&D investment, and a positive correlation between cluster density and regional innovation indicators. It was concluded that technology companies have played an important role in fostering innovation and competitiveness in the northern region of Ecuador.
Journal: Data and Metadata
Pages: 196
Volume: 4
Year: 2025
DOI: 10.56294/dm2025196
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:196:id:1056294dm2025196

Template-Type: ReDIF-Article 1.0
Author-Name: Luz Tobar Subía
Author-Name-First: Luz
Author-Name-Last: Tobar Subía
Author-Name: Cristian Tasiguano Pozo
Author-Name-First: Cristian
Author-Name-Last: Tasiguano Pozo
Author-Name: Fernando Valencia
Author-Name-First: Fernando
Author-Name-Last: Valencia
Author-Name: David Villarreal
Author-Name-First: David
Author-Name-Last: Villarreal
Author-Name: Christian Vásquez
Author-Name-First: Christian
Author-Name-Last: Vásquez
Author-Name: Guillermo Mosquera Canchingre
Author-Name-First: Guillermo
Author-Name-Last: Mosquera Canchingre
Title: Implementation of Industry 4.0 in metallurgical factories
Abstract: This paper presents the preliminary results of a study on implementing Industry 4.0 in metallurgical factories. It showed that adopting Industry 4.0 technologies generates a productive environment characterized by real-time sensing, with high adaptability, flexibility, self-learning capacity, and fault tolerance. In the context of the Ecuadorian industry, particularly in micro, small, and medium-sized enterprises (MSMEs), there is limited integration of industrial technologies, both at the software and hardware levels. Additionally, many factories do not perceive the relevance of implementing solutions based on Industry 4.0 in key areas such as production, quality control, and maintenance. The study presents three case studies of metalworking factories that emerged as small locksmith workshops and analyzes the challenges related to incorporating new know-how in these industries. The findings concluded that Industry 4.0 has transformative potential for the value chain, facilitating the development of innovative products and services.
Journal: Data and Metadata
Pages: 195
Volume: 4
Year: 2025
DOI: 10.56294/dm2025195
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:195:id:1056294dm2025195

Template-Type: ReDIF-Article 1.0
Author-Name: Kevin Vinueza
Author-Name-First: Kevin
Author-Name-Last: Vinueza
Author-Name: Lucía Sandoval-Pillajo
Author-Name-First: Lucía
Author-Name-Last: Sandoval-Pillajo
Author-Name: Adriana Giret-Boggino
Author-Name-First: Adriana
Author-Name-Last: Giret-Boggino
Author-Name: Diego Trejo-España
Author-Name-First: Diego
Author-Name-Last: Trejo-España
Author-Name: Marco Pusdá-Chulde
Author-Name-First: Marco
Author-Name-Last: Pusdá-Chulde
Author-Name: Iván García-Santillán
Author-Name-First: Iván
Author-Name-Last: García-Santillán
Title: Automatic weed quantification in potato crops based on a modified convolutional neural network using drone images
Abstract: Identifying and quantifying weeds is a crucial aspect of agriculture for efficiently controlling them. Weeds compete with the crop for nutrients, minerals, physical space, sunlight, and water, causing problems in crops ranging from low production to economic losses and environmental deterioration of the land. Weed quantification is generally a manual process requiring significant time and precision. Convolutional Neural Networks (CNN) are very common in weed quantification. Thus, the purpose of this research is the adaptation of the ResNeXt50 CNN architecture for semantic segmentation tasks, focused on the automatic quantification of weeds (Broadleaf dock, Dandelion, Kikuyo grass, and other unidentified classes) in potato fields using RGB images acquired by the DJI Mavic 2 Pro drone. The analytical model was trained following the Knowledge Discovery in Databases (KDD) methodology using Python and the TensorFlow-Keras frameworks. The results indicate that the modified ResNeXt50 model presented a mean IoU of 0.7350, a performance comparable to the values reported by other authors considering fewer weed classes. The Student´s t-test and Pearson correlation coefficient were applied to contrast the weed coverage from the model predictions and the ground truth, indicating no statistically significant differences between both measurements in most weed classes.
Journal: Data and Metadata
Pages: 194
Volume: 4
Year: 2025
DOI: 10.56294/dm2025194
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:194:id:1056294dm2025194

Template-Type: ReDIF-Article 1.0
Author-Name: Edison Guaichico
Author-Name-First: Edison
Author-Name-Last: Guaichico
Author-Name: Marco Pusdá-Chulde
Author-Name-First: Marco
Author-Name-Last: Pusdá-Chulde
Author-Name: MacArthur Ortega-Bustamante
Author-Name-First: MacArthur
Author-Name-Last: Ortega-Bustamante
Author-Name: Pedro Granda
Author-Name-First: Pedro
Author-Name-Last: Granda
Author-Name: Iván García-Santillán
Author-Name-First: Iván
Author-Name-Last: García-Santillán
Title: Mobile app for real-time academic attendance registration based on MobileFaceNet Convolutional neural network
Abstract: The attendance record monitors the student's participation in university academic activities, reflecting the commitment to their professional training. However, traditional systems require moderate time to perform this activity and can be susceptible to fraud and errors. In today's technological landscape, facial recognition has become an effective solution to problems in various fields. Currently, all university professors own smartphones. Considering this advantage, this article proposes to develop a mobile application for the registration of academic attendance using advanced artificial intelligence technologies such as Multitasking Cascade Convolutional Networks (MTCNN) in facial detection, MobileFaceNet in facial feature extraction (facial vector) and the Euclidean distance function in the calculation of similarity between obtained vectors. MobileFaceNet was evaluated in Python, using a personalized dataset of top-level students of the Software career of the Universidad Técnica del Norte, achieving an accuracy of 98.9% and 99.4% in LWF. The models were then integrated into a mobile app developed with Android Studio. Finally, the time required to register attendance was compared using the university academic platform (SIIU) and the facial recognition mobile application. The benchmarking showed a 24-second reduction of 33% in attendance registration time.
Journal: Data and Metadata
Pages: 193
Volume: 4
Year: 2025
DOI: 10.56294/dm2025193
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:193:id:1056294dm2025193

Template-Type: ReDIF-Article 1.0
Author-Name: Jhonny Barzola
Author-Name-First: Jhonny
Author-Name-Last: Barzola
Author-Name: Hernan Pérez
Author-Name-First: Hernan
Author-Name-Last: Pérez
Author-Name: Ramiro Vasquez
Author-Name-First: Ramiro
Author-Name-Last: Vasquez
Author-Name: Francisco Naranjo
Author-Name-First: Francisco
Author-Name-Last: Naranjo
Author-Name: Jaen Sánchez
Author-Name-First: Jaen
Author-Name-Last: Sánchez
Title: Assessment of the Incidence of the “Parque Solar Salinas” Photovoltaic Plant on the Medium Voltage Grid of the Emelnorte S.A. Company: A Comprehensive Report
Abstract: This article presents a comprehensive analysis of the impact of the 'Parque Solar Salinas' photovoltaic (PV) plant on the medium voltage grid of Emelnorte S.A., a power distribution company located in the northern region of Ecuador. The main objective was to evaluate the power quality delivered by the plant, in compliance with the standards set by the Electricity Regulation and Control Agency of Ecuador (ARCONEL). The study was based on a detailed review of electrical generation, specifically focusing on solar PV energy. The components and topologies of PV plants, such as inverters, solar panels, and transformers, were described in depth. Additionally, the National Electrical Code regulations related to power quality were analyzed. The technical details of the plant is described, and the steps to determine the power quality are presented in the grid. Data were extracted from the plant, including variables such as active power, reactive power, voltage, and current. Short-term (Pst) and long-term (Plt) flicker were also analyzed and interpreted about solar radiation. The results obtained in this study were presented in a technical procedure manual that allowed for the evaluation of power quality for a photovoltaic plant when connected to a medium voltage grid.
Journal: Data and Metadata
Pages: 191
Volume: 4
Year: 2025
DOI: 10.56294/dm2025191
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:191:id:1056294dm2025191

Template-Type: ReDIF-Article 1.0
Author-Name: Gabriela Elizabeth Cárdenas Rosero
Author-Name-First: Gabriela Elizabeth
Author-Name-Last: Cárdenas Rosero
Author-Name: Cathy Pamela Guevara Vega
Author-Name-First: Cathy Pamela
Author-Name-Last: Guevara Vega
Author-Name: Pablo Landeta-López
Author-Name-First: Pablo
Author-Name-Last: Landeta-López
Title: Website Protection: An Evaluation of the Web Application Firewall
Abstract: Introduction: In recent years, a significant increase in attacks targeting web applications has been observed. These attacks compromise application integrity, disrupt services, and have devastating consequences regarding data loss, reputational damage, and financial costs. Objective: The objective was to evaluate the effectiveness of the Web Application Firewall (WAF) using the OWASP methodology to detect and neutralize attacks on the Universidad Técnica del Norte’s web server. Results: The results were to categorize the main types of attacks detected by the WAF, analyze the most frequent attacks blocked by the firewall, and implement an additional layer of security on the web server. Conclusions: It was concluded that the WAF detects suspicious or potentially malicious activity in web traffic but fails to identify all cyber threats comprehensively. In addition, the WAF report, broken down each month with the number of frequent attack events identified as malicious, is a crucial tool for the web administrator.
Journal: Data and Metadata
Pages: 190
Volume: 4
Year: 2025
DOI: 10.56294/dm2025190
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:190:id:1056294dm2025190

Template-Type: ReDIF-Article 1.0
Author-Name: Alexander Guevara-Vega
Author-Name-First: Alexander
Author-Name-Last: Guevara-Vega
Author-Name: Jorge Luis Montesdeoca Erazo
Author-Name-First: Jorge Luis
Author-Name-Last: Montesdeoca Erazo
Author-Name: Cathy Guevara-Vega
Author-Name-First: Cathy
Author-Name-Last: Guevara-Vega
Title: Streaming Standards and Codecs to improve TV service in mobile environments
Abstract: Introduction: Streaming technology has become a means of communication with a high rate of application for audio and video transmission such as television services through cell phones. Public and private companies, organizations or individuals of any kind apply streaming, however, the use of standards is limited so the quality of the transmission is affected. Objective: To apply streaming standards and codecs to support audio and video transmission in a mobile environment. Method: The XP methodology was applied as a framework for the development of a mobile streaming application and the completeness of the functional adequacy feature of ISO 25010 was evaluated. Results: The test plan for the application's operation was optimally executed. The adequate functionality of the application with respect to its completeness was 84.62%, which qualification is very acceptable. Conclusion: The implementation of new codecs facilitated the streaming transmission of the developed application.
Journal: Data and Metadata
Pages: 189
Volume: 4
Year: 2025
DOI: 10.56294/dm2025189
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:189:id:1056294dm2025189

Template-Type: ReDIF-Article 1.0
Author-Name: Pablo Landeta-López
Author-Name-First: Pablo
Author-Name-Last: Landeta-López
Author-Name: Cathy Guevara-Vega
Author-Name-First: Cathy
Author-Name-Last: Guevara-Vega
Title: Computational experiments in Computer Science: A bibliometric study
Abstract: Introduction: Computational Experiments are crucial in various fields, including biological sciences, engineering, social sciences, etc., and are a powerful tool for understanding complex systems, optimizing processes, and driving innovation. Their importance lies in their ability to integrate with experimental methods, facilitate simulation-based learning, and provide cost-effective, scalable, and flexible solutions for analyzing complex systems. The purpose of this study is to make a bibliometric analysis of the research related to Computational Experiments in Computer Science. Methods: This bibliometric analysis was performed using information from 2013 and 2024 from the Scopus and Web of Science databases, with published articles This bibliometric study followed the guidelines proposed in the publication “How to conduct a bibliometric analysis: An overview and guidelines” by the author Gonthu N. To answer the research questions, the number of articles per year, number of articles per country, number of articles per subject area, list of main journals, and citation analysis were analyzed. Results: The results show that Scopus has more publications on the subject, China is the country that publishes more on the subject, Mathematics is the predominant subject area, finally, a co-occurrence analysis was performed where a total of 27 clusters were found in Scopus and 10 clusters in WoS. From this, the 10 most relevant keywords in each of the databases were identified. Conclusions: This review can be a basis in order that researchers to have a starting point for the current state of publications on Computational Experiments for future research.
Journal: Data and Metadata
Pages: 188
Volume: 4
Year: 2025
DOI: 10.56294/dm2025188
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:188:id:1056294dm2025188

Template-Type: ReDIF-Article 1.0
Author-Name: Irving Reascos
Author-Name-First: Irving
Author-Name-Last: Reascos
Author-Name: Fernando Garrido
Author-Name-First: Fernando
Author-Name-Last: Garrido
Author-Name: Carpio Pineda
Author-Name-First: Carpio
Author-Name-Last: Pineda
Author-Name: Fausto Salazar-Fierro
Author-Name-First: Fausto
Author-Name-Last: Salazar-Fierro
Author-Name: Ricardo Pomasqui
Author-Name-First: Ricardo
Author-Name-Last: Pomasqui
Author-Name: Jessica Cachipuendo
Author-Name-First: Jessica
Author-Name-Last: Cachipuendo
Title: Evaluation of the Integrated University Information System at Universidad Técnica del Norte Using the DeLone and McLean Success Model
Abstract: This study aims to evaluate the Integrated University Information System (SIIU) at Universidad Técnica del Norte by applying the DeLone and McLean Information Systems Success Model. The SIIU plays a crucial role in academic management by integrating key modules, such as executive, faculty, and student portfolios. Despite its long-standing implementation, a comprehensive assessment of the system's performance and its impact on academic activities had not been conducted until now. The research specifically focuses on assessing the SIIU's impact from the students' perspective, applying the six dimensions of the DeLone and McLean model: system quality, information quality, service quality, use/intention to use, user satisfaction, and net impacts. Four assessments were carried out over two years (May 2022, February 2023, July 2023, and July 2024), using a 28-item survey to measure these dimensions. The findings confirm the validity of the DeLone and McLean model in evaluating information systems and reveal a positive trend in students' perceptions of the "SIIU – Student Portfolio" over the past years. The results also provide a detailed breakdown of the system's strengths, weaknesses, and areas for improvement, as experienced by students. These insights offer valuable guidance for the SIIU management team, facilitating targeted improvements to optimize the system’s contribution to the university’s academic environment.
Journal: Data and Metadata
Pages: 187
Volume: 4
Year: 2025
DOI: 10.56294/dm2025187
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:187:id:1056294dm2025187

Template-Type: ReDIF-Article 1.0
Author-Name: Vladimir Bonilla Venegas
Author-Name-First: Vladimir
Author-Name-Last: Bonilla Venegas
Author-Name: Guillermo Mosquera Canchingre
Author-Name-First: Guillermo
Author-Name-Last: Mosquera Canchingre
Author-Name: Miguel Sánchez Muyulema
Author-Name-First: Miguel
Author-Name-Last: Sánchez Muyulema
Author-Name: Nelson Gutiérrez Suquillo
Author-Name-First: Nelson
Author-Name-Last: Gutiérrez Suquillo
Author-Name: Jonnathan Ismael Chamba Cruz
Author-Name-First: Jonnathan Ismael
Author-Name-Last: Chamba Cruz
Title: Application of Model-Based Design for Filtering sEMG Signals Using Wavelet Transform
Abstract: The aim of this study was the integration of model-based design and Wavelet transform techniques for filtering surface electromyography (sEMG) signals. In the first stage the noises and interferences that disturb sEMG signals were analyzed to implement a digital filter in a low-cost embedded system that filters these signals. It was shown that the noises and interferences are caused by various sources. Sources of interference and noise can be divided into internal and external. Internal noise is caused by the electrodes, EMG signals of other muscles, and noise associated with the functioning of other organs such as the heart or stomach. The external noises are due to the electrical environment, the most prominent of which is the direct interference of the power hum, produced by the incorrect grounding of other devices and electromotors. For the analysis of the digital filter, sEMG signals from the biceps muscle were used when the elbow joint was at rest and during flexion and extension movements. Signals from 10 participants who did not have any atrophies or pathologies in the muscle were considered for this stage. Denoising of sEMG signals was performed using different wavelets; the smallest error was observed when using the biorthogonal wavelet 3/5 of level 6 with the soft thresholding method. The wavelet filter was implemented using the V-model, and the Processor in The Loop (PIL) tests helped to determine the characteristics of the embedded system where the digital filter was implemented. The digital filter code was implemented on an ESP32 board due to its processing speed of 328 ms.
Journal: Data and Metadata
Pages: 186
Volume: 4
Year: 2025
DOI: 10.56294/dm2025186
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:186:id:1056294dm2025186

Template-Type: ReDIF-Article 1.0
Author-Name: David Leonardo Rodríguez Portes
Author-Name-First: David Leonardo
Author-Name-Last: Rodríguez Portes
Author-Name: Mario Bernabé Ron Egas
Author-Name-First: Mario Bernabé
Author-Name-Last: Ron Egas
Author-Name: Daisy Elizabeth Imbaquingo Esparza
Author-Name-First: Daisy Elizabeth
Author-Name-Last: Imbaquingo Esparza
Title: Evaluation of the information technology security of the GAD municipal de Esmeraldas based on internal control standards.
Abstract: This research focuses on an audit of information technology security, compliance with current legal regulations, Internal Control Standard (ICS) 410, and the need to constantly evaluate the control environment of a municipality. The type of research was mixed: bibliographic-descriptive, bibliographic for the elaboration of the frame of reference with the collection of existing information in similar research, articles, and regulations; descriptive to collect, analyze and present the information obtained, both through the techniques used (survey, interview, and observation) in the field work and with the application of analytical, deductive and inductive methods, which provided a more complete view of the problem. During the presentation and discussion of the results, an analytical and refined exposition of the main findings was made, evidencing the level of IT risk and the low level of compliance with internal control standards, both those promulgated by the Comptroller General of the State and those established by ISO27001:2022. In the final report, due to the low incidence of the mechanisms implemented on the security of IT assets and existing technological infrastructure, in addition to the conclusions, recommendations and corrective actions that the institution should incorporate to formalize and strengthen its information security management system, through an improvement plan that involves the implementation of institutional security policies, were also included.
Journal: Data and Metadata
Pages: 185
Volume: 4
Year: 2025
DOI: 10.56294/dm2025185
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:185:id:1056294dm2025185

Template-Type: ReDIF-Article 1.0
Author-Name: Mythrayee D
Author-Name-First: Mythrayee
Author-Name-Last: D
Author-Name: Dinesh Kumar Anguraj
Author-Name-First: Dinesh
Author-Name-Last: Kumar Anguraj
Author-Name: M
Author-Name-First: M
Author-Name-Last: M
Author-Name: V. K. Gnanavel
Author-Name-First: V. K.
Author-Name-Last: Gnanavel
Title: An Efficient Model for Optimizing Hyperparameters in AlexNet for Precise Malignancy Detection in Lung and Colon Histopathology Images with CSIP-EHE
Abstract: Cancer, a lethal disease stemming from genetic anomalies and biochemical irregularities, presents a major global health challenge, with lung and colon cancers being significant contributors to morbidity and mortality. Timely and precise cancer detection is crucial for optimal treatment decisions, and machine learning and deep learning techniques offer a promising solution for expediting this process. In this research, a pre-trained neural network, specifically AlexNet, was fine-tuned with modifications to four layers to adapt it to a dataset comprising histopathological images of lung and colon tissues. Additionally, a Bayesian optimization approach was employed for hyperparameter tuning in Convolutional Neural Networks (CNNs) to enhance recognition accuracy while maintaining computational efficiency. The research utilized a comprehensive dataset divided into five classes, and in cases of suboptimal results, a Counteracting Suboptimal Image Processing (CSIP) strategy was applied, focusing on improving images of underperforming classes to reduce processing time and effort.
Journal: Data and Metadata
Pages: 184
Volume: 4
Year: 2025
DOI: 10.56294/dm2025184
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:184:id:1056294dm2025184

Template-Type: ReDIF-Article 1.0
Author-Name: Yanir Bayona Arévalo
Author-Name-First: Yanir
Author-Name-Last: Bayona Arévalo
Author-Name: Matilde Bolaño García
Author-Name-First: Matilde
Author-Name-Last: Bolaño García
Title: Scientific production on dialogical pedagogy: a bibliometric analysis
Abstract: Paulo Freire’s dialogical pedagogy provides teachers with a framework for their professional practice, offering educators strategies for teaching and learning. The main objective of this research is to determine the contributions of Paulo Freire’s dialogical pedagogy to teaching praxis from bibliometric analysis, in terms of increasing impact and incidence in educational processes, knowing its structure, production, and utilization of information for pedagogical practices. A descriptive bibliometric study in Scopus database was conducted, applying a technique of exploratory and descriptive bibliographic document collection to analyze research related to the research topics. A total of 781 documents were retrieved from the Scopus database on the topic under study, of which 32,5 % were open access, involving 1317 authors, with an average of 8,1 citations per document (1,42 Field-Weighted Citation Impact). Original articles represented three-quarters of the total documents, indicating research with new contributions to knowledge, while 12,4 % were book chapters and the remaining 11,8 % were Reviews, Books, Conference Papers, Editorials, and Errata. The top 10 countries with the highest number of published documents in the research area are the United States, United Kingdom and Australia. The analysis carried out revealed that there is significant progress in the area of research related to dialogic pedagogy and its scientific evolution
Journal: Data and Metadata
Pages: 7
Volume: 2
Year: 2023
DOI: 10.56294/dm20237
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:7:id:1056294dm20237

Template-Type: ReDIF-Article 1.0
Author-Name: Sonia Castellanos
Author-Name-First: Sonia
Author-Name-Last: Castellanos
Author-Name: Claudia Figueroa
Author-Name-First: Claudia
Author-Name-Last: Figueroa
Title: Cognitive accessibility in health care institutions. Pilot study and instrument proposal
Abstract: Introduction: cognitive accessibility is part of the general accessibility framework. Cognitive accessibility means that services are simple, consistent, clear, multimodal, error tolerant, and focused, with all users in mind. Objectives: to validate a questionnaire on cognitive accessibility to be applied to health professionals. Methods: the study is of a quantitative approach, with a non-experimental and cross-sectional design, developed between March and June 2022. The sample consisted of 130 health professionals from Argentina, selected through purposive sampling. Results: the validation process was carried out in three stages. Internal consistency analysis (reliability) was performed using Cronbach's Alpha. The descriptive results with the 17 items showed a variance of 4,445 for each item, a total variance of 13,049, with a total Cronbach's Alpha of 0,701, indicating that the instrument presents internal consistency. Conclusions: it was possible to verify that the scores of both Cronbach's Alpha and the factorial analysis allow us to affirm that the instrument has the necessary metric aspects to be used in future research, considering that it had a prior assessment by expert criteria. It can be assumed that this article becomes the starting point for future studies, in which it is intended to continue the line of research, which allows the analysis of cognitive accessibility in the context of health professionals
Journal: Data and Metadata
Pages: 22
Volume: 2
Year: 2023
DOI: 10.56294/dm202322
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:22:id:1056294dm202322

Template-Type: ReDIF-Article 1.0
Author-Name: William Castillo González
Author-Name-First: William
Author-Name-Last: Castillo González
Title: Evaluation of the scientific production of the Instituto de Investigaciones en Microbiología y Parasitología Médica (UBA-CONICET)
Abstract: Introduction: SciVal is a bibliometric tool used to assess the scientific output of institutions, such as the Institute of Research on Medical Microbiology and Parasitology (IMPaM), doubly dependent on Buenos Aires University (UBA) and the National Council of Scientific and Technical Research (CONICET). IMPaM studies medical microbiology and parasitology and has many research projects. Assessing it through SciVal will make it possible to identify areas of strengths and weaknesses to improve the scientific output at that institution. Goal: assess the scientific output of IMPaM with SciVal, describing the methodology, results, conclusions, and recommendations to improve research at that institution. Methods: the scientific production was analyzed, examining the research areas, the influence of that institution in the field of study, financing, and available resources. A database of researchers was used to carry out the analysis, and the scientific output was compared with similar institutions. Results: the study found that more than one-half of the articles of that Institution are open access, and fostering their publication in open access journals without any embargo period is suggested. Besides, it was stressed that the most representative thematic areas are related to the social object of that Institution and that international collaboration is essential to scientific research. Finally, a decrease in citations by publication was noticed, keeping the impact of weighted citations by field, which suggests that the articles keep their relevance in their area. Conclusions: IMPaM researches and publishes in Medicine, Immunology and Microbiology, Biochemistry, Genetics and Molecular Biology. Even though the number of open-access articles is large, it is below average in the Impact of Weighted Citations by Field, and it is necessary to strengthen the international collaboration links and widen thematic diversity to keep their relevance in scientific research
Journal: Data and Metadata
Pages: 23
Volume: 2
Year: 2023
DOI: 10.56294/dm202323
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:23:id:1056294dm202323

Template-Type: ReDIF-Article 1.0
Author-Name: Denis Gonzalez Argote
Author-Name-First: Denis
Author-Name-Last: Gonzalez Argote
Title: Thematic Specialization of Institutions with Academic Programs in the Field of Data Science
Abstract: Introduction: data science careers are on the rise due to the growing demand for technical skills in this area. Data science careers focus on collecting, organizing, and analyzing data to identify patterns and trends, which allows organizations to make informed decisions and develop effective solutions. Aim: to analyze the thematic specialization of institutions with academic programs in the area of data science. Methods: the Scopus database was used to conduct a bibliometric analysis aimed at examining the thematic specialization of institutions with academic programs in the field of data science. SciVal, a bibliometric analysis tool, was employed to extract the relevant data. The study period ranged from 2012 to 2021. Results: nine higher education institutions were found to offer undergraduate or graduate degrees in the field of data science. There was no correlation found between RSI and Field-Weighted Citation Impact (r=0,05355; P=0,8912; 95%CI: -0,6331 to 0,6930). Therefore, it cannot be claimed that specialization in the subject area studied influences the greater impact of research. On the other hand, recent accreditation did not influence greater specialization (r=0,1675; P=0,6667; 95%CI: -0,5588 to 0,7484). Additionally, no differences were found regarding academic level. Conclusions: the analysis of the thematic specialization of institutions with academic programs in the field of data science shows low scientific production in this field. Moreover, more than half of the analyzed higher education institutions have thematic specialization below the global average. This suggests that there is still a long way to go for these institutions to achieve adequate specialization and compete internationally in the field of data science
Journal: Data and Metadata
Pages: 24
Volume: 2
Year: 2023
DOI: 10.56294/dm202324
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:24:id:1056294dm202324

Template-Type: ReDIF-Article 1.0
Author-Name: Carlos Alberto Gómez Cano
Author-Name-First: Carlos Alberto
Author-Name-Last: Gómez Cano
Author-Name: Verenice Sánchez Castillo
Author-Name-First: Verenice
Author-Name-Last: Sánchez Castillo
Author-Name: Tulio Andrés Clavijo Gallego
Author-Name-First: Tulio Andrés
Author-Name-Last: Clavijo Gallego
Title: Mapping the Landscape of Netnographic Research: A Bibliometric Study of Social Interactions and Digital Culture
Abstract: Introduction: netnography is a research method that has emerged in response to the growing popularity of online communication and social networks. Aim: to analyze communication patterns about netnography in the Scopus database. Methods: a bibliometric study was conducted in the Scopus database on netnography. The analysis was conducted globally, by country, and by institution. Results: a total of 11173 documents and 2213 authors were recovered. 35,1 % of the documents were open access. The global field-weighted citation impact was 1,27. the most productive ones in the following order: United Kingdom (275 documents), United States (223 documents), Australia (165 documents), Brazil (100 documents), and France (83 documents). Conclusions: the results show that netnography is an emerging area of research, with a wide geographic and thematic diversity, that has experienced steady growth in recent years and is being explored in a variety of contexts, from market research to the analysis of social dynamics online
Journal: Data and Metadata
Pages: 25
Volume: 2
Year: 2023
DOI: 10.56294/dm202325
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:25:id:1056294dm202325

Template-Type: ReDIF-Article 1.0
Author-Name: Daisy Bencomo García
Author-Name-First: Daisy
Author-Name-Last: Bencomo García
Author-Name: Lissette Cárdenas de Baños
Author-Name-First: Lissette
Author-Name-Last: Cárdenas de Baños
Author-Name: Niurka Hernández Labrada
Author-Name-First: Niurka
Author-Name-Last: Hernández Labrada
Author-Name: Jhossmar Cristians Auza Santivañez
Author-Name-First: Jhossmar Cristians
Author-Name-Last: Auza Santivañez
Author-Name: Idrian García García
Author-Name-First: Idrian
Author-Name-Last: García García
Author-Name: Sergio González García
Author-Name-First: Sergio
Author-Name-Last: González García
Title: Academic results during the epidemic period at the Faculty of Medical Sciences Miguel Enríquez
Abstract: Introduction: the years 2020 and 2021 were characterized by the COVID-19 epidemic in Cuba, which caused the adaptation of academic courses, with the premise of making the training process more flexible, based on the suspension of face-to-face activities and the modification of the teaching curriculum. Objective: to describe the state exam results during the epidemic period. Methods: an observational, descriptive, retrospective study was carried out based on analyzing the promotion reports and the official models 36.19 and 36.20 of the Postgraduate Department, corresponding to 2020-2021. Results: 173 residents took the state examination, 111 from medical specialties and 62 from stomatological specialties, with promotion of 100 %. 49,7 % obtained final grades above 95 points and 78,0 % above 90 points in the state exam. The residents of the Dermatology and Intensive and Emergency Medicine specialties received the best teaching results. Conclusions: the Faculty of Medical Sciences "Miguel Enríquez", during the epidemic period, graduated, with quality, all the residents who took the state exam
Journal: Data and Metadata
Pages: 27
Volume: 2
Year: 2023
DOI: 10.56294/dm202327
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:27:id:1056294dm202327

Template-Type: ReDIF-Article 1.0
Author-Name: Waseem Hassan
Author-Name-First: Waseem
Author-Name-Last: Hassan
Title: Sri Lanka Published 234 Research Papers in Psychiatry from 2012 to 2021: Comparison with 76 Research Fields
Journal: Data and Metadata
Pages: 28
Volume: 2
Year: 2023
DOI: 10.56294/dm202328
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:28:id:1056294dm202328

Template-Type: ReDIF-Article 1.0
Author-Name: William Castillo González
Author-Name-First: William
Author-Name-Last: Castillo González
Title: How much does a citation cost?: A case study based on CONICET's budget
Abstract: Introduction: CONICET has been fundamental in the training of a large number of researchers and the promotion of science in Argentine society. Objective: describe the relative cost per published article and citation received for articles published by authors affiliated with CONICET. Methods: a bibliometric study was carried out in which the scientific production of CONICET was analyzed in the Scopus database and the CONICET budget from 2016 to 2021. Results: a decrease in the CONICET budget was observed, only recovering in the last year but without reaching the historical maximum studied. On the other hand, as previously mentioned, it was commented that the citations decreased despite the increase in the number of articles. Faced with this panorama, the theoretical cost of an article and that of a bibliographical citation can be presented. So, for example, for the year 2021, the cost of publishing an article was 41014,09 USD, and the cost of a citation was 9442,77 USD. Conclusions: we cannot minimize the budgetary expenses of a government institution of thousands of workers to simple final products that are articles when in between are the expenses of salaries, awareness campaigns, building construction and its maintenance or things that have nothing to do with it. With science (or yes) how to pay the water bill of an institute; but if we can get closer to a theoretical cost of the articles and citations produced by Argentine scientists
Journal: Data and Metadata
Pages: 29
Volume: 2
Year: 2023
DOI: 10.56294/dm202329
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:29:id:1056294dm202329

Template-Type: ReDIF-Article 1.0
Author-Name: Jorge Márquez Molina
Author-Name-First: Jorge
Author-Name-Last: Márquez Molina
Author-Name: Jhossmar Cristians Auza Santivañez
Author-Name-First: Jhossmar Cristians
Author-Name-Last: Auza Santivañez
Author-Name: Edwin Cruz Choquetopa
Author-Name-First: Edwin
Author-Name-Last: Cruz Choquetopa
Author-Name: Jose Bernardo Antezana Muñoz
Author-Name-First: Jose Bernardo
Author-Name-Last: Antezana Muñoz
Author-Name: Osman Arteaga Iriarte
Author-Name-First: Osman
Author-Name-Last: Arteaga Iriarte
Author-Name: Helen Fernández Burgoa
Author-Name-First: Helen
Author-Name-Last: Fernández Burgoa
Title: Early prediction of acute kidney injury in neurocritical patients: relevance of renal resistance index and intrarenal venous Doppler as diagnostic tools
Abstract: Introduction: Implementing renal POCUS in critical care is a valuable tool complementing the physical examination of critical patients. As it is noninvasive, accessible, innocuous, and economical, it makes it possible to assess, at the bedside of patients, renal perfusion via ultrasound measurements such as the renal resistance index (RRI) and intrarenal venous Doppler (IRVD), which are considered early predictors of the acute renal lesion. Goals: Determine the relationship between the renal resistance index (RRI) and the degree of acute renal lesion according to KDIGO in neurocritical patients. Correlate the alterations to intrarenal venous Doppler (IRVD) flow with the degree of the acute renal lesion, according to KDIGO. Methods: An observational, analytical, prospective, longitudinal study was carried out in an ICU with an influx of neurocritical patients. Forty-three (43) patients participated. Their renal resistance index (RRI) and intrarenal venous Doppler (IRVD) were measured upon admission, 72 hours later, and 7 days after admission. Which of these tools better predicts acute renal lesions according to KDIGO was assessed. Results: In the study with 43 critical patients, no significant correlation was found between the RRI value and the acute renal lesion, according to KDIGO. On the contrary, a significant relation was found between intrarenal venous Doppler (IRVD) upon admission, 72 hours later, and 7 days after admission with the acute renal lesion according to KDIGO, with a value of r: 43=0.95 (P=0.54); 0.49 (P=0.001); 0.58 (P=0.000). When analyzing via the classification tree, it was determined that the variables better predicting the risk of suffering from an acute renal lesion before its occurrence are the measurement of intrarenal venous Doppler (IRVD) 7 days after admission and the value of the accumulated water balance. Conclusions: There is a positive and significant correlation between intrarenal venous Doppler (IRVD) and the acute renal lesion. Intrarenal venous Doppler (IRVD) and the accumulated water balance better predict the risk of suffering from an acute renal lesion in critical patients. In contrast, the renal resistance index (RRI) was unrelated to the acute renal lesion in the studied population
Journal: Data and Metadata
Pages: 30
Volume: 2
Year: 2023
DOI: 10.56294/dm202330
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:30:id:1056294dm202330

Template-Type: ReDIF-Article 1.0
Author-Name: Eduardo Enrique Chibas Muñoz
Author-Name-First: Eduardo Enrique
Author-Name-Last: Chibas Muñoz
Author-Name: Annier Jesús Fajardo Quesada
Author-Name-First: Annier Jesús
Author-Name-Last: Fajardo Quesada
Author-Name: Karina Vidal Díaz
Author-Name-First: Karina
Author-Name-Last: Vidal Díaz
Author-Name: Nayaxi Reyes Domínguez
Author-Name-First: Nayaxi
Author-Name-Last: Reyes Domínguez
Title: Data-driven decision-making to improve the diagnosis of cancer patients in the province of Guantanamo: a case study of epidemiological behavior during the year 2019
Abstract: Introduction: cancer is a disease caused by neoplastic cells that multiply uncontrollably, invading other tissues autonomously and at a distance. There are many types of cancer that can be prevented by avoiding certain risk factors. Objective: to describe the epidemiological behavior of patients with cancer diagnosis in Guantanamo province in 2019. Methods: an observational, descriptive and cross-sectional study was conducted in patients diagnosed with cancer in the province of Guantánamo, belonging to the country Cuba, during the year 2019. The universe was composed by the 1697 cases reported in that period. The variables age, sex, municipality and main location of the cancer were studied. The primary source of data was the Health Statistical Yearbook of Guantánamo Province. Results: it was observed that the age group older than 60 years had the highest incidence, with 1176 patients, which represents 69,29 %. The male sex was the most representative, with 870 patients, equivalent to 51,26 %. Prostate cancer was the most prevalent cancer in the male population, with 220 patients, representing 25,28 %. Conclusions: cancer is an important health problem for the Guantanamo population, especially in the age group over 60 years old. Male sex has a higher incidence, and prostate, breast and skin cancer are the most frequent in the population studied
Journal: Data and Metadata
Pages: 33
Volume: 2
Year: 2023
DOI: 10.56294/dm202333
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:33:id:1056294dm202333

Template-Type: ReDIF-Article 1.0
Author-Name: Alioska Jessica Martínez García
Author-Name-First: Alioska Jessica
Author-Name-Last: Martínez García
Author-Name: Yeny Roxana Estrada Cahuapaza
Author-Name-First: Yeny Roxana
Author-Name-Last: Estrada Cahuapaza
Author-Name: Grover Marín Mamani
Author-Name-First: Grover
Author-Name-Last: Marín Mamani
Author-Name: Vitaliano Enríquez Mamani
Author-Name-First: Vitaliano
Author-Name-Last: Enríquez Mamani
Author-Name: Kely Lelia Cotacallapa Ochoa
Author-Name-First: Kely Lelia
Author-Name-Last: Cotacallapa Ochoa
Author-Name: Francisco Curro Pérez
Author-Name-First: Francisco
Author-Name-Last: Curro Pérez
Title: Thermal evaluation of a rustic building prototype at 1/5 scale with vegetal envelope during the winter in southern Peru
Abstract: The purpose of the study was to demonstrate the benefits of a model for scientific research in the sense that a construction system with a vegetated enclosure could benefit the internal environment of Juliaca in winter. To do this, we used an experimental procedure to compare the thermal resistance of a fifth-scale adobe high Andean house without vegetation and a house built in the climatic zone with vegetated facades. It simultaneously records the internal surface temperature, the internal air temperature, and the external environmental conditions. The results obtained show that the use of photosystems in buildings is an effective passive technique to reduce energy consumption due to its ability to insulate and protect internal thermal conditions
Journal: Data and Metadata
Pages: 34
Volume: 2
Year: 2023
DOI: 10.56294/dm202334
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:34:id:1056294dm202334

Template-Type: ReDIF-Article 1.0
Author-Name: V. Lakshmi Narasimhan
Author-Name-First: V.
Author-Name-Last: Lakshmi Narasimhan
Author-Name: G. Basupi
Author-Name-First: G.
Author-Name-Last: Basupi
Title: Deep learning based analysis of student aptitude for programming at college freshman level
Abstract: Predicting Freshman student’s aptitude for computing is critical for researchers to understand the underlying aptitude for programming. Dataset out of a questionnaire taken from various Senior students in a high school in the city of Kanchipuram, Tamil Nadu, India was used, where the questions related to their social and cultural back- grounds and their experience with computers. Several hypotheses were also generated. The datasets were analyzed using three machine learning algorithms namely, Back- propagation Neural Network (BPN) and Recurrent Neural Network (RNN) (and its variant, Gated Recurrent Network (GNN)) with K-Nearest Neighbor (KNN) used as the classifier. Various models were obtained to validate the under- pinning set of hypotheses clusters. The results show that the BPN model achieved a high degree of accuracies on various metrics in predicting Freshman student’s aptitude for computer programming
Journal: Data and Metadata
Pages: 38
Volume: 2
Year: 2023
DOI: 10.56294/dm202338
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:38:id:1056294dm202338

Template-Type: ReDIF-Article 1.0
Author-Name: Khalid Lali
Author-Name-First: Khalid
Author-Name-Last: Lali
Author-Name: Abdellatif Chakor
Author-Name-First: Abdellatif
Author-Name-Last: Chakor
Title: Improving the Security and Reliability of a Quality Marketing Information System: A Priority Prerequisite for Good Strategic Management of a Successful Entrepreneurial Project
Abstract: Thanks to the security policy of the marketing information system which includes physical, administrative and logical safeguards, organizations are today able to design marketing and sales strategies that enable them to effectively respond and satisfy their customers' needs and expectations in a timely and cost effective manner and this by protecting the relevant information and data circulating in the said information system against any attempt at attack or malicious intrusion which seeks only to harm its reliability, confidentiality, integrity, availability and credibility. Indeed with this security policy we arrive easily to identify each discrepancy observed in the behavior of persons accessing this marketing information system as well as each mismatch between the service provided to users and the service expected by them, a context that pushes this security system to generate automatically some countermeasures such as encryption, decryption, hashing, electronic signature, intrusion detection and prevention and certification
Journal: Data and Metadata
Pages: 40
Volume: 2
Year: 2023
DOI: 10.56294/dm202340
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:40:id:1056294dm202340

Template-Type: ReDIF-Article 1.0
Author-Name: Khalid Lali
Author-Name-First: Khalid
Author-Name-Last: Lali
Author-Name: Abdellatif Chakor
Author-Name-First: Abdellatif
Author-Name-Last: Chakor
Author-Name: Hayat El Boukhari
Author-Name-First: Hayat
Author-Name-Last: El Boukhari
Title: The Digitalization of Production Processes : A Priority Condition for the Success of an Efficient Marketing Information System. Case of the Swimwear Anywhere Company
Abstract: The digitalization of production operations is considered today as a decisive condition capable of stimulating the spontaneous and regular use of an effective and operational marketing information system. Certainly, thanks to digitalisation, companies can: increase their profitability; simplify working methods, automate production processes and interactions between the various employees responsible for monitoring the smooth running of production activities as well as between the latter and the heads of the marketing department who prepare the marketing strategies to be executed. Indeed, if companies want to increase their sales volumes and be able to take advantage of the new opportunities that digital will offer them, they are encouraged and better than ever to quickly computerize their production processes. To do this, they must rely on well-documented marketing strategies that emphasize customer orientation and ensure that the latter receives a personalized offer while benefiting from the operational functionalities provided by marketing information systems
Journal: Data and Metadata
Pages: 41
Volume: 2
Year: 2023
DOI: 10.56294/dm202341
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:41:id:1056294dm202341

Template-Type: ReDIF-Article 1.0
Author-Name: Moulay Driss Hanafi
Author-Name-First: Moulay
Author-Name-Last: Driss Hanafi
Author-Name: Khalid Lali
Author-Name-First: Khalid
Author-Name-Last: Lali
Author-Name: Houda Kably
Author-Name-First: Houda
Author-Name-Last: Kably
Author-Name: Abdellatif Chakor
Author-Name-First: Abdellatif
Author-Name-Last: Chakor
Title: The English Proficiency and the Inevitable Resort to Digitalization: A Direction to Follow and Adopt to Guarantee the Success of Women Entrepreneurs in the World of Business and Enterprises
Abstract: In this paper, an attempt has been made to highlight the importance of the English language and ICT in the entrepreneurial endeavors of Moroccan women. The development of ICT and the rise of English as the major lingua franca of worldwide business have been followed by a considerable increase in academic interest in a variety of topics connected to language choice and usage in the business and professional sectors. In business, it's important for Moroccan women entrepreneurs to be good at ICT and speak English well. The integration of digital technologies into female entrepreneurship has developed a new approach called "digital entrepreneurship." This method offers numerous benefits for Moroccan female entrepreneurs, namely economic development, women's empowerment, and access to worldwide markets. The power of English and new digital paradigms has changed how Moroccan businesswomen work and communicate with each other. This has changed business practices and given Moroccan businesswomen new opportunities
Journal: Data and Metadata
Pages: 42
Volume: 2
Year: 2023
DOI: 10.56294/dm202342
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:42:id:1056294dm202342

Template-Type: ReDIF-Article 1.0
Author-Name: Lázaro Ernesto Horta Martínez
Author-Name-First: Lázaro Ernesto
Author-Name-Last: Horta Martínez
Author-Name: Melissa Sorá Rodríguez
Author-Name-First: Melissa
Author-Name-Last: Sorá Rodríguez
Title: Some metrics on scientific production about fractures
Abstract: Introduction: the rapid and precipitous increase in the number of scientific journals has outlined the hardship of perpetrating periodic assessments of their scientific production or that of a specific area of ​​knowledge. Scientific production is directly related to scientific activity. Objective: describe the production on fractures in the Cuban Journal of Orthopedics and Traumatology. Methods: a metric, descriptive and cross-sectional analysis of the articles published on fractures in the Cuban Journal of Orthopedics and Traumatology (RCOT) was carried out from from 2013 to 2022. Results: a total of 37 articles were collected; 19 (51,4 %) original articles, 12 (32,4 %) case presentations, 4 (10,8 %) bibliographic reviews, 1 (2,7 %) special article and 1 (2,7 %) letter to the editor. The year with the highest scientific production was 2023 (n=19; 51,4 %), in the period 2015-2019 no scientific contributions were reported. The total originality rate is 51,4 %, with 2013 being the year with the highest rate with 100 %, although together with 2014 they represent the most unproductive years within the productive ones, 2014 has an originality rate of 0 %. Conclusions: the production on fractures in the RCOT had a notable representation in the period 2020-2021 and showed a tendency to progress in this regard; but work still needs to be done in order to endorse optimal visibility of the contents on this topic, as well as a greater citation of these, to which the publication in English and the persuasion of collaborators of other nationalities could greatly contribute
Journal: Data and Metadata
Pages: 43
Volume: 2
Year: 2023
DOI: 10.56294/dm202343
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:43:id:1056294dm202343

Template-Type: ReDIF-Article 1.0
Author-Name: Néstor Eloy Gonzales Sucasaire
Author-Name-First: Néstor Eloy
Author-Name-Last: Gonzales Sucasaire
Title: Data analysis of vehicular noise pollution and its perception in the cities of Juliaca and Puno, Puno region - 2021
Abstract: Noise pollution generated by vehicular traffic can have a significant impact on the quality of life of residents, affecting their physical and emotional well-being. To determine the relationship between vehicular noise pollution and the perception of the population in the cities of Juliaca and Puno, a descriptive and correlational study was carried out. Data were collected using registration forms and questionnaires, using 10 representative sampling points on the roads with the highest traffic and surveying 584 randomly selected people. The results revealed sound pressure levels that exceed the limits established by regulations in both cities. Minimum values of 67,84 dB in Puno and 68,03 dB in Juliaca, and maximum values of 83,86 dB and 78,83 dB, respectively, were found. In addition, a positive but low correlation (r = 0,142) was identified between noise pollution and population perception. These findings highlight the exposure of the population to vehicular noise pollution levels that exceed the permissible limits, which can have negative consequences for health and well-being. It is necessary to implement effective measures to reduce noise pollution and improve the quality of life of residents in both cities. These results provide valuable information for the development of appropriate mitigation strategies
Journal: Data and Metadata
Pages: 44
Volume: 2
Year: 2023
DOI: 10.56294/dm202344
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:44:id:1056294dm202344

Template-Type: ReDIF-Article 1.0
Author-Name: María del Carmen Marín Prada Marín Prada
Author-Name-First: María del Carmen Marín Prada
Author-Name-Last: Marín Prada
Author-Name: Nayra Condori Villca
Author-Name-First: Nayra
Author-Name-Last: Condori Villca
Author-Name: Francisco Gutiérrez Garcia
Author-Name-First: Francisco
Author-Name-Last: Gutiérrez Garcia
Author-Name: Carlos Antonio Rodriguez García
Author-Name-First: Carlos Antonio
Author-Name-Last: Rodriguez García
Author-Name: Miguel Ángel Martínez Morales
Author-Name-First: Miguel Ángel
Author-Name-Last: Martínez Morales
Author-Name: Jhossmar Cristians Auza Santiváñez
Author-Name-First: Jhossmar Cristians
Author-Name-Last: Auza Santiváñez
Author-Name: Fidel Aguilar Medrano
Author-Name-First: Fidel
Author-Name-Last: Aguilar Medrano
Title: Chronic kidney disease and its risk stratification in Cuba
Abstract: Introduction: Epidemiological risk stratification in health is a tool effective in identifying where the main problems lie in a program health, to distribute resources where they are most needed. kidney disease chronic is a metabolic endocrine syndrome, brings disability to people who suffer, has become one of the main causes of death in the world, in our country has seen an increase in the last ten years. Objective: Stratify mortality with CKD in Cuba and characterize some sociodemographic variables from 2011-2020. Method: The universe consisted of 35031 deceased with CKD in Cuba, percentages, crude, specific and specific rates were calculated. standardized by age, sex, causes of death, by province of residence and color of the skin. The stratification by provinces was classified as very high risk, high risk, medium and low risk. Results: There was a total of 35031 deaths, the risk of die older in men, older adults with black skin color. The main cause of death hypertensive kidney disease. The standardized rates showed slow and sustained increase in all provinces. Very high risk provinces Artemisa (22,15), Cienfuegos (19,36) and the Isla de la Juventud Special Municipality (18,72). Conclusions: Risk stratification presented differences in the country, the main cause of death was hypertensive kidney disease, older adults have higher risk of dying, although it is important to pay attention to the group that includes working age
Journal: Data and Metadata
Pages: 49
Volume: 2
Year: 2023
DOI: 10.56294/dm202349
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:49:id:1056294dm202349

Template-Type: ReDIF-Article 1.0
Author-Name: Silfredo Damian Vergara Danies
Author-Name-First: Silfredo Damian
Author-Name-Last: Vergara Danies
Author-Name: Daniela Carolina Ariza Celis
Author-Name-First: Daniela Carolina
Author-Name-Last: Ariza Celis
Author-Name: Liseth Maria Perpiñan Duitama
Author-Name-First: Liseth Maria
Author-Name-Last: Perpiñan Duitama
Title: Strategic guidelines for intelligent traffic control
Abstract: The objective of this study was to establish strategic guidelines to solve the existing vehicular mobility problems in the District of Riohacha, proposing the adoption of advanced technologies to optimize traffic management in the city. The methodology of the study consisted in the application of surveys and the review of relevant bibliography. The results allowed the identification of various intelligent traffic control tools used in different regions of the world, determining their applicability and benefits for the context of Riohacha, where there was a notable lack of traffic signals. It was concluded that the implementation of the technological tools proposed in this study could offer effective solutions to the mobility challenges faced by the District of Riohacha
Journal: Data and Metadata
Pages: 51
Volume: 2
Year: 2023
DOI: 10.56294/dm202351
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:51:id:1056294dm202351

Template-Type: ReDIF-Article 1.0
Author-Name: María del Carmen Becerra
Author-Name-First: María del Carmen
Author-Name-Last: Becerra
Author-Name: Alicia Aballay
Author-Name-First: Alicia
Author-Name-Last: Aballay
Author-Name: María Romagnano
Author-Name-First: María
Author-Name-Last: Romagnano
Title: Reflections on Healthcare Document Management in the Age of 4.0 Technologies
Abstract: The purpose of this study is to examine certain aspects associated with the 4.0 Revolution in the field of health data, with particular emphasis on decision-making and organizational models implemented in the information systems of the health sector. This analysis is conducted in the context following the implementation of the Federal Unified Program for the Computerization of the Digital Medical Record, which establishes a unified registry of patient data. The practices and tools used in document management of health data, biometric and genetic data are identified and examined, which, due to their sensitive nature, require rigorous protection. Various aspects related to the responsible provision of health services are discussed. For efficient and effective management of health systems, both public and private, the implications of using technologies in health from the perspective of safety and privacy are considered
Journal: Data and Metadata
Pages: 52
Volume: 2
Year: 2023
DOI: 10.56294/dm202352
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:52:id:1056294dm202352

Template-Type: ReDIF-Article 1.0
Author-Name: Amal Fadhil Mohammed
Author-Name-First: Amal Fadhil
Author-Name-Last: Mohammed
Author-Name: Hayder A Nahi
Author-Name-First: Hayder
Author-Name-Last: A Nahi
Author-Name: Akmam Majed Mosa
Author-Name-First: Akmam
Author-Name-Last: Majed Mosa
Author-Name: Inas Kadhim
Author-Name-First: Inas
Author-Name-Last: Kadhim
Title: Secure E-healthcare System Based on Biometric Approach
Abstract: A secure E-health care system is satisfying by maintaining data authenticity and privacy. Authentic users only access and edit medical records, any alteration in the medical records may result in a misdiagnosis and, as a result, harm the patient's life. Biometric method and watermarking modes are utilized to satisfy goal, such as Discrete Wavelet Transform (DWT), Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT) and Least Significant Bit (LSB). In this work focused on a biometric watermarking system where the iris code of the sender programmed as a sender authentication key. The confidentiality of the patient information is safeguarded via encrypting it with an XOR algorithm and embedding the key in the DCT image. The algorithm has demonstrated which is suggested system has met earlier constraints. We used samples of original watermarked images with PSNR value, embedding time and extraction time, the lowest embedding time was 0,0709 and the PSNR value was 49,2369
Journal: Data and Metadata
Pages: 56
Volume: 2
Year: 2023
DOI: 10.56294/dm202356
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:56:id:1056294dm202356

Template-Type: ReDIF-Article 1.0
Author-Name: Lissette Cárdenas de Baños
Author-Name-First: Lissette
Author-Name-Last: Cárdenas de Baños
Author-Name: Rossana Planas Labrada
Author-Name-First: Rossana
Author-Name-Last: Planas Labrada
Author-Name: Niurka de la C Almaguer Fernández
Author-Name-First: Niurka de la C
Author-Name-Last: Almaguer Fernández
Author-Name: María Teresa Dieguez Calderón
Author-Name-First: María Teresa
Author-Name-Last: Dieguez Calderón
Author-Name: Sergio González García
Author-Name-First: Sergio
Author-Name-Last: González García
Title: Postgraduate training at the Universidad de Ciencias Médicas de La Habana
Abstract: Introduction: it is an essential requirement during the teaching process in the postgraduate the continuous scientific updating of the faculty members, both from the thematic and pedagogical point of view. Teachers must have skills to transmit their knowledge to students. The faculty is essential to achieve quality in postgraduate teaching. Objective: to characterize the postgraduate training of the University of Medical Sciences of Havana. Methods: observational, descriptive, retrospective study, where the specialties, settings and faculty of each of the medical schools and postgraduate training centers of the university were described during the year 2021. The primary source for data collection was the databases of the Postgraduate Department of the UCMH. Results: the study included 11 faculties of Medical Sciences and 4 Postgraduate Centers, with 265 accredited scenarios and training in 69 specialties. In the year 2021, of 6 108 teachers, only 6,4 % are consultants, 7,1 % are associate profesor and 31,9 % assistants. 18,9 % of the teachers have a research category and 8,5 % are doctors of science (PhD). The tutor/resident ratio was 0,69. The distribution of teachers with higher categories, PhDs in science and teachers with research category shows great variability, depending on the postgraduate training center. Conclusions: during the year 2021, postgraduate training at the UCMH was characterized by its heterogeneity, with 69 specialties, several training centers; where the quality of the faculty depends on the training scenario
Journal: Data and Metadata
Pages: 58
Volume: 2
Year: 2023
DOI: 10.56294/dm202358
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:58:id:1056294dm202358

Template-Type: ReDIF-Article 1.0
Author-Name: Emmanuel Vivien Oluchi
Author-Name-First: Emmanuel
Author-Name-Last: Vivien Oluchi
Author-Name: Maryjane Efemini
Author-Name-First: Maryjane
Author-Name-Last: Efemini
Author-Name: Dauda Oseni Yahaya
Author-Name-First: Dauda
Author-Name-Last: Oseni Yahaya
Author-Name: Bolaji David Oladokun
Author-Name-First: Bolaji David
Author-Name-Last: Oladokun
Title: Application of blockchain technology to 21st century library services: Benefits and best practices
Abstract: The fourth industrial revolution has paved the way for emerging technologies, and among them, blockchain stands out for its unprecedented ability to create and trade value in library organizations. This research paper explores the potential application of blockchain technologies in 21st-century library services. By conducting a systematic analysis of the literature, this study examines how libraries can harness blockchain to support innovative services and meet global demands. The study suggests that the recent advancements in blockchain have led to a fourth generation of the technology, which possesses disruptive capabilities across diverse fields, including library and information science. The paper proposes that blockchain can enhance library services such as collection development, circulation services, research, data management, and storage. It is important to note that this paper represents the original ideas of the authors and does not rely on copyrighted materials. Furthermore, it highlights that blockchain remains a vast and underexplored area of research, presenting both challenges and opportunities for library professionals seeking to provide diverse library services
Journal: Data and Metadata
Pages: 59
Volume: 2
Year: 2023
DOI: 10.56294/dm202359
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:59:id:1056294dm202359

Template-Type: ReDIF-Article 1.0
Author-Name: Jaisson Cenci
Author-Name-First: Jaisson
Author-Name-Last: Cenci
Author-Name: Daiane Silva Santos Da Cruz
Author-Name-First: Daiane
Author-Name-Last: Silva Santos Da Cruz
Author-Name: Pedro Dentice Da Silva Leite
Author-Name-First: Pedro Dentice
Author-Name-Last: Da Silva Leite
Author-Name: Maximiliano Sérgio Cenci
Author-Name-First: Maximiliano Sérgio
Author-Name-Last: Cenci
Author-Name: Anelise Fernandes Montagner
Author-Name-First: Anelise
Author-Name-Last: Fernandes Montagner
Title: Adherence to preprints’ publication in Dentistry by Brazilian researchers
Abstract: Aim: the objective of this study was to evaluate the adherence to the preprint publication format by a sample of Brazilian researchers. Methods: searches were carried out, in September 2021, on the MedArxiv, OSF, and SciELO preprints platforms, looking for publications in preprint format by all Brazilian researchers of graduate programs in dentistry (n=211) who were productivity fellows in 2021 (PQ). Searches were performed by typing the authors’ full names and the possible variations, as indicated by each author's curriculum, openly available on the Lattes website platform. The Friedman test, with the Durbin-Conover post-hoc (α=0,05) was applied in order to compare the three platforms. Spearman's correlation test (α=0,05) was performed to assess the possible correlations between the number of preprints and age, career stage, and the researcher’s scholarship level variables. Results: from the 211 researchers searched, 22 (10,4 %) published 1 (one) preprint on at least one platform. A total of 39 published preprints were found at MedArxiv (n=19, 48,7 %), SciELO preprints (n=18, 46,2 %), and OSF platforms (n=2, 5,1 %). There was no difference between the adherence to MedArxiv and SciELO preprints (p = 0,731). However, the OSF platform presented the lowest adherence, statistically differing from MedArxiv (p=0,008) and SciELO preprints platforms (p=0,003). In addition, no correlation was found between the publication of preprints and the researcher's age (p=0,128), career stage (p=0,248), or the researcher's scholarship level (p=0,661). Conclusion: it was possible to observe a low adherence to the preprints’ publications by Brazilian researchers’ productivity fellows of graduate programs in dentistry
Journal: Data and Metadata
Pages: 60
Volume: 2
Year: 2023
DOI: 10.56294/dm202360
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:60:id:1056294dm202360

Template-Type: ReDIF-Article 1.0
Author-Name: Louridi Nabaouia
Author-Name-First: Louridi
Author-Name-Last: Nabaouia
Author-Name: Samira Douzi
Author-Name-First: Samira
Author-Name-Last: Douzi
Author-Name: El Ouahidi Bouabid
Author-Name-First: El Ouahidi
Author-Name-Last: Bouabid
Title: Explainable machine learning for coronary artery disease risk assessment and prevention
Abstract: Coronary Artery Disease (CAD) is an increasingly prevalent ailment that has a significant impact on both longevity and quality of life. Lifestyle, genetics, nutrition, and stress are all significant contributors to rising mortality rates. CAD is preventable through early intervention and lifestyle changes. As a result, low-cost automated solutions are required to detect CAD early and help healthcare professionals treat chronic diseases efficiently. Machine learning applications in medicine have increased due to their ability to detect data patterns. Employing machine learning to classify the occurrence of coronary artery disease could assist doctors in reducing misinterpretation. The research project entails the creation of a coronary artery disease diagnosis system based on machine learning. Using patient medical records, we demonstrate how machine learning can help identify if an individual will acquire coronary artery disease. Furthermore, the study highlights the most critical risk factors for coronary artery disease. We used two machine learning approaches, Catboost and LightGBM classifiers, to predict the patient with coronary artery disease. We employed various data augmentation methods, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAE), to solve the imbalanced data problem. Optuna was applied to optimize hyperparameters. The proposed method was tested on the real-world dataset Z-Alizadeh Sani. The acquired findings were satisfactory, as the model could predict the likelihood of cardiovascular disease in a particular individual by combining Catboost with VAE, which demonstrated good accuracy compared to the other approaches. The proposed model is evaluated using a variety of metrics, including accuracy, recall, f-score, precision, and ROC curve. Furthermore, we used the SHAP values and Boruta Feature Selection (BFS) to determine essential risk factors for coronary artery disease
Journal: Data and Metadata
Pages: 65
Volume: 2
Year: 2023
DOI: 10.56294/dm202365
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:65:id:1056294dm202365

Template-Type: ReDIF-Article 1.0
Author-Name: Mohammed Moutaib
Author-Name-First: Mohammed
Author-Name-Last: Moutaib
Author-Name: Mohammed Fattah
Author-Name-First: Mohammed
Author-Name-Last: Fattah
Author-Name: Yousef Farhaoui
Author-Name-First: Yousef
Author-Name-Last: Farhaoui
Author-Name: Badraddine Aghoutane
Author-Name-First: Badraddine
Author-Name-Last: Aghoutane
Author-Name: Moulhime El Bekkali
Author-Name-First: Moulhime
Author-Name-Last: El Bekkali
Title: Extraction of fetal electrocardiogram signal based on K-means Clustering
Abstract: Fetal electrocardiograms (ECG) provide crucial information for the interventions and diagnoses of pregnant women at the clinical level. Maternal signals are robust, making retrieval and detection of Fetal ECGs difficult. In this article, we propose a solution based on Machine Learning by adapting the k-means clustering to detect the fetal ECG by recording the ECGs. In our first preprocessing part, we tried normalized and segmented ECG waveform. Next, we used the Euclidean distance to measure similarity. To identify a certain number of centroids in our data, the results classified into two classes are represented in the last part through graphs and compared with other algorithms, such as the CNN classifier, to demonstrate the effectiveness of this innovative approach, which can be deployed in real-time
Journal: Data and Metadata
Pages: 84
Volume: 2
Year: 2023
DOI: 10.56294/dm202384
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:84:id:1056294dm202384

Template-Type: ReDIF-Article 1.0
Author-Name: Filiberto Fernando Ochoa Paredes
Author-Name-First: Filiberto Fernando
Author-Name-Last: Ochoa Paredes
Author-Name: Manuel Enrique Chenet Zuta
Author-Name-First: Manuel Enrique
Author-Name-Last: Chenet Zuta
Author-Name: Segundo Waldemar Rios Rios
Author-Name-First: Segundo Waldemar
Author-Name-Last: Rios Rios
Author-Name: Anwar Julio Yarin Achachagua
Author-Name-First: Anwar Julio
Author-Name-Last: Yarin Achachagua
Title: Decision-Making in Tourism Management and its Impact on Environmental Awareness
Abstract: The objective was to establish the impact of the management of the tourism system on the environmental awareness of the population of Lunahuana-Cañete, period 2022, the method used was a basic study, the design was without any experiment, in a single time and descriptive, quantitative and deductive approach. The population and test was 120 workers who work in the tourism and gastronomic areas, a non-probabilistic sensal sampling was used. As a result, 86,6 % of respondents stated that the management of the tourism system is well implemented and basically implemented; the cultural, economic, environmental and social dimensions are between basically implemented and very well implemented. The environmental awareness variable was rated with 60,0 % medium level, 36,7 % high level, 3,3 % low level, and the cognitive, affective, active and behavioral dimensions were rated as high level with an average of over 70 %. The inferential statistical results indicate that the management of the Tourist System has a significant influence on the environmental awareness of the inhabitant, in the same way for the specific premise 1, it was confirmed that the cultural dimension is linked in a preponderant way with the environmental awareness of the inhabitant, for the specific premise 2, it was confirmed that the cultural dimension is associated in an important way with the environmental awareness of the inhabitant, for the specific premise 3, it was confirmed that the environmental dimension is linked with the environmental awareness of the inhabitant. And finally for the specific premise 4, it was confirmed that the social dimension is linked to the environmental awareness of the inhabitant, all the premises or hypotheses refer to the inhabitant of Lunahuana, Cañete, Lima, 2022
Journal: Data and Metadata
Pages: 85
Volume: 2
Year: 2023
DOI: 10.56294/dm202385
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:85:id:1056294dm202385

Template-Type: ReDIF-Article 1.0
Author-Name: Jenny Victoria Manosalvas Tapia
Author-Name-First: Jenny Victoria
Author-Name-Last: Manosalvas Tapia
Author-Name: Víctor Hugo Parreño Gallo
Author-Name-First: Víctor Hugo
Author-Name-Last: Parreño Gallo
Author-Name: Noemi Estefani Morales Morales
Author-Name-First: Noemi Estefani
Author-Name-Last: Morales Morales
Author-Name: Tatiana Lucrecia Pancho Chavarrea
Author-Name-First: Tatiana Lucrecia
Author-Name-Last: Pancho Chavarrea
Title: Comparison between CAD-CAM and conventional techniques in the manufacture of fixed zirconia prostheses
Abstract: The use of CAD-CAM technology represents an advanced alternative to optimizing the production of fixed zirconia dental prostheses. This study focused on making a comparison between CAD-CAM techniques and conventional techniques for the production of these prostheses, evaluating various aspects. A methodology was used that combined a literature review based on high-impact academic databases and qualitative interviews with experts in the field. The findings revealed that both CAD-CAM and conventional techniques can achieve aesthetically satisfactory results in the manufacture of zirconia prostheses, depending on the experience and skill of the dental professional. No substantial differences were found to suggest that one technique alters the properties of zirconia significantly compared to the other. However, it was highlighted that CAD-CAM manufacturing systems offer advantages in terms of high quality and precision in fixed dental restorations. The choice between using one or another technology should be based on a detailed evaluation of the specific needs of the patient, considering the expertise of the dentist and the desired quality of the final result. This integrative approach ensures that the best technological option is considered based on the clinical context and patient expectations
Journal: Data and Metadata
Pages: 90
Volume: 2
Year: 2023
DOI: 10.56294/dm202390
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:90:id:1056294dm202390

Template-Type: ReDIF-Article 1.0
Author-Name: Jonathan Martínez Líbano
Author-Name-First: Jonathan
Author-Name-Last: Martínez Líbano
Author-Name: Nicole González Campusano
Author-Name-First: Nicole
Author-Name-Last: González Campusano
Author-Name: Javiera Pereira Castillo
Author-Name-First: Javiera
Author-Name-Last: Pereira Castillo
Author-Name: Juan Carlos Oyanedel
Author-Name-First: Juan Carlos
Author-Name-Last: Oyanedel
Author-Name: María Mercedes Yeomans Cabrera
Author-Name-First: María Mercedes
Author-Name-Last: Yeomans Cabrera
Title: Psychometric Properties of the Social Media Addiction Scale (SMAS) on Chilean University Students
Abstract: Introduction: the use and abuse of social networks are harming the mental health of university students. Objective: to adapt and validate the Social Media Addiction Scale (SMAS) for the Chilean context to have a reliable instrument to measure addiction to social networks. The sample comprised 686 university students (mean age=28,04, SD=8,4), 71,1 % female, 28,4 % male, and 0,5 % other genders. Methods: confirmatory factor analysis (CFA) using the weighted least squares means and variances method (WLSMV) was used for this study. Results: reliability was Cronbach's alpha α=0,841. The SMAS yielded two factors that explained 53,433 % of the variance. The CFA yielded very good fit indicators such as CFI=0,959, TLI=0,949, and RMSEA=0,060. Conclusions: based on the results described above, we can affirm that the SMAS is a good instrument to measure social network addiction in college students
Journal: Data and Metadata
Pages: 91
Volume: 2
Year: 2023
DOI: 10.56294/dm202391
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:91:id:1056294dm202391

Template-Type: ReDIF-Article 1.0
Author-Name: Mohamed Khalifa Boutahir
Author-Name-First: Mohamed Khalifa
Author-Name-Last: Boutahir
Author-Name: Abdelaaziz Hessane
Author-Name-First: Abdelaaziz
Author-Name-Last: Hessane
Author-Name: Imane Lasri
Author-Name-First: Imane
Author-Name-Last: Lasri
Author-Name: Salma Benchikh
Author-Name-First: Salma
Author-Name-Last: Benchikh
Author-Name: Yousef Farhaoui
Author-Name-First: Yousef
Author-Name-Last: Farhaoui
Author-Name: Mourade Azrour
Author-Name-First: Mourade
Author-Name-Last: Azrour
Title: Dynamic Threshold Fine-Tuning in Anomaly Severity Classification for Enhanced Solar Power Optimization
Abstract: This study explores an innovative approach to anomaly severity classification within the realm of solar power optimization. Leveraging established machine learning algorithms—including Isolation Forest (IF), Local Outlier Factor (LOF), and Principal Component Analysis (PCA)—we introduce a novel framework marked by dynamic threshold fine-tuning. This adaptive paradigm aims to refine the accuracy of anomaly classification under varying environmental conditions, addressing factors such as dust storms and equipment irregularities. The research builds upon datasets derived from Errachidia, Morocco. Results underscore the effectiveness of dynamically adjusting severity thresholds in optimizing anomaly classification and subsequently improving the overall efficiency of solar power generation. The study not only reaffirms the robustness of the initial framework but also emphasizes the practical significance of fine-tuning anomaly severity classification for real-world applications in solar energy management. By providing a more nuanced perspective on anomaly detection, this research advances our understanding of the intricate precision required for optimal solar power generation efficiency. The findings contribute valuable insights into the broader field of machine learning applications in renewable energy, offering a pathway for the refinement of existing frameworks for enhanced sustainability and operational effectiveness
Journal: Data and Metadata
Pages: 94
Volume: 2
Year: 2023
DOI: 10.56294/dm202394
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:94:id:1056294dm202394

Template-Type: ReDIF-Article 1.0
Author-Name: Serafeim A. Triantafyllou
Author-Name-First: Serafeim A.
Author-Name-Last: Triantafyllou
Title: A detailed study on implementing new approaches in the Game of Life
Abstract: In 1952, Alan Turing who is considered as a father of Computer Science, based on his previous scientific research on the theory of computation, he emphasized how important is the analysis of pattern formation in nature and developed a theory. In his theory, he described specific patterns in nature that could be formed from basic chemical systems. Turing in his previous studies in the theory of computation, he had constantly worked on symmetrical patterns that could be formed simultaneously and realized the necessity for further analysis of pattern formation in biological problems. However, it was until the late 1960s, when John Conway was the first to introduce the "Game of Life", an innovative mathematical game based on cellular automata, having a purpose to utilize the fundamental entities, called as cells, in two possible states described as "dead" or "alive". This paper tries to contribute to a better understanding of the "Game of Life" by implementing algorithmic approaches of this problem in PASCAL and Python programming languages. Also, inside the paper numerous variations and extensions of the Conway's Game of Life are proposed that introduce new ideas and concepts. Furthermore, several machine learning algorithms to learn patterns from large sets of Game of Life simulations and generate new rules or strategies are described in detail
Journal: Data and Metadata
Pages: 95
Volume: 2
Year: 2023
DOI: 10.56294/dm202395
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:95:id:1056294dm202395

Template-Type: ReDIF-Article 1.0
Author-Name: Daniel Cristóbal Andrade Girón
Author-Name-First: Daniel Cristóbal
Author-Name-Last: Andrade Girón
Author-Name: William Joel Marín Rodriguez
Author-Name-First: William Joel
Author-Name-Last: Marín Rodriguez
Author-Name: Marcelo Zúñiga Rojas
Author-Name-First: Marcelo
Author-Name-Last: Zúñiga Rojas
Author-Name: Edgar Tito Susanibar Ramirez
Author-Name-First: Edgar Tito
Author-Name-Last: Susanibar Ramirez
Author-Name: Irina Patricia Calvo Rivera
Author-Name-First: Irina Patricia
Author-Name-Last: Calvo Rivera
Title: Quality Management System for Higher Education: A Systematic Review
Abstract: Global organizations currently face the challenge of managing massive volumes of data and knowledge efficiently. The consolidation of the knowledge society is manifesting itself in an evident way, driving university institutions to reconfigure both their academic and administrative processes in order to achieve excellence in their functions. In this context, the central purpose of this research is to present a comprehensive systematic review of the implementation of Quality Management Systems (QMS) in the field of higher education. In order to address this issue with the utmost rigor, a systematic review was carried out incorporating the fundamental pillars outlined in the PRISMA statement. In an initial phase, a selection of 883 papers was carried out from preeminent documentary sources, namely: Scopus, IEEE and Web Science. Subsequently, the final review was confined to a corpus of 23 research papers. The results derived from this thorough review show that the paradigm embodied by the ISO 9001 model prevails as the most predominant approach, with 69,56 % representativeness in the set of studies analyzed. In contrast, the EFQM, TQM and Malcom Baldrige models showed a more modest presence, each accounting for 4,35 % of the total number of studies examined. In addition, fundamental aspects have been identified that both facilitate and condition the process of implementing QMS
Journal: Data and Metadata
Pages: 100
Volume: 2
Year: 2023
DOI: 10.56294/dm2023100
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:100:id:1056294dm2023100

Template-Type: ReDIF-Article 1.0
Author-Name: Justiniano Felix Palomino Quispe
Author-Name-First: Justiniano Felix
Author-Name-Last: Palomino Quispe
Author-Name: Camilo Fermín García Huamantumba
Author-Name-First: Camilo Fermín
Author-Name-Last: García Huamantumba
Author-Name: Elvira García Huamantumba
Author-Name-First: Elvira
Author-Name-Last: García Huamantumba
Author-Name: Arturo García Huamantumba
Author-Name-First: Arturo
Author-Name-Last: García Huamantumba
Author-Name: Edwin Eduardo Pacherres Serquen
Author-Name-First: Edwin Eduardo
Author-Name-Last: Pacherres Serquen
Author-Name: Luis Villar Requis Carbajal
Author-Name-First: Luis
Author-Name-Last: Villar Requis Carbajal
Author-Name: Alisson Lizbeth Castro León
Author-Name-First: Alisson Lizbeth
Author-Name-Last: Castro León
Author-Name: Leopoldo Choque Flores
Author-Name-First: Leopoldo
Author-Name-Last: Choque Flores
Author-Name: Domingo Zapana Diaz
Author-Name-First: Domingo
Author-Name-Last: Zapana Diaz
Author-Name: Carlos Enrique Guanilo Paredes
Author-Name-First: Carlos Enrique
Author-Name-Last: Guanilo Paredes
Title: Quantitative Evaluation of the Impact of Artificial Intelligence on the Automation of Processes
Abstract: Introduction: in the current era, Artificial Intelligence (AI) has profoundly transformed the operation and management of business processes, being essential for competitiveness. This article focuses on quantitatively evaluating the impact of AI on the automation of business processes, seeking to support decision making. Objective: this study aims to carry out a quantitative evaluation of the impact of AI on business processes. Robust methods are used to measure and analyze key variables related to AI adoption. Methods: the methodology combines secondary data and company surveys. Public business databases are accessed and financial data is collected, in addition to analyzing Key Performance Indicators (KPI). A random selection of companies is made for the surveys, a structured questionnaire is used and the data is subjected to rigorous statistical analysis. Result: quantitative results show significant impact of AI on business processes. The average reduction in operating costs reaches 26 %, the improvement in the quality of products and services is 30 %, and an average increase of 20 % in profit margins is observed. Possible moderators that influence these results are identified. Conclusion: this quantitative study supports the strategic importance of AI in business, demonstrating substantial improvements in efficiency, quality and decision making. Despite its limitations, it offers a solid framework for decision-making and future research in the field of AI and business automation
Journal: Data and Metadata
Pages: 101
Volume: 2
Year: 2023
DOI: 10.56294/dm2023101
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:101:id:1056294dm2023101

Template-Type: ReDIF-Article 1.0
Author-Name: Abdelaaziz Hessane
Author-Name-First: Abdelaaziz
Author-Name-Last: Hessane
Author-Name: Mohamed Khalifa Boutahir
Author-Name-First: Mohamed
Author-Name-Last: Khalifa Boutahir
Author-Name: Ahmed El Youssefi
Author-Name-First: Ahmed
Author-Name-Last: El Youssefi
Author-Name: Yousef Farhaoui
Author-Name-First: Yousef
Author-Name-Last: Farhaoui
Author-Name: Badraddine Aghoutane
Author-Name-First: Badraddine
Author-Name-Last: Aghoutane
Title: Empowering Date Palm Disease Management with Deep Learning: A Comparative Performance Analysis of Pretrained Models for Stage-wise White-Scale Disease Classification
Abstract: Deep Learning (DL) has revolutionized crop management practices, with disease detection and classification gaining prominence due to their impact on crop health and productivity. Addressing the limitations of traditional methods, such as reliance on handcrafted features, sensitivity to small datasets, limited adaptability, and scalability issues, deep learning enables accurate disease detection, real-time monitoring, and precision agriculture practices. Its ability to analyze and extract features from images, handle multimodal data, and adapt to new data patterns paves the way for a more sustainable and productive agricultural future. This study evaluates six pre-trained deep-learning models designed for stage-wise classification of white-scale date palm disease (WSD). The study assesses key metrics such as accuracy, sensitivity to training data volume, and inference time to identify the most effective model for accurate WSD stage-wise classification. For model development and assessment, we employed a dataset of 1,091 colored date palm leaflet images categorized into four distinct classes: healthy, low infestation degree, medium infestation degree, and high infestation degree. The results reveal the MobileNet model as the top performer, demonstrating superior accuracy and inference time compared to the other models and state of the art methods. The MobileNet model achieves high classification accuracy with only 60 % of the training data. By harnessing the power of deep learning, this study enhances disease management practices in date palm agriculture, fostering improved crop yield, reduced losses, and sustainable food production
Journal: Data and Metadata
Pages: 102
Volume: 2
Year: 2023
DOI: 10.56294/dm2023102
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:102:id:1056294dm2023102

Template-Type: ReDIF-Article 1.0
Author-Name: Najia Khouibiri
Author-Name-First: Najia
Author-Name-Last: Khouibiri
Author-Name: Yousef Farhaoui
Author-Name-First: Yousef
Author-Name-Last: Farhaoui
Title: Analyzing the Influence of Cloud Business Intelligence on Small and Medium Enterprises A Case Study of Morocco
Abstract: Business intelligence (BI) has long been a crucial factor in bolstering organizational competitiveness, offering strategic insights that shape decision-making and propel business expansion. The advent of cloud computing has further amplified data sharing and collaboration. This study advocates for the adoption of Cloud BI as an innovative tool to bolster the economic growth of small- and medium-sized enterprises (SMEs) in Morocco. We emphasize the interconnectedness of these businesses' performance with the overall well-being of the Moroccan economy, underscoring the need for regulatory bodies to prioritize not only financial support but also a keen focus on technological advancements. We explore how technological integration can enhance the competitive edge of SMEs. Finally, we conclude by presenting a framework that incorporates the migration of BI to the cloud within the realm of Cloud BI. Drawing inspiration from prior research, we propose modifications tailored to address the specific concerns of SMEs in embracing cloud BI technology
Journal: Data and Metadata
Pages: 104
Volume: 2
Year: 2023
DOI: 10.56294/dm2023104
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:104:id:1056294dm2023104

Template-Type: ReDIF-Article 1.0
Author-Name: Mohamed Sabiri
Author-Name-First: Mohamed
Author-Name-Last: Sabiri
Author-Name: Yousef Farhaoui
Author-Name-First: Yousef
Author-Name-Last: Farhaoui
Author-Name: Agoujil Said
Author-Name-First: Agoujil
Author-Name-Last: Said
Title: Utilizing Data Mining and Machine Learning for Enhancing Bachelor's Degree Outcomes and Predicting Students' Academic Success
Abstract: This paper aims to conceptualize, design, and implement a Data Mining (DM) system integrated with machine learning within the realm of school management. The primary objective is to support the educational community and decision-makers in addressing the issue of school dropout and enhancing success rates at the certificate levels in Morocco, specifically focusing on the bachelor's degree examination in the qualifying cycle. The proposed system categorizes students five months prior to the exam date, facilitating targeted academic interventions for those at risk of course repetition or discontinuation. The DM system, operational throughout the school year, enhances the precision and effectiveness of schools and provincial administrations by identifying areas requiring additional support to improve end-of-year success rates and student performance. Project development is rooted in the collection and analysis of existing data from various departmental information systems, utilizing classification and regression algorithms to predict learner performance, success rates, and overall outcomes at the conclusion of certificate levels
Journal: Data and Metadata
Pages: 105
Volume: 2
Year: 2023
DOI: 10.56294/dm2023105
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:105:id:1056294dm2023105

Template-Type: ReDIF-Article 1.0
Author-Name: Lucio Arnulfo Ferrer Peñaranda
Author-Name-First: Lucio Arnulfo
Author-Name-Last: Ferrer Peñaranda
Author-Name: Lindomira Castro Llaja
Author-Name-First: Lindomira
Author-Name-Last: Castro Llaja
Author-Name: Mercedes Lulilea Ferrer Mejía
Author-Name-First: Mercedes Lulilea
Author-Name-Last: Ferrer Mejía
Author-Name: Zoila Rosa Díaz Tavera
Author-Name-First: Zoila Rosa
Author-Name-Last: Díaz Tavera
Author-Name: Fernando Martin Ramirez Wong
Author-Name-First: Fernando Martin
Author-Name-Last: Ramirez Wong
Author-Name: Leonardo Velarde Dávila
Author-Name-First: Leonardo
Author-Name-Last: Velarde Dávila
Author-Name: Roberto Carlos Dávila Morán
Author-Name-First: Roberto Carlos
Author-Name-Last: Dávila Morán
Title: Recommended practices for the open publication of epidemiological research data and reports
Abstract: Introduction: epidemiology plays a fundamental role in public health by providing evidence for decision making. However, the lack of access to data limits the evaluation and replicability of epidemiological studies. Objective: establish recommended practices for the open publication of epidemiological research data and reports, in order to maximize their value and accessibility. Method: a systematic review of open publication guidelines was conducted. Good practices were identified in the stages of collection, storage, publication and dissemination of epidemiological information. Results: consensus was found on the importance of using standardized instruments, documenting metadata, storing data in repositories with open licenses, assigning digital identifiers and publishing in open access journals. Conclusions: the adoption of these recommended practices will substantially improve the quality, replicability and use of epidemiological research. This will strengthen transparency, scientific collaboration and evidence-based decision making
Journal: Data and Metadata
Pages: 108
Volume: 2
Year: 2023
DOI: 10.56294/dm2023108
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:108:id:1056294dm2023108

Template-Type: ReDIF-Article 1.0
Author-Name: Khaoula Taji
Author-Name-First: Khaoula
Author-Name-Last: Taji
Author-Name: Fadoua Ghanimi
Author-Name-First: Fadoua
Author-Name-Last: Ghanimi
Title: Enhancing Plant Disease Classification through Manual CNN Hyperparameter Tuning
Abstract: Diagnosing plant diseases is a challenging task due to the complex nature of plants and the visual similarities among different species. Timely identification and classification of these diseases are crucial to prevent their spread in crops. Convolutional Neural Networks (CNN) have emerged as an advanced technology for image identification in this domain. This study explores deep neural networks and machine learning techniques to diagnose plant diseases using images of affected plants, with a specific emphasis on developing a CNN model and highlighting the importance of hyperparameters for precise results. The research involves processes such as image preprocessing, feature extraction, and classification, along with a manual exploration of diverse hyperparameter settings to evaluate the performance of the proposed CNN model trained on an openly accessible dataset. The study compares customized CNN models for the classification of plant diseases, demonstrating the feasibility of disease classification and automatic identification through machine learning-based approaches. It specifically presents a CNN model and traditional machine learning methodologies for categorizing diseases in apple and maize leaves, utilizing a dataset comprising 7023 images divided into 8 categories. The evaluation criteria indicate that the CNN achieves an impressive accuracy of approximately 98,02 %
Journal: Data and Metadata
Pages: 112
Volume: 2
Year: 2023
DOI: 10.56294/dm2023112
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:112:id:1056294dm2023112

Template-Type: ReDIF-Article 1.0
Author-Name: Rajae Ghanimi
Author-Name-First: Rajae
Author-Name-Last: Ghanimi
Author-Name: Fadoua
Author-Name-First: Fadoua
Author-Name-Last: Fadoua
Author-Name: Ilyas Ghanimi
Author-Name-First: Ilyas
Author-Name-Last: Ghanimi
Author-Name: Abdelmajid Soulaymani
Author-Name-First: Abdelmajid
Author-Name-Last: Soulaymani
Title: An artificial intelligence-based approach for an urgent detection of the pesticide responsible of intoxication
Abstract: Acute poisoning by pesticides in Morocco is an important public health issue, because the use of pesticides has become both massive and anarchic. This is the cause of deaths whose incidence is unfortunately increasing. Unfortunately, these deaths are not always accidental. Pesticides are also used as a means of suicide; according to the WHO, these are means suicide chemicals most used in the world, since, out of the 800 000 suicides recorded per year, more than a third are caused by this type of product. Even more serious, these suicides are currently being observed among children and teenagers. Faced with this alarming figure, and in order to prevent deaths and improve emergency treatment of cases of pesticide poisoning, it becomes important to use the potential of artificial intelligence in the treatment of these admissions. Our approach is essentially based on machine learning algorithms, including decision support software capable of predicting, based on major clinical signs, the most likely pesticide responsible of the intoxication in the triage room. This, before moving on to the confirmation stage based on biological and toxicological investigations, which are often costly and time-consuming
Journal: Data and Metadata
Pages: 114
Volume: 2
Year: 2023
DOI: 10.56294/dm2023114
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:114:id:1056294dm2023114

Template-Type: ReDIF-Article 1.0
Author-Name: William Joel Marín Rodriguez
Author-Name-First: William Joel
Author-Name-Last: Marín Rodriguez
Author-Name: Daniel Cristóbal Andrade Girón
Author-Name-First: Daniel Cristóbal
Author-Name-Last: Andrade Girón
Author-Name: Zúñiga Rojas
Author-Name-First: Zúñiga Rojas
Author-Name-Last: Zúñiga Rojas
Author-Name: Edgar Tito Susanibar Ramirez
Author-Name-First: Edgar Tito
Author-Name-Last: Susanibar Ramirez
Author-Name: Irina Patricia Calvo Rivera
Author-Name-First: Irina Patricia
Author-Name-Last: Calvo Rivera
Author-Name: Jose Luis Ausejo Sanchez
Author-Name-First: Jose Luis
Author-Name-Last: Ausejo Sanchez
Author-Name: Felix Gil Caro Soto
Author-Name-First: Felix Gil
Author-Name-Last: Caro Soto
Title: Artificial Intelligence and Augmented Reality in Higher Education: a systematic review
Abstract: Augmented reality is a technology that combines elements of the real and virtual world to enhance the user experience by providing additional information and enriching interaction. In education, AR has been used to enhance the teaching of complex concepts by providing interactive content and immersive experiences. This review examines various aspects related to the implementation of AR in higher education, including its educational benefits, impact on student motivation and engagement, and its effectiveness in achieving learning objectives. Associated challenges and limitations, such as device availability and effective experience design, are also explored. The results indicate that AR can improve content comprehension and retention, encourage active student participation, and enhance collaborative learning. However, significant challenges are identified, such as the initial investment in technology and the need for adequate teacher training. In addition, diversity in institutional infrastructure and resources may limit the widespread adoption of AR in higher education. In conclusion, augmented reality in higher education offers promising potential to enhance teaching and learning, but its successful implementation requires careful considerations of pedagogy, accessibility, and overcoming technological barriers. It highlights the need for further research to thoroughly understand its impact and maximize its benefits in academic training
Journal: Data and Metadata
Pages: 121
Volume: 2
Year: 2023
DOI: 10.56294/dm2023121
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:121:id:1056294dm2023121

Template-Type: ReDIF-Article 1.0
Author-Name: Rodrigo Lagos
Author-Name-First: Rodrigo
Author-Name-Last: Lagos
Author-Name: Matías Espinoza
Author-Name-First: Matías
Author-Name-Last: Espinoza
Author-Name: Alejandro Cubillos
Author-Name-First: Alejandro
Author-Name-Last: Cubillos
Title: Design of a Risk Scoring System for Post Surgical Adverse Events on Neuro-oncological patients
Abstract: This paper aims to validate and subsequently design a Risk Scoring System based on Lohman et al.(14) risk calculator for patients undergoing brain or spinal tumor surgery. Three models were tested: replication of Lohman's methodology, modification of risk groups, and development of a custom risk calculator. The replication of Lohman's instrument did not show significant correlations with adverse events in the study population. However, the adapted risk calculator demonstrated promising predictive performance for unplanned reoperation at 30 days, indicating good utility. The study suggests the potential applicability of the adapted risk calculator for predicting unplanned reoperation within 30 days for patients undergoing brain or spinal tumor surgery. Further research with larger samples and less missing data is recommended to confirm and enhance the utility of the proposed risk calculator. The results could be used to optimize decision-making and improve the quality of care for neuro-oncological surgery patients
Journal: Data and Metadata
Pages: 125
Volume: 2
Year: 2023
DOI: 10.56294/dm2023125
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:125:id:1056294dm2023125

Template-Type: ReDIF-Article 1.0
Author-Name: Salah Eddine Didi
Author-Name-First: Salah
Author-Name-Last: Eddine Didi
Author-Name: Imane Halkhams
Author-Name-First: Imane
Author-Name-Last: Halkhams
Author-Name: Abdelhafid Es Saqy
Author-Name-First: Abdelhafid
Author-Name-Last: Es Saqy
Author-Name: Mohammed Fattah
Author-Name-First: Mohammed
Author-Name-Last: Fattah
Author-Name: Younes Balboul
Author-Name-First: Younes
Author-Name-Last: Balboul
Author-Name: Said Mazer
Author-Name-First: Said
Author-Name-Last: Mazer
Author-Name: Moulhime El Bekkali
Author-Name-First: Moulhime
Author-Name-Last: El Bekkali
Title: Creation of a soft circular patch antenna for 5G at a frequency of 2.45 GHz dedicated to biomedical applications
Abstract: Telemedicine technology is one of the key achievements of recent years. This technology is based on biomedical devices that contain essential components, in-cluding antennas. Biomedical antennas ensure the exchange of data between de-vices installed on the human body and the external environment. This paper pre-sents the study and design of a flexible circular patch antenna implanted on a bio-sourced substrate for industrial, scientific, and medical applications. The frequen-cy chosen for the study is 2,45GHz. Return loss and radiation pattern measure-ments. An improvement in the gain of this antenna is also investigated in this study. This antenna offers adequate performance to meet the needs of 5G users. This antenna is printed on a polyester substrate with a thickness of h=2,85cm, a relative permittivity εr=3,2, a loss tangent equal to 0,003, and a patch radius equal to 2,11cm. In addition, this antenna provides the following results: reflection co-efficient S11=-26,59dB, bandwidth BW=0,12GHz, gain G=5,6, directivity D=5,8dB, and efficiency η=96,55 %
Journal: Data and Metadata
Pages: 127
Volume: 2
Year: 2023
DOI: 10.56294/dm2023127
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:127:id:1056294dm2023127

Template-Type: ReDIF-Article 1.0
Author-Name: Lingeswari Sivagnanam
Author-Name-First: Lingeswari
Author-Name-Last: Sivagnanam
Author-Name: N. Karthikeyani Visalakshi
Author-Name-First: N.
Author-Name-Last: Karthikeyani Visalakshi
Title: Detection of bipolar disorder by means of ensemble machine learning classifier
Abstract: The accurate diagnosis of bipolar disorder is extremely challenging, due to unpredictable mood swings, behaviors, sleep, judgment, and inability to think, which makes it difficult to make a proper diagnosis. This paper aims to investigate the application of ensemble classifiers in classifying bipolar disorder and to compare their performance with existing methods. Herein, the work involves a thorough analysis of diagnostic precision and performance metrics. According to a study, an existing classifier achieved an accuracy rate of 87 % in bipolar disorder classification. In addition, the two most widely used classifiers, which are Random Forest and Decision Tree, achieved accuracy rates of 90 % and 86 %, respectively. These results highlight the performance baseline against which the proposed ensemble classifier is evaluated. Notably, the proposed ensemble classifier shows excellent results in bipolar disorder classification thereby, achieving an impressive accuracy rate of 98 %. This considerable improvement in accuracy marks a significant stride in diagnostic precision, showcasing the potential of ensemble classifiers in enhancing bipolar disorder detection. The results of this study have given substantial implications for the field of mental health diagnosis, offering a promising avenue for a more accurate and reliable classification of bipolar disorder. This research reinforces the significance of advanced machine learning techniques and their potential to revolutionize the approach to diagnose and to manage mental health conditions
Journal: Data and Metadata
Pages: 134
Volume: 2
Year: 2023
DOI: 10.56294/dm2023134
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:134:id:1056294dm2023134

Template-Type: ReDIF-Article 1.0
Author-Name: V. Sushma Sri
Author-Name-First: V.
Author-Name-Last: Sushma Sri
Author-Name: V. Hima Sailu
Author-Name-First: V.
Author-Name-Last: Hima Sailu
Author-Name: U. Pradeepthi
Author-Name-First: U.
Author-Name-Last: Pradeepthi
Author-Name: P. Manogyna Sai
Author-Name-First: P.
Author-Name-Last: Manogyna Sai
Author-Name: M. Kavitha
Author-Name-First: M.
Author-Name-Last: Kavitha
Title: Disease Detection using Region-Based Convolutional Neural Network and ResNet
Abstract: In recent times, various techniques have been employed in agriculture to address different aspects. These techniques encompass strategies to enhance crop yield, identify hidden pests, and implement effective pest reduction methods, among others. Presented in this study a novel strategy which focuses on identification of plant leaf infections in agricultural fields using drones. By employing cameras on drones with high resolution, we take precise pictures of plant leaves, ensuring comprehensive coverage of the entire area. These images serve as datasets for Deep Learning algorithms, including Convolutional Neural Networks(CNN), Resnet, ReLu enabling the early detection of infections. The deep learning models leverage the captured images to identify and classify infections at their initial stages. The usage of R-CNN and ResNet technology in agriculture field has brought the tremendous change when we detect the disease in earlier stage of crop. Thus the farmer can take the pest preventive measures in the beginning stage to avoid crop failure
Journal: Data and Metadata
Pages: 135
Volume: 2
Year: 2023
DOI: 10.56294/dm2023135
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:135:id:1056294dm2023135

Template-Type: ReDIF-Article 1.0
Author-Name: M. Kalaimani
Author-Name-First: M.
Author-Name-Last: Kalaimani
Author-Name: AN. Sigappi
Author-Name-First: AN.
Author-Name-Last: Sigappi
Title: Posture Recognition in Bharathanatyam Images using 2D-CNN
Abstract: The postures are important for conveying emotions, expressing artistic intent, and preserving appropriate technique. Posture recognition in dance is essential for several reasons, as it improving the performance and overall artistic expression of the dancer. The Samapadam, Aramandi, and Muzhumandi are three postures that serve as the foundation for the Bharathanatyam dance style. This work proposes a model designed to recognize the posture portrayed by the dancer. The proposed methodology employs the pre-trained 2D-CNN model fine-tuned using the Bharathanatyam dance image dataset and evaluates the model performance
Journal: Data and Metadata
Pages: 136
Volume: 2
Year: 2023
DOI: 10.56294/dm2023136
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:136:id:1056294dm2023136

Template-Type: ReDIF-Article 1.0
Author-Name: Younes JAMOULI
Author-Name-First: Younes
Author-Name-Last: JAMOULI
Author-Name: Samir TETOUANI
Author-Name-First: Samir
Author-Name-Last: TETOUANI
Author-Name: Omar CHERKAOUI
Author-Name-First: Omar CHERKAOUI
Author-Name-Last: Omar CHERKAOUI
Author-Name: Aziz SOULHI
Author-Name-First: Aziz
Author-Name-Last: SOULHI
Title: To diagnose industry 4.0 by maturity model: the case of Moroccan clothing industry
Abstract: In 2011, the German government launched the visionary initiative known as Industry 4.0, with the goal of positioning itself at the forefront of cutting-edge manufacturing and the shift towards digital transformation. In the wake of this transformative wave, numerous manufacturers are continuously exploring avenues to bolster their capabilities and remain competitive in the market. This empirical study adopts a maturity model inspired by the Economic Development Board's Singapore Smart Industry Readiness Index. The model empowers companies to perform self-assessments, facilitating a systematic and comprehensive alignment with the principles of Industry 4.0. The research delves into the assessment of Industry 4.0 maturity within the Moroccan clothing industry, examining clustering index factors and the influence of key factors on companies' self-assessment. The results classify 252 Moroccan Clothing enterprises into three distinct categories, highlighting a strong positive correlation among process, technology, and organization. Significantly, a majority of the 252 companies evaluated using the maturity model still appear to be in early stages or partially mature, necessitating significant improvements and a reevaluation of their Industry 4.0 transformation strategies. Conclusively, the Singapore Smart Industry Readiness Index proves to be a valuable tool for conducting self-assessments within Moroccan-based enterprises. These findings offer practical guidance for both industry practitioners and researchers seeking to navigate the complexities of Industry 4.0 maturity and grouping
Journal: Data and Metadata
Pages: 137
Volume: 2
Year: 2023
DOI: 10.56294/dm2023137
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:137:id:1056294dm2023137

Template-Type: ReDIF-Article 1.0
Author-Name: Md Alimul Haque
Author-Name-First: Md
Author-Name-Last: Alimul Haque
Author-Name: Sultan Ahmad
Author-Name-First: Sultan
Author-Name-Last: Ahmad
Author-Name: Deepa Sonal
Author-Name-First: Deepa
Author-Name-Last: Sonal
Author-Name: Hikmat A. M. Abdeljaber
Author-Name-First: Hikmat A. M.
Author-Name-Last: Abdeljaber
Author-Name: B.K. Mishra
Author-Name-First: B.K.
Author-Name-Last: Mishra
Author-Name: A.E.M. Eljialy
Author-Name-First: A.E.M.
Author-Name-Last: Eljialy
Author-Name: Sultan Alanazi
Author-Name-First: Sultan
Author-Name-Last: Alanazi
Author-Name: Jabeen Nazeer
Author-Name-First: Jabeen
Author-Name-Last: Nazeer
Title: Achieving Organizational Effectiveness through Machine Learning Based Approaches for Malware Analysis and Detection
Abstract: Introduction: as technology usage grows at an exponential rate, cybersecurity has become a primary concern. Cyber threats have become increasingly advanced and specific, posing a severe risk to individuals, businesses, and even governments. The growing complexity and sophistication of cyber-attacks are posing serious challenges to traditional cybersecurity methods. As a result, machine learning (ML) techniques have emerged as a promising solution for detecting and preventing these attacks. Aim: this research paper offers an extensive examination of diverse machine learning algorithms that have the potential to enhance the intelligence and overall functionality of applications. Methods: the main focus of this study is to present the core principles of distinct machine learning methods and demonstrate their versatile applications in various practical fields such as cybersecurity systems, smart cities, healthcare, e-commerce, and agriculture. By exploring these applications, this paper contributes to the understanding of how machine learning techniques can be effectively employed across different domains. The article then explores the current and future prospects of ML in cybersecurity. Results: this paper highlights the growing importance of ML in cybersecurity and the increasing demand for skilled professionals who can develop and implement ML-based solutions. Conclusion: overall, the present article presents a thorough examination of the role of machine learning (ML) in cybersecurity, as well as its current and future prospects. It can be a valuable source of information for researchers, who seek to grasp the potential of ML in enhancing cybersecurity
Journal: Data and Metadata
Pages: 139
Volume: 2
Year: 2023
DOI: 10.56294/dm2023139
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:139:id:1056294dm2023139

Template-Type: ReDIF-Article 1.0
Author-Name: Salma Benchikh
Author-Name-First: Salma
Author-Name-Last: Benchikh
Author-Name: Jarou Tarik
Author-Name-First: Jarou
Author-Name-Last: Tarik
Author-Name: Mohamed khalifa Boutahir
Author-Name-First: Mohamed khalifa
Author-Name-Last: Boutahir
Author-Name: Elmehdi Nasri
Author-Name-First: Elmehdi
Author-Name-Last: Nasri
Author-Name: roa Lamrani
Author-Name-First: roa
Author-Name-Last: Lamrani
Title: Improving Photovoltaic System Performance with Artificial Neural Network Control
Abstract: Photovoltaic systems play a pivotal role in renewable energy initiatives. To enhance the efficiency of solar panels amid changing environmental conditions, effective Maximum Power Point Tracking (MPPT) is essential. This study introduces an innovative control approach based on an Artificial Neural Network (ANN) controller tailored for photovoltaic systems. The aim is to elevate the precision and adaptability of MPPT, thereby improving solar energy harvesting. This research integrated an ANN controller into a photovoltaic system in order dynamically optimize the operating point of solar panels in response to environmental changes. The performance of the ANN controller was compared with traditional MPPT approaches using simulation in Simulink/Matlab. The results of the simulation showed that the ANN controller performed better than the traditional MPPT techniques, highlighting the effectiveness of this method for dynamically changing solar panel performance. The ANN particularly demonstrates higher precision and adaptability when environmental conditions vary. The strategy consistently achieves and maintains the maximum power point, enhancing overall energy harvesting efficiency. The integration of an ANN controller marks a significant advance in solar energy control. The study highlights the superiority of the ANN controller through rigorous simulations, demonstrating increased accuracy and adaptability. This approach not only proves effective, but also has the potential to outperform other MPPT strategies in terms of stability and responsiveness
Journal: Data and Metadata
Pages: 144
Volume: 2
Year: 2023
DOI: 10.56294/dm2023144
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:144:id:1056294dm2023144

Template-Type: ReDIF-Article 1.0
Author-Name: Zainab Rasheed
Author-Name-First: Zainab
Author-Name-Last: Rasheed
Author-Name: Sameh Ghwanmeh
Author-Name-First: Sameh
Author-Name-Last: Ghwanmeh
Author-Name: Abedallah Zaid Abualkishik
Author-Name-First: Abedallah Zaid
Author-Name-Last: Abualkishik
Title: Harnessing Artificial Intelligence for Personalized Learning: A Systematic Review
Abstract: Introduction: the document presents a comprehensive review of the utilization of Artificial Intelligence (AI) in personalized learning within the educational context. The study aims to investigate the various approaches to using ML algorithms for personalizing educational content, the impact and implications of these approaches on student performance, and the challenges and limitations associated with AI in personalized learning. The research questions are structured around these three broad areas, focusing on the AI methods used in education, their impact on students' academic outcomes, and the challenges and limitations associated with AI. Methods: the study employed a systematic literature review methodology, utilizing a structured and replicable search strategy to identify relevant research material from high-impact peer-reviewed journals published between 2015 and 2023. Inclusion and exclusion criteria were applied to select studies that focused on AI in education for personalized learning. Data collection involved extracting relevant data from the selected studies, and a thematic analysis was conducted to identify themes related to the research questions. The selected studies were graded based on their quality, and the results were summarized in a narrative synthesis. Results: the analysis of the selected research papers revealed the significance of adaptive learning systems, recommender systems, NLP techniques, and intelligent tutoring systems in tailoring educational content to individual students. These approaches have demonstrated their effectiveness in enhancing student engagement, improving learning outcomes, and providing personalized feedback. However, the study also identified challenges and limitations that need to be addressed for the successful implementation of AI in personalized learning. Conclusions: the study identified several limitations, including potential bias toward certain research areas, contextual factors influencing the effectiveness of ML algorithms, and the need for further research to examine the applicability of different approaches across diverse contexts. The findings highlight the research gaps, limitations, and potential future research areas in the field of AI-based personalized learning in education
Journal: Data and Metadata
Pages: 146
Volume: 2
Year: 2023
DOI: 10.56294/dm2023146
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:146:id:1056294dm2023146

Template-Type: ReDIF-Article 1.0
Author-Name: Ghita Ibrahimi
Author-Name-First: Ghita
Author-Name-Last: Ibrahimi
Author-Name: Wijdane Merioumi
Author-Name-First: Wijdane
Author-Name-Last: Merioumi
Author-Name: Bouchra Benchekroun
Author-Name-First: Bouchra
Author-Name-Last: Benchekroun
Title: Fostering innovation through collective intelligence: a literature review
Abstract: In the twenty-first century, Collective intelligence (CI) arose as a social phenomenon to assist organizations in managing future uncertainty. It pushes a broad diverse group to come up with new solutions that outperform those uncovered within the organization itself. Accordingly, CI has been widely acknowledged as a means to foster innovation, and develop, and sustain an organization's creative potential. This paper aims to conduct a literature review to examine the existing body of literature regarding the ways collective intelligence improves innovation. The findings emphasized the importance of collective intelligence in fueling a firm’s knowledge and innovation in all of its forms to overcome public and private organizational challenges. Furthermore, our review underlined the mediating role of information technology in taking full advantage of collective intelligence via digital platforms. In addition, our analysis pointed out the multifaceted traits of collective intelligence as reflected in the literature under several terms, including crowdsourcing. Our research revealed several gaps in the current literature, including insufficient analysis and modeling of the relationship between the two concepts. Finally, we concluded our paper by identifying the limits of our research and suggesting avenues for future studies on collective intelligence and innovation
Journal: Data and Metadata
Pages: 149
Volume: 2
Year: 2023
DOI: 10.56294/dm2023149
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:149:id:1056294dm2023149

Template-Type: ReDIF-Article 1.0
Author-Name: Manal Benzyane
Author-Name-First: Manal
Author-Name-Last: Benzyane
Author-Name: Mourade Azrour
Author-Name-First: Mourade
Author-Name-Last: Azrour
Author-Name: Imad Zeroual
Author-Name-First: Imad
Author-Name-Last: Zeroual
Author-Name: Said Agoujil
Author-Name-First: Said
Author-Name-Last: Agoujil
Title: Investigating the Influence of Convolutional Operations on LSTM Networks in Video Classification
Abstract: Video classification holds a foundational position in the realm of computer vision, involving the categorization and labeling of videos based on their content. Its significance resonates across various applications, including video surveil-lance, content recommendation, action recognition, video indexing, and more. The primary objective of video classification is to automatically analyze and comprehend the visual information embedded in videos, facilitating the efficient organization, retrieval, and interpretation of extensive video collections. The integration of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks has brought about a revolution in video classification. This fusion effectively captures both spatial and temporal dependencies within video sequences, leveraging the strengths of CNNs in extracting spatial features and LSTMs in modeling sequential and temporal information. ConvLSTM and LRCN (Long-term Recurrent Convolutional Networks) are two widely embraced architectures that embody this fusion. This paper seeks to investigate the impact of convolutions on LSTM networks in the context of video classification, aiming to compare the performance of ConvLSTM and LRCN
Journal: Data and Metadata
Pages: 152
Volume: 2
Year: 2023
DOI: 10.56294/dm2023152
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:152:id:1056294dm2023152

Template-Type: ReDIF-Article 1.0
Author-Name: Ali Omari Alaoui
Author-Name-First: Ali
Author-Name-Last: Omari Alaoui
Author-Name: Omaima El Bahi
Author-Name-First: Omaima
Author-Name-Last: El Bahi
Author-Name: Mohamed Rida Fethi
Author-Name-First: Mohamed
Author-Name-Last: Rida Fethi
Author-Name: Othmane Farhaoui
Author-Name-First: Othmane
Author-Name-Last: Farhaoui
Author-Name: Ahmad El Allaoui
Author-Name-First: Ahmad
Author-Name-Last: El Allaoui
Author-Name: Yousef Farhaoui
Author-Name-First: Yousef
Author-Name-Last: Farhaoui
Title: Pre-trained CNNs: Evaluating Emergency Vehicle Image Classification
Abstract: In this paper, we aim to provide a comprehensive analysis of image classification, specifically in the context of emergency vehicle classification. We have conducted an in-depth investigation, exploring the effectiveness of six pre-trained Convolutional Neural Network (CNN) models. These models, namely VGG19, VGG16, MobileNetV3Large, MobileNetV3Small, MobileNetV2, and MobileNetV1, have been thoroughly examined and evaluated within the domain of emergency vehicle classification. The research methodology utilized in this study is carefully designed with a systematic approach. It includes the thorough preparation of datasets, deliberate modifications to the model architecture, careful selection of layer operations, and fine-tuning of the model compilation. To gain a comprehensive understanding of the performance, we conducted a detailed series of experiments. We analyzed nuanced performance metrics such as accuracy, loss, and training time, considering important factors in the evaluation process. The results obtained from this study provide a comprehensive understanding of the advantages and disadvantages of each model. Moreover, they emphasize the crucial significance of carefully choosing a suitable pre-trained Convolutional Neural Network (CNN) model for image classification tasks. Essentially, this article provides a comprehensive overview of image classification, highlighting the crucial significance of pre-trained CNN models in achieving precise outcomes, especially in the demanding field of emergency vehicle classification
Journal: Data and Metadata
Pages: 153
Volume: 2
Year: 2023
DOI: 10.56294/dm2023153
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:153:id:1056294dm2023153

Template-Type: ReDIF-Article 1.0
Author-Name: Abdelhak Khadraoui
Author-Name-First: Abdelhak
Author-Name-Last: Khadraoui
Author-Name: Elmoukhtar Zemmouri
Author-Name-First: Elmoukhtar
Author-Name-Last: Zemmouri
Title: Pyramid Scene Parsing Network for Driver Distraction Classification
Abstract: In recent years, there has been a persistent increase in the number of road accidents worldwide. The US National Highway Traffic Safety Administration reports that distracted driving is responsible for approximately 45 percent of road accidents. In this study, we tackle the challenge of automating the detection and classification of driver distraction, along with the monitoring of risky driving behavior. Our proposed solution is based on the Pyramid Scene Parsing Network (PSPNet), which is a semantic segmentation model equipped with a pyramid parsing module. This module leverages global context information through context aggregation from different regions. We introduce a lightweight model for driver distraction classification, where the final predictions benefit from the combination of both local and global cues. For model training, we utilized the publicly available StateFarm Distracted Driver Detection Dataset. Additionally, we propose optimization techniques for classification to enhance the model’s performance
Journal: Data and Metadata
Pages: 154
Volume: 2
Year: 2023
DOI: 10.56294/dm2023154
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:154:id:1056294dm2023154

Template-Type: ReDIF-Article 1.0
Author-Name: Khaoula Taji
Author-Name-First: Khaoula
Author-Name-Last: Taji
Author-Name: Badr Elkhalyly
Author-Name-First: Badr
Author-Name-Last: Elkhalyly
Author-Name: Yassine Taleb Ahmad
Author-Name-First: Yassine
Author-Name-Last: Taleb Ahmad
Author-Name: Ilyas Ghanimi
Author-Name-First: Ilyas
Author-Name-Last: Ghanimi
Author-Name: Fadoua Ghanimi
Author-Name-First: Fadoua
Author-Name-Last: Ghanimi
Title: Securing Smart Agriculture: Proposed Hybrid Meta-Model and Certificate-based Cyber Security Approaches
Abstract: The Internet of Things is a decentralized network of physically connected devices that communicate with other systems and devices over the internet. As the number of IoT-based devices continues to grow at an exponential rate, this technology has the potential to improve nearly every aspect of daily life, from smart networks and transportation to home automation and agriculture. However, the absence of adequate security measures on all levels of the IoT poses a significant security risk, with the potential for cyber-attacks and data theft. While scholars have suggested various security measures, there are still gaps that need to be addressed. In this study, we analyzed previous research and proposed metamodels for security, IoT, and machine learning. We then proposed a new IoT-based smart agriculture model with integrated security measures to mitigate cyber- attacks and increase agricultural output. Our model takes into account the unique features of the smart farming domain and offers a framework for securing IoT devices in this specific application area. Moreover, in order to mitigate a range of cyber security attacks across various layers of IoT, we introduced two certificate-based schemes named CBHA and SCKA for smart agriculture. A comparative analysis of their security with existing literature demonstrates their superior robustness against diverse attacks. Additionally, security testing utilizing scyther affirms the resilience and security of both CBHA and SCKA, establishing them as viable options for ensuring security in smart agriculture
Journal: Data and Metadata
Pages: 155
Volume: 2
Year: 2023
DOI: 10.56294/dm2023155
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:155:id:1056294dm2023155

Template-Type: ReDIF-Article 1.0
Author-Name: Ahmed Bichri
Author-Name-First: Ahmed
Author-Name-Last: Bichri
Author-Name: Hamid Mazouz
Author-Name-First: Hamid
Author-Name-Last: Mazouz
Author-Name: Souad Abderafi
Author-Name-First: Souad
Author-Name-Last: Abderafi
Title: Study of phosphoric acid slurry rheological behavior in the attack reactor and development of a model to control its viscosity using artificial intelligence
Abstract: This work aims to determine the rheological properties of the industrial phosphoric acid slurry and its behavior under the operating conditions of the phosphoric acid production process. For that, several experimental tests on the slurry were carried out, using a Rheometer (Anton Paar), which testing the effect of temperature and solid content. The results show that, for a fixed solids rate, the viscosity of the slurry decreases with temperatures from 75°C to 82°C and increases for temperatures above 82°C considered as the maximum temperature required by the process. This phenomenon is due to the morphological change of the gypsum which corresponds to the range of calcium sulfate hemihydrate formation. For a fixed temperature, the viscosity increases with increasing slurry solid content (31 % to 37 %). The viscosity increases with the shear gradient. Increasing the solid charge in the slurry increases its resistance to flow and movement. Thus, the slurry has a higher tendency to settle. A comparative study of four rheological models, Casson, Bingham, Ostwald and Herschel-Buckley, led to the selection of the Herschel-Bulkley model. This predicts the behavior of the phosphate slurry with a correlation coefficient of 99,9 % and a MAE less than 4 %. Overall, the results show that the threshold flow of the slurry is negligible, and its behavior is nonlinear. Thus, the slurry is a non-Newtonian fluid, with a dilatant rheological behavior. The various tests carried out enabled us to measure the viscosity of the phosphoric acid suspension for different solids contents and at different temperatures. The results obtained enabled us to study the rheological behavior and develop an artificial neural network model to control the viscosity of the slurry at the phosphoric acid attack tank
Journal: Data and Metadata
Pages: 160
Volume: 2
Year: 2023
DOI: 10.56294/dm2023160
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:160:id:1056294dm2023160

Template-Type: ReDIF-Article 1.0
Author-Name: Mr. Rohit
Author-Name-First: Mr.
Author-Name-Last: Rohit
Author-Name: Kapil Sethi
Author-Name-First: Kapil
Author-Name-Last: Sethi
Author-Name: Mudassir Khan
Author-Name-First: Mudassir
Author-Name-Last: Khan
Author-Name: Ashish Raina
Author-Name-First: Ashish
Author-Name-Last: Raina
Title: Machine Learning Model for Prediction of the Chemicals Harmfulness on Staff and Guests in the Hospitality Industry: A Pilot Study
Abstract: This article examines the trend around the adoption of machine learning in the hotel business in light of the significance of new technologies. According to previous research, the hospitality industry uses a variety of chemicals for cleaning. Cleaning supplies are the housekeeping department's primary tool in their daily routine to keep rooms and common areas clean and tidy. Guest and staff don't know the harmfulness of these chemicals. Providing hospitality that meets the needs of guests requires not only a positive attitude, but also high-quality and excellent services that keep guests warm, relaxed, and comfortable. But in some incidents, we find that the guest and staff health is affected by the chemicals. Also, no one worked on predicting the chemical's effects on staff and guest health in the hospitality sector with the use of Machine Learning models. For this purpose, data is collected from different hotels of Delhi NCR in India. There were two distinct fields utilized for assessment and instruction. For the investigation, machine learning methods were employed. The research project employed five machine learning methods. The newly developed MHC-CNN algorithm achieved the highest accuracy (93,75) in comparison to other cutting-edge machine learning techniques. The created technique can be expanded upon and applied in many hotels all around the world
Journal: Data and Metadata
Pages: 161
Volume: 2
Year: 2023
DOI: 10.56294/dm2023161
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:161:id:1056294dm2023161

Template-Type: ReDIF-Article 1.0
Author-Name: Prachi Jain
Author-Name-First: Prachi
Author-Name-Last: Jain
Author-Name: Vinod Maan
Author-Name-First: Vinod
Author-Name-Last: Maan
Title: Optimizing Emotion Recognition of Non-Intrusive E-Walking Dataset
Abstract: Emotion recognition being a complex task because of its valuable usages in critical fields like Robotics, human-computer interaction and mental health has recently gathered huge attention. The selection and optimization of suitable feature sets that can accurately capture the underlying emotional states is one of the critical challenges in Emotion Recognition. Metaheuristic optimization techniques have shown promise in addressing this challenge by efficiently exploring the large and complex feature space. This research paper proposes a novel framework for emotion recognition that uses metaheuristic optimization. The key idea behind metaheuristic optimization is to explore the search space in an intelligent way, by generating candidate solutions and iteratively improving them until an optimal or near-optimal solution is found. The accuracy & robustness of emotion identification systems can be enhanced by optimizing the metaheuristic optimization. The major contribution of this research is to develop a Chiropteran Mahi Metaheuristic optimization which emphasizes the weights updating in the classifier for improving the accuracy of the proposed system
Journal: Data and Metadata
Pages: 162
Volume: 2
Year: 2023
DOI: 10.56294/dm2023162
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:162:id:1056294dm2023162

Template-Type: ReDIF-Article 1.0
Author-Name: Ángel Acevedo Duque
Author-Name-First: Ángel
Author-Name-Last: Acevedo Duque
Author-Name: Agustín Álvarez Herranz
Author-Name-First: Agustín
Author-Name-Last: Álvarez Herranz
Author-Name: Enrique Marinao Artigas
Author-Name-First: Enrique
Author-Name-Last: Marinao Artigas
Title: Scientometrics study of country branding and its contribution to sustainable development in nations
Abstract: The main economic powers are focusing on a sustainable economic recovery following the crises triggered by systemic risks. In this context of global renewal, the opportunity arises to promote long-term collective goals and avoid unsustainable setbacks in the social, economic, and environmental realms. This article aims to conduct a critical analysis of the scientific production on country branding and its contribution to sustainable development. From 1991 to 2023, there is an interesting scientific contribution from researchers worldwide, although the years 2022 and 2023 lack production. Through scientometrics analysis using data from Web of Science (JCR and ESCI), 103 articles were identified in the knowledge categories "Country Brand" and "Sustainable Development." Laws such as Price, Zipf, Lotka, Bradford, and the Hirsch index were applied. The results reveal contributions from authors and institutions at a global level, highlighting the international relevance of the subject. Global precedents in country branding research are emphasized, aiming to establish a connection between this field and the sustainable development of nations. With this article, the authors seek to rekindle interest in this theme, promoting a comprehensive approach to the sustainable future of nations
Journal: Data and Metadata
Pages: 163
Volume: 2
Year: 2023
DOI: 10.56294/dm2023163
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:163:id:1056294dm2023163

Template-Type: ReDIF-Article 1.0
Author-Name: Aymane Ezzaim
Author-Name-First: Aymane
Author-Name-Last: Ezzaim
Author-Name: Aziz Dahbi
Author-Name-First: Aziz
Author-Name-Last: Dahbi
Author-Name: Abdelfatteh Haidine
Author-Name-First: Abdelfatteh
Author-Name-Last: Haidine
Author-Name: Abdelhak Aqqal
Author-Name-First: Abdelhak
Author-Name-Last: Aqqal
Title: Enhancing Academic Outcomes through an Adaptive Learning Framework Utilizing a Novel Machine Learning-Based Performance Prediction Method
Abstract: Introduction:E landscapes have been transformed by technological advancements, enabling adaptive and flexible learning through AI-based and decision-oriented adaptive learning systems. The increasing importance of this solutions is underscored by the pivotal role of the learner model, representing the core of the teaching-learning dynamic. This model, encompassing qualities, knowledge, abilities, behaviors, preferences, and unique distinctions, plays a crucial role in customizing the learning experience. It influences decisions related to learning materials, teaching strategies, and presentation styles. Objective: This study meets the need for applying AI-driven adaptive learning in education, implementing a novel method that uses self-esteem (ES), emotional intelligence (EQ), and demographic data to predict student performance and adjust the learning process. Methods: Our study involved collecting and processing data, constructing a predictive machine learning model, implementing it as an online solution, and conducting an experimental study with 146 high school students in computer science and French as foreign language. The aim was to tailor the teaching-learning process to the learners' performance. Results: significant correlations were observed between self-esteem, emotional intelligence, demographic data, and final grades. The predictive model demonstrated a 90 % accuracy rate. In the experimental group, the results indicated higher scores, with an average of 15,78/20 compared to the control group's 12,53/20 in computer science. Similarly, in French as a foreign language, the experimental group achieved an average of 13,78/20, surpassing the control group's 10,47/20. Conclusion: the achieved results motivate the creation of a multifactorial AI-driven adaptive learning platform. Recognizing the necessity for improvement, we aim to refine the predicted performance score through the incorporation of a diagnostic evaluation, ensuring an optimal grouping of learners
Journal: Data and Metadata
Pages: 164
Volume: 2
Year: 2023
DOI: 10.56294/dm2023164
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:164:id:1056294dm2023164

Template-Type: ReDIF-Article 1.0
Author-Name: Nehal Ettaloui
Author-Name-First: Nehal
Author-Name-Last: Ettaloui
Author-Name: Sara Arezki
Author-Name-First: Sara
Author-Name-Last: Arezki
Author-Name: Taoufiq Gadi
Author-Name-First: Taoufiq
Author-Name-Last: Gadi
Title: An Overview of Blockchain-Based Electronic Health Records and Compliance with GDPR and HIPAA
Abstract: The healthcare sector plays a pivotal role in both generating and relying on vast amounts of data, emphasizing the significance of collecting, managing, and sharing information. Technological advancements have facilitated the transformation of healthcare data into electronic health records (EHRs). These digital records are disseminated among various stakeholders, including patients, healthcare professionals, providers, insurance companies, and pharmacies. Given the sensitivity of healthcare information, the assimilation of new technologies is paramount. Blockchain technology, with its immutable nature and decentralized features, has emerged as a promising solution to instigate changes in the healthcare system. In the healthcare domain, where confidentiality is crucial, strict regulations are in place to safeguard patient privacy. Frameworks like the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) are designed to mitigate the risks associated with health data breaches. Although blockchain's characteristics, such as enhanced interoperability, anonymity, and access control, can improve the overall landscape of health data management, it is imperative for blockchain applications to adhere to existing regulatory frameworks for practical implementation. This paper delves into the examination of the compliance of blockchain-based EHR systems with regulations like HIPAA and GDPR. Additionally, it introduces a Blockchain-based EHR model specifically crafted to seamlessly align with regulatory requirements, ensuring its viability and effectiveness in real-world scenarios
Journal: Data and Metadata
Pages: 166
Volume: 2
Year: 2023
DOI: 10.56294/dm2023166
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:166:id:1056294dm2023166

Template-Type: ReDIF-Article 1.0
Author-Name: Sohaib Khalid
Author-Name-First: Sohaib
Author-Name-Last: Khalid
Author-Name: Driss Effina
Author-Name-First: Driss
Author-Name-Last: Effina
Title: Quantifying Urban Dynamics: An Investigation of Employment Mobility, Spatial Proximity, and Residential Attractiveness in Moroccan Small Cities Applying Data Science Methods
Abstract: The primary objective of this study is to delve into the intricate interplay between workforce mobility and the spatial proximity to agglomerations, and their collective impact on the residential attractiveness of small cities in Morocco. Initially, we meticulously estimated the net migration rate, a robust and widely acknowledged metric within scholarly discourse, employed to gauge the territorial magnetism. Subsequently, employing this metric as the dependent variable, we embarked on a thorough examination of how the mobility of the workforce and territorial proximity to agglomerations synergistically shape the attractiveness of small cities. The assessment of the net migration rate unearthed a pattern of dispersion, a phenomenon that catalyzed our adoption of quantile regression modeling. Therefore, our rigorous analysis has unveiled a compelling revelation: the geographical proximity of small cities exerts a pronounced influence on their allure. Specifically, a closer adjacency to agglomeration zones invariably results in an augmented residential attractiveness. Furthermore, our research has discerned a robust correlation between heightened workforce mobility and an amplified migratory interest in small Moroccan cities. These compelling findings challenge the prevailing notion that the residential magnetism of small cities in Morocco hinges solely on their socio-economic profile. Instead, it underscores the profound impact wielded by their spatial disposition and the dynamic movements of the workforce
Journal: Data and Metadata
Pages: 167
Volume: 2
Year: 2023
DOI: 10.56294/dm2023167
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:167:id:1056294dm2023167

Template-Type: ReDIF-Article 1.0
Author-Name: Abdelhamid El Beghdadi
Author-Name-First: Abdelhamid
Author-Name-Last: El Beghdadi
Author-Name: Mohammed Merzougui
Author-Name-First: Mohammed
Author-Name-Last: Merzougui
Author-Name: Ahmad El Allaoui
Author-Name-First: Ahmad
Author-Name-Last: El Allaoui
Title: Gray Level Homogeneity Analysis: A Novel Approach
Abstract: In this article, we propose a method that helps us to analyze the homogeneity of gray levels locally by calculating a coefficient for each pixel based on the nature of neighboring pixels. This principle of encoding pixels according to their adjacent neighbors is described the nature of the distribution of gray levels within the image and measures their degree of homogeneity locally. This allows us to detect the different regions of the image and their contours based on the coefficient of homogeneity of the gray levels. In addition, this allows us to exploit these homogeneity coefficients to restructure regions of the image, extract and enhance the image contours while reducing the noise present in the image. This homogeneity study principle has several functions in the study and analysis of image texture, as do other methods of homogeneity assessment, such as the local contrast descriptor (LCD) and the co-occurrence matrix
Journal: Data and Metadata
Pages: 170
Volume: 2
Year: 2023
DOI: 10.56294/dm2023170
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:170:id:1056294dm2023170

Template-Type: ReDIF-Article 1.0
Author-Name: Mariame Oumoulylte
Author-Name-First: Mariame
Author-Name-Last: Oumoulylte
Author-Name: Ali Omari Alaoui
Author-Name-First: Ali
Author-Name-Last: Omari Alaoui
Author-Name: Yousef Farhaoui
Author-Name-First: Yousef
Author-Name-Last: Farhaoui
Author-Name: Ahmad El Allaoui
Author-Name-First: Ahmad
Author-Name-Last: El Allaoui
Author-Name: Abdelkhalak Bahri
Author-Name-First: Abdelkhalak
Author-Name-Last: Bahri
Title: Convolutional Neural Network-Based Approach For Skin Lesion Classification
Abstract: Skin cancer represents one of the primary forms of cancer arising from various dermatological disorders. It can be further categorized based on morphological characteristics, coloration, structure, and texture. Given the rising incidence of skin cancer, its significant mortality rates, and the substantial costs associated with medical treatment, the imperative lies in early detection to promptly diagnose symptoms and initiate appropriate interventions. Traditionally, skin cancer diagnosis and detection involve manual screening and visual examination conducted by dermatologists. these techniques are complex, error-prone, and time-consuming. Machine learning algorithms, particularly deep learning approaches, have been applied to analyze images of skin lesions, detect potential cancerous growths, and provide predictions regarding the likelihood of malignancy. In this paper, we have developed an optimized deep convolutional neural network (DCNN) specifically tailored for classifying skin lesions into benign and malignant categories. Thereby, enhancing the precision of disease diagnosis. Our study encompassed the utilization of a dataset comprising 3,297 dermoscopic images. To enhance the model's performance, we applied rigorous data preprocessing techniques and softmax activation algorithms. The suggested approach employs multiple optimizers, including Adam, RMSProp, and SGD, all configured with a learning rate of 0.0001. The outcomes of our experiments reveal that the Adam optimizer outperforms the others in distinguishing benign and malignant skin lesions within the ISIC dataset, boasting an accuracy score of 84 %, a loss rate of 32 %, a recall rating of 85 %, a precision score of 85 %, a f1-score of 85 %, and a ROC-AUC of 83 %
Journal: Data and Metadata
Pages: 171
Volume: 2
Year: 2023
DOI: 10.56294/dm2023171
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:171:id:1056294dm2023171

Template-Type: ReDIF-Article 1.0
Author-Name: Nerio Enriquez Gavilan
Author-Name-First: Nerio
Author-Name-Last: Enriquez Gavilan
Author-Name: Yolanda Yauri Paquiyauri
Author-Name-First: Yolanda Yauri Paquiyauri
Author-Name-Last: Yolanda Yauri Paquiyauri
Author-Name: Brian Meneses Claudio
Author-Name-First: Brian
Author-Name-Last: Meneses Claudio
Author-Name: Aydeé Lopez Curasma
Author-Name-First: Aydeé
Author-Name-Last: Lopez Curasma
Author-Name: Julio Romero Sandoval
Author-Name-First: Julio
Author-Name-Last: Romero Sandoval
Title: Pedagogical Management and Managerial Leadership in the Secondary Educational Institutions of Network 6, UGEL 06, Ate, 2020
Abstract: The main objective of this research was to determine the relationship between pedagogical management (PM) and directive leadership (ML). The research was conducted with a quantitative approach, basic type, correlational level, non-experimental design, cross-sectional and hypothetical deductive method. Non-probabilistic convenience sampling was applied considering a sample made up of 60 teachers from secondary level educational institutions of Network 6, UGEL 06, of the Ate district. The validity of the expert judgment and the confirmation of the reliability were fulfilled through Cronbach's Alpha (pedagogical management = 0,974 and directive leadership = 0,909). The survey technique was used and through two instruments (questionnaires) the data were collected via Google forms. The results obtained were (P = 0,000, Rho = 0,586), it is concluded that there is a moderate positive significant correlation between the study variables
Journal: Data and Metadata
Pages: 172
Volume: 2
Year: 2023
DOI: 10.56294/dm2023172
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:172:id:1056294dm2023172

Template-Type: ReDIF-Article 1.0
Author-Name: Mariame Oumoulylte
Author-Name-First: Mariame
Author-Name-Last: Oumoulylte
Author-Name: Abdelkhalak Bahri
Author-Name-First: Abdelkhalak
Author-Name-Last: Bahri
Author-Name: Yousef Farhaoui
Author-Name-First: Yousef
Author-Name-Last: Farhaoui
Author-Name: Ahmad El Allaoui
Author-Name-First: Ahmad
Author-Name-Last: El Allaoui
Title: An efficient prediction system for diabetes disease based on machine learning algorithms
Abstract: Diabetes is a persistent medical condition that arises when the pancreas loses its ability to produce insulin or when the body is unable to utilize the insulin it generates effectively. In today's world, diabetes stands as one of the most prevalent and, unfortunately, one of the deadliest diseases due to certain complications. Timely detection of diabetes plays a crucial role in facilitating its treatment and preventing the disease from advancing further. In this study, we have developed a diabetes prediction model by leveraging a variety of machine learning classification algorithms, including K-Nearest Neighbors (KNN), Naive Bayes, Support Vector Machine (SVM), Decision Tree, Random Forest, and Logistic Regression, to determine which algorithm yields the most accurate predictive outcomes. we employed the famous PIMA Indians Diabetes dataset, comprising 768 instances with nine distinct feature attributes. The primary objective of this dataset is to ascertain whether a patient has diabetes based on specific diagnostic metrics included in the collection. In the process of preparing the data for analysis, we implemented a series of preprocessing steps. The evaluation of performance metrics in this study encompassed accuracy, precision, recall, and the F1 score. The results from our experiments indicate that the K-nearest neighbors’ algorithm (KNN) surpasses other algorithms in effectively differentiating between individuals with diabetes and those without in the PIMA dataset
Journal: Data and Metadata
Pages: 173
Volume: 2
Year: 2023
DOI: 10.56294/dm2023173
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:173:id:1056294dm2023173

Template-Type: ReDIF-Article 1.0
Author-Name: Ali Benaissa
Author-Name-First: Ali
Author-Name-Last: Benaissa
Author-Name: Abdelkhalak Bahri
Author-Name-First: Abdelkhalak
Author-Name-Last: Bahri
Author-Name: Ahmad El Allaoui
Author-Name-First: Ahmad
Author-Name-Last: El Allaoui
Author-Name: My Abdelouahab Salahddine
Author-Name-First: My
Author-Name-Last: Abdelouahab Salahddine
Title: Transformative Progress in Document Digitization: An In-Depth Exploration of Machine and Deep Learning Models for Character Recognition
Abstract: Introduction: this paper explores the effectiveness of character recognition models for document digitization, leveraging diverse machine learning and deep learning techniques. The study, driven by the increasing relevance of image classification in various applications, focuses on evaluating Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and VGG16 with transfer learning. The research employs a challenging French alphabet dataset, comprising 82 classes, to assess the models' capacity to discern intricate patterns and generalize across diverse characters. Objective: This study investigates the effectiveness of character recognition models for document digitization using diverse machine learning and deep learning techniques. Methods: the methodology initiates with data preparation, involving the creation of a merged dataset from distinct sections, encompassing digits, French special characters, symbols, and the French alphabet. The dataset is subsequently partitioned into training, test, and evaluation sets. Each model undergoes meticulous training and evaluation over a specific number of epochs. The recording of fundamental metrics includes accuracy, precision, recall, and F1-score for CNN, RNN, and VGG16, while SVM and KNN are evaluated based on accuracy, macro avg, and weighted avg. Results: the outcomes highlight distinct strengths and areas for improvement across the evaluated models. SVM demonstrates remarkable accuracy of 98,63 %, emphasizing its efficacy in character recognition. KNN exhibits high reliability with an overall accuracy of 97 %, while the RNN model faces challenges in training and generalization. The CNN model excels with an accuracy of 97,268 %, and VGG16 with transfer learning achieves notable enhancements, reaching accuracy rates of 94,83 % on test images and 94,55 % on evaluation images. Conclusion: our study evaluates the performance of five models—Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and VGG16 with transfer learning—on character recognition tasks. SVM and KNN demonstrate high accuracy, while RNN faces challenges in training. CNN excels in image classification, and VGG16, with transfer learning, enhances accuracy significantly. This comparative analysis aids in informed model selection for character recognition applications
Journal: Data and Metadata
Pages: 174
Volume: 2
Year: 2023
DOI: 10.56294/dm2023174
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:174:id:1056294dm2023174

Template-Type: ReDIF-Article 1.0
Author-Name: Franklin Moza Villalobos
Author-Name-First: Franklin
Author-Name-Last: Moza Villalobos
Author-Name: Juan Natividad Villanueva
Author-Name-First: Juan
Author-Name-Last: Natividad Villanueva
Author-Name: Brian Meneses Claudio
Author-Name-First: Brian
Author-Name-Last: Meneses Claudio
Title: Use of Convolutional Neural Networks (CNN) to recognize the quality of oranges in Peru by 2023
Abstract: Introduction: the agricultural sector in Peru has witnessed a notable increase in the production of oranges, which has promoted the essential use of convolutional neural networks (CNN). The ability to interpret images by visual artificial intelligence has been fundamental for the analysis and processing of these images, especially in the detection and classification of fruits, standing out in the specific case of oranges. Objective: conduct a systematic literature review (RSL) to evaluate the neural networks used in the classification of oranges in Peru. Method: an RSL was carried out using the PICO strategy to search the Scopus database. The selection criteria included studies that used convolutional neural networks to classify the quality status of oranges in the Peruvian context. Results: all the studies reviewed were based on the use of convolutional neural networks (CNN) for fruit classification, using various architectures and techniques. Some studies focused on a single specific fruit, while others addressed the classification of multiple types of fruits, highlighting the importance of the number and variety of images for training the networks. Conclusions: convolutional neural networks show effectiveness in orange classification, but the quality of the images and the variety of data are essential to improve accuracy
Journal: Data and Metadata
Pages: 175
Volume: 2
Year: 2023
DOI: 10.56294/dm2023175
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:175:id:1056294dm2023175

Template-Type: ReDIF-Article 1.0
Author-Name: Othmane Farhaoui
Author-Name-First: Othmane
Author-Name-Last: Farhaoui
Author-Name: Mohamed Rida Fethi
Author-Name-First: Mohamed
Author-Name-Last: Rida Fethi
Author-Name: Imad Zeroual
Author-Name-First: Imad
Author-Name-Last: Zeroual
Author-Name: Ahmad El Allaoui
Author-Name-First: Ahmad
Author-Name-Last: El Allaoui
Title: Toward Innovative Recognition of Handwritten Arabic Characters: A Hybrid Approach with SIFT, BoVW, and SVM classification
Abstract: The goal of handwriting recognition has been a top priority for those who want to enter data into computer systems for more than thirty years. In several fields, the advent of handwriting recognition technology is highly anticipated. OCR technology has made it possible for computers to recognize characters as visual objects and collect data about their unique characteristics in recent years. In particular, several studies in this field have focused on Arabic writing. The use of machines to examine handwritten papers is the first step in the character identification process. The identification of specific Arabic characters is the main goal of this particular investigation. In computer vision, Arabic character recognition is very important since it's necessary to correctly recognize and classify Arabic letters and characters in manuscripts. In this research, an innovative approach based on identifying Arabic character characteristics using BoVW (bag of visual words) and SIFT (Scale Invariant Feature Transform) features is proposed. These features are clustered using k-means clustering to produce a dictionary. Following that, SVM (Support Vector Machine) is utilized to classify the word images in a visual codebook created using these terms. The proposed approach is an innovative method to deal with the difficulties associated with Arabic hand-writing recognition. The utilization of BoVW and SIFT features is expected to enhance the system's robustness in recognizing and classifying Arabic characters. The proposed approach will be experimentally evaluated using a dataset that includes a variety of Arabic characters written in various styles. The results of this study will offer important new perspectives on the effectiveness and practicality of the approach suggested
Journal: Data and Metadata
Pages: 176
Volume: 2
Year: 2023
DOI: 10.56294/dm2023176
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:176:id:1056294dm2023176

Template-Type: ReDIF-Article 1.0
Author-Name: Mohamed Rida Fethi
Author-Name-First: Mohamed
Author-Name-Last: Rida Fethi
Author-Name: Othmane Farhaoui
Author-Name-First: Othmane
Author-Name-Last: Farhaoui
Author-Name: Imad Zeroual
Author-Name-First: Imad
Author-Name-Last: Zeroual
Author-Name: Ahmad El Allaoui
Author-Name-First: Ahmad
Author-Name-Last: El Allaoui
Title: A Progressive Approach to Arabic Character Recognition Using a Modified Freeman Chain Code Algorithm
Abstract: Arabic character identification presents a significant obstacle to the comprehension and analysis of Arabic text. This paper presents an improved technique that generates Freeman code from handwritten Arabic characters. This code provides the shortest code length without losing character information, accounting for all handwritten Arabic character variants. We tested this code using a set of Arabic characters in various formats to identify Arabic characters in order to take use of the code generated by our enhanced method. We also performed a comparison between our Freeman code and codes generated in other related research. In light of this, the code that we obtained correctly represents the Arabic letter in all of its variants, including the ones that the algorithms in previous publications did not consider. Consequently, our novel method based on Freeman coding represents a significant advancement in Arabic character recognition. Furthermore, our method provides a successful way of identifying and presenting Arabic characters
Journal: Data and Metadata
Pages: 178
Volume: 2
Year: 2023
DOI: 10.56294/dm2023178
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:178:id:1056294dm2023178

Template-Type: ReDIF-Article 1.0
Author-Name: Noredine Hajraoui
Author-Name-First: Noredine
Author-Name-Last: Hajraoui
Author-Name: Mourade Azrour
Author-Name-First: Mourade
Author-Name-Last: Azrour
Author-Name: Ahmad El Allaoui
Author-Name-First: Ahmad
Author-Name-Last: El Allaoui
Title: Classification of diseases in tomato leaves with Deep Transfer Learning
Abstract: Plant diseases are important factors because they significantly affect the quality, quantity, and yield of agricultural products. Therefore, it is important to detect and diagnose these diseases at an early stage. The overall objective of this study is to develop an acceptable deep learning model to correctly classify diseases on tomato leaves in RGB color images. To address this challenge, we use a new approach based on combining two deep learning models VGG16 and ResNet152v2 with transfer learning. The image dataset contains 55 000 images of tomato leaves in 5 different classes, 4 diseases and one healthy class. The results of our experiment are promising and encouraging, showing that the proposed model achieves 99,08 % accuracy in training, 97,66 % in validation, and 99,0234 % in testing
Journal: Data and Metadata
Pages: 181
Volume: 2
Year: 2023
DOI: 10.56294/dm2023181
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:181:id:1056294dm2023181

Template-Type: ReDIF-Article 1.0
Author-Name: Amine El Haddadi
Author-Name-First: Amine
Author-Name-Last: El Haddadi
Author-Name: Oumaima El Haddadi
Author-Name-First: Oumaima
Author-Name-Last: El Haddadi
Author-Name: Mohamed Cherradi
Author-Name-First: Mohamed
Author-Name-Last: Cherradi
Author-Name: Fadwa Bouhafer
Author-Name-First: Fadwa
Author-Name-Last: Bouhafer
Author-Name: Anass El Haddadi
Author-Name-First: Anass
Author-Name-Last: El Haddadi
Author-Name: Ahmed El Allaoui
Author-Name-First: Ahmed
Author-Name-Last: El Allaoui
Title: Data Lake Management System based on Topic Modeling
Abstract: In an environment full of competitiveness, data is a valuable asset for any company looking to grow. It represents a real competitive economic and strategic lever. The most reputable companies are not only concerned with collecting data from heterogeneous data sources, but also with analyzing and transforming these datasets into better decision-making. In this context, the data lake continues to be a powerful solution for storing large amounts of data and providing data analytics for decision support. In this paper, we examine the intelligent data lake management system that addresses the drawbacks of traditional business intelligence, which is no longer capable of handling data-driven demands. Data lakes are highly suitable for analyzing data from a variety of sources, particularly when data cleaning is time-consuming. However, ingesting heterogeneous data sources without any schema represents a major issue, and a data lake can easily turn into a data swamp. In this study, we implement the LDA topic model for managing the storage, processing, analysis, and visualization of big data. To assess the usefulness of our proposal, we evaluated its performance based on the topic coherence metric. The results of these experiments showed our approach to be more accurate on the tested datasets
Journal: Data and Metadata
Pages: 183
Volume: 2
Year: 2023
DOI: 10.56294/dm2023183
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:183:id:1056294dm2023183

Template-Type: ReDIF-Article 1.0
Author-Name: Ali Benaissa
Author-Name-First: Ali
Author-Name-Last: Benaissa
Author-Name: Abdelkhalak Bahri
Author-Name-First: Abdelkhalak
Author-Name-Last: Bahri
Author-Name: Ahmad El Allaoui
Author-Name-First: Ahmad
Author-Name-Last: El Allaoui
Author-Name: My Abdelouahab Salahddine
Author-Name-First: My
Author-Name-Last: Abdelouahab Salahddine
Title: Build a Trained Data of Tesseract OCR engine for Tifinagh Script Recognition
Abstract: This article introduces a methodology for constructing a trained dataset to facilitate Tifinagh script recognition using the Tesseract OCR engine. The Tifinagh script, widely used in North Africa, poses a challenge due to the lack of built-in recognition capabilities in Tesseract. To overcome this limitation, our approach focuses on image generation, box generation, manual editing, charset extraction, and dataset compilation. By leveraging Python scripting, specialized software tools, and Tesseract's training utilities, we systematically create a comprehensive dataset for Tifinagh script recognition. The dataset enables the training and evaluation of machine learning models, leading to accurate character recognition. Experimental results demonstrate high accuracy, precision, recall, and F1 score, affirming the effectiveness of the dataset and its potential for practical applications. The results highlight the robustness of the OCR system, achieving an outstanding accuracy rate of 99,97 %. The discussion underscores its superior performance in Tifinagh character recognition, exceeding the findings in the field. This methodology contributes significantly to enhancing OCR technology capabilities and encourages further research in Tifinagh script recognition, unlocking the wealth of information contained in Tifinagh documents
Journal: Data and Metadata
Pages: 185
Volume: 2
Year: 2023
DOI: 10.56294/dm2023185
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:185:id:1056294dm2023185

Template-Type: ReDIF-Article 1.0
Author-Name: Nath Chandamita
Author-Name-First: Nath
Author-Name-Last: Chandamita
Author-Name: Bhairab Sarma
Author-Name-First: Bhairab
Author-Name-Last: Sarma
Title: A Grapheme to Phoneme Based Text to Speech Conversion Technique in Unicode Language
Abstract: Text-to-speech conversion can be done with two approaches: dictionary-based (database) approach and grapheme-to-phoneme (G2P) mapping. One of the drawbacks of this approach is its performance depends on the size of the dictionary or database. In the case of domain specific conversion, a simple rule -based technique is used to play pre-recorded audio for each equivalent token. It is easy to design but its limitation is mapping with the sound database and availability of the audio file in the database. In general, grapheme to phoneme conversion can be used in any domain. Advantages are the limited size of the database required, ease of mapping and compliance with domain. However, G2P suffers from pronounce ambiguity (formation of audio output). This paper will discuss about the grapheme-to -phoneme mapping and its application in text to speech conversion system. In this work, Assamese (an Indian scheduled Unicode language) is used as the experimental language and its performance is analysis with another Unicode language (Hindi). English (ASCII) language will be used as a benchmark to compare with the target language
Journal: Data and Metadata
Pages: 191
Volume: 2
Year: 2023
DOI: 10.56294/dm2023191
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:191:id:1056294dm2023191

Template-Type: ReDIF-Article 1.0
Author-Name: D Harshavardhan
Author-Name-First: D
Author-Name-Last: Harshavardhan
Author-Name: K Saisree
Author-Name-First: K
Author-Name-Last: Saisree
Author-Name: S Ragavarshini
Author-Name-First: S
Author-Name-Last: Ragavarshini
Title: Parturition Detection Using Oxytocin Secretion Level and Uterine Muscle Contraction Intensity
Abstract: The "Parturition Detection Sensor Belt," also known as the "Labor Pain Detection Sensor Belt," represents a novel advancement in maternal health monitoring. "Parturition Detection Sensor Belt" designed to simultaneously predict oxytocin levels and monitor uterine muscle contractions. This innovative system combines real-time prediction of oxytocin levels and simultaneous monitoring of uterine muscle contractions to provide a comprehensive solution for parturition detection. By integrating cutting-edge sensor technology and deep learning algorithms, the system offers precise, non-invasive monitoring during labor. The oxytocin level predictions aid in understanding maternal well-being, while the real-time uterine muscle contraction monitoring ensures early detection of labor progression. This interdisciplinary approach leverages advancements in biomedical engineering and data analysis, holding promise for improving the safety and care of expectant mothers. The "Parturition Detection Sensor Belt" has the potential to revolutionize the field of obstetrics by offering a versatile tool for healthcare providers, enhancing maternal health, and facilitating data-driven research in this critical domain. A correlation is developed between oxytocin release and muscle contraction which turns out to be nearly 0,899836. This infers that the two factors that we are considering as important parameters are having a strong association with each other
Journal: Data and Metadata
Pages: 195
Volume: 2
Year: 2023
DOI: 10.56294/dm2023195
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:195:id:1056294dm2023195

Template-Type: ReDIF-Article 1.0
Author-Name: E. Banu
Author-Name-First: E.
Author-Name-Last: Banu
Author-Name: A. Geetha
Author-Name-First: A.
Author-Name-Last: Geetha
Title: Hybrid Convolutional Neural Network with Whale Optimization Algorithm (HCNNWO) Based Plant Leaf Diseases Detection
Abstract: Plant diseases appear to be posing a serious danger to the production and availability of food globally. The main factor affecting the quality and productivity of agricultural products is the health of the plants. In this paper, we describe a modified plant disease detection using deep convolutional neural networks in real time. By employing image processing techniques to enlarge the plant illness photos, the plant disease sets of data were initially produced. To recognise plant illnesses, a system called Convolutional Neural Network combined with Wolf Optimisation algorithm (CNN-WO) was used. Finally, the Whale Optimization algorithm (WO) is used to maximise and optimizes getting input. And it is given to CNN's learning rate for classification process. This paper presents an image segmentation and classification technique to automatically identify plant leaf diseases. The suggested strategy increased accuracy, sensitivity, precision, F1 measure, and specificity of plant disease detection. According to this study, HCNNWO real detectors have improved, which would require deep learning. It would be an effective method for determining plant illnesses and other diseases within plants. According to the evaluation report, the suggested method offers good reliability. To evaluate how well the suggested algorithm performs in comparison to cutting-edge techniques such as SVM, BPNN and CNN, experiments are conducted on datasets that are openly accessible
Journal: Data and Metadata
Pages: 196
Volume: 2
Year: 2023
DOI: 10.56294/dm2023196
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:196:id:1056294dm2023196

Template-Type: ReDIF-Article 1.0
Author-Name: Sanjeev Kumar Bhatt
Author-Name-First: Sanjeev
Author-Name-Last: Kumar Bhatt
Author-Name: S. Srinivasan
Author-Name-First: S.
Author-Name-Last: Srinivasan
Author-Name: Piyush Prakash
Author-Name-First: Piyush
Author-Name-Last: Prakash
Title: Brain Tumor Segmentation Pipeline Model Using U-Net Based Foundation Model
Abstract: Medical professionals often rely on Magnetic Resonance Imaging (MRI) to obtain non-invasive medical images. One important use of this technology is brain tumor segmentation, where algorithms are used to identify tumors in MRI scans of the brain. The foundation model Pipeline is based on U-Net Architecture to handle medical image segmentation and has been fine-tuned in the research paper to segment brain tumors. The model will be further trained on various medical images to segment images for various bio-medical purposes and used as part of the Generative AI functional model framework. Accurate segmentation of tumors is essential for treatment planning and monitoring, and this approach can potentially improve patient outcomes and quality of life
Journal: Data and Metadata
Pages: 197
Volume: 2
Year: 2023
DOI: 10.56294/dm2023197
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:197:id:1056294dm2023197

Template-Type: ReDIF-Article 1.0
Author-Name: Yamilé Rodríguez Sotomayor
Author-Name-First: Yamilé
Author-Name-Last: Rodríguez Sotomayor
Author-Name: Lee Yang Díaz Chieng
Author-Name-First: Lee Yang
Author-Name-Last: Díaz Chieng
Author-Name: Luis Ernesto Paz Enrique
Author-Name-First: Luis Ernesto
Author-Name-Last: Paz Enrique
Author-Name: Hilda Lidia Iznaga Brooks
Author-Name-First: Hilda Lidia
Author-Name-Last: Iznaga Brooks
Author-Name: Katsuyori Pérez Mola
Author-Name-First: Katsuyori
Author-Name-Last: Pérez Mola
Author-Name: Jimmy Javier Calás Torres
Author-Name-First: Jimmy Javier
Author-Name-Last: Calás Torres
Title: Gender approach in the activity and scientific production of Cuban medical university journals
Abstract: Introduction: science and scientific production are spaces given to men for centuries, although in the 21st century, there are gaps in this sense. Aim: to describe from a gender perspective the scientific production in the academic journals of Cuban medical universities, period 2017-2021. Method: a descriptive bibliometric study was carried out. The universe consisted of all the academic journals of Cuban medical universities in the period 2017-2021, they were reviewed between December 2022 and March 2023. The sampling was non-probabilistic and the sample was 19. Only regular numbers were considered. Results: women were 19 % of the magazine directors and 44 % of the editorial board composition. 57,5 % of the editorials were written by men. 2,6 % of the articles dealt with the gender approach. 61,8 % of the authors were women and 38,2 % were men. Women were 55,7 % of the lead authors. In 32,3 % of the articles, women predominated as authors and 23,7 % were written only by women; gender equality represented 14,1 %. Conclusions: an empowerment of women in Cuba is evident as they are the majority within scientific production in medical universities. However, this is not reflected in the direction of the magazines and the composition of their editorial committees, where men predominate, nor in publications with a gender focus, which is still insufficient
Journal: Data and Metadata
Pages: 199
Volume: 2
Year: 2023
DOI: 10.56294/dm2023199
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:199:id:1056294dm2023199

Template-Type: ReDIF-Article 1.0
Author-Name: Younes Jamouli
Author-Name-First: Younes
Author-Name-Last: Jamouli
Author-Name: Samir Tetouani
Author-Name-First: Samir
Author-Name-Last: Tetouani
Author-Name: Omar Cherkaoui
Author-Name-First: Omar
Author-Name-Last: Cherkaoui
Author-Name: Aziz Soulhi
Author-Name-First: Aziz
Author-Name-Last: Soulhi
Title: A model for Industry 4.0 readiness in manufacturing industries
Abstract: In the context of digital transformation, to assess the current state of manufacturing companies, a readiness model is proposed in this paper. Using a literature review and a framework considering maturity as an 'input' enabler and not as an 'output'. Three dimensions are considered in this model (Organization maturity, Technology maturity, and Process Maturity), to assess the company readiness (Ready or Not ready). Allowing compagnies to identify their readiness for Industry 4.0 (I4.0) adoption, by developing a decision support model, is the goal of this research. This model based on Fuzzy Inference System, considers the three decision criteria and then ranks the enterprise according to its output indicator. For the validation of this proposed model, an experimental study was conducted to assess the readiness of 2 manufacturing companies, a multinational in automotive sector and an SME in Apparel sector. The proposed model meets the desired objective and is therefore retained for the evaluation of the readiness to I4.0 in different manufacturing contexts
Journal: Data and Metadata
Pages: 200
Volume: 2
Year: 2023
DOI: 10.56294/dm2023200
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:200:id:1056294dm2023200

Template-Type: ReDIF-Article 1.0
Author-Name: Asmaa BENCHAMA
Author-Name-First: Asmaa
Author-Name-Last: BENCHAMA
Author-Name: Khalid ZEBBARA
Author-Name-First: Khalid
Author-Name-Last: ZEBBARA
Title: Novel Approach to Intrusion Detection: Introducing GAN-MSCNN-BILSTM with LIME Predictions
Abstract: This paper introduces an innovative intrusion detection system that harnesses Generative Adversarial Networks (GANs), Multi-Scale Convolutional Neural Networks (MSCNNs), and Bidirectional Long Short-Term Memory (BiLSTM) networks, supplemented by Local Interpretable Model-Agnostic Explanations (LIME) for interpretability. Employing a GAN, the system generates realistic network traffic data, encompassing both normal and attack patterns. This synthesized data is then fed into an MSCNN-BiLSTM architecture for intrusion detection. The MSCNN layer extracts features from the network traffic data at different scales, while the BiLSTM layer captures temporal dependencies within the traffic sequences. Integration of LIME allows for explaining the model's decisions. Evaluation on the Hogzilla dataset, a standard benchmark, showcases an impressive accuracy of 99,16 % for multi-class classification and 99,10 % for binary classification, while ensuring interpretability through LIME. This fusion of deep learning and interpretability presents a promising avenue for enhancing intrusion detection systems by improving transparency and decision support in network security
Journal: Data and Metadata
Pages: 202
Volume: 2
Year: 2023
DOI: 10.56294/dm2023202
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:202:id:1056294dm2023202

Template-Type: ReDIF-Article 1.0
Author-Name: Rayda Villalobos Castro
Author-Name-First: Rayda
Author-Name-Last: Villalobos Castro
Author-Name: Segundo Ríos Ríos
Author-Name-First: Segundo
Author-Name-Last: Ríos Ríos
Author-Name: Fernando Ochoa Paredes
Author-Name-First: Fernando
Author-Name-Last: Ochoa Paredes
Author-Name: Miguel Vargas Tasayco
Author-Name-First: Miguel
Author-Name-Last: Vargas Tasayco
Author-Name: Yrene Uribe Hernandez
Author-Name-First: Yrene
Author-Name-Last: Uribe Hernandez
Title: Balanced scorecard in the business development of MSMEs in the district of San Vicente de Cañete
Abstract: Introduction: This research was carried out on the topic: “Balanced scorecard in the business development of MSMEs in the district of San Vicente de Cañete, 2021”. Objective: Determine the influence of the balanced scorecard on the business development of MSMEs in the province of Cañete, 2021, so that the balanced scorecard indicators are considered the choice of MSMEs for an improvement in decision making. Method: In this scenario, an applied methodology was developed, with a quantitative approach, a non-experimental, transversal and correlational design. A questionnaire was used as a survey of 68 managers of MSMEs, which was made up of 23 questions on a Likert scale, these being validated with expert judgment. Results: The results achieved allowed us to confirm the hypotheses raised that the balanced scorecard has a significant influence on the business development of MSMEs in the province of Cañete, 2021, as well as the secondary hypotheses were confirmed. Conclusions: The same ones that stated that the balanced scorecard significantly influences economic profitability, product quality, resource optimization and innovation in MSMEs in the province. de Cañete, 2021
Journal: Data and Metadata
Pages: 203
Volume: 2
Year: 2023
DOI: 10.56294/dm2023203
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:203:id:1056294dm2023203

Template-Type: ReDIF-Article 1.0
Author-Name: Young Chool Choi
Author-Name-First: Young
Author-Name-Last: Chool Choi
Author-Name: Kim Gamin
Author-Name-First: Kim
Author-Name-Last: Gamin
Author-Name: Jeon Yunseo
Author-Name-First: Jeon
Author-Name-Last: Yunseo
Author-Name: Yona Cavallini
Author-Name-First: Yona
Author-Name-Last: Cavallini
Title: Utilizing Topic Modelling and AHP (Analytical Hierarchy Process) for Setting Policy Priorities to Strengthen Official Development Assistance at Local Government Level
Abstract: In Korea, aid projects to developing countries at central government level are increasing in number significantly every year, yet at local government level their scale is extremely small. Recognizing this problem, this study aims to set policy priorities to strengthen official development assistance (ODA) at local government level in Korea. Accordingly, we analysed the important issues relating to ODA projects at local government level by performing topic modelling analysis method. On the basis of these analysis results, policy priorities were derived using the AHP method. The analysis suggests that in Korea, in order to revitalize ODA projects at local government level, a dedicated department that can professionally handle these projects must be established within each local authority. Furthermore, it is important to recruit and deploy professional administrators who can utilize these dedicated departments to discover new ODA projects
Journal: Data and Metadata
Pages: 204
Volume: 2
Year: 2023
DOI: 10.56294/dm2023204
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:204:id:1056294dm2023204

Template-Type: ReDIF-Article 1.0
Author-Name: Andrés Sebastián Guevara Pabón
Author-Name-First: Andrés Sebastián
Author-Name-Last: Guevara Pabón
Author-Name: Erika Marcela León Revelo
Author-Name-First: Erika Marcela
Author-Name-Last: León Revelo
Author-Name: Leonel Gerardo Ruano Yarpaz
Author-Name-First: Leonel Gerardo
Author-Name-Last: Ruano Yarpaz
Title: Proposal for a vaccination scheme for children based on risk factors identified in the province of Imbabura, Ibarra Cantón
Abstract: In Ecuador there are risk factors that make it impossible for mothers with children to be vaccinated against different diseases. The objective of the research was to propose a comprehensive vaccination scheme based on the factors that may affect its implementation by the residents of the province of Imbabura, canton Ibarra. The research was carried out in a quantitative, transversal, descriptive, correlational modality with the support of analytical-synthetic, inductive-deductive, historical-logical and systemic methods with the application of a survey which was processed in the SPPS. 58 % of the mothers surveyed do not comply with the regular schedule for fear of contagion, with 45 % there are almost always biological in the health units when they have attended a scheduled appointment, 47 % stated that they do not have enough time to several reasons, with 69 % at the moment having some knowledge about the vaccines that are administered to infants and as a last factor is that 58 % of the people surveyed, their children under 5 years of age lack vaccines, this concludes that there are many factors related to the COVID-19 pandemic, which affected the child population, causing them to fall behind in the regular schedule, causing children to have no defenses to combat the different diseases that vaccines protect us, so it is important the intervention of mothers of families and/or legal guardians to comply with the immunization schedule
Journal: Data and Metadata
Pages: 212
Volume: 2
Year: 2023
DOI: 10.56294/dm2023212
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:212:id:1056294dm2023212

Template-Type: ReDIF-Article 1.0
Author-Name: María Eugenia Paredes Herrera
Author-Name-First: María Eugenia
Author-Name-Last: Paredes Herrera
Author-Name: Oswaldo Damián Miranda Rosero
Author-Name-First: Oswaldo Damián
Author-Name-Last: Miranda Rosero
Author-Name: Adriana Nicole Tobar Peñaherrera
Author-Name-First: Adriana Nicole
Author-Name-Last: Tobar Peñaherrera
Author-Name: María de los Ángeles Salazar Durán
Author-Name-First: María de los Ángeles
Author-Name-Last: Salazar Durán
Title: Analysis of epigenetic knowledge in the management of periodontal diseases
Abstract: The most common periodontal diseases worldwide are periodontitis and gingivitis. These are infections that affect the structures that support and protect the teeth, known as the supporting periodontium and the protective periodontium, respectively. Despite being so prevalent and having a considerable impact, there is a significant lack of clear and accessible information aimed at the general public about the processes that give rise to them, especially with regard to genetic and epigenetic aspects. This study investigated the relationship between epigenetic knowledge and periodontal health in the population of Los Ríos, examining how understanding epigenetic factors can influence the management of periodontitis and gingivitis, common periodontal diseases that affect the supporting structures and tooth protection. Despite the clinical importance of these disorders, there is a notable lack of accessible information on their underlying genetic and epigenetic mechanisms. The study used surveys and interviews to assess knowledge of epigenetics among residents, and descriptive and inferential statistical analyzes revealed a significant association between epigenetic knowledge and advanced oral health practices. The majority of respondents were found to have limited knowledge about epigenetics, highlighting the urgent need for focused educational programs. Those with a deeper understanding showed more proactive and personalized oral health practices. These findings underscore the importance of epigenetics education as a key strategy to improve periodontal health in the community
Journal: Data and Metadata
Pages: 226
Volume: 2
Year: 2023
DOI: 10.56294/dm2023226
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:226:id:1056294dm2023226

Template-Type: ReDIF-Article 1.0
Author-Name: Laydi Diana Milagros Caycho Araujo
Author-Name-First: Laydi Diana Milagros
Author-Name-Last: Caycho Araujo
Author-Name: Miriam Viviana Ñañez Silva
Author-Name-First: Miriam Viviana
Author-Name-Last: Ñañez Silva
Title: Implementation of biosafety protocols in tourist services: Perception and resilience of key actors
Abstract: This research addressed the analysis of the perception and resilience of key actors in the implementation of biosecurity protocols to enhance tourist services in risky situations. A case study approach was used, and in-depth interviews were conducted to gather significant data, which were processed using the Atlas.ti software. The findings of the research underscore the essential importance of implementing biosecurity protocols for the success and growth of accommodation establishments, reaffirming their commitment to the safety and well-being of all involved. These protocols are also crucial for a safe and sustainable reactivation of the gastronomic sector. Despite regulatory limitations, providers of recreational and complementary tourism services demonstrate a clear willingness to adapt and implement biosecurity measures, ensuring a secure tourist experience. It is concluded that biosecurity protocols are fundamental for the economic reactivation of tourism establishments in the district, instilling confidence and safety in tourists, which encourages travel and visits to these places. Additionally, the significance of personnel training and the need for a well-structured contingency plan to effectively respond to risky situations in the tourism industry are highlighted
Journal: Data and Metadata
Pages: 228
Volume: 2
Year: 2023
DOI: 10.56294/dm2023228
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:228:id:1056294dm2023228

Template-Type: ReDIF-Article 1.0
Author-Name: Luz Martínez Ríos
Author-Name-First: Luz
Author-Name-Last: Martínez Ríos
Author-Name: Jorge Franco Medina
Author-Name-First: Jorge
Author-Name-Last: Franco Medina
Author-Name: Segundo Ríos Ríos
Author-Name-First: Segundo
Author-Name-Last: Ríos Ríos
Author-Name: Hugo Morán Requena
Author-Name-First: Hugo
Author-Name-Last: Morán Requena
Author-Name: Fernando Ochoa Paredes
Author-Name-First: Fernando
Author-Name-Last: Ochoa Paredes
Author-Name: Yrene Uribe Hernandez
Author-Name-First: Yrene
Author-Name-Last: Uribe Hernandez
Title: The capacity for technological innovation and level of entrepreneurship in the students of the National University of Cañete
Abstract: Introduction: motivation is fundamental in this research that was carried out in the province of Cañete, Lima-Peru, with the purpose of finalizing the capacity for technological innovation that influences the level of entrepreneurship of the students of the Faculty of Business Sciences of the professional school of administration and accounting, knowing their skills and experiences when having or carrying out business ideas and the active participation of students in an incubator. Objective: this research seeks to analyze students in terms of their entrepreneurial skills and abilities in addition to identifying the role that teachers play in terms of encouraging and encouraging initiative behavior towards creativity and entrepreneurship. Method: this research work is quasi-experimental; it is to determine how the capacity for technological innovation influences the level of entrepreneurship of students at the National University of Cañete. Manage the type of control and compare with the experimental one. We worked with a population of 200 students, of which the questionnaire was applied to a sample of 80 students with 20 Likert-type items, handling 3 important dimensions with the independent variable. Results: there are 6 dimensions of which the high level of responses is managed (83,8 %), being effective, presenting a better result in the experimental than the control with (41,3 %). The limitations of the students focus on the level of entrepreneurship, the skills and attitudes of (88,8 %), entrepreneurial capacity of (87,5 %) and entrepreneurial experience of (81.3) in their levels of effectiveness. Conclusions: in conclusion, this research carried out in the province of Cañete, Lima-Peru, has shown that the capacity for technological innovation has a significant impact on the level of entrepreneurship of the students of the faculty of business sciences of the professional school of administration and accounting from the National University of Cañete. The findings highlight the importance of integrating technological innovation and entrepreneurship in higher education to prepare students for the business world and foster entrepreneurship in the next generation of professionals
Journal: Data and Metadata
Pages: 229
Volume: 2
Year: 2023
DOI: 10.56294/dm2023229
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:229:id:1056294dm2023229

Template-Type: ReDIF-Article 1.0
Author-Name: Lisbhet Mendoza Cabello
Author-Name-First: Lisbhet
Author-Name-Last: Mendoza Cabello
Author-Name: Segundo Ríos Ríos
Author-Name-First: Segundo
Author-Name-Last: Ríos Ríos
Author-Name: Hugo Morán Requena
Author-Name-First: Hugo
Author-Name-Last: Morán Requena
Author-Name: Filiberto Ochoa Paredes
Author-Name-First: Filiberto
Author-Name-Last: Ochoa Paredes
Author-Name: Fernando Ochoa Paredes
Author-Name-First: Fernando
Author-Name-Last: Ochoa Paredes
Author-Name: Yrene Uribe Hernandez
Author-Name-First: Yrene
Author-Name-Last: Uribe Hernandez
Title: Motivation and entrepreneurship in the students of the last cycles of administration of the National University of Cañete
Abstract: Introduction: motivation is fundamental in the entrepreneurship of the students of the National University of Cañete. Motivation is the drive that a person feels to achieve their goals. Objective: the contribution that this study will provide will be to encourage students to undertake, in turn new jobs will be generated, a source of income and greater economic development in the province of Cañete. This research work was carried out at the National University of Cañete, which is in the district of San Vicente, belonging to the province of Cañete – department of Lima. The objective was to determine how motivation is associated with entrepreneurship in students in the final cycles of Administration at the National University of Cañete, 2021. Method: the type of research used was basic, with a quantitative - deductive, correlational - descriptive level and non-experimental - cross-sectional design. The variables used were motivation and entrepreneurship, the dimensions of the research were the following: need for achievement, need for affiliation, need for power, innovation, risk management and proactivity. The population was made up of 178 students from the last cycles of the professional Administration degree at the National University of Cañete. The total sample was 73 students. For the data collection process, the questionnaire was used as an instrument and the survey of the study variables according to the Likert scale was used as a technique. Likewise, in the descriptive and inferential analysis of the data, Microsoft Excel programs and SPSS version 25 statistics were used. Results: finally, to determine the correlation between the variables, the Pearson parametric test was used, through which the following results were obtained with a P-Value = 0,00, that is, a P < 0,05, which establishes that motivation is associated with entrepreneurship of the students of the last administration cycles of the National University of Cañete. Conclusions: in conclusion, the results of this research reveal a clear and significant association between motivation and entrepreneurship among students in the final cycles of Administration at the National University of Cañete. Additionally, the importance of entrepreneurship education in the academic curriculum to prepare students for an entrepreneurial future and the potential impact on local economic development by generating employment and income opportunities is highlighted. These findings establish a solid foundation for future research and emphasize the relevance of promoting entrepreneurship in academic and community settings
Journal: Data and Metadata
Pages: 230
Volume: 2
Year: 2023
DOI: 10.56294/dm2023230
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:230:id:1056294dm2023230

Template-Type: ReDIF-Article 1.0
Author-Name: Ana Chaman Bardalez
Author-Name-First: Ana
Author-Name-Last: Chaman Bardalez
Author-Name: Alberto Ramón Osorio
Author-Name-First: Alberto
Author-Name-Last: Ramón Osorio
Author-Name: Segundo Ríos Ríos
Author-Name-First: Segundo
Author-Name-Last: Ríos Ríos
Author-Name: Miguel Vargas Tasayco
Author-Name-First: Miguel
Author-Name-Last: Vargas Tasayco
Author-Name: Yrene Uribe Hernandez
Author-Name-First: Yrene
Author-Name-Last: Uribe Hernandez
Title: Strategic planning and organizational culture
Abstract: Introduction: The thesis “Strategic planning and organizational culture of Bodega y Viñedos Santa María S.A.C. of the district of Lunahuaná - Cañete 2021” highlights that strategic planning is a structured process through which an organization considers where it wants to go and how it is going to achieve it. Likewise, organizational culture is that culture (set of values, beliefs, and other representative characteristics of a group of people) that are reflected within an organization and that identify it as such. Objective: Determine how strategic planning is associated with the organizational culture of Bodega y Viñedos Santa María S.A.C. of the district of Lunahuaná - Cañete 2021. Method: Research with a quantitative approach, basic type, non-experimental cross-sectional design, and correlational level. The population was made up of 10 people from the company and the research sample was made up of the entire population. Two surveys were used: strategic planning and organizational culture, composed of 28 and 60 questions for each instrument, respectively, which were validated by expert judgment and reliability through Cronbach's alpha, respecting ethical considerations. Results: The results obtained consider that strategic planning is significantly associated with the organizational culture of Bodega y Viñedos Santa María S.A.C. of the district of Lunahuaná - Cañete 2021, because Spearman's Rho statistical test is 0,764, which according to the correlation analysis table is considered a very strong positive correlation. In relation to the dimensions: involvement, consistency, adaptability, and mission, with strategic planning it was found that they have a significant association. Conclusions: In conclusion, there is an association between both variables. Therefore, the implementation of strategic planning in micro and small businesses (MYPE) establishes the procedure to follow and the appropriate organizational culture contributes to the fulfillment of what is planned, allowing the continuous improvement of the company and the scope of business success
Journal: Data and Metadata
Pages: 231
Volume: 2
Year: 2023
DOI: 10.56294/dm2023231
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:231:id:1056294dm2023231

Template-Type: ReDIF-Article 1.0
Author-Name: Carlos Alberto Valverde González
Author-Name-First: Carlos Alberto
Author-Name-Last: Valverde González
Author-Name: Lexter Ivan Mihalache Bernal
Author-Name-First: Lexter Ivan
Author-Name-Last: Mihalache Bernal
Author-Name: Vanessa Del Cisne Pinza Vera
Author-Name-First: Vanessa
Author-Name-Last: Del Cisne Pinza Vera
Title: Meta-analytic analysis of the role of hemostatic evaluation in optimizing surgical results
Abstract: Introduction: pre-surgical clinical assessment constitutes an essential practice in internal medicine, focused on the identification of risk factors to reduce perioperative morbidity and mortality. Objective: this study seeks to critically evaluate the effectiveness of preoperative coagulation tests to predict bleeding complications in elective non-cardiac surgeries, in order to validate their clinical relevance and adjust practices to real needs based on solid evidence. Method: a meta-analysis approach was employed to synthesize data from multiple studies examining the correlation between preoperative coagulation testing and intra- and postoperative bleeding complications. Study selection was based on rigorous criteria to include only those with patients without a history of hematological diseases who underwent elective non-cardiac surgeries. Results: the results indicate that alterations in coagulation tests are not significantly associated with an increased risk of hemorrhagic complications, as evidenced by a relative risk (RR) of less than one. This suggests that routine performance of these tests in the study population may not be necessary and does not contribute significantly to the safety of the surgical patient. Conclusions: the study supports a review of clinical guidelines to reduce unnecessary defensive medical practices and encourage evidence-based decisions. It is recommended to limit preoperative coagulation testing to patients with identifiable risk factors or clinical manifestations of hemostatic disorders, avoiding widespread and unsubstantiated procedures that overload health systems without improving clinical outcomes
Journal: Data and Metadata
Pages: 246
Volume: 2
Year: 2023
DOI: 10.56294/dm2023246
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:246:id:1056294dm2023246

Template-Type: ReDIF-Article 1.0
Author-Name: Andrea Katherine Miranda Anchundia
Author-Name-First: Andrea Katherine
Author-Name-Last: Miranda Anchundia
Author-Name: Lourdes Elizabeth Menéndez Oña
Author-Name-First: Lourdes Elizabeth
Author-Name-Last: Menéndez Oña
Author-Name: Ana Fernanda Ocaña Tovar
Author-Name-First: Ana Fernanda
Author-Name-Last: Ocaña Tovar
Title: Optimization of the tunneling technique in the treatment of gingival recessions
Abstract: Introduction: gingival recession has presented significant aesthetic and functional challenges for patients, making it imperative to search for effective surgical techniques that improve periodontal results. Therefore, the present study has focused on optimizing the tunneling technique with a subepithelial connective tissue graft, through the implementation and evaluation of different clinical strategies that improve the results of surgery and patient safety. Method: the VIKOR method was used for multi-criteria decision making, which allowed the analysis of several strategies based on specific criteria related to the results of surgery and patient safety. Eight evaluation criteria were established and six strategies were rated in two aspects, one focused on satisfaction with the results and the other on patient safety and health. Results: the advanced training and clinical practice strategies and comprehensive periodontal health program were identified as the most effective, showing high scores in technical competence, patient satisfaction, adherence to the protocol, and reduction of complications. Conclusions: the tunneling technique with a graft of subepithelial connective tissue is effective for the treatment of gingival recessions. The success of this technique has critically depended on surgeon training, adherence to standardized protocols, and an integrated approach that has included patient education and rigorous follow-up. The objective and systematic evaluation of the proposed strategies allowed us to highlight the importance of a well-informed and managed clinical practice
Journal: Data and Metadata
Pages: 254
Volume: 2
Year: 2023
DOI: 10.56294/dm2023254
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:254:id:1056294dm2023254

Template-Type: ReDIF-Article 1.0
Author-Name: Blanca Cristina Estrella López
Author-Name-First: Blanca Cristina Estrella
Author-Name-Last: López
Author-Name: Roberto Javier Aguilar Berrezueta
Author-Name-First: Roberto Javier
Author-Name-Last: Aguilar Berrezueta
Author-Name: Silvio Amable Machuca Vivar
Author-Name-First: Silvio Amable
Author-Name-Last: Machuca Vivar
Title: Challenges and strategies in the treatment of juvenile type 2 diabetes: a case analysis
Abstract: Type 2 diabetes mellitus is today the most common type of diabetes, characterized by insulin resistance and dysfunction of the beta cells of the pancreas. This condition is especially prevalent in adults, although in recent times it has increased significantly among young people. Factors such as obesity, lack of physical activity, poor sleep quality, family history are known contributors. The objective of this work is to develop effective intervention strategies and improve the management of the disease in adolescents. A case study was carried out and using the PESTEL method, strategies are proposed to comprehensively address the challenges associated with type 2 diabetes mellitus in adolescents, in addition to improving not only individual care but also the social and political environment that affects them youths
Journal: Data and Metadata
Pages: 255
Volume: 2
Year: 2023
DOI: 10.56294/dm2023255
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:255:id:1056294dm2023255

Template-Type: ReDIF-Article 1.0
Author-Name: Julio Rodrigo Morillo Cano
Author-Name-First: Julio Rodrigo
Author-Name-Last: Morillo Cano
Author-Name: Melba Esperanza Narváez Jaramillo
Author-Name-First: Melba Esperanza
Author-Name-Last: Narváez Jaramillo
Author-Name: María Belén Morillo Chamorro
Author-Name-First: María Belén
Author-Name-Last: Morillo Chamorro
Author-Name: Sara Ximena Guerrón Enríquez
Author-Name-First: Sara Ximena
Author-Name-Last: Guerrón Enríquez
Title: Characterization of the agenesis of the corpus callosum, through the presentation of a clinical case
Abstract: Agenesis of the corpus callosum (ACC) is an anomaly that consists of the partial or total absence, congenitally or due to a neuropathological condition, of this structure, due to alterations in development; thus being defined by its absence and not by its manifestations. The causes of agenesis of the corpus callosum have not yet been clear and it is proposed that it occurs due to multiple factors, such as vitamin deficiency, radiation exposure, prenatal and toxic infections, smoking, maternal diabetes and genetic causes. Prenatal diagnosis can be made by ultrasound and magnetic resonance imaging from week 20 of gestation. Neuroimaging diagnostic tools are useful and magnetic resonance imaging is one of the ideal and most sensitive methods for postnatal demonstration of the anomaly. The purpose of this research is to investigate the characteristics of agenesis of the corpus callosum, through the presentation of a clinical case where the relevant observations that allow its diagnosis are presented. The case presented highlights the importance of considering agenesis of the corpus callosum as a spectrum of clinical conditions that can present with a wide variety of signs and symptoms. In this report, a combination of neurological anomalies is observed, which highlight the complexity and diversity of this condition. Therefore, it is critical that healthcare professionals be alert to potential differential diagnoses and consider agenesis of the corpus callosum in the context of patients with neurological or developmental symptoms
Journal: Data and Metadata
Pages: 337
Volume: 2
Year: 2023
DOI: 10.56294/dm2023337
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:337:id:1056294dm2023337

Template-Type: ReDIF-Article 1.0
Author-Name: Sara Ximena Guerrón Enríquez
Author-Name-First: Sara Ximena
Author-Name-Last: Guerrón Enríquez
Author-Name: Daniela Solimar Ortega Armas
Author-Name-First: Daniela Solimar
Author-Name-Last: Ortega Armas
Author-Name: María Verónica Aveiga Hidalgo
Author-Name-First: María Verónica
Author-Name-Last: Aveiga Hidalgo
Author-Name: Zuly Rivel Nazate Chuga
Author-Name-First: Zuly Rivel
Author-Name-Last: Nazate Chuga
Title: Assessment of Satisfaction Level of Patients with Chronic Conditions Regarding Nursing Staff Service During Home Visits
Abstract: The research was conducted in the San Francisco de Sigsipamba parish, Pimampiro Canton, with the objective of determining the level of satisfaction of chronic patients regarding the role of nursing in home visits. A mixed methodology, both qualitative and quantitative, was utilized, with a non-experimental design that was exploratory, descriptive, and correlational in scope. The methods employed included analytical-synthetic, inductive-deductive, historical-logical, systemic, and scientific observation as part of the empirical research. A survey using the Iadov method was applied to assess the overall satisfaction of the patients regarding the visit of the nursing staff. The results indicated a prevalence of dissatisfaction among the patients, with 47 % showing significant levels of dissatisfaction compared to 51 % reporting some degree of satisfaction. The largest group, however, corresponds to those most dissatisfied, highlighting the need to address key issues in the service to improve the quality and experience of the patient. Additionally, a set of strategies was developed aimed at training healthcare personnel and minimizing the negative impacts resulting from inappropriate practices by nursing staff in patients with chronic diseases
Journal: Data and Metadata
Pages: 338
Volume: 2
Year: 2023
DOI: 10.56294/dm2023338
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:338:id:1056294dm2023338

Template-Type: ReDIF-Article 1.0
Author-Name: Aurelia María Cleonares Borborus
Author-Name-First: Aurelia María
Author-Name-Last: Cleonares Borborus
Author-Name: Jaime Fernando Armijos Moreta
Author-Name-First: Jaime Fernando
Author-Name-Last: Armijos Moreta
Author-Name: Amalia Fernanda Vera Veloz
Author-Name-First: Amalia Fernanda
Author-Name-Last: Vera Veloz
Title: Proposal for Early Prevention Actions of Oral Neoplasms: A Multidisciplinary and Analytical Approach
Abstract: The study aimed to identify factors contributing to oral cancer prevention in Latin America through an exploratory review of recent scientific literature on the disease's prevalence among adult populations. It utilized a comprehensive analysis of selected publications, focusing on demographic and clinical variables such as age, gender, geographical origin, oral topography, clinical extent, and the morphological type of observed lesions. Data analysis employed a descriptive and comparative methodology that helped categorize and assess case distribution. A higher susceptibility was noted in men aged 50 to 79, in contrast to women, whose prevalence was primarily between 50 and 54 years. The study also quantified key risk factors associated with oral cancer development. Preventive measures proposed were evaluated by a multidisciplinary panel of experts, including dentists, dental assistants, general practitioners, and nurses, using the PROMETHEE Method. This ensured an objective and structured evaluation, supporting informed and effective decision-making. The research emphasized the importance of prevention and early detection of oral cancer, highlighting that early diagnosis can significantly improve patient prognosis. It reinforced the need for implementing effective, well-founded strategies to combat this disease in the region.
Journal: Data and Metadata
Pages: 339
Volume: 2
Year: 2023
DOI: 10.56294/dm2023339
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:339:id:1056294dm2023339

Template-Type: ReDIF-Article 1.0
Author-Name: Aurelia María Cleonares Borbor
Author-Name-First: Aurelia María
Author-Name-Last: Cleonares Borbor
Author-Name: Jaime Fernando Armijos Moreta
Author-Name-First: Jaime Fernando
Author-Name-Last: Armijos Moreta
Author-Name: Amalia Fernanda Vera Veloz
Author-Name-First: Amalia Fernanda
Author-Name-Last: Vera Veloz
Title: Impact of Catheter Ablation on Mitral Regurgitation and Atrial Fibrillation: A Case Study
Abstract: Mitral regurgitation is recognized as the most common valvulopathy among cardiac valve disorders, with causes divided into primary and secondary components. Traditionally, the secondary component has been associated with the dilation of the ventricular cavity and ring; however, recent interpretations also include the dilation of the left atrium as a significant factor, especially when the dimensions of the left ventricular cavity are preserved. This new perspective has led to a reinterpretation of the pathogenesis, diagnosis, and treatment of atrial functional mitral regurgitation. The research focused on a case study of patients with this condition and atrial fibrillation, who were treated through catheter ablation. This approach was supported by an exhaustive bibliographic review conducted in databases such as MEDLINE via PUBMED. The findings reveal that atrial functional mitral regurgitation, now recognized as a distinct pathological entity, challenges the previous belief that only the dilation of the left ventricle and mitral ring were the causes of the disease. Furthermore, it was determined that catheter ablation is effective not only for restoring sinus rhythm but also for improving valvular function and quality of life in patients with this condition and atrial fibrillation
Journal: Data and Metadata
Pages: 340
Volume: 2
Year: 2023
DOI: 10.56294/dm2023340
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:340:id:1056294dm2023340

Template-Type: ReDIF-Article 1.0
Author-Name: Jonathan Luis Gonzabay Muñoz
Author-Name-First: Jonathan Luis
Author-Name-Last: Gonzabay Muñoz
Author-Name: Mónica Gabriela Chachalo Sandoval
Author-Name-First: Mónica Gabriela
Author-Name-Last: Chachalo Sandoval
Author-Name: Santiago Xavier Peñarreta Quezada
Author-Name-First: Santiago Xavier
Author-Name-Last: Peñarreta Quezada
Title: Prevention of bile duct injuries due to cholecystectomy
Abstract: The human organism sometimes presents problems that require surgical intervention to provide care and a solution. Numerous investigations have dedicated their development to the search for more advanced methods that allow medical care to be provided with the least possible invasion. Among the most developed techniques in surgery is laparoscopic cholecystectomy. This technique is widely used worldwide in the treatment of biliary problems due to conditions such as biliary lithiasis pathology. The application of the procedures to be carried out in this technique has required constant preparation of the surgical staff, who are not exempt from making errors during the procedure that could cause other effects on the patient. The research carries out a bibliographic review of the application of this technique and the study of the results obtained in research on the application of the technique. A group of procedures are analyzed to take into account in the diagnosis, the decision to operate and the application of the procedure itself to reduce any impact on the patient. The existence of cases in which medical intervention has caused new effects on the patient that lead to changes in lifestyle is recognized. However, the results show that this technique is widely spread and effective in the treatment of biliary conditions in people who have difficulties of this type
Journal: Data and Metadata
Pages: 341
Volume: 2
Year: 2023
DOI: 10.56294/dm2023341
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:341:id:1056294dm2023341

Template-Type: ReDIF-Article 1.0
Author-Name: Icler Naun Sisalema Aguilar
Author-Name-First: Icler Naun
Author-Name-Last: Sisalema Aguilar
Author-Name: Fernando Bolívar Mena Hidalgo
Author-Name-First: Fernando
Author-Name-Last: Bolívar Mena Hidalgo
Author-Name: Alice Mishell Mantilla Moreira
Author-Name-First: Alice Mishell
Author-Name-Last: Mantilla Moreira
Author-Name: Francisco Gabriel Morejón Vallejo
Author-Name-First: Francisco Gabriel
Author-Name-Last: Morejón Vallejo
Title: Analysis of corona virus mortality rates in a public health unit in the city of Santo Domingo de los Tsachilas
Abstract: The global health crisis generated by the Coronavirus pandemic resulted in a global imbalance in several spheres. This research aims to provide a detailed summary of the clinical-epidemiological characteristics of the disease by analyzing data from a public institution in Santo Domingo de los Tsáchilas between 2020 and 2022. Using a retrospective cross-sectional approach, the researchers analyzed data from a health unit database. Non-probabilistic convenience sampling was chosen. The findings revealed that positive cases represented 35 % in 2020, 41 % in 2021, and 22 % in 2022 of the total periods studied. The most common symptoms included fever, cough, and dyspnea. In addition, a high mortality rate was observed during 2020, with 63 % of the total deaths, adding up to a total of 874 deaths over the three years analyzed. This study provides valuable evidence on the progression and impact of COVID-19 in a public health context, highlighting the variability in case prevalence and symptom severity over time. Health prevention actions were proposed taking as experience the data provided by this research.
Journal: Data and Metadata
Pages: 342
Volume: 2
Year: 2023
DOI: 10.56294/dm2023342
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:342:id:1056294dm2023342

Template-Type: ReDIF-Article 1.0
Author-Name: Lexter Iván Mihalache Bernal
Author-Name-First: Lexter Iván
Author-Name-Last: Mihalache Bernal
Author-Name: Carlos Alberto Valverde González
Author-Name-First: Carlos Alberto
Author-Name-Last: Valverde González
Author-Name: Vanessa Del Cisne Pinza Vera
Author-Name-First: Vanessa
Author-Name-Last: Del Cisne Pinza Vera
Title: Analysis of Patient Protection in a Medical Institution
Abstract: At the General Hospital Santo Domingo, patient safety was a priority in 2022, focusing on practices that prevented unnecessary harm during healthcare delivery. A quantitative study with a descriptive, exploratory, and field approach was conducted, using methodological tools such as the questionnaire from the Agency for Healthcare Research and Quality and structured interviews with the institution's director. The sample included 100 operational staff and the administrative director, evaluating labor, social, and personal parameters in five safety categories. It was noted that 65 % of the health personnel confirmed full compliance with the patient safety program guidelines. Furthermore, a strategic analysis was performed that significantly contributed to improving patient safety at the institution. This analysis highlighted the need for ongoing training and updating parameters to meet the specific needs of the establishment, aiming to enhance the patient safety culture. It was concluded that, although the management of patient safety was efficient, it was imperative to continue training and adapting practices to optimize patient protection at the hospital
Journal: Data and Metadata
Pages: 343
Volume: 2
Year: 2023
DOI: 10.56294/dm2023343
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:343:id:1056294dm2023343

Template-Type: ReDIF-Article 1.0
Author-Name: Manuel Conrado Ezcurdia Barzaga
Author-Name-First: Manuel Conrado
Author-Name-Last: Ezcurdia Barzaga
Author-Name: Carlos Alejandro Troya Altamirano
Author-Name-First: Carlos Alejandro
Author-Name-Last: Troya Altamirano
Author-Name: Evelyn Carolina Betancourt Rubio
Author-Name-First: Evelyn Carolina
Author-Name-Last: Betancourt Rubio
Title: Evaluation and Prioritization of Training Programs for the Management of Marfan Syndrome
Abstract: Introduction: Marfan Syndrome has been a connective tissue disease affecting multiple systems of the body, requiring an interdisciplinary diagnostic and therapeutic approach. The variability in presentation and potential complications underscored the need for precise and specialized medical training. Therefore, the general objective of this research is to evaluate specific training programs to improve the diagnosis and management of Marfan Syndrome by identifying critical gaps in current medical practice. Method: multicriteria decision-making methodologies were used, including the AHP Saaty method to quantify the incidence of diagnostic gaps and the MOORA method to prioritize medical training programs based on the improvement of the diagnosis and treatment of Marfan Syndrome. Results: the analysis revealed that insufficient knowledge and incomplete clinical evaluations are the main gaps. Priority training programs included the advanced course in clinical genetics and the training program in echocardiography, noted for their direct capacity to improve clinical outcomes. Conclusions: medical education in genetics and echocardiography must be prioritized to effectively address Marfan Syndrome. The implementation of these programs constitutes the support to close the identified gaps. Thus, they significantly improve early diagnosis and management of complications, promoting interdisciplinary collaboration in patient care
Journal: Data and Metadata
Pages: 344
Volume: 2
Year: 2023
DOI: 10.56294/dm2023344
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:344:id:1056294dm2023344

Template-Type: ReDIF-Article 1.0
Author-Name: Diana Carolina Freire Villena
Author-Name-First: Diana Carolina
Author-Name-Last: Freire Villena
Author-Name: Luis Darío Pérez Villalba
Author-Name-First: Luis Darío
Author-Name-Last: Pérez Villalba
Author-Name: Melanie Cristina Ulloa Poveda
Author-Name-First: Melanie Cristina
Author-Name-Last: Ulloa Poveda
Title: Optimization of surgical selection in the treatment of irritation fibroids
Abstract: Irritation fibroma is a benign, non-neoplastic lesion that arises in the oral mucosa due to chronic irritations or trauma. This focal hyperplasia of collagenized fibrous connective tissue typically presents as an exophytic, firm, asymptomatic, pink nodule with a smooth surface and a well-defined border. Removing this lesion not only allows for diagnostic confirmation, it also prevents additional complications, relieves symptoms in symptomatic cases, and improves the patient's aesthetics and comfort. Surgical intervention is the recommended treatment and can be carried out using various techniques, such as conventional surgical excision, laser surgery, electrosurgery and cryosurgery. The choice of surgical method is based on specific characteristics of the fibroid and physician preferences, with conventional surgical excision frequently preferred as it allows complete control over tissue removal. This study details the case of a patient with an irritation fibroma located in the lateral region of the tongue, for which the VIKOR method was used as a multi-criteria decision-making tool to select the most appropriate surgical technique. The final decision favored conventional surgical excision, based on multicriteria assessment that included aspects such as the size and location of the lesion, available resources, risks, and aesthetic results. The management of irritation fibroma must be careful and well-founded, favoring techniques that ensure complete removal of the lesion with minimal complications and ensuring adequate postoperative follow-up to evaluate the patient's evolution and prevent recurrences
Journal: Data and Metadata
Pages: 345
Volume: 2
Year: 2023
DOI: 10.56294/dm2023345
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:345:id:1056294dm2023345

Template-Type: ReDIF-Article 1.0
Author-Name: Melany Michelle Abril Moya
Author-Name-First: Melany Michelle Abril
Author-Name-Last: Moya
Author-Name: Graciela Alexandra Carrera Aldaz
Author-Name-First: Graciela Alexandra
Author-Name-Last: Carrera Aldaz
Author-Name: Francisco Xavier Poveda Paredes
Author-Name-First: Francisco Xavier
Author-Name-Last: Poveda Paredes
Title: Characterization of Trichinella spiralis and its incidence in Ecuador
Abstract: Trichinella spiralis is a parasite that can infect humans through consumption of infected raw or undercooked meat, especially pork, wild boar, bear and other wild animals. Trichinella infection can cause a disease called trichinosis, which can result in serious symptoms such as fever, muscle pain, facial and eye swelling, and in severe cases, cardiac and respiratory complications. The main objective of this research is to characterize the pathogenicity mechanisms of Trichinella spiralis and its incidence in Ecuador through a bibliographic review. A bibliographic review of a retrospective descriptive narrative type was carried out. Trichinella spiralis infection has a suppressive effect on the immune system, making it totally invisible to invasive elimination attacks. The parasite, as its life cycle progresses, changes its morphology and excretion, which allows it to migrate with lymph or blood, invading skeletal muscle. The lack of mechanisms for detecting Trichinella in pork and monitoring safe meat handling and cooking practices constitute a food safety problem in Ecuador. The specific incidence in humans of Trichinella spiralis in Ecuador is not widely documented in scientific literature or public health sources. However, although trichinosis is a parasitic disease that is not very common in Ecuador, it is necessary for health professionals to be attentive to this problem and work together to implement effective control and prevention strategies
Journal: Data and Metadata
Pages: 346
Volume: 2
Year: 2023
DOI: 10.56294/dm2023346
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:346:id:1056294dm2023346

Template-Type: ReDIF-Article 1.0
Author-Name: Edisson Vladimir Maldonado Mariño
Author-Name-First: Edisson Vladimir
Author-Name-Last: Maldonado Mariño
Author-Name: Dario Orlando Siza Saquinga
Author-Name-First: Dario Orlando
Author-Name-Last: Siza Saquinga
Author-Name: Diego Eduardo Guato Canchinia
Author-Name-First: Diego Eduardo
Author-Name-Last: Guato Canchinia
Author-Name: Alexander Javier Ramos Velastegui
Author-Name-First: Alexander Javier
Author-Name-Last: Ramos Velastegui
Title: Systematization of research on the incidence of pesticides in people, use of biomarkers
Abstract: Currently the use of pesticides in agriculture has expanded in the search for greater productivity. These products can harm people's health in various ways. These effects can be captured through the use of genotoxicity biomarkers. The objective of this research is to systematize studies on biomarkers of genotoxicity of people exposed to pesticides in South America. The PRISMA method was applied to determine the studies to be analyzed. 15 documents met the inclusion criteria. Among the adverse health effects perceived in studies are neurological, respiratory, dermatological and endocrine disorders, as well as an increased risk of cancer. The main biomarkers identified are the comet assay, the cytokinesis blockade micronucleus assay, and the buccal cytoma micronucleus assay. Polymerase chain reaction, chromosomal aberrations, flow cytometry, and fluorescence in situ hybridization were also taken into account. Limitations were determined by biomarker. The usefulness of using multiple biomarkers is highlighted for a more complete and precise evaluation of pesticide exposure and genotoxic damage in agricultural workers in South America. The establishment of protective measures for workers against the use of pesticides and opting for the use of pesticides of biological origin will contribute to the preservation of people's health
Journal: Data and Metadata
Pages: 350
Volume: 2
Year: 2023
DOI: 10.56294/dm2023350
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:350:id:1056294dm2023350

Template-Type: ReDIF-Article 1.0
Author-Name: Filiberto Fernando Ochoa Paredes
Author-Name-First: Filiberto Fernando
Author-Name-Last: Ochoa Paredes
Author-Name: Segundo Waldemar Rios Rios
Author-Name-First: Segundo Waldemar
Author-Name-Last: Rios Rios
Author-Name: Manuel Enrique Chenet Zuta
Author-Name-First: Manuel Enrique
Author-Name-Last: Chenet Zuta
Author-Name: Anwar Julio Yarin Achachagua
Author-Name-First: Anwar Julio
Author-Name-Last: Yarin Achachagua
Author-Name: Soledad del Rosario Olivares Zegarra
Author-Name-First: Soledad del Rosario
Author-Name-Last: Olivares Zegarra
Title: Management of the tourist system in the environmental awareness of the inhabitants of Lunahuana
Abstract: The objective was to establish the impact of the Management of the tourist System on the environmental awareness of the population of Lunahuana-Cañete period 2022, the method that was used is a basic study, the design without any experiment, in a single time and descriptive, quantitative and deductive approach. The population and test was 120 workers who work in the field of tourism, gastronomy, a proven non-probabilistic sensible was used. As a result, 86,6 % of those surveyed state that the management of the Tourism system is well implemented and basically implemented, both the cultural, economic, environmental and social dimensions are between basically implemented and very well implemented. The environmental awareness variable was qualified with 60,0 % at a medium level, 36,7 % at a high level, and 3,3 % with a low level, and the cognitive, affective, active, and behavioral dimensions were qualified as a high level on average. greater than 70 %. The inferential statistical results indicate that the management of the Tourist System significantly influences the environmental awareness of the inhabitant, in the same way for the specific premise 1, it is established that the cultural dimension is significantly related to the environmental awareness of the inhabitant, for the specific premise 2 , it is guaranteed that the cultural dimension is significantly related to the environmental awareness of the population, for specific premise 3, it is guaranteed. The environmental dimension is related to the environmental awareness of the population. And finally for the specific premise 4 it is guaranteed that the social dimension is related to the environmental awareness of the resident, all the premises or hypotheses are about the resident of Lunahuana, Cañete, Lima, 2022
Journal: Data and Metadata
Pages: 107
Volume: 2
Year: 2023
DOI: 10.56294/dm2023107
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:107:id:1056294dm2023107

Template-Type: ReDIF-Article 1.0
Author-Name: Solomon Olusegun Oyetola
Author-Name-First: Solomon
Author-Name-Last: Olusegun Oyetola
Author-Name: Bolaji David Oladokun
Author-Name-First: Bolaji David
Author-Name-Last: Oladokun
Author-Name: Charity Ezinne Maxwell
Author-Name-First: Charity
Author-Name-Last: Ezinne Maxwell
Author-Name: Solomon Obotu Akor
Author-Name-First: Solomon
Author-Name-Last: Obotu Akor
Title: Artificial intelligence in the library: gauging the potential application and implications for contemporary library services in Nigeria
Abstract: Purpose: libraries may become obsolete in the twenty-first century unless they begin to harness new technology and improve information and service delivery. This paper examines the potential application and implications of artificial intelligence for contemporary library services in Nigeria. Methods: this paper adopts the expository research approach to evaluate the application and implication of artificial intelligence in contemporary library services in Nigeria. Through systematic analysis of literature, the study addresses how academic libraries can utilize artificial intelligence to support innovative library services. Findings: the column emphasizes that, academic libraries in Nigeria have not yet adopted and applied AI, in spite of the potential that it holds for libraries. Given that there has been relatively little study linking artificial intelligence (AI) to librarianship, this may be because there is a low degree of awareness and adoption of AI's importance in libraries. Conclusions: this column is the original idea from the authors and does not reflect on any copyrighted materials. The column recommended that, academic libraries in Nigeria should fully embrace artificial intelligence like chatbots, barcodes, RFIDs, and robots for delivering quality services and libraries should also leverage on the opportunities presented by artificial intelligence to reconnect their remote users, and consequently re-establish their relevance among the user community
Journal: Data and Metadata
Pages: 36
Volume: 2
Year: 2023
DOI: 10.56294/dm202336
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:36:id:1056294dm202336

Template-Type: ReDIF-Article 1.0
Author-Name: Rose Mary Favier Rodríguez
Author-Name-First: Rose Mary Favier
Author-Name-Last: Rodríguez
Author-Name: Gloritza Rodríguez Matos
Author-Name-First: Gloritza
Author-Name-Last: Rodríguez Matos
Author-Name: Eduardo Enrique Chibas Muñoz
Author-Name-First: Eduardo Enrique
Author-Name-Last: Chibas Muñoz
Title: Exploring links between toxic-environmental factors and hematologic malignancies: considerations for data-driven health decision making
Abstract: Introduction: some toxic-environmental factors are frequently related to malignant hematological diseases, which have increased in recent years. Objective: to describe the frequency of appearance of toxic-environmental factors in patients diagnosed with hematological neoplasms. Method: a study was conducted cross-sectional descriptive study in patients diagnosed with hematological pathologies in the year 2020, a structured interview was applied to them looking for toxic-environmental factors, occupation, contact with toxic substances, origin, drinking water, toxic habits, diet, among others. Patients who used pesticides were given a second interview that collected: the type of substance, exposure time, time since last exposure, history of poisoning, protection measures, knowledge and application of the same. Results: patients with lymphoma predominated. non-Hodgkin (25,5 %), ages between 61-80 years (50 %), farmers (31 %), rural origin (57 %), those who had contact with toxins (64,4 %). The most used toxin was pesticides, the average exposure time was 14 years, the last contact 9,2 years, protection measures are used only sometimes, their need is unknown and 90 % do not know any protection measure. Conclusions: according to the results it is necessary to create strategies to guide the population about this risk and how to modify it
Journal: Data and Metadata
Pages: 39
Volume: 2
Year: 2023
DOI: 10.56294/dm202339
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:39:id:1056294dm202339

Template-Type: ReDIF-Article 1.0
Author-Name: Adhitia Erfina
Author-Name-First: Adhitia
Author-Name-Last: Erfina
Author-Name: M Rifki Nurul Ramdani Alamsyah
Author-Name-First: M Rifki Nurul
Author-Name-Last: Ramdani Alamsyah
Title: Implementation of Naive Bayes classification algorithm for Twitter user sentiment analysis on ChatGPT using Python programming language
Abstract: ChatGPT (Generative Pre-Trained Transformer) is a chatbot that is being widely used by the public. This technology is based on Artificial Intelligence and is capable of having conversational interactions with its users just like humans, but in the form of automated text. Because of this capability, online forums such as Brainly and the like can be overtaken by these smart chatbots. Therefore, this study was conducted to determine the positive and negative sentiments towards ChatGPT using Naive Bayes Classification algorithm on 5000 Twitter users. Data was collected by scraping technique and Python programming language was used in data analysis. The results showed that the majority of Twitter users had a positive sentiment of 57,6 % towards ChatGPT, while the negative sentiment reached 42,4 %. The resulting classification model had an accuracy of 80 %, indicating a good classification model in determining sentiment probabilities. These findings provide a basis for the development of better AI chatbot technology that can meet user needs. The results of this study provide insights into user sentiment towards ChatGPT and can be used as a reference for future AI chatbot development
Journal: Data and Metadata
Pages: 45
Volume: 2
Year: 2023
DOI: 10.56294/dm202345
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:45:id:1056294dm202345

Template-Type: ReDIF-Article 1.0
Author-Name: Mario Macea Anaya
Author-Name-First: Mario
Author-Name-Last: Macea Anaya
Author-Name: Ruben Baena Navarro
Author-Name-First: Ruben
Author-Name-Last: Baena Navarro
Author-Name: Yulieth Carriazo Regino
Author-Name-First: Yulieth
Author-Name-Last: Carriazo Regino
Author-Name: Julio Alvarez Castillo
Author-Name-First: Julio
Author-Name-Last: Alvarez Castillo
Author-Name: Jhoan Contreras-Florez
Author-Name-First: Jhoan
Author-Name-Last: Contreras-Florez
Title: Designing a Framework for the Appropriation of Information Technologies in University Teachers: A Four-Phase Approach
Abstract: The implementation of Information Technology (IT) in university education encompasses multiple aspects, from the incorporation of accessible technologies to the disruptive transformation of learning through emerging technologies. This article proposes a conceptual framework that describes four phases of IT adoption by university teachers: Technology Adoption, Online Collaboration and Feedback, Technology Exploration and Experimentation, and Adoption of Emerging Technologies. Each phase is detailed, starting from the integration of accessible technological tools to the incorporation of emerging technologies such as artificial intelligence, virtual and augmented reality, to create innovative and transformative learning experiences. This article is based on bibliographic references that support each phase and underline the importance of personalizing learning, promoting interaction between students and teachers, and applying project-based approaches to enrich the educational process
Journal: Data and Metadata
Pages: 53
Volume: 2
Year: 2023
DOI: 10.56294/dm202353
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:53:id:1056294dm202353

Template-Type: ReDIF-Article 1.0
Author-Name: Stephany Romero Tobias
Author-Name-First: Stephany
Author-Name-Last: Romero Tobias
Author-Name: Geomar Molina Bolívar
Author-Name-First: Geomar
Author-Name-Last: Molina Bolívar
Author-Name: Iris Jiménez Pitre
Author-Name-First: Iris
Author-Name-Last: Jiménez Pitre
Title: Comprehensive analysis of water quality in the middle and lower basin of the Marquesote River Colombian
Abstract: Different biotic indices have been used for the integral analysis of hydrographic basins in Colombia, using benthic microinverters and physicochemical parameters. A comprehensive analysis of water quality was performed in the low Marquesote river basin, using benthic macroinvertebrates and the ETP index (Ephemeroptera-Trichoptera-Plecoptera) as a biological indicator and some physicochemical parameters. Work was worked on the middle and lower basin on the Marquesote River at two times of the year (dry and rainy); standardized methods for water physicochemical variables were applied, for benthic fauna collected using a Surber network, and identifying them up to the taxonomic level of family. The study observed 22 families and 1388 individuals, where 49 % represent PTSD, indicating regular water quality; however, physicochemical variables had wide variation, noting that pH showed the greatest variability based on an analysis of major components. Environmental quality in the Marquesote river basin is compromised according to the indicators used, a more detailed study of the sources of pollution and dynamics of macroinvertebrates could provide a greater ecological knowledge of the basin
Journal: Data and Metadata
Pages: 54
Volume: 2
Year: 2023
DOI: 10.56294/dm202354
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:54:id:1056294dm202354

Template-Type: ReDIF-Article 1.0
Author-Name: María Eugenia Ramírez
Author-Name-First: María Eugenia
Author-Name-Last: Ramírez
Author-Name: Misael Ron
Author-Name-First: Misael
Author-Name-Last: Ron
Author-Name: Gladys Mago
Author-Name-First: Gladys
Author-Name-Last: Mago
Author-Name: Estela Hernandez Runque
Author-Name-First: Estela
Author-Name-Last: Hernandez Runque
Author-Name: María Del Carmen Martínez
Author-Name-First: María Del Carmen
Author-Name-Last: Martínez
Author-Name: Evelin Escalona
Author-Name-First: Evelin
Author-Name-Last: Escalona
Title: Proposal for an epidemiological surveillance program for the prevention of occupational accidents and diseases in workers exposed to carbon dioxide (CO2) at a Venezuelan brewing company
Abstract: Introduction: in manufacturing companies, specifically in the brewery, there are processes that involve the handling and use of chemical agents, such as carbon dioxide (CO2), this is the reason why workers are exposed to this agent. In the studied company, an accident was caused by exposure to this substance. Objective: to propose an epidemiological surveillance program for the prevention of occupational accidents and diseases in workers exposed to carbon dioxide (CO2) in a Venezuelan brewery. Methods: a qualitative-quantitative, field, descriptive, feasible project-type research was carried out, with the epidemiological surveillance program as the unit of analysis. Documentary review, direct observation and the interview were used as data collection techniques, and the observation guide, the sociodemographic form and the field diary were used as instruments. Results: the machine room has 18 workers, which shows that the workforce is composed of men over 40 years of age. Among the main causes of consultation of workers to the medical service are headache with 24,1 %, followed by fatigue with 20,6 % and then dizziness with 13,7 %. Conclusion: we propose an Epidemiological Surveillance Program aimed at machine room workers exposed to Carbon Dioxide (CO2), since there is no system that collects complete information on the working conditions and health of its workers, thus failing to comply with the legal framework governing the subject
Journal: Data and Metadata
Pages: 55
Volume: 2
Year: 2023
DOI: 10.56294/dm202355
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:55:id:1056294dm202355

Template-Type: ReDIF-Article 1.0
Author-Name: Emilio Manuel Zayas Somoza
Author-Name-First: Emilio Manuel
Author-Name-Last: Zayas Somoza
Author-Name: Vilma Fundora Álvarez
Author-Name-First: Vilma
Author-Name-Last: Fundora Álvarez
Author-Name: Roberto Carlos Morejón Alderete
Author-Name-First: Roberto Carlos
Author-Name-Last: Morejón Alderete
Title: Latin American scientific production on malnutrition in ambulatory older adults with progression to sarcopenia
Abstract: Introduction: malnutrition is a global problem that affects millions of people around the world, especially the elderly. Among the possible consequences of malnutrition in the elderly is sarcopenia or loss of muscle mass. Objective: to characterize the trends and impact of scientific production on malnutrition in ambulatory older adults with progression to sarcopenia published in Scopus between 2019 and 2022 in the Latin American context. Method: an observational, descriptive, cross-sectional, bibliometric study was carried out. The data used in the study in question were obtained from the Dimensions database. Pearson's linear correlation was used to perform the trend analysis of the data. Results: the most productive years were 2020 (175 articles) and 2021 (160 articles), with the least productive being 2022 (31 articles). The year with the highest number of citations was represented by 2019 (15795 citations) for 53,74 % and the year with the lowest number was 2022 (2141 citations) for 7,29 %. Of the total citations, 6552 were considered self-citations. The results corroborate the hegemony of countries like Brazil (176 articles) and Mexico (110 articles). Cuba ranks 14th in Latin America with respect to the production of articles on the subject of study. Conclusions: a low Latin American scientific production on malnutrition in ambulatory older adults with progression to sarcopenia was evidenced in journals indexed in Scopus, with published articles and citations that follow a direct line towards reduction
Journal: Data and Metadata
Pages: 89
Volume: 2
Year: 2023
DOI: 10.56294/dm202389
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:89:id:1056294dm202389

Template-Type: ReDIF-Article 1.0
Author-Name: Juan Carlos Cotrina Aliaga
Author-Name-First: Juan Carlos
Author-Name-Last: Cotrina Aliaga
Author-Name: Danny Alonso Lizarzaburu Aguinaga
Author-Name-First: Danny Alonso
Author-Name-Last: Lizarzaburu Aguinaga
Author-Name: Teresa Marianella Gonzales Moncada
Author-Name-First: Teresa Marianella
Author-Name-Last: Gonzales Moncada
Author-Name: Jorge Luis Ilquimiche Melly
Author-Name-First: Jorge Luis
Author-Name-Last: Ilquimiche Melly
Author-Name: Yoni Magali Maita Cruz
Author-Name-First: Yoni Magali
Author-Name-Last: Maita Cruz
Author-Name: Segundo Pio Vasquez Ramos
Author-Name-First: Segundo Pio
Author-Name-Last: Vasquez Ramos
Title: Data, Digital Tools and Meaningful Learning: An Analysis in Today's Educational Context
Abstract: This research aims to address educational inequalities and improve the quality of education in the country, through a comprehensive and systematic review of the literature related to the topic of "Digital Tools and Meaningful Learning" in the current educational context. The key findings of each study were examined, including the methodologies used, the results obtained and the relevant conclusions. The papers were categorized and grouped according to common themes and emerging trends in the relationship between digital tools and meaningful learning. Special attention was paid to the limitations and challenges identified in the literature. In conclusion, the use of digital tools in the classroom can contribute significantly to the teaching-learning process, as long as they are implemented effectively and existing educational inequalities are addressed. Effective strategies implemented in Latin America to close the digital divide and reduce educational inequalities were identified
Journal: Data and Metadata
Pages: 96
Volume: 2
Year: 2023
DOI: 10.56294/dm202396
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:96:id:1056294dm202396

Template-Type: ReDIF-Article 1.0
Author-Name: Jorge Burdiles Aguirre
Author-Name-First: Jorge
Author-Name-Last: Burdiles Aguirre
Author-Name: Nicole Hidd Cuitiño
Author-Name-First: Nicole
Author-Name-Last: Hidd Cuitiño
Author-Name: Jaime Crisosto Alarcón
Author-Name-First: Jaime
Author-Name-Last: Crisosto Alarcón
Author-Name: Carlos Rojas
Author-Name-First: Carlos
Author-Name-Last: Rojas
Title: Evolution and characteristics of speech and language therapist services in a high complexity Chilean hospital according to monthly statistical records (REM)
Abstract: Introduction: speech and language therapist services has been extensively described. However, in Chile the evolution and characteristics of these services at hospital level and especially during the last years (pre and post COVID-19) are unknown. The exploration of these data could contribute to the development of strategies and decision making at the local level. Objective: to determine the evolution and characteristics of speech and language therapist services between the years 2015-2022 in a Chilean high complexity hospital. Methods: by means of a quantitative, transectional and descriptive design, 96 databases corresponding to the Monthly Statistical Records (REM) between January 2015 and December 2022 were reviewed. The variables analyzed were: number of initial and intermediate evaluations, hospital rehabilitation sessions, home rehabilitation and procedures-activities performed. Results: an oscillating increase in the number of speech and language therapist services performed between 2015-2022 was observed. Preference is given to hospital rehabilitation sessions (95,626 services) followed by initial evaluations (11,550). By specific area, the highest number of benefits was obtained by swallowing rehabilitation (22,594), while individual and group auditory rehabilitation only presented 7 and 11 records respectively. Conclusions: the analysis of the REM exhibits an incremental evolution of the registry of speech and language therapist services, especially since the last three years (2020 onwards), this despite the fluctuations observed during the previous years (2015-2019). This increase would be directly related to the increase in the hiring of professionals, improvement of supplies and equipment, incorporation of the speech therapist to pathologies with explicit health guarantees (GES) and the need for professional staffing due to the COVID-19 contingency
Journal: Data and Metadata
Pages: 97
Volume: 2
Year: 2023
DOI: 10.56294/dm202397
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:97:id:1056294dm202397

Template-Type: ReDIF-Article 1.0
Author-Name: Freddy Lalaleo Analuisa
Author-Name-First: Freddy
Author-Name-Last: Lalaleo Analuisa
Author-Name: Amanda Martínez Yacelga
Author-Name-First: Amanda
Author-Name-Last: Martínez Yacelga
Title: Evaluation of the effectiveness of strategies under the perspectives of the Balanced ScoreCard
Abstract: The objective of this research is to know the effectiveness of business strategies from the perspective of the Balanced ScoreCard in medium-sized companies in the city of Ambato. The research consisted of an explanatory methodology with a qualitative and quantitative approach with a descriptive research design, the study population was based on micro-enterprises in the province of Tungurahua, in this sense for the sample, 22 medium-sized companies. of the city of Ambato were determined. A survey structured by 16 questions distributed in four dimensions was applied: financial perspective, customer perspective, internal processes perspective and learning and growth perspective. Among the main results is the low financial perspective (27,3 %). The customer perspective is the lowest point in 45,4 % of companies, on the other hand, there is a very low perspective of internal processes (27,3 %) and finally, the management focused on learning and knowledge perspective maintains the same deficiencies of the previous dimensions (22,7 %). In this context, it is concluded that the companies selected for the study have a low level of use of the Balanced ScoreCard perspectives. The results of the correlation analysis show that the financial perspective varies directly with the change in customer perspectives and internal processes and the customer perspective varies directly with respect to internal processes
Journal: Data and Metadata
Pages: 106
Volume: 2
Year: 2023
DOI: 10.56294/dm2023106
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:106:id:1056294dm2023106

Template-Type: ReDIF-Article 1.0
Author-Name: Roberto Carlos Dávila Morán
Author-Name-First: Roberto Carlos
Author-Name-Last: Dávila Morán
Author-Name: Rafael Alan Castillo Sáenz
Author-Name-First: Rafael Alan
Author-Name-Last: Castillo Sáenz
Author-Name: Alfonso Renato Vargas Murillo
Author-Name-First: Alfonso Renato
Author-Name-Last: Vargas Murillo
Author-Name: Leonardo Velarde Dávila
Author-Name-First: Leonardo
Author-Name-Last: Velarde Dávila
Author-Name: Elvira García Huamantumba
Author-Name-First: Elvira
Author-Name-Last: García Huamantumba
Author-Name: Camilo Fermín García Huamantumba
Author-Name-First: Camilo Fermín
Author-Name-Last: García Huamantumba
Author-Name: Renzo Fidel Pasquel Cajas
Author-Name-First: Renzo Fidel
Author-Name-Last: Pasquel Cajas
Author-Name: Carlos Enrique Guanilo Paredes
Author-Name-First: Carlos Enrique
Author-Name-Last: Guanilo Paredes
Title: Application of Machine Learning Models in Fraud Detection in Financial Transactions
Abstract: Introduction: fraud detection in financial transactions has become a critical concern in today's financial landscape. Machine learning techniques have become a key tool for fraud detection given their ability to analyze large volumes of data and detect subtle patterns. Objective: evaluate the performance of machine learning techniques such as Random Forest and Convolutional Neural Networks to identify fraudulent transactions in real time. Methods: a real-world data set of financial transactions was obtained from various institutions. Data preprocessing techniques were applied that include multiple imputation and variable transformation. Models such as Random Forest, Convolutional Neural Networks, Naive Bayes and Logistic Regression were trained and optimized. Performance was evaluated using metrics such as F1 score. Results: random Forests and Convolutional Neural Networks achieved an F1 score greater than 95% on average, exceeding the target threshold. Random Forests produced the highest average F1 score of 0,956. It was estimated that the models detected 45 % of fraudulent transactions with low variability. Conclusions: the study demonstrated the effectiveness of machine learning models, especially Random Forests and Convolutional Neural Networks, for accurate real-time fraud detection. Its high performance supports the application of these techniques to strengthen financial security. Future research directions are also discussed
Journal: Data and Metadata
Pages: 109
Volume: 2
Year: 2023
DOI: 10.56294/dm2023109
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:109:id:1056294dm2023109

Template-Type: ReDIF-Article 1.0
Author-Name: Edwin Gustavo Estrada Araoz
Author-Name-First: Edwin Gustavo
Author-Name-Last: Estrada Araoz
Author-Name: Marilú Farfán Latorre
Author-Name-First: Marilú
Author-Name-Last: Farfán Latorre
Author-Name: Willian Gerardo Lavilla Condori
Author-Name-First: Willian Gerardo
Author-Name-Last: Lavilla Condori
Author-Name: Jhemy Quispe Aquise
Author-Name-First: Jhemy
Author-Name-Last: Quispe Aquise
Author-Name: Maribel Mamani Roque
Author-Name-First: Maribel
Author-Name-Last: Mamani Roque
Author-Name: Franklin Jara Rodríguez
Author-Name-First: Franklin
Author-Name-Last: Jara Rodríguez
Title: Scientific production in the Scopus database of a public university in southeastern Peru
Abstract: Introduction: the scientific production of universities serves as a key indicator of their commitment to research and knowledge generation. It represents the collective efforts of educators, researchers, and students contributing to the advancement of science and technology. Objective: to analyze the scientific production in the Scopus database of the Universidad Nacional Amazónica de Madre de Dios (UNAMAD). Methods: the research adopted a bibliometric and retrospective approach. An analysis of Scopus-indexed documents was conducted, evaluating the number of publications, authors, journals of publication, types of documents, language of publication, authorship order, funding sources, knowledge areas to which the documents belong, and co-authorship networks. Results: a total of 172 documents indexed in the Scopus database were identified, indicating a rising trend in production in recent years. Most documents were original articles, published in foreign journals in the English language, with collaboration from researchers affiliated with UNAMAD and no declared funding sources. Additionally, a higher number of documents were observed in the fields of Social Sciences and Environmental Sciences. Conclusions: in recent years, there has been a notable increase in UNAMAD's scientific production in the Scopus database. Nevertheless, it remains relatively limited in comparison to other universities in the Amazonian region and throughout Peru
Journal: Data and Metadata
Pages: 111
Volume: 2
Year: 2023
DOI: 10.56294/dm2023111
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:111:id:1056294dm2023111

Template-Type: ReDIF-Article 1.0
Author-Name: Mohammed Moutaib
Author-Name-First: Mohammed
Author-Name-Last: Moutaib
Author-Name: Mohammed Fattah
Author-Name-First: Mohammed
Author-Name-Last: Fattah
Author-Name: Yousef Farhaoui
Author-Name-First: Yousef
Author-Name-Last: Farhaoui
Author-Name: Badraddine Aghoutane
Author-Name-First: Badraddine
Author-Name-Last: Aghoutane
Author-Name: Moulhime El Bekkali
Author-Name-First: Moulhime
Author-Name-Last: El Bekkali
Title: Fetal and Maternal Electrocardiogram ECG Prediction using Convolutional Neural Networks
Abstract: Predicting fetal and maternal electrocardiograms (ECGs) is crucial in advanced prenatal monitoring. In this study, we explore the effectiveness of Convolutional Neural Networks (CNNs), using a carefully developed methodology to predict the category of fetal (F) or maternal (M) ECGs. In the first part, we trained a CNN model to predict fetal and maternal ECG images. In the following sections, the study results will be revealed. The CNN model demonstrated its ability to effectively discriminate between fetal and maternal patterns using automatically learned features
Journal: Data and Metadata
Pages: 113
Volume: 2
Year: 2023
DOI: 10.56294/dm2023113
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:113:id:1056294dm2023113

Template-Type: ReDIF-Article 1.0
Author-Name: Berrami Hind
Author-Name-First: Berrami
Author-Name-Last: Hind
Author-Name: Zineb Serhier
Author-Name-First: Zineb
Author-Name-Last: Serhier
Author-Name: Jallal Manar
Author-Name-First: Jallal
Author-Name-Last: Manar
Author-Name: Mohammed Bennani Othmani
Author-Name-First: Mohammed
Author-Name-Last: Bennani Othmani
Title: Chatbots for medical students exploring medical students’ attitudes and concerns towards artificial intelligence and medical chatbots
Abstract: Introduction: artificial intelligence (AI) encompasses the concept of automated machines that can perform tasks typically carried out by humans, doctor-patient communication will increasingly rely on the integration of artificial intelligence (AI) in healthcare, especially in medicine and digital assistant systems like chatbots. The objective of this study is to explore the understanding, utilization, and apprehensions of future doctors at the Faculty of Medicine in Casablanca regarding the adoption of artificial intelligence, particularly intelligent chatbots. Methods: a cross-sectional study was conducted among students from the 1st to 5th year at the Faculty of Medicine and Pharmacy in Casablanca. Probability sampling was implemented using a clustered and stratified approach based on the year of study. Electronic forms were distributed to randomly selected groups of students. Results: among the participants, 52 % of students fully agreed to utilize chatbots capable of answering health-related queries, while 39 % partially agreed to use chatbots for providing diagnoses regarding health conditions. About concerns, 77 % of the respondents expressed fear regarding reduced transparency regarding the utilization of personal data, and 66 % expressed concerns about diminished professional autonomy. Conclusion: Moroccan Medical students are open to embracing AI in the field of medicine. The study highlights their ability to grasp the fundamental aspects of how AI and chatbots will impact their daily work, while the overall attitude towards the use of clinical AI was positive, participants also expressed certain concerns
Journal: Data and Metadata
Pages: 115
Volume: 2
Year: 2023
DOI: 10.56294/dm2023115
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:115:id:1056294dm2023115

Template-Type: ReDIF-Article 1.0
Author-Name: Rolando Eslava Zapata
Author-Name-First: Rolando
Author-Name-Last: Eslava Zapata
Author-Name: Rómulo Esteban Montilla
Author-Name-First: Rómulo Esteban
Author-Name-Last: Montilla
Author-Name: Edixon Chacón Guerrero
Author-Name-First: Edixon
Author-Name-Last: Chacón Guerrero
Author-Name: Carlos Alberto Gómez Cano
Author-Name-First: Carlos Alberto
Author-Name-Last: Gómez Cano
Author-Name: Edgar Gómez Ortiz
Author-Name-First: Edgar
Author-Name-Last: Gómez Ortiz
Title: Social Responsibility: A bibliometric analysis of research state and its trend
Abstract: Introduction: social responsibility is related to organizations' commitment to society and the environment. Recent research has shown the relationship between organizations' performance and some indicators such as economic performance or corporate image. Objective: this study analyzes the research on social responsibility to know the trend of studies. Method: based on qualitative and quantitative research and with bibliometric techniques, a statistical analysis is made with the Vosviewer program of 1639 publications from the Scopus database to map the research based on publications, authors, and citations. Results: the geographical distribution shows that the United States and the United Kingdom have the most published documents. They have the greatest scientific impact and a strong collaboration network. From the above, it is evident that social responsibility research has been approached from different angles to verify its relationship with economic, societal, or environmental variables. There is a wide field of knowledge that scholars can address. Conclusions: the results indicate that central research topics include the connection of social responsibility with advancing technologies, globalization, and climate change. Mapping the co-occurrence of keywords by authors reveals four clusters related to ethics and social responsibility, corporate governance, corporate social responsibility, and sustainable development
Journal: Data and Metadata
Pages: 117
Volume: 2
Year: 2023
DOI: 10.56294/dm2023117
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:117:id:1056294dm2023117

Template-Type: ReDIF-Article 1.0
Author-Name: Idrian García García
Author-Name-First: Idrian
Author-Name-Last: García García
Author-Name: Sergio González García
Author-Name-First: Sergio
Author-Name-Last: González García
Author-Name: Hamna Coello Caballero
Author-Name-First: Hamna
Author-Name-Last: Coello Caballero
Author-Name: Lisbel Garzón Cutiño
Author-Name-First: Lisbel
Author-Name-Last: Garzón Cutiño
Author-Name: Lourdes Hernández Cuétara
Author-Name-First: Lourdes
Author-Name-Last: Hernández Cuétara
Title: Analysis of scientific publications by professors of a Faculty of Medical Sciences
Abstract: Introduction: scientific publications are considered the final step of a research and are an excellent tool to characterize the scientific output of a university. Objective: to characterize the scientific production of the faculty of the Faculty of Medical Sciences "Miguel Enriquez", based on their scientific publications, in the period 2016-2022. Methods: a descriptive, cross-sectional, retrospective, observational, descriptive study was carried out. The universe was constituted by the publications of the faculty professors, grouped by teaching departments. Articles, complete books and chapters, and monographs were included. The publications were analyzed according to the time of dedication of the professor to the teaching activity, and annual indexes of scientific productivity were calculated. Results: a total of 845 scientific publications were counted in a faculty composed of 444 professors from 17 teaching departments. In a quarter of them, the main author was from the Diagnostic Means department. The number of authorships per professor was 1487 during the period, with the Clinical Sciences Department standing out. Most of the works were published in journals indexed in prestigious international databases (Groups I-II), with a predominance of publications by full-time professors. The highest indicators of annual productivity, both per department and per professor, were obtained by the Diagnostic Means and Graduate and Research departments. Professors with a scientific degree and full professors and researchers were the most productive. Conclusions: The analysis of seven years of scientific publications of the faculty of the "Miguel Enriquez" Faculty shows that there is a diminished scientific production, which mainly corresponds to the professors of higher rank or category
Journal: Data and Metadata
Pages: 118
Volume: 2
Year: 2023
DOI: 10.56294/dm2023118
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:118:id:1056294dm2023118

Template-Type: ReDIF-Article 1.0
Author-Name: Yuleydi Alcaide Guardado
Author-Name-First: Yuleydi
Author-Name-Last: Alcaide Guardado
Author-Name: Luis Enrique Jiménez Franco
Author-Name-First: Luis Enrique
Author-Name-Last: Jiménez Franco
Author-Name: Claudia Díaz de la Rosa
Author-Name-First: Claudia
Author-Name-Last: Díaz de la Rosa
Author-Name: Enrique Acosta Figueredo
Author-Name-First: Enrique
Author-Name-Last: Acosta Figueredo
Author-Name: Juan Luis
Author-Name-First: Juan Luis
Author-Name-Last: Juan Luis
Title: Student scientific group: “Technology and Science”: a look from the sustainable development goals
Abstract: Introduction: currently, work with young people plays an important role in advancing the Sustainable Development Goals. Objective: describe the scientific-technological contributions of the Student Scientific Group: "Technology and Science" linked to the achievement of the Sustainable Development Goals. Methods: observational, descriptive and cross-sectional study, from January 2021 to March 2023 at the University of Medical Sciences of Cienfuegos, Cuba. Study variables: scientific advice and technological support in carrying out virtual health scientific events, technological solutions, virtual courses and work with digital social networks as spaces for the exchange of knowledge and community work in health promotion and prevention. Results: the GCE is made up of 14 medical science students. The contributions linked to the objectives are shown (3,4,11). Advice and training for the development of events in the different interactive virtual platforms were highlighted. They supported developers and programmers in the creation of Android Mobile Applications. They stood out in the preparation of professors and teachers for the work with the Virtual Teaching-Learning Environments. They gave training courses on digital social networks to the members of the chair of the University for the Elderly and they joined the community work in health promotion and prevention. Conclusions: inspiring positive changes and transformations of the new generations in society is vital in order to contribute to the achievement of the Sustainable Development Goals
Journal: Data and Metadata
Pages: 119
Volume: 2
Year: 2023
DOI: 10.56294/dm2023119
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:119:id:1056294dm2023119

Template-Type: ReDIF-Article 1.0
Author-Name: Emilio Manuel Zayas Somoza
Author-Name-First: Emilio Manuel
Author-Name-Last: Zayas Somoza
Author-Name: Vilma Fundora Álvarez
Author-Name-First: Vilma
Author-Name-Last: Fundora Álvarez
Author-Name: Roberto Carlos Morejón Alderete
Author-Name-First: Roberto Carlos
Author-Name-Last: Morejón Alderete
Title: Latin American scientific production on malnutrition in ambulatory older adults with progression to sarcopenia in Scopus
Abstract: Introduction: malnutrition is a global problem that affects millions of people around the world, especially the elderly. Among the possible consequences of malnutrition in the elderly is sarcopenia or loss of muscle mass. Objective: to characterize the trends and impact of scientific production on malnutrition in ambulatory older adults with progression to sarcopenia published in Scopus between 2019 and 2022 in the Latin American context. Methods: an observational, descriptive, cross-sectional, bibliometric study was carried out. The data used in the study in question were obtained from the Dimensions database. Pearson's linear correlation was used to perform the trend analysis of the data. Results: the most productive years were 2020 (175 articles) and 2021 (160 articles), with the least productive being 2022 (31 articles). The year with the highest number of citations was represented by 2019 (15795 citations) for 53,74 % and the year with the lowest number was 2022 (2141 citations) for 7,29 %. Of the total citations, 6552 were considered self-citations. The results corroborate the hegemony of countries like Brazil (176 articles) and Mexico (110 articles). Cuba ranks 14th in Latin America with respect to the production of articles on the subject of study. Conclusions: a low Latin American scientific production on malnutrition in ambulatory older adults with progression to sarcopenia was evidenced in journals indexed in Scopus, with published articles and citations that follow a direct line towards reduction
Journal: Data and Metadata
Pages: 120
Volume: 2
Year: 2023
DOI: 10.56294/dm2023120
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:120:id:1056294dm2023120

Template-Type: ReDIF-Article 1.0
Author-Name: Sergio Peñafiel
Author-Name-First: Sergio
Author-Name-Last: Peñafiel
Author-Name: Analia Hurtado
Author-Name-First: Analia
Author-Name-Last: Hurtado
Author-Name: Marcela Aguirre
Author-Name-First: Marcela
Author-Name-Last: Aguirre
Author-Name: Inti Paredes
Author-Name-First: Inti
Author-Name-Last: Paredes
Author-Name: Vladimir Pizarro
Author-Name-First: Vladimir
Author-Name-Last: Pizarro
Title: Implementation and evaluation of an oncological case management system among public and private healthcare providers in Chile
Abstract: This article presented the implementation, results, and usability evaluation of a software solution designed to manage oncological cases between healthcare centers. The software was developed to facilitate the exchange of clinical and administrative data for patients referred to the Arturo López Peréz Foundation (FALP) through a charitable program. The software underwent iterative development and included features such as user roles, patient list, progress tracking, document upload and viewer, chat, DICOM viewer, sharing, download, and API integration. The usability of the software was evaluated using the System Usability Scale (SUS) questionnaire, which showed high levels of usability and user satisfaction. The software proved successful in facilitating the coordination and continuity of care for patients referred to FALP and received positive feedback from users. The results of this study highlight the effectiveness and value of the software solution in improving case management and information exchange in the Chilean healthcare system. Future plans include expanding the software for internal patient management at FALP and extending its use to other institutions
Journal: Data and Metadata
Pages: 122
Volume: 2
Year: 2023
DOI: 10.56294/dm2023122
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:122:id:1056294dm2023122

Template-Type: ReDIF-Article 1.0
Author-Name: Marcela Aguirre
Author-Name-First: Marcela
Author-Name-Last: Aguirre
Author-Name: Sergio Peñafiel
Author-Name-First: Sergio
Author-Name-Last: Peñafiel
Author-Name: April Anlage
Author-Name-First: April
Author-Name-Last: Anlage
Author-Name: Emily Brown
Author-Name-First: Emily
Author-Name-Last: Brown
Author-Name: Cecilia Enriquez Chavez
Author-Name-First: Cecilia
Author-Name-Last: Enriquez Chavez
Author-Name: Inti Paredes
Author-Name-First: Inti
Author-Name-Last: Paredes
Title: Comparative Analysis of Classification Models for Predicting Cancer Stage in a Chilean Cancer Center
Abstract: This study aimed to develop a predictive model for cancer stage using data from a Chilean cancer registry. Several factors, including cancer type, patient age, medical history, and time delay between diagnosis and treatment, were examined to determine their association with cancer stage. Multiple supervised multi-class classification methods were tested, and the best-performing models were identified. The results showed that the random forest, SVM polynomial, and composite models performed well across different stages, although distinguishing between Stages II and III was more challenging. The most important features for predicting cancer stage were found to be cancer type, TNM variables, and diagnostic extension. Variables related to treatment timing and sequence also showed some importance. It was emphasized that the results of predictive models should be interpreted carefully to avoid overprediction or underprediction. Clinical context and additional information should be considered to enhance the accuracy of predictions. The small dataset and limitations in data availability posed challenges in accurately predicting cancer stage for different cancer types. Implementing the predictive model can have various benefits, including informing treatment decisions, assessing disease severity, and optimizing resource allocation. Further research and expansion of the model's scope were recommended to improve its performance and impact. Overall, the study emphasized the potential of predictive models in cancer staging and highlighted the need for ongoing advancements in this field
Journal: Data and Metadata
Pages: 123
Volume: 2
Year: 2023
DOI: 10.56294/dm2023123
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:123:id:1056294dm2023123

Template-Type: ReDIF-Article 1.0
Author-Name: Carolina Villalobos
Author-Name-First: Carolina
Author-Name-Last: Villalobos
Author-Name: Carla Cavallera
Author-Name-First: Carla
Author-Name-Last: Cavallera
Author-Name: Matías Espinoza
Author-Name-First: Matías
Author-Name-Last: Espinoza
Author-Name: María Francisca Cid
Author-Name-First: María Francisca
Author-Name-Last: Cid
Author-Name: Inti Paredes
Author-Name-First: Inti
Author-Name-Last: Paredes
Title: Toward Efficiency and Accuracy: Implementation of a Semiautomated Data Capture and Processing Model for the Construction of a Hospital-based Tumor Registry in Chile
Abstract: Introduction: the innovative implementation of a Hospital-based cancer registry (HBCR) at the Arturo López Pérez Oncology Institute (FALP), showcasing the transition from a manual data extraction model to a semi-automation of the process. The purpose of this publication is to compare both methodologies by assessing their efficiency and accuracy. Methods: the analysis was conducted by comparing the complete dataset of the FALP HBCR from 2017 to 2021. The efficiency variable is analyzed, taking into account the total execution time of the registration process, and the precision variable was measured through the internal data consistency method using the IARCcrg Tools Software Results: in terms of efficiency, the analysis reveals that in 2017, employing a manual approach without automation, it was necessary to analyze 13 061 cases over 144 weeks with an average of 4 registrars to achieve a total of 3 211 cases fully registered. In contrast, over the subsequent 4 years (2018 to 2021), with varying degrees of automation, 65 088 cases were analyzed within 115 weeks, employing an average of 8 registrars, resulting in 13 537 fully registered. This method demonstrated to be 3 times more efficient. Regarding precision, the manual approach exhibited a 5 % error rate in registered cases, whereas the automated approach showed a 0,6 % error rate during the 2018-2021 period. Conclusion: the obtained results highlight the significant impact of semi-automating the tumor registration process through the utilization of tools for data capture and processing, achieving a threefold increase in efficiency and reducing errors to 0,6 %
Journal: Data and Metadata
Pages: 124
Volume: 2
Year: 2023
DOI: 10.56294/dm2023124
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:124:id:1056294dm2023124

Template-Type: ReDIF-Article 1.0
Author-Name: Nicolás Bravo
Author-Name-First: Nicolás
Author-Name-Last: Bravo
Author-Name: Inti Paredes
Author-Name-First: Inti
Author-Name-Last: Paredes
Author-Name: Luis Loyola
Author-Name-First: Luis
Author-Name-Last: Loyola
Author-Name: Gonzalo Vargas
Author-Name-First: Gonzalo
Author-Name-Last: Vargas
Title: Use of 5G technology for oncological surgery streaming
Abstract: This paper discusses the benefits of surgery streaming and tele-mentoring, as well as the use of 5G technology in surgical procedures. The paper describes the advantages of using wireless 5G broadband as a low-latency and large-bandwidth capacity connection, which can solve problems with cables and large equipment in the surgery room. The Chilean oncology clinic Fundación Arturo López Pérez coordinated an international project with Japanese companies NTT Data and Allm Inc. to implement a proof of concept using 5G technology for the transmission of an oncological surgery. This project consisted of the installation of a local 5G network, its configuration and testing, and the realization of the first broadcast of a robotic partial nephrectomy in Latin America using the 5G broadband. The paper provides details on the hardware infrastructure and components used in the project
Journal: Data and Metadata
Pages: 126
Volume: 2
Year: 2023
DOI: 10.56294/dm2023126
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:126:id:1056294dm2023126

Template-Type: ReDIF-Article 1.0
Author-Name: Jorge Contreras
Author-Name-First: Jorge
Author-Name-Last: Contreras
Author-Name: Andrés Cepeda
Author-Name-First: Andrés
Author-Name-Last: Cepeda
Title: Implementation of a course on disruptive technologies for nursing students in Chile
Abstract: Several institutions and countries have recognized the need to integrate disruptive technologies in the training of health professionals. An elective course on disruptive technologies in health for nursing was developed, structured in 5 units: a) innovation in health and nursing, b) creation of apps and virtual environments, c) digital manufacturing for nursing, d) sensors and internet of things, and e) data science in health. For its implementation, the didactic model proposed by Jorba and Sanmartí was considered; and for the evaluation of the units and the impact of the course, Urquidi's extended model of technological adoption was used. Forty-four students participated (39 women and 5 men), with an average age of 23 years. According to the technology acceptance model, statistically significant differences were found between the pre- and post-intervention groups in all dimensions of the model (Wilcoxon test, p < 0,05). In addition, a positive correlation was found between ease of use, subjective norm and intention to use the technologies taught. The implementation of the disruptive technologies course proved to be effective in the development of technological skills among nursing students in Chile
Journal: Data and Metadata
Pages: 129
Volume: 2
Year: 2023
DOI: 10.56294/dm2023129
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:129:id:1056294dm2023129

Template-Type: ReDIF-Article 1.0
Author-Name: Gilberto Murillo González
Author-Name-First: Gilberto
Author-Name-Last: Murillo González
Author-Name: German Martínez Prats
Author-Name-First: German
Author-Name-Last: Martínez Prats
Author-Name: Verónica Vázquez Vidal
Author-Name-First: Verónica
Author-Name-Last: Vázquez Vidal
Title: Technological disinformation: factors and causes of cybernaut identity theft in the digital world
Abstract: The contribution of technology in the development of our daily activities has taken a giant step in the dependence of the citizen-technology-society with the integration of the Internet without glimpsing a border. It is therefore necessary to safeguard personal information if you have an active digital life. The identification of the factors and causes that lead to identity theft is a requirement for the technical and operational literacy of citizens, who are easy victims. This article aims to analyze some aspects of causes and factors of identity theft of citizens of the municipality of the center of the State of Tabasco. A quantitative instrument was designed, applied via Internet to a population of 3,158. The results show that citizens are unaware of several aspects of security in the environment of digital services, which, depending on gender, age and level of education, are captive in some scenario of digital insecurity. Keywords: Cybersecurity; Knowledge Society; Digital Ecosystem; Business Intelligence; E-Commerce
Journal: Data and Metadata
Pages: 133
Volume: 2
Year: 2023
DOI: 10.56294/dm2023133
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:133:id:1056294dm2023133

Template-Type: ReDIF-Article 1.0
Author-Name: Y Sri Lalitha
Author-Name-First: Y
Author-Name-Last: Sri Lalitha
Author-Name: P Gayatri
Author-Name-First: P
Author-Name-Last: Gayatri
Author-Name: I Laxmi Bindu
Author-Name-First: I
Author-Name-Last: Laxmi Bindu
Author-Name: Ganapathi Raju
Author-Name-First: Ganapathi
Author-Name-Last: Raju
Title: Risk Analysis of Diabetic Leg Amputation : A Systematic Study
Abstract: Diabetic Foot Ulcer is considered a critical complication of diabetes, characterized by injuries and frequent exposure of the diabetic patient's foot. Approximately 20 % of diabetic patients may develop foot ulcers, with around 10 % requiring hospitalization due to additional complications. Typically, these ulcers affect individuals who have had diabetes for more than ten years. Neglecting or leaving Diabetic Foot Ulcers untreated can result in severe damage, leading to worsened infections and potentially necessitating amputation, often accompanied by multiple complications that may even result in mortality. Therefore, early prediction of foot-threatening risks is crucial to prevent worsening situations. In this work visualization methods are applied for a better understanding of the dataset to draw meaningful insights and to observe the behavior of amputation risks in diabetic patients. The feature values fluctuate, so selecting the best feature from a combination of statistical and graphical data analysis is not trivial. Data visualization techniques (data-driven approach), and statistical analysis were used to select important features, that lead to leg amputation. The Machine learning models were implemented to forecast foot ulcers depending on clinical outcomes. A predicted accuracy of 85 % is observed using Ensemble Methods
Journal: Data and Metadata
Pages: 140
Volume: 2
Year: 2023
DOI: 10.56294/dm2023140
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:140:id:1056294dm2023140

Template-Type: ReDIF-Article 1.0
Author-Name: Ricardo Javier Albarracín Vanoy
Author-Name-First: Ricardo Javier
Author-Name-Last: Albarracín Vanoy
Title: Logistics 4.0: Exploring Artificial Intelligence Trends in Efficient Supply Chain Management
Abstract: Introduction: in the current era of globalization and digitalization, international logistics faces unique challenges and opportunities. The growing demand for efficient supply chain management, combined with the need to reduce costs and improve services, has led to the adoption of advanced technologies such as Artificial Intelligence (AI). AI has become a key catalyst in the transformation of logistics, giving way to what is known as Logistics 4.0. This paper explores the most recent trends of AI in international logistics and its integration into education, with a specific focus on the San Mateo University Foundation. Methods: this mixed study, combining qualitative and quantitative methods, begins with quantitative data collection and analysis, followed by a qualitative phase. The qualitative approach focuses on students' perceptions of logistics training, while the quantitative approach describes how they perceive AI tools. The research included students and companies in Bogota, analyzing their familiarity with AI and its implementation in practice. Results: the findings indicate that AI is increasingly relevant in logistics, especially in process automation and data-driven decision making. Most companies surveyed have a good understanding of AI, but less than half implement it in their operations. Students recognize the importance of AI in logistics and its positive impact on education. There is consensus on the role of AI in improving educational quality, highlighting its usefulness in optimizing processes and personalizing learning. Conclusions: the research highlights the crucial role of AI in modern logistics and its ability to improve operational efficiency. The integration of AI in international business education is critical to enrich students' learning experience and prepare them for the challenges of the labor market. The blended methodology used is effective in gaining a holistic view of AI integration in logistics and its educational impact. The conclusions provide guidelines for curriculum development in international business with a focus on international logistics, aligning curricula with emerging trends in logistics and AI
Journal: Data and Metadata
Pages: 145
Volume: 2
Year: 2023
DOI: 10.56294/dm2023145
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:145:id:1056294dm2023145

Template-Type: ReDIF-Article 1.0
Author-Name: R. Uma Maheswari
Author-Name-First: R.
Author-Name-Last: Uma Maheswari
Author-Name: N. Sudha
Author-Name-First: N.
Author-Name-Last: Sudha
Title: Hybrid Feature Extraction and Capsule Neural Network Model for Fake News Detection
Abstract: The introduction and widespread use of social media has altered how information is generated and disseminated, along with the expansion of the Internet. Through social media, information is now more quickly, cheaply, and easily available. Particularly harmful content includes misinformation propagated by social media users, such as false news. Users find it simple to post comments and false information on social networks. Realising the difference between authentic and false news is the biggest obstacle. The current study's aim of identifying bogus news involved the deployment of a capsule neural network. However, with time, this technique as a whole learns how to report user accuracy. This paper offers a three-step strategy for spotting bogus news on social networks as a solution to this issue. Pre-processing is executed initially to transform unstrsuctrured data into a structured form. The second part of the project brought the HFEM (Combined Feature Extraction Model), which also revealed new relationships between themes, authors, and articles as well as undiscovered features of false news. based on a collection of traits that were explicitly and implicitly collected from text. This study creates a capsule neural network model in the third stage to concurrently understand how creators, subjects, and articles are presented. This work uses four performance metrics in evaluations of the suggested classification algorithm using on existing public data sets
Journal: Data and Metadata
Pages: 190
Volume: 2
Year: 2023
DOI: 10.56294/dm2023190
Handle: RePEc:dbk:datame:v:2:y:2023:i::p:190:id:1056294dm2023190

Template-Type: ReDIF-Article 1.0
Author-Name: Ankur Goyal
Author-Name-First: Ankur
Author-Name-Last: Goyal
Author-Name: Pronita Mukherjee
Author-Name-First: Pronita
Author-Name-Last: Mukherjee
Author-Name: Dipra Mitra
Author-Name-First: Dipra
Author-Name-Last: Mitra
Author-Name: Shiv Kant
Author-Name-First: Shiv
Author-Name-Last: Kant
Author-Name: Khalid Almalki
Author-Name-First: Khalid
Author-Name-Last: Almalki
Author-Name: Suliman Mohamed Fati
Author-Name-First: Suliman
Author-Name-Last: Mohamed Fati
Title: A Two-stage Approach for Word Searching in Handwritten Document Images
Abstract: Introduction; Despite the rise of electronic papers, handwritten paper documents remain important. Current technologies make document digitization, storage, compression, and transmission easy and affordable. But semi-automatic document image processing needs specific technology to extract document information accurately. Typed textual searches are used to get information from Digital Libraries. Objective; Generally, in a document, there exists a varying number of characters in different words. That is why searching a word in a whole document is incorporate mismatched word images in the fetched word image and also increases the time consumption to complete the task. Method; Keeping this idea in mind, the words having different number of characters with respect to the search word are discarded at the beginning as preprocessing. Result; To confirm the outstanding words in the document page as probable search word, a voting-based approach has been used for doing this, a modified HOG feature descriptor is extracted from each word image, then 5 distance-matching metrics are calculated, fed to a voting schema with the help of threshold value of each metrics, calculated beforehand. Conclusion; Here 3 types of voting is performed, first 2, with the varying no of metrics vote for positivity of the search word and in the last one three distance metrics are used among which if more than one votes for the positivity the model will indicate the word as a search word.
Journal: Data and Metadata
Pages: 54
Volume: 4
Year: 2025
DOI: 10.56294/dm202554
Handle: RePEc:dbk:datame:v:4:y:2025:i::p:54:id:1056294dm202554