Template-Type: ReDIF-Article 1.0 Author-Name: Melvin Octavio Fiallos Gonzales Author-Name-First: Melvin Octavio Author-Name-Last: Fiallos Gonzales Author-Name: Leocadio Fiallos Gonzales Author-Name-First: Leocadio Author-Name-Last: Fiallos Gonzales Title: Artificial Intelligence as a Pedagogical Resource in Initial Teacher Training Abstract: This study examines the integration of Artificial Intelligence (AI) in initial teacher education, focusing on its role in strengthening classroom curriculum management through teaching and assessment methodologies. A quantitative and descriptive design was applied using a Likert-type scale, with confirmatory factor analysis for validation and Cronbach’s alpha for reliability, complemented by inferential analysis. Findings indicate that teaching how to teach with AI is shaped by classroom practices, influencing how AI-based processes are perceived and their alignment with competency achievement. Results suggest that pre-service teachers develop greater confidence in using AI for didactic design, although only a small percentage view AI’s organization and presentation as fully coherent and useful for verification and continuous improvement. Journal: LatIA Pages: 367 Volume: 3 Year: 2025 DOI: 10.62486/latia2025367 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:367:id:1062486latia2025367 Template-Type: ReDIF-Article 1.0 Author-Name: Geovanne Farell Author-Name-First: Geovanne Author-Name-Last: Farell Author-Name: Delsina Faiza Author-Name-First: Delsina Author-Name-Last: Faiza Author-Name: Vera Irma Delianti Author-Name-First: Vera Irma Author-Name-Last: Delianti Author-Name: Rido Wahyudi Author-Name-First: Rido Author-Name-Last: Wahyudi Author-Name: Agariadne Dwinggo Samala Author-Name-First: Agariadne Dwinggo Author-Name-Last: Samala Author-Name: Nurullah Taş Author-Name-First: Nurullah Author-Name-Last: Taş Title: Integrating AI-Based Natural Language Processing in Vocational Education: Usability, Learning Gains, and Student Engagement in Indonesia Abstract: Introduction: The advancement of Artificial Intelligence (AI) has brought substantial changes to education, particularly through AI-based digital assistants. Objective: This study developed and evaluated an AI-powered digital assistant equipped with Natural Language Processing (NLP) capabilities, specifically designed for Indonesian vocational schools. Methods: Adopting the 4D development model (Define, Design, Develop, Disseminate), the system was created using machine learning algorithms and NLP to enhance interactivity and personalization. The assistant enables natural language interaction, provides real-time feedback, and adapts learning material difficulty to students’ comprehension levels. The system was tested with 100 vocational school students, with usability assessed using the System Usability Scale (SUS) and learning gains measured through pre- and post-tests. Results: Results showed a SUS score of 71.05, indicating good usability, and a significant improvement in post-test scores compared to pre-test scores (p < 0.001), reflecting enhanced conceptual understanding, engagement, and motivation. Conclusions: These findings demonstrate the potential of AI-powered NLP assistants to enrich vocational education and prepare students for technology-driven industrial demands. Journal: LatIA Pages: 362 Volume: 3 Year: 2025 DOI: 10.62486/latia2025362 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:362:id:1062486latia2025362 Template-Type: ReDIF-Article 1.0 Author-Name: Muthu Selvam Author-Name-First: Muthu Author-Name-Last: Selvam Title: Ethical AI for Personalized Banking: Addressing Bias and Fairness Challenges Abstract: Introduction: The integration of Artificial Intelligence (AI) into personalized banking has enhanced service delivery in areas such as loan processing, credit assessment, and fraud detection. Despite these advancements, ethical concerns, especially algorithmic bias and lack of fairness, pose significant challenges. This study addresses the need for equitable AI systems that promote transparency, fairness, and regulatory compliance in the banking sector. Objective: This study aims to develop and implement a comprehensive framework for integrating ethical principles into AI-driven banking systems, with a focus on mitigating algorithmic bias, enhancing fairness, and improving transparency in personalized banking services. Methods: A comprehensive methodology is proposed that integrates bias-aware data collection, fairness-constrained machine-learning models, and explainable AI (XAI) techniques. Tools such as Shapley Additive Explanations (SHAPs) and Local Interpretable Model-Agnostic Explanations (LIMEs) are applied to interpret model outputs. Adversarial debiasing and fairness-aware learning algorithms were employed to identify and mitigate systemic biases in financial data. Alternative data sources, including utility and rental payment histories, were incorporated to enhance inclusivity. Results: The implementation of the proposed framework demonstrates improved fairness in decision-making without significantly compromising model accuracy. Bias metrics show measurable reductions in disparate impacts across the demographic groups. Explainability tools enhance transparency, enabling a more transparent communication of AI decisions to both users and regulators. Conclusions: Embedding ethical principles into AI-driven banking systems is critical to ensuring fairness, regulatory alignment, and public trust. The structured framework presented in this study supports the development of responsible AI systems to mitigate bias, enhance explainability, and foster financial inclusion. This approach serves as the foundation for building equitable and accountable AI applications in modern banking. Journal: LatIA Pages: 361 Volume: 3 Year: 2025 DOI: 10.62486/latia2025361 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:361:id:1062486latia2025361 Template-Type: ReDIF-Article 1.0 Author-Name: Julie Uy Cabato Author-Name-First: Julie Uy Author-Name-Last: Cabato Title: From Awareness to Practice: Exploring the Knowledge, Attitudes, and Practices of Secondary ESL Teachers in the Philippines Toward ChatGPT in Education Abstract: The rise of generative artificial intelligence (AI), particularly ChatGPT, has brought significant changes to educational practice. While research has largely emphasized student use, the perspectives of teachers, especially those in English as a second language (ESL) instruction, remain limited. This study examined the knowledge, attitudes, and practices (KAP) of 181 Filipino secondary ESL teachers in Zamboanga City regarding ChatGPT integration in language teaching. Using a descriptive-comparative quantitative design, data were gathered through the validated KAP-CQ39 instrument and analyzed via SPSS. The findings revealed that participants demonstrated a moderate level of knowledge, a somewhat positive attitude, and high positive usage of ChatGPT. Gender-based comparisons revealed no significant differences across the KAP dimensions. The item-level analysis highlighted the uneven awareness of ChatGPT’s features, ethical implications, and varied implementation in classroom settings. These findings suggest a growing interest among ESL educators in engaging with AI tools, although knowledge gaps and ethical uncertainties persist. The study highlights the need for targeted training, institutional support, and clear guidelines to foster the responsible and effective use of ChatGPT in language education. This study contributes to a deeper understanding of AI adoption in linguistically diverse educational contexts within the Philippine context. Journal: LatIA Pages: 360 Volume: 3 Year: 2025 DOI: 10.62486/latia2025360 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:360:id:1062486latia2025360 Template-Type: ReDIF-Article 1.0 Author-Name: Qiuye Guo Author-Name-First: Qiuye Author-Name-Last: Guo Author-Name: Sanghyun Kim Author-Name-First: Sanghyun Author-Name-Last: Kim Title: Research on Intelligent Recommendation Algorithm of Short Videos Based on Graph Neural Network Abstract: The rapid development of short video platforms has put forward higher requirements for the accuracy and personalization of content recommendation systems. In this paper, a short video recommendation algorithm based on Graph Neural Network (GNN) is studied, which improves the recommendation performance by fusing multimodal features such as video, audio, and text. The key technologies such as graph convolution neural network, graph attention network and graph pooling operator are analyzed, and a multimodal recommendation framework is constructed by combining self-supervised contrastive learning and local feature encoder to effectively deal with complex user-content interactions. In this paper, several algorithms are compared on TikTok and MovieLens datasets. The experimental results show that the SHL algorithm significantly improves the recommendation accuracy and user personalized satisfaction on TikTok and MovieLens datasets, which is generalizable. Journal: LatIA Pages: 347 Volume: 3 Year: 2025 DOI: 10.62486/latia2025347 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:347:id:1062486latia2025347 Template-Type: ReDIF-Article 1.0 Author-Name: Marianna Zhumbei Author-Name-First: Marianna Author-Name-Last: Zhumbei Author-Name: Halyna Apelt Author-Name-First: Halyna Author-Name-Last: Apelt Author-Name: Nadiia Savchuk Author-Name-First: Nadiia Author-Name-Last: Savchuk Author-Name: Oleksandr Akimov Author-Name-First: Oleksandr Author-Name-Last: Akimov Author-Name: Inna Tsymbal Author-Name-First: Inna Author-Name-Last: Tsymbal Title: Leveraging Gamification to Sustain Student Motivation and Emotional Resilience in Higher Education During Wartime: Case Studies from Ukraine Abstract: In the conditions of war in Ukraine, the educational process underwent profound transformations, accompanied by a decrease in student motivation and an increase in emotional exhaustion. The relevance of the topic was due to the need to find effective psychological and pedagogical tools to support student engagement in times of crisis. The purpose of the study was to find out the impact of gamification on student motivation; the object was the educational process in higher education institutions during the war. The research methodology was based on a questionnaire, comparative analysis, qualitative interviews, and empirical observation of gamified educational practices in three higher education institutions. The results of the study showed that team gamification, the use of adaptive online platforms, and instant feedback mechanisms were the most effective in wartime. Gamification was shown to increase academic engagement, reduce anxiety, and raise satisfaction with the learning process. Particularly high rates were recorded among psychology and engineering students. Gamified elements, such as virtual rewards, interactive missions, and cooperative tasks, proved to be effective not only in terms of learning but also in providing emotional support. The practical significance of the results lay in the possibility of adapting the cases to other educational contexts and developing strategies to overcome motivational decline during a crisis. The findings could be useful for teachers, educational administrators, and content developers who were looking for innovative solutions in the face of uncertainty. Journal: LatIA Pages: 345 Volume: 3 Year: 2025 DOI: 10.62486/latia2025345 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:345:id:1062486latia2025345 Template-Type: ReDIF-Article 1.0 Author-Name: Mykhailo Lukash Author-Name-First: Mykhailo Author-Name-Last: Lukash Author-Name: Yevhenii Chuprun Author-Name-First: Yevhenii Author-Name-Last: Chuprun Author-Name: Oksana Lysak Author-Name-First: Oksana Author-Name-Last: Lysak Author-Name: Anatolii Husakovskyi Author-Name-First: Anatolii Author-Name-Last: Husakovskyi Author-Name: Kyrylo Hanhanov Author-Name-First: Kyrylo Author-Name-Last: Hanhanov Title: AI evolution and its role in transforming the automation of commercial activities Abstract: This article examined the impact of artificial intelligence (AI) on the automation of business processes, focusing on how intelligent systems enhanced management efficiency and operational optimization. Special attention was given to cognitive neuro-fuzzy models and their role in transforming business processes in the digital era. The study was timely, considering the exponential growth of data and the complexity of modern organizational structures, which demanded fast, accurate, and adaptive management solutions. AI technologies provided such capabilities, while companies that failed to adopt them risked losing competitive advantage amid ongoing digital transformation. The study aimed to develop and justify a conceptual approach to automating business processes through AI. To achieve this, two primary methods were applied: cognitive modeling using semantic M-networks to reflect human imaginative thinking in process structures, and reinforcement learning to optimize processes based on feedback mechanisms. The methodology combined theoretical literature analysis, mathematical modeling, and empirical examination of real business processes. The findings demonstrated that integrating AI significantly improved overall business process efficiency by reducing complexity, costs, and feedback loops, while enhancing control, regulation, and financial outcomes. The M-network model illustrated how AI adapted processes to dynamic environments and supported decision-making through visualized cognitive maps. Future research directions included advancing cognitive learning algorithms to handle larger datasets, designing adaptive AI interfaces tailored to individual user behavior, and exploring AI’s influence on cross-functional collaboration to foster comprehensive digital management ecosystems. Journal: LatIA Pages: 344 Volume: 3 Year: 2025 DOI: 10.62486/latia2025344 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:344:id:1062486latia2025344 Template-Type: ReDIF-Article 1.0 Author-Name: Iurii Bedratyi Author-Name-First: Iurii Author-Name-Last: Bedratyi Author-Name: Nadiia Vasylieva Author-Name-First: Nadiia Author-Name-Last: Vasylieva Author-Name: Olena Perunova Author-Name-First: Olena Author-Name-Last: Perunova Author-Name: Oleksii Volokhov Author-Name-First: Oleksii Author-Name-Last: Volokhov Author-Name: Larysa Chekmarova Author-Name-First: Larysa Author-Name-Last: Chekmarova Title: Global Digital Transformation: Ensuring the Protection of IP Rights Abstract: Today, intellectual property (IP) is a key global development driver. Its institution forms the basis of the economy, its virtually inexhaustible resource. Against the backdrop of large-scale and rapid digitalisation of public life, intellectual property is acquiring the functions of a toolkit for forming an up-to-date digital market, which requires a study of the transformation of the IP institution. The article aims to analyse the key trends in developing the system of intellectual property rights protection against the background of the digitalisation of global society. It examines the functionality of IP in the new digital era and outlines the main related risks and challenges. The study finds that rapid informatisation has become a key cause of large-scale infringements of IP rights. It examines modern innovative technologies and effective approaches in the practical experience of developed countries in protecting intellectual property rights. The article analyses modern scholars' positions regarding assessing the current level of IP rights protection. It highlights the need to integrate advanced digital technologies in terms of the IP protection strategy and identifies and analyses the most effective ones. The study establishes that this process may require separate targeted measures within the legislative and legal regulation framework. It has been proved that this problem should be addressed through a comprehensive global upgrade of IP legislation to introduce and strengthen a generally favourable legal regime that considers innovation trends to the maximum extent possible. It is substantiated that today, upgrading traditional legal approaches to protecting intellectual property rights is necessary. Journal: LatIA Pages: 343 Volume: 3 Year: 2025 DOI: 10.62486/latia2025343 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:343:id:1062486latia2025343 Template-Type: ReDIF-Article 1.0 Author-Name: Fatimazahra Ouahouda Author-Name-First: Fatimazahra Author-Name-Last: Ouahouda Author-Name: Khadija Achtaich Author-Name-First: Khadija Author-Name-Last: Achtaich Author-Name: Naceur Achtaich Author-Name-First: Naceur Author-Name-Last: Achtaich Title: Tinkercad and mBlock: Tools for Educational Robotics and Coding Learning A Case of Ghandi Primary school Morocco Abstract: This study explores the impact of Tinkercad and mBlock on the en gagement and computational thinking skills of students at Ghandi Primary School. Through interactive robotics and coding activities, students demonstrated increased motivation, problem-solving abilities, and confidence in programming. Using Arduino and block-based coding, they developed a deeper understanding of STEM concepts in a hands-on learning environment. The findings suggest that integrating educational robotics into primary education enhances student engage ment and fosters essential 21st-century skills. Journal: LatIA Pages: 342 Volume: 3 Year: 2025 DOI: 10.62486/latia2025342 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:342:id:1062486latia2025342 Template-Type: ReDIF-Article 1.0 Author-Name: Nataliia Shevtsova Author-Name-First: Nataliia Author-Name-Last: Shevtsova Author-Name: Alina Аndroshchuk Author-Name-First: Alina Author-Name-Last: Аndroshchuk Author-Name: Viktoriya Syno Author-Name-First: Viktoriya Author-Name-Last: Syno Author-Name: Iryna Aleksandruk Author-Name-First: Iryna Author-Name-Last: Aleksandruk Author-Name: Oleksandr Maliuha Author-Name-First: Oleksandr Author-Name-Last: Maliuha Title: Teaching German within digital paradigm of education: AI-based approaches and tools Abstract: In view of the increased popularity of AI tools in teaching foreign languages, particularly German, and the corresponding concerns that arose, this article explored the futuristic prospects of learning German with AI. It examined how these technologies had revolutionized the learning process and what learners could expect in the future. The study’s methodology was based on a systemic paradigm and involved the use of content analysis and elements of case studies, relying on a wide array of literature sources extracted from general and specialized scientometric databases. The findings showed that AI in language teaching represented a powerful approach to engaging students and enhancing learning outcomes. The most innovative methods, such as the integration of massively multiplayer online role-playing games (MMORPGs) into educational processes, yielded the most effective results. The study attempted to outline the correlation between various AI-based teaching approaches and existing educational theories that characterized the contemporary educational landscape. Furthermore, it proposed an appropriate schematic model that could serve as a foundation for further research in the field, including studies with an interdisciplinary focus. Journal: LatIA Pages: 340 Volume: 3 Year: 2025 DOI: 10.62486/latia2025340 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:340:id:1062486latia2025340 Template-Type: ReDIF-Article 1.0 Author-Name: Mohammad Sidiq Author-Name-First: Mohammad Author-Name-Last: Sidiq Author-Name: Jyoti Sharma Author-Name-First: Jyoti Author-Name-Last: Sharma Author-Name: Aksh Chahal Author-Name-First: Aksh Author-Name-Last: Chahal Author-Name: Krishna Reddy Vajrala Author-Name-First: Krishna Reddy Author-Name-Last: Vajrala Author-Name: Sachin Gupta Author-Name-First: Sachin Author-Name-Last: Gupta Title: Role of Artificial Intelligence in Cross-sectional Studies in Rural India: Prospects, Obstacles, and Future Directions Abstract: Cross-sectional studies are critical as sources of the health, socio-economic, and demographic dynamics of rural populations in India. However, these studies suffer from some drawbacks, including logistics issues, data validity, and limited funding. Recent advances in AI have demonstrated the possibility of enhancing various aspects of cross-sectional study design, data acquisition, and statistical and interpretational methods. This manuscript outlines how AI can complement cross-sectional studies in rural India, describes the challenges of AI implementation, and envisions ways in which AI options may be incorporated into future rural health research. Journal: LatIA Pages: 336 Volume: 3 Year: 2025 DOI: 10.62486/latia2025336 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:336:id:1062486latia2025336 Template-Type: ReDIF-Article 1.0 Author-Name: Petros Chavula Author-Name-First: Petros Author-Name-Last: Chavula Author-Name: Fredrick Kayusi Author-Name-First: Fredrick Author-Name-Last: Kayusi Author-Name: Gilbert Lungu Author-Name-First: Gilbert Author-Name-Last: Lungu Author-Name: Agnes Uwimbabazi Author-Name-First: Agnes Author-Name-Last: Uwimbabazi Title: Enhancing Agricultural Resilience in Malawi: The Impact of Simple Irrigation Adoption and AI-Driven Solutions on Smallholder Farmers in Kamudidi Abstract: Agriculture is the backbone of Malawi’s economy, yet smallholder farmers face significant challenges due to erratic rainfall, water scarcity, and inefficient irrigation practices. This study examines the impact of simple irrigation adoption on maize productivity and household income among smallholder farmers in Kamudidi, Malawi. Using Propensity Score Matching (PSM), we compare farmers who adopt simple irrigation with those who rely on traditional rain-fed agriculture. The results show that irrigation adapters produce, on average, 244.21 more kilograms of maize and experience a 6562.79 Malawian Kwacha increase in household total expenditure compared to non-adopters. These findings underscore the role of irrigation in improving food security and economic stability. Furthermore, the study explores the potential of Artificial Intelligence (AI) to optimize irrigation practices through predictive analytics, weather forecasting, and smart water management. While AI-driven solutions can enhance decision-making and resource allocation, challenges such as limited digital literacy, infrastructure constraints, and financial barriers hinder widespread adoption. The study highlights the need for targeted policies, including access to affordable credit, farmer training programs, and investment in digital infrastructure, to facilitate both irrigation and AI adoption. Overall, the research provides valuable insights into how simple irrigation and AI-driven solutions can enhance agricultural resilience. Policymakers and development agencies should prioritize interventions that improve irrigation access and integrate AI to support smallholder farmers, ultimately fostering sustainable agricultural growth and rural development in Malawi. Journal: LatIA Pages: 335 Volume: 2 Year: 2024 DOI: 10.62486/latia2025335 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:335:id:1062486latia2025335 Template-Type: ReDIF-Article 1.0 Author-Name: Ranta Butarbutar Author-Name-First: Ranta Author-Name-Last: Butarbutar Title: Uncovering Online Collaborative Learning in Teaching English for Specific Purposes Abstract: This study aimed to investigate how ESP teachers facilitate online collaboration in teaching English. Furthermore, this study sought to explore ESP teachers’ and students’ collaboration learning (OCL) in public administration (PA) courses. To achieve this, the researchers utilized semi-structured questionnaires and open-ended questions to gather data, which were then analyzed thematically. The findings revealed that ESP teachers employ online collaboration in PA courses in three distinct phases: pre-OCL, during OCL, and post-OCL. They perceived OCL through four themes: context, interaction, impact, and challenges. However, it is important to note that this study did not incorporate the use of a true experiment or direct observation in either the control or class intervention, which is a limitation that should be addressed in future studies. Future studies should explore the potential benefits of ESP-administration-based augmented reality. This study implies that OCL in ESP teaching necessitates innovation and creativity from ESP teachers, as it extends beyond simply emphasizing communicative competence. Journal: LatIA Pages: 334 Volume: 3 Year: 2025 DOI: 10.62486/latia2025334 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:334:id:1062486latia2025334 Template-Type: ReDIF-Article 1.0 Author-Name: Amit Singh Author-Name-First: Amit Author-Name-Last: Singh Title: From Past to Present: The Evolution of Data Breach Causes (2005–2025) Abstract: This review aims to analyze the changing causes of data breaches over two decades by synthesising evidence from various data breach investigation reports and regulatory filings. The methodology involves examining trends in threat actors, actions, and motives identified in reports such as the Verizon Data Breach Investigations Report (DBIR) series from 2008 to 2024, California Attorney General's reports, and the Privacy Rights Clearinghouse. (1,2,3) The findings reveal an evolution through distinct phases: an initial period (roughly 2008-2010) dominated by external breaches leveraging hacking and malware, a subsequent era (2011-2019) marked by the rise of sophisticated cybercrime, including increased phishing and the emergence of defined incident patterns, and a more recent epoch (2020-2024) characterised by a significant surge in ransomware attacks, exploitation of systemic vulnerabilities, and the convergence of financially motivated and nation-state actors. Throughout these periods, human factors and errors have consistently contributed to successful breaches. In conclusion, the landscape of data breaches have shifted from simpler external attacks to more complex and disruptive campaigns, where human vulnerabilities remain a key enabler, and the emerging landscape includes AI-driven threats that are being explored by both attackers and defenders, necessitating continuous adaptation of defence strategies to address both traditional weaknesses and novel AI-related risks. Journal: LatIA Pages: 333 Volume: 3 Year: 2025 DOI: 10.62486/latia2025333 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:333:id:1062486latia2025333 Template-Type: ReDIF-Article 1.0 Author-Name: Fredrick Kayusi Author-Name-First: Fredrick Author-Name-Last: Kayusi Author-Name: Petros Chavula Author-Name-First: Petros Author-Name-Last: Chavula Author-Name: Gilbert Lungu Author-Name-First: Gilbert Author-Name-Last: Lungu Author-Name: Hockings Mambwe Author-Name-First: Hockings Author-Name-Last: Mambwe Title: AI-Driven Climate Modeling: Validation and Uncertainty Mapping – Methodologies and Challenges Abstract: Climate models are fundamental for predicting future climate conditions and guiding mitigation and adaptation strategies. This study aims to enhance the accuracy and reliability of climate modeling by integrating artificial intelligence (AI) techniques for validation and uncertainty mapping. AI-driven approaches, including machine learning-based parameterization, ensemble simulations, and probabilistic modeling, offer improvements in model precision, quality assurance, and uncertainty quantification. A systematic review methodology was applied, selecting peer-reviewed studies from 2000 to 2023 that focused on climate modeling, validation, and uncertainty estimation. Data sources included observational records, satellite measurements, and global reanalysis datasets. The study analyzed key AI-driven methodologies used for improving model accuracy, including statistical downscaling techniques and deep learning-based uncertainty prediction frameworks. Findings indicate that AI-enhanced models significantly improve climate projections by refining parameterization, enhancing bias correction, and optimizing uncertainty quantification. Machine learning applications facilitate more accurate predictions of meteorological phenomena, including temperature and precipitation variability. However, challenges remain in addressing observational biases, inter-model inconsistencies, and computational limitations. The study concludes that AI-driven advancements provide critical improvements in climate model reliability, yet ongoing refinements are necessary to address persistent uncertainties. Enhancing observational datasets, refining computational techniques, and strengthening model validation frameworks will be essential for reducing uncertainty. Effective communication of climate model outputs, including uncertainty mapping, is crucial for supporting informed policy decisions. AI-driven climate modeling is a rapidly evolving field, and continuous innovation will be key to improving predictive accuracy and resilience in climate adaptation strategies. Journal: LatIA Pages: 332 Volume: 3 Year: 2025 DOI: 10.62486/latia2025332 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:332:id:1062486latia2025332 Template-Type: ReDIF-Article 1.0 Author-Name: Hockings Mambwe Author-Name-First: Hockings Author-Name-Last: Mambwe Author-Name: Petros Chavula Author-Name-First: Petros Author-Name-Last: Chavula Author-Name: Fredrick Kayusi Author-Name-First: Fredrick Author-Name-Last: Kayusi Author-Name: Gilbert Lungu Author-Name-First: Gilbert Author-Name-Last: Lungu Author-Name: Agnes Uwimbabazi Author-Name-First: Agnes Author-Name-Last: Uwimbabazi Title: Machine learning and AI for security mechanisms: A Systematic Literature Review Using a PRISMA Framework Abstract: Cyber threats are evolving rapidly, posing significant risks to individuals, organizations, and digital infrastructure. Traditional cybersecurity measures, which rely on predefined rules and static defence mechanisms, struggle to counter emerging threats such as zero-day attacks and advanced persistent threats (APTs). The integration of artificial intelligence (AI) and machine learning (ML) into cybersecurity presents a transformative approach, enhancing threat detection, anomaly identification, and automated response mechanisms. This study systematically reviews the role of ML and AI in cybersecurity defence using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. A comprehensive literature search was conducted across multiple academic databases, identifying and analyzing studies published within the last decade. The review focuses on AI-driven cybersecurity applications, including intrusion detection systems (IDS), malware analysis, and anomaly detection in cloud and IoT environments. Findings indicate that ML models, such as neural networks, support vector machines, and ensemble learning techniques, improve detection accuracy and adaptability to evolving threats. AI-driven automated response systems enhance incident mitigation, reducing reliance on human intervention. However, challenges such as adversarial attacks, data privacy concerns, and computational resource demands persist. The study concludes that AI and ML significantly enhance cybersecurity resilience but require continuous advancements in model robustness, interpretability, and ethical considerations. Future research should focus on refining AI-driven security mechanisms, addressing adversarial vulnerabilities, and improving regulatory frameworks to maximize AI’s potential in cybersecurity. Journal: LatIA Pages: 331 Volume: 2 Year: 2024 DOI: 10.62486/latia2025331 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:331:id:1062486latia2025331 Template-Type: ReDIF-Article 1.0 Author-Name: Petros Chavula Author-Name-First: Petros Author-Name-Last: Chavula Author-Name: Fredrick Kayusi Author-Name-First: Fredrick Author-Name-Last: Kayusi Author-Name: Gilbert Lungu Author-Name-First: Gilbert Author-Name-Last: Lungu Author-Name: Hockings Mambwe Author-Name-First: Hockings Author-Name-Last: Mambwe Author-Name: Agnes Uwimbabazi Author-Name-First: Agnes Author-Name-Last: Uwimbabazi Title: AI Application in Climate-Smart Agricultural Technologies: A Synthesis Study Abstract: Climate change poses significant challenges to global agriculture, necessitating innovative solutions to enhance sustainability and productivity. Artificial intelligence (AI) has emerged as a key enabler in climate-smart agricultural technologies (CSAT), offering data-driven approaches to optimize resource use, mitigate climate risks, and improve decision-making. This study aims to evaluate AI's integration into CSAT, focusing on its applications, benefits, and adoption challenges, particularly in climate-vulnerable regions. A bibliographic review employing machine learning (ML) and natural language processing (NLP) techniques was conducted to analyze over 40,000 scientific articles from global academic databases. Topic modeling and classification algorithms were applied to identify key trends, adoption barriers, and implementation pathways for AI-driven CSAT. The study also incorporated expert validation through the Delphi method to refine AI-generated insights and ensure their alignment with real-world agricultural challenges. Findings indicate that AI enhances decision-making in conservation agriculture, precision farming, water management, and market intelligence. AI-powered tools facilitate early pest detection, optimize irrigation schedules, and provide real-time climate advisory services, significantly improving agricultural resilience and food security. However, major barriers to AI adoption include high implementation costs, limited digital literacy, and inadequate infrastructure, particularly in low-income regions. Despite these challenges, AI-driven CSAT presents significant potential to transform agriculture, especially in climate-affected areas. Strategic investments in digital literacy, infrastructure development, and supportive policy frameworks are essential to facilitate AI adoption. Strengthening interdisciplinary collaboration among researchers, policymakers, and farmers will be crucial in advancing sustainable agricultural practices and ensuring long-term food security. Journal: LatIA Pages: 330 Volume: 2 Year: 2024 DOI: 10.62486/latia2025330 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:330:id:1062486latia2025330 Template-Type: ReDIF-Article 1.0 Author-Name: Linety Juma Author-Name-First: Linety Author-Name-Last: Juma Author-Name: Petros Chavula Author-Name-First: Petros Author-Name-Last: Chavula Author-Name: Fredrick Kayusi Author-Name-First: Fredrick Author-Name-Last: Kayusi Author-Name: Michael Keari Omwenga Author-Name-First: Michael Author-Name-Last: Keari Omwenga Author-Name: Rashmi Mishra Author-Name-First: Rashmi Author-Name-Last: Mishra Author-Name: Timothy Mwewa Author-Name-First: Timothy Author-Name-Last: Mwewa Title: AI in Dissertation Examination: Opportunities for Undergraduates and Postgraduates in Zambia, Rwanda, and Kenya Abstract: The integration of Artificial Intelligence (AI) in dissertation examination presents a transformative opportunity for higher education institutions in Zambia, Rwanda, and Kenya. As student enrollments continue to rise, universities face challenges in efficiently evaluating dissertations while maintaining academic integrity. AI-driven tools offer innovative solutions by automating tasks such as plagiarism detection, language quality assessment, and contract cheating identification. This study aims to explore the opportunities, challenges, and impact of AI adoption in dissertation assessment across selected universities. A mixed-methods research design was employed, incorporating surveys, semi-structured interviews, and data analysis from AI-assisted dissertation evaluations at Copperbelt University (Zambia), the University of Rwanda, and Jomo Kenyatta University of Agriculture and Technology (Kenya). Findings indicate that AI enhances efficiency by reducing faculty workload and improving feedback quality for students. However, challenges such as digital literacy gaps, infrastructure limitations, and concerns over AI’s fairness and ethical implications hinder full adoption. Despite these obstacles, there is strong support among students and faculty for AI integration, provided it is complemented by human oversight. The study concludes that AI has significant potential to revolutionize dissertation evaluation but requires investment in infrastructure, faculty training, and policy frameworks to ensure responsible implementation. Collaboration among universities, policymakers, and technology providers is essential to optimizing AI-driven dissertation assessment while upholding academic rigour. Journal: LatIA Pages: 329 Volume: 3 Year: 2025 DOI: 10.62486/latia2025329 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:329:id:1062486latia2025329 Template-Type: ReDIF-Article 1.0 Author-Name: Muthu Selvam Author-Name-First: Muthu Author-Name-Last: Selvam Author-Name: Rubén González Vallejo Author-Name-First: Rubén Author-Name-Last: González Vallejo Title: Ethical and Privacy Considerations in AI-Driven Language Learning Abstract: Artificial intelligence (AI) has revolutionized language learning by enabling personalized and adaptive education; however, these advancements also raise ethical and privacy concerns, including algorithmic bias, data security risks, and a lack of transparency in AI-driven decision-making. This study examines these challenges, focusing on fairness, linguistic diversity, and the balance between automated and human instruction, with the goal of proposing ethical guidelines for the responsible adoption of AI in language education. Through a literature review and comparative analysis, ethical and privacy risks in AI-powered language learning tools were explored, assessing bias detection algorithms, transparency frameworks, and privacy-preserving techniques to identify best practices. The findings indicate that AI-driven language tools tend to exhibit biases that disadvantage underrepresented linguistic groups, raising concerns about fairness while also exposing privacy risks due to inadequate security measures. Implementing ethical AI frameworks that incorporate fairness-aware algorithms, explainable AI models, and robust data protection mechanisms enhances user trust and security. Therefore, addressing these issues is essential for ensuring the ethical integration of AI in language education, where a hybrid approach combining AI with human instruction emerges as the most responsible solution. Lastly, future research should focus on regulatory compliance and adaptive learning models to strengthen AI ethics in education. Journal: LatIA Pages: 328 Volume: 3 Year: 2025 DOI: 10.62486/latia2025328 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:328:id:1062486latia2025328 Template-Type: ReDIF-Article 1.0 Author-Name: Sunitha Purushottam Ashtikar Author-Name-First: Sunitha Author-Name-Last: Purushottam Ashtikar Author-Name: Geetha Manoharan Author-Name-First: Geetha Author-Name-Last: Manoharan Author-Name: Sowmya Muppidi Author-Name-First: Sowmya Author-Name-Last: Muppidi Title: Navigating Education in the Age of Generative AI Abstract: The educational landscape is quickly evolving, presenting many opportunities. At the same time, there are tests to be passed when Generative Artificial Intelligence (AI) comes into the picture. Better rides going ways to alter education to enroll in the AI era, worth of the best integration of Generative AI technologies. To start, our deliberation will open discussions on how generative AI can precipitate authentic revolutions in the enhancement of learning experiences, customized tutorials, and generating very different contextualization. We continue to explore evils to the integration of AI that has cropped up as a result; issues of ethics, privacy, and educator training, all stand as major adversaries in this context. Therefore, our theoretical proposal is drawn from literature and empirical studies. It offers a structure by which lecturers or schools may integrate Generative AI effectively. The framework pertains to curriculum realignment, teacher training programs, augmented infrastructure, and a robustly piloted/code of ethics. We are further provoked to encourage and improve collaboration among scholars, technologists, policymakers, and stakeholders about ensuring the conscientious and ethical use of AI in educational settings. It is an asset valuable for educators, users, and policy developers keen on inserting the energy of Generative AI into the consistently disorderly order of the AI era. Cutting-edge methodologies and inclusive of a culture of adaptive change, education can now truly flourish in a world increasingly shaped by AI, supported by modern-day learners and teachers in the twenty-first century and beyond. Journal: LatIA Pages: 327 Volume: 3 Year: 2025 DOI: 10.62486/latia2025327 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:327:id:1062486latia2025327 Template-Type: ReDIF-Article 1.0 Author-Name: Petros Chavula Author-Name-First: Petros Author-Name-Last: Chavula Author-Name: Fredrick Kayusi Author-Name-First: Fredrick Author-Name-Last: Kayusi Author-Name: Bismark Agura Kayus Author-Name-First: Bismark Author-Name-Last: Agura Kayus Title: Linking New Information Technologies to Agricultural Economics: The Role of Artificial Intelligence Integration Abstract: Artificial Intelligence (AI) is revolutionizing agricultural economics by optimizing productivity, reducing costs, and enhancing decision-making processes. This paper explores the integration of AI technologies—such as machine learning, predictive analytics, and automation—into agricultural economic frameworks. AI-driven innovations, including precision farming, yield forecasting, and supply chain management, are reshaping agricultural practices by improving efficiency and sustainability. Furthermore, AI facilitates data-driven policymaking, enabling governments and stakeholders to address food security, market fluctuations, and resource allocation more effectively. Despite its benefits, AI adoption in agriculture faces challenges, including high implementation costs, data privacy concerns, and the digital divide between developed and developing regions. The study highlights case studies and real-world applications demonstrating AI’s impact on economic growth and sustainable agricultural development. The findings suggest that strategic investment in AI infrastructure, combined with supportive policies and education, can accelerate its adoption and maximize its economic benefits. Ultimately, AI integration holds the potential to transform agricultural economies by fostering innovation, resilience, and sustainability. Journal: LatIA Pages: 326 Volume: 2 Year: 2024 DOI: 10.62486/latia2025326 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:326:id:1062486latia2025326 Template-Type: ReDIF-Article 1.0 Author-Name: Petros Chavula Author-Name-First: Petros Author-Name-Last: Chavula Author-Name: Fredrick Kayusi Author-Name-First: Fredrick Author-Name-Last: Kayusi Author-Name: Bismark Agura Kayus Author-Name-First: Bismark Author-Name-Last: Agura Kayus Title: Application of Artificial Intelligence in Tree Care in Sub-Saharan Africa Abstract: Artificial intelligence (AI) has emerged as a transformative tool in various industries, including environmental conservation and tree care. In Sub-Saharan Africa, where deforestation, climate change, and inadequate tree management pose significant challenges, AI presents opportunities for improving tree care practices. This study explores the application of AI technologies in tree monitoring, disease detection, and sustainable management strategies within the region. Utilizing a combination of literature review and case study analysis, the research evaluates AI-driven approaches such as remote sensing, machine learning models, and automated data collection for assessing tree care and forest dynamicos. The findings indicate that AI enhances early disease detection, optimizes resource allocation, and supports decision-making for conservation efforts. However, challenges such as limited technological infrastructure, high implementation costs, and the need for specialized expertise hinder widespread adoption. The study concludes that while AI holds significant potential for revolutionizing tree care in Sub-Saharan Africa, strategic investments in digital infrastructure, policy support, and capacity building are essential for its successful integration into forestry and environmental management practices. Journal: LatIA Pages: 325 Volume: 1 Year: 2023 DOI: 10.62486/latia2025325 Handle: RePEc:dbk:rlatia:v:1:y:2023:i::p:325:id:1062486latia2025325 Template-Type: ReDIF-Article 1.0 Author-Name: Khritish Swargiary Author-Name-First: Khritish Author-Name-Last: Swargiary Title: Interpretable AI for Behavioral Prediction: An Ethical Laboratory Experiment on Snack Choice Prediction Abstract: Introduction: The application of artificial intelligence (AI) in behavioral prediction has shown promise across domains like mental health, autonomous vehicles, and consumer behavior. However, challenges such as algorithmic bias, lack of interpretability, and ethical concerns persist. This study addresses these gaps by developing an interpretable AI model to predict snack choices in a controlled laboratory experiment. Methods: A random forest classifier was trained to predict participants’ snack choices (healthy vs. unhealthy) based on contextual factors (hunger, mood, time of day) and historical choices. Data were collected from 75 adults over 10 sessions, with features engineered to capture both immediate and longitudinal patterns. Model performance was evaluated using accuracy, precision, recall, and feature importance analysis. Results: The model achieved 85.33% accuracy, with hunger level, historical choices, and mood identified as the most influential predictors. Performance improved over sessions (peaking at 93.33% accuracy in sessions 8–9), highlighting the value of longitudinal data. Subgroup analyses showed consistent performance across age, gender, and BMI, with higher accuracy for participants with healthier habits and higher socioeconomic status. Conclusions: This study demonstrates the feasibility of interpretable AI models in predicting dietary behavior while addressing ethical concerns through rigorous data anonymization and informed consent protocols. The findings underscore the potential of AI to inform personalized interventions for healthier eating habits and provide a framework for ethical AI implementation in behavioral research. Journal: LatIA Pages: 324 Volume: 3 Year: 2025 DOI: 10.62486/latia2025324 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:324:id:1062486latia2025324 Template-Type: ReDIF-Article 1.0 Author-Name: Victor Chigbundu Nwaiwu Author-Name-First: Victor Author-Name-Last: Chigbundu Nwaiwu Author-Name: Sreemoy Kanti Das Author-Name-First: Sreemoy Author-Name-Last: Kanti Das Title: AI-assisted abnormal CXR findings and correlation with behavioral risk factors: A Public Health Radiography approach to formulating policies and effective interventions Abstract: Introduction: Cardiovascular, respiratory and related diseases (CVRDs) constitute over 40% cause of death worldwide, mostly reported in low-and-middle-income countries. The catastrophic effect of this spans across poor health outcomes, severe economic loss and significant societal consequences. Responding to this situation necessitates collective strategy to prevent further deterioration as these conditions are closely related, share common risk factors as well as control measures at the clinical, population and policy levels. Thus, this study is aimed at understanding the distribution of AI-assisted abnormal adult chest X-ray (CXR) and examine relationship with behavioral factors; to lay foundation for planned interventions. Methods: Prospective mixed-methods research, cross-sectional in nature, conducted across six top-rated hospitals in Nigeria, representing the six geopolitical zones of the country via purposive sampling technique. Quantitative aspect involved data collection on demographics and abnormal findings from AI-assisted technology, while Qualitative aspect explored individual’s behavioral choices in relation to risk factors. Informed consent and ethical approval were obtained; SPSS software utilized for descriptive and correlation analysis. Results: Cardiomegaly(15.35%), pleural effusion(14.03%), fibrous opacities(10.43%), pleural capping(8.51%), pulmonary mass(7.91%), apical opacities(7.55%), consolidation(6.59%), infiltration(5.88%) among the sixteen abnormal findings in decreasing order of magnitude. An early onset of these anomalies at 30 years was noted, hitting peak values at 40-44 years. A significant percentage of the population engages in unhealthy lifestyle, found to positively correlate with these anomalies in varying degrees; low education levels, health education gaps, poor income and environmental challenges clearly seen. Conclusion: A Public Health Radiography approach- AI assisted, engaging with empirical evidence provides a novel and valuable strategy in designing effective interventions and policy making to address CVRDs burden. Journal: LatIA Pages: 323 Volume: 3 Year: 2025 DOI: 10.62486/latia2025323 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:323:id:1062486latia2025323 Template-Type: ReDIF-Article 1.0 Author-Name: Petros Chavula Author-Name-First: Petros Author-Name-Last: Chavula Author-Name: Fredrick Kayusi Author-Name-First: Fredrick Author-Name-Last: Kayusi Title: Systematic Review on the Application of Nanotechnology and Artificial Intelligence in Agricultural Economics Abstract: The convergence of nanotechnology and artificial intelligence (AI) represents a transformative force in agricultural economics, offering innovative solutions to longstanding challenges such as productivity inefficiencies, environmental degradation, and unsustainable resource use. This study presents a systematic literature review (SLR) aimed at synthesising theoretical frameworks, applications, and economic implications associated with these technologies in agriculture. A structured search strategy was developed using Boolean operators to combine key terms related to nanotechnology, AI, and machine learning. Comprehensive searches were conducted across six academic databases—Springer, IEEE Xplore, ACM, Science Direct, Wiley, and Google Scholar—complemented by manual and snowballing techniques. From an initial pool of 840 records, 55 studies met the inclusion criteria after rigorous screening and eligibility assessment. Findings indicate that nanotechnology enhances nutrient delivery, pest control, and crop monitoring through nanosensors and nano-fertilisers, while AI facilitates data-driven decision-making, yield prediction, and resource optimisation in precision farming. Despite promising results, challenges such as high initial investment, technological complexity, and limited access for smallholder farmers remain significant. The review concludes that the integration of nanotechnology and AI can improve agricultural efficiency, economic viability, and environmental sustainability. However, targeted investments, capacity-building, and interdisciplinary collaboration are essential to bridge the gap between innovation and implementation in developing economies. Journal: LatIA Pages: 322 Volume: 3 Year: 2025 DOI: 10.62486/latia2025322 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:322:id:1062486latia2025322 Template-Type: ReDIF-Article 1.0 Author-Name: Petros Chavula Author-Name-First: Petros Author-Name-Last: Chavula Author-Name: Fredrick Kayusi Author-Name-First: Fredrick Author-Name-Last: Kayusi Title: Integrating AI to Assess Community Roles in Environmental Safeguarding During Mining: Implications for ESIA in SSA Abstract: This study investigates the role of local communities in environmental safeguarding during mining operations in Sub-Saharan Africa (SSA) and its implications for Environmental and Social Impact Assessments (ESIAs). While mining drives economic development, it often imposes environmental and social costs on local populations. The study critiques existing ESIA frameworks for privileging top-down, technocratic models that marginalize community voices. Using a systematic scoping review of 62 peer-reviewed empirical studies published since 2010, the research analyzes community participation and safeguarding practices through thematic coding and AI-powered tools like natural language processing. The findings underscore that local communities possess unique monitoring capacities, contextual knowledge, and culturally grounded environmental ethics that can enhance ESIA efficacy. These communities often respond more effectively than regulatory authorities to environmental infractions. The study also identifies structural barriers such as tokenistic participation, poverty, and policy exclusion that undermine meaningful engagement. It recommends embedding community-driven perspectives within ESIA processes by strengthening collaborative frameworks, recognizing indigenous knowledge systems, and leveraging AI to ensure inclusive and transparent evaluations. Furthermore, it argues for a shift toward participatory governance models that empower communities as co-regulators of environmental standards. By reframing ESIA as a dynamic socio-environmental negotiation, the study offers practical insights for policy reform, corporate responsibility, and sustainable development in SSA’s mining sectors. Journal: LatIA Pages: 321 Volume: 3 Year: 2025 DOI: 10.62486/latia2025321 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:321:id:1062486latia2025321 Template-Type: ReDIF-Article 1.0 Author-Name: Evans Omosa Nyamwaka Author-Name-First: Evans Author-Name-Last: Omosa Nyamwaka Title: Integrating Traditional African Music into Modern Education Using Digital Platform and Artificial Intelligence Abstract: This study examined how traditional African music might be incorporated into contemporary education, emphasizing how it affects learning, preservation of culture, and educational development. It also examined recent studies on potential, difficulties, and implementation frameworks. Modern teaching techniques must be balanced with cultural authenticity for successful assimilation. Results indicate that students' musical proficiency, cultural awareness, and cognitive abilities are all improved by traditional African music. Qualified teachers, useful instructional resources, utilization of artificial intelligence, and cultural preservation techniques are essential components of implementation. Cultural barriers, a lack of resources, and gaps in teacher preparation are still problems, but new technologies and creative teaching strategies provide answers. These problems must be addressed, especially the disparity in resources and quality of instruction between urban and rural areas. The study emphasizes how traditional African music shape’s cultural identity and aids in schooling. Through an analysis of how traditional African music promotes cross-cultural competency and cultural awareness in contemporary schooling, this study makes a distinctive contribution. Curriculum design, teacher education, and policy development gaps are filled by integrating educational frameworks, technological advancements, and cultural preservation techniques. The study offers suggestions for improving resource distribution, developing programs, and creating comprehensive curriculum guidelines that combine classic and contemporary teaching approaches in order to integrate music education globally. Curriculum design, teacher preparation, and educational policy are all significantly impacted by these revelations. Journal: LatIA Pages: 320 Volume: 3 Year: 2025 DOI: 10.62486/latia2025320 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:320:id:1062486latia2025320 Template-Type: ReDIF-Article 1.0 Author-Name: Michael Keari Omwenga Author-Name-First: Michael Keari Author-Name-Last: Omwenga Author-Name: Fredrick Kayusi Author-Name-First: Fredrick Author-Name-Last: Kayusi Author-Name: Everlyne Chebet Author-Name-First: Everlyne Author-Name-Last: Chebet Author-Name: Linety Juma Author-Name-First: Linety Author-Name-Last: Juma Author-Name: Bismark Agura Kayus Author-Name-First: Bismark Author-Name-Last: Agura Kayus Author-Name: Rhoda Moraa Siro Author-Name-First: Rhoda Author-Name-Last: Moraa Siro Author-Name: Catherine Munga Author-Name-First: Catherine Author-Name-Last: Munga Author-Name: Petros Chavula Author-Name-First: Petros Author-Name-Last: Chavula Title: African Education Systems in the Role of Artificial Intelligence (AI) in Automated Decision-Making Abstract: The main areas of focus for reforming educational systems are the incorporation of artificial intelligence (AI). Although artificial intelligence (AI) has many applications, its use in education to improve learning, develop employable skills, and help people adjust to life in the AI era has not been as common. The study evaluated current policy initiatives and delved into African information and communication technology (ICT) policies about the education sector to offer policy suggestions for AI educational institutions from an African viewpoint. The targeted population was junior and senior secondary schools in Kenya, a range of stakeholders in the field of education, including educators, parents, Ministry of Education representatives, and other pertinent parties. A sample of 125 participants was used. The study employed a descriptive research design. A combination of articles, research papers, reports, briefs, and books a along with interviews and questionnaires, was used to collect opinions and insights from these participants, and qualitative content analysis was involved. Its goal was to promote a thorough comprehension of the ethical issues related to the application of AI in the Kenyan educational system. The result indicated that 29 (28.2 %) were very familiar with automated decision-making, 63 (61.2 %) had reported some level of familiarity, while 11 (10.6 %) admitted did not know automated decision-making. The mean was fairly close to the true mean of the general population, as indicated by the extremely small total SE of 0.07. Drawing on the result, the report offers the policy priorities and suggestions that are crucial for establishing a supportive environment for the growth of AI and for governance in the African education sector. Journal: LatIA Pages: 319 Volume: 3 Year: 2025 DOI: 10.62486/latia2025319 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:319:id:1062486latia2025319 Template-Type: ReDIF-Article 1.0 Author-Name: Petros Chavula Author-Name-First: Petros Author-Name-Last: Chavula Author-Name: Fredrick Kayusi Author-Name-First: Fredrick Author-Name-Last: Kayusi Title: Challenges in Sub-Saharan Africa’s Food Systems and the Potential Role of AI Abstract: Sub-Saharan Africa (SSA) faces persistent food insecurity due to low agricultural productivity, limited access to modern technologies, and growing climate variability. This study explores the transformative potential of Artificial Intelligence (AI) to enhance food systems across SSA. The objective is to assess how AI applications—such as machine learning, remote sensing, and big data analytics—can address systemic inefficiencies in cereal crop production, with a focus on barley, millet, and sorghum. Using a systematic review approach aligned with PRISMA guidelines, literature from 2015–2025 was analyzed across multiple databases to identify empirical studies and models related to AI in SSA agriculture. Results reveal that AI can significantly improve crop monitoring, yield forecasting, and resource optimization. However, adoption barriers such as inadequate infrastructure, financial constraints, and the digital divide persist. The study concludes that while AI holds significant promise, its success in SSA depends on inclusive policies, capacity building, and localized data governance. It recommends interdisciplinary research, investment in rural digital infrastructure, and participatory innovation frameworks to empower smallholder farmers and ensure equitable AI deployment. This review provides a roadmap for integrating AI into SSA food systems to enhance resilience, productivity, and food security. Journal: LatIA Pages: 318 Volume: 3 Year: 2025 DOI: 10.62486/latia2025318 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:318:id:1062486latia2025318 Template-Type: ReDIF-Article 1.0 Author-Name: Khaoula Asmar Author-Name-First: Khaoula Author-Name-Last: Asmar Author-Name: Marwa El Jai Author-Name-First: Marwa Author-Name-Last: El Jai Author-Name: Yassamine El Jai Author-Name-First: Yassamine Author-Name-Last: El Jai Author-Name: Latifa Belfakir Author-Name-First: Latifa Author-Name-Last: Belfakir Title: Incorporating AI-Generated Duolingo within Collaborative SLL: Spoken English Students at FLDM-USMBA as a case study Abstract: In today’s digital age, the combination of AI-generated learning platforms and Second Language Learning (SLL) has revolutionized the way students learn new languages, mostly because these tools provide individuals with personalized learning content that is actively adjusted and tailored so that students can dynamically enhance their language skills according to their strengths, weaknesses, and progress. Thus, the prospect of such tools expands beyond self-paced learning and creates considerable opportunities for improvement in collaborative language learning settings. This article explores the integration of Duolingo into group-based learning contexts, focusing on its potential to enhance collaborative Second Language Learning (SLL) through its gamified structure and community features such as leaderboards, clubs, and challenges. The study adopts a mixed-methods exploratory design and was conducted among 189 Bachelors (BA) students enrolled in Spoken English (SE) classes during the 2024-2025 academic year at Sidi Mohamed Ben Abdellah University of Fes, Morocco (USMBA). Being a case study, the study investigates how Duolingo; as an AI-generated Language Learning Tool, and how its collaboration-focused features influence students’ motivation, engagement, and communication skills within a collaborative SLL framework. The study argues that when used alongside traditional classroom methods, Duolingo serves as a powerful tool for promoting both individual and group-based language acquisition, thereby enhancing the overall learning experience. Journal: LatIA Pages: 317 Volume: 3 Year: 2025 DOI: 10.62486/latia2025317 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:317:id:1062486latia2025317 Template-Type: ReDIF-Article 1.0 Author-Name: Amit Singh Author-Name-First: Amit Author-Name-Last: Singh Title: English Title: The State of Quantum Computing: Hardware, Algorithms, and Emerging Networks Abstract: This review article examines the current landscape and recent advancements in quantum computing, emphasizing its roots in quantum mechanics and its growing influence across various computational fields. A thorough analysis of recent literature, including academic publications and industry white papers, highlights significant progress in qubit technologies, quantum algorithms, and the emerging area of quantum networking. The findings indicate enhanced fabrication of quantum processors with higher qubit counts and improved stability and coherence. Additionally, developments in quantum algorithms suggest the potential for considerable speedups compared to classical methods for specific problems. Research into quantum key distribution and the prospect of a quantum internet points to promising advancements in secure communication. However, challenges surrounding error correction, scalability, and the practical implementation of quantum systems remain critical. In conclusion, quantum computing is pivotal, showcasing tangible progress toward solving real-world problems. However, it continues to grapple with substantial hurdles in achieving fully fault-tolerant and scalable systems. Ongoing interdisciplinary research and development efforts are vital to unlocking this technology's transformative potential and addressing its broader societal implications. Journal: LatIA Pages: 316 Volume: 3 Year: 2025 DOI: 10.62486/latia2025316 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:316:id:1062486latia2025316 Template-Type: ReDIF-Article 1.0 Author-Name: Nadia Chafiq Author-Name-First: Nadia Author-Name-Last: Chafiq Author-Name: Mohamed Ghazouani Author-Name-First: Mohamed Author-Name-Last: Ghazouani Author-Name: Rokaya El Gounidi Author-Name-First: Rokaya Author-Name-Last: El Gounidi Title: From Manual Review to AI Automation: An NLP-Powered System for Efficient CV Processing in Academic Admissions Abstract: Manual screening of thousands of admissions of master's program applications at Hassan II University of Casablanca is a time and labor-intensive task. Towards this challenge, we designed a machine-based solution utilizing Natural Language Processing (NLP) for summarization and CV ranking on a large set of CVs. Our solution relies on pre-trained spaCy and Hugging Face Transformers-based Named Entity Recognition (NER) models for the retrieval of information such as education, experience, and skills. We then incorporated extractive summarization by using BERT-based models for the selection of the most informative sentences and then the abstractive summarization by utilizing advanced language models such as LLAMA for the summaries to be coherent and easy. We verified our system by conducting a case study of the master's program of Big Data and Data Science by running a set of 2,325 CVs. The model gave very good results like a 72,67 % ROUGE-1 Recall, 74,32 % ROUGE-2 Recall, 73,15 % ROUGE-1 Precision, 57,28 % ROUGE-2 Precision, and 82% Named Entity Recognition (NER) Precision. The system processed a CV on average in 3,84 seconds. We also integrated a conversation bot (chatbot) that allows admissions teams to search the CVs uploaded in real time for improved decision-making effectiveness and significantly decreasing the administrative burden. The promise of NLP-driven automation stands out from this research as a scalable as well as efficient method of screening numerous applicants. Journal: LatIA Pages: 315 Volume: 3 Year: 2025 DOI: 10.62486/latia2025315 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:315:id:1062486latia2025315 Template-Type: ReDIF-Article 1.0 Author-Name: Jay Rodel C. Serdenia Author-Name-First: Jay Rodel Author-Name-Last: C. Serdenia Author-Name: Alexandhrea Hiedie Dumagay Author-Name-First: Alexandhrea Author-Name-Last: Hiedie Dumagay Author-Name: Keir A. Balasa Author-Name-First: Keir Author-Name-Last: A. Balasa Author-Name: Elenieta A. Capacio Author-Name-First: Elenieta Author-Name-Last: A. Capacio Author-Name: Lovelle Diocess S. Lauzon Author-Name-First: Lovelle Diocess Author-Name-Last: S. Lauzon Title: Attitude, Acceptability, and Perceived Effectiveness of Artificial Intelligence in Education: A Quantitative Cross-sectional Study among Future Teachers Abstract: This study investigated the extent of prospective teachers’ acceptance, attitudes, and perceived effectiveness of artificial intelligence (AI) in education. It also examined whether these perceptions varied according to gender and age group. Using a descriptive-correlational design, data were gathered from 392 teacher education students enrolled in a state-managed university in southwestern Mindanao. The results revealed that the respondents generally demonstrated moderate acceptance, favorable attitudes, and positive perceptions of AI effectiveness in the teaching and learning process. While no statistically significant differences were found between genders, moderate effect sizes suggested subtle variations worth further exploration. Significant differences were observed across age groups, with older individuals reporting higher levels of AI acceptance. Strong and significant correlations among acceptance, attitude, and perceived effectiveness affirmed the interconnected nature of belief, emotion, and evaluation in shaping readiness for AI integration. These findings support the Technology Acceptance Model and the Theory of Planned Behavior. In light of these results, it is recommended that teacher education programs integrate AI literacy and practical training, with targeted support for younger students to enhance digital confidence and preparedness. Journal: LatIA Pages: 313 Volume: 3 Year: 2025 DOI: 10.62486/latia2025313 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:313:id:1062486latia2025313 Template-Type: ReDIF-Article 1.0 Author-Name: Kateryna Kolos Author-Name-First: Kateryna Author-Name-Last: Kolos Author-Name: Oleg Kubrak Author-Name-First: Oleg Author-Name-Last: Kubrak Author-Name: Yuliya Olimpiyeva Author-Name-First: Yuliya Author-Name-Last: Olimpiyeva Author-Name: Pavlo Ihnatenko Author-Name-First: Pavlo Author-Name-Last: Ihnatenko Author-Name: Olena Furtat Author-Name-First: Olena Author-Name-Last: Furtat Title: Automation of Production Management Processes Using Artificial Intelligence: Impact on the Efficiency and Resilience of Manufacturing Systems Abstract: The rapid technological advancement and global competition provokes the automation of production management processes through artificial intelligence. This study investigates the integration of artificial intelligence into production management and its influence on the efficiency and resilience of manufacturing systems. The research is motivated by the growing relevance of AI within the paradigm of Industry 4.0, where advanced digital technologies are transforming traditional production models. The main objective is assessing how AI technologies – such as machine learning, deep learning, predictive analytics, and intelligent automation – enhance core production functions, including planning, quality control, maintenance, logistics, and energy management. The study applies a mixed-method approach, combining comparative analysis, case study evaluation, and content analysis of scientific and industrial data. Empirical evidence (1653 records) was drawn from both international (e.g., Siemens, Fanuc, Bosch) and Ukrainian (e.g., Interpipe, Kernel) manufacturing companies. Results after screening, filtration, validation, verification and exclusion (50 records) demonstrate measurable improvements in key performance indicators, such as reduced downtime, decreased defect rates, increased logistical accuracy, and optimized energy use. At the same time, the paper addresses the challenges accompanying AI integration, including cybersecurity risks, social impacts, regulatory gaps, and organizational readiness. The research concludes that AI not only improves operational performance but also strengthens adaptive capacity and strategic stability, contributing to the formation of intelligent, self-learning, and data-driven production systems. This article will be of particular interest to production managers, industrial engineers, innovation strategists, policymakers, and academic researchers seeking to understand and apply AI for sustainable industrial transformation. Journal: LatIA Pages: 311 Volume: 3 Year: 2025 DOI: 10.62486/latia2025311 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:311:id:1062486latia2025311 Template-Type: ReDIF-Article 1.0 Author-Name: Juan Ignacio Ruiz Author-Name-First: Juan Ignacio Author-Name-Last: Ruiz Author-Name: Verónica Olocco Author-Name-First: Verónica Author-Name-Last: Olocco Author-Name: Alfredo Mario Baronio Author-Name-First: Alfredo Mario Author-Name-Last: Baronio Title: Adoption of artificial intelligence technologies in Argentine external auditing Abstract: This study analyzes the adoption of artificial intelligence (AI) technologies in external auditing in Argentina, within a context where these tools promise to optimize processes and enhance the quality of professional judgment. Despite a high level of awareness regarding AI, its practical application remains limited and uneven. The objective was to analyze the adoption of artificial intelligence technologies in external auditing in Argentina. A mixed-methods approach with a descriptive design was employed. A total of 236 certified public accountants were surveyed between August 2024 and February 2025, and the quantitative findings were complemented by semi-structured interviews. The results show that although 97% of respondents are familiar with the concept of AI, only 12% apply it in their auditing work. The main barriers identified were the lack of specialized training, limited technical skills, and organizational resistance to change. Among the most valued benefits are time savings, increased accuracy, and improved detection of irregularities. The analysis allowed for the identification of three user profiles: young innovators, neutral professionals, and older individuals willing to adopt but lacking training. The study concludes that promoting targeted training programs, clear regulatory frameworks, and an innovation-oriented organizational culture is essential to bridge the gap between technological discourse and its effective implementation. Journal: LatIA Pages: 310 Volume: 3 Year: 2025 DOI: 10.62486/latia2025310 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:310:id:1062486latia2025310 Template-Type: ReDIF-Article 1.0 Author-Name: Rony Flores-Flores Author-Name-First: Rony Author-Name-Last: Flores-Flores Author-Name: Katty Alamo-Larrañaga Author-Name-First: Katty Author-Name-Last: Alamo-Larrañaga Author-Name: Elia Anacely Córdova-Calle Author-Name-First: Elia Anacely Author-Name-Last: Córdova-Calle Author-Name: Jonathan Lee Arévalo-Pinchi Author-Name-First: Jonathan Lee Author-Name-Last: Arévalo-Pinchi Author-Name: Jesús Rodríguez-Sánchez Author-Name-First: Jesús Author-Name-Last: Rodríguez-Sánchez Author-Name: Jorge Raúl Navarro-Cabrera Author-Name-First: Jorge Raúl Author-Name-Last: Navarro-Cabrera Title: Generative artificial intelligence in tourism: current status and emerging trends Abstract: Introduction: Generative Artificial Intelligence (GAI) has consolidated in recent years as a disruptive technology with the capacity to transform the tourism sector through experience personalization, content generation, and process optimization. Methods: A bibliometric approach was applied to analyze the scientific literature on GAI in tourism. Data were retrieved from the Scopus database, considering publications between 2020 and 2025. Records were processed using the Biblioshiny tool. Indicators evaluated included annual scientific production, sources, institutions, keywords, collaboration networks, and thematic maps. Results: A total of 412 documents were identified. Scientific production grew exponentially from 2022 onward, reaching over 150 articles in 2023 and 180 in 2024. Current Issues in Tourism and International Journal of Hospitality Management were the most productive journals. The University of Macau led institutional output, while China and the United States were the countries with the highest levels of international collaboration. Thematic maps revealed consolidated, specialized, and emerging topics. Conclusions: GAI in tourism is a rapidly expanding and multidisciplinary field, with notable geographic and institutional concentration. The findings provide a foundation for guiding research strategies, technological investment, and training, highlighting the need to integrate ethical and sustainability dimensions into its future development. Journal: LatIA Pages: 306 Volume: 3 Year: 2025 DOI: 10.62486/latia2025306 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:306:id:1062486latia2025306 Template-Type: ReDIF-Article 1.0 Author-Name: Víctor Hugo González Torres Author-Name-First: Víctor Hugo Author-Name-Last: González Torres Author-Name: Elisabeth Viviana Lucero Baldevenites Author-Name-First: Elisabeth Viviana Author-Name-Last: Lucero Baldevenites Author-Name: Manuel de Jesús Azpilcueta Ruiz Esparza Author-Name-First: Manuel de Jesús Azpilcueta Author-Name-Last: Ruiz Esparza Author-Name: Pedro Luis Bracho-Fuenmayor Author-Name-First: Pedro Luis Author-Name-Last: Bracho-Fuenmayor Author-Name: Claudia Patricia Caballero De Lamarque Author-Name-First: Claudia Patricia Caballero Author-Name-Last: De Lamarque Title: Artificial intelligence in Latin American higher education: implementations, ethical challenges, and pedagogical effectiveness Abstract: Artificial intelligence is establishing itself as a catalyst for transformation in the regional university sector, generating growing yet uneven academic output. This research conducted a systematic review following the PRISMA methodology on applications of artificial intelligence in Latin American higher education. The results from the 421 studies obtained during the bibliometric stage indicate that research is geographically and institutionally concentrated in a limited set of approaches and practices. In this regard, a notable prevalence of studies on Machine Learning applications, as well as Natural Language Processing, was observed. From a practical standpoint, 30 studies were selected for qualitative analysis. These texts agreed that the implementation process of these technologies continues to face structural challenges. Notably, poor infrastructure conditions, as well as deficiencies in teacher training, were identified as the main obstacles to implementing these technologies. The analyzed studies also concurred on the inadequate treatment of algorithmic biases or data protection in application policies proposed by the literature. Consequently, a key recommendation of this research is the urgent need for studies aimed at evaluating short-term outcomes, as well as analyzing the long-term sustainability of such innovations. Journal: LatIA Pages: 304 Volume: 3 Year: 2025 DOI: 10.62486/latia2025304 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:304:id:1062486latia2025304 Template-Type: ReDIF-Article 1.0 Author-Name: Iryna Zinkiv Author-Name-First: Iryna Author-Name-Last: Zinkiv Author-Name: Iryna Konovalova Author-Name-First: Iryna Author-Name-Last: Konovalova Author-Name: Iryna Polska Author-Name-First: Iryna Author-Name-Last: Polska Author-Name: Olena Roshchenko Author-Name-First: Olena Author-Name-Last: Roshchenko Author-Name: Tetiana Rozhnova Author-Name-First: Tetiana Author-Name-Last: Rozhnova Title: Integration of Artificial Intelligence into the Curricula of Higher Education Institutions Abstract: Introduction: The study explored how artificial intelligence (AI) is transforming the teaching of scientific disciplines by enabling personalized learning and simplifying the understanding of complex concepts. Particular attention was given to the role of AI tools in providing individual academic support. Methods: A qualitative analysis was conducted based on 71 publications retrieved from Google Scholar, ResearchGate, and Scopus databases. The selected sources covered theoretical and empirical research on AI implementation in higher education curricula. Results: The findings indicated that AI technologies enhanced students’ engagement with the learning material and facilitated comprehension of abstract and complex phenomena across various disciplines. However, several barriers to integration were identified. These included insufficient technical infrastructure and inadequate teacher training, both of which limited the effective use of AI tools in many higher education institutions. Conclusions: To ensure successful AI integration into educational programmes, it is essential to establish robust technological infrastructures and develop comprehensive professional development initiatives for academic staff. When effectively implemented, AI has the potential to support individualized learning experiences and significantly influence the broader educational ecosystem by fostering the evolution of student-centered thinking. Journal: LatIA Pages: 300 Volume: 3 Year: 2025 DOI: 10.62486/latia2025300 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:300:id:1062486latia2025300 Template-Type: ReDIF-Article 1.0 Author-Name: Nataliia Holubenko Author-Name-First: Nataliia Author-Name-Last: Holubenko Author-Name: Nataliia Yuhan Author-Name-First: Nataliia Author-Name-Last: Yuhan Author-Name: Iryna Tsypniatova Author-Name-First: Iryna Author-Name-Last: Tsypniatova Author-Name: Yuliia Holovashchenko Author-Name-First: Yuliia Author-Name-Last: Holovashchenko Author-Name: Oleksandra Nuzban Author-Name-First: Oleksandra Author-Name-Last: Nuzban Title: The Impact of Artificial Intelligence on the Development of Methods of Critical Text Analysis in Modern Philology Abstract: Introduction: this study aimed to evaluate the influence of artificial intelligence (AI), particularly deep learning and natural language processing (NLP) technologies, on the transformation of critical text analysis in contemporary philology. Aim: the research focused on how AI-driven approaches modify traditional linguistic and literary methodologies. Methods: a qualitative literature review was conducted to examine recent academic contributions at the intersection of philology and AI. Sources were selected from peer-reviewed journals covering linguistics, computational philology, and digital humanities. Results: the analysis revealed that AI-based algorithms, especially deep learning models, enhanced the detection of latent textual structures such as lexical patterns, stylistic markers, and semantic clusters. These technologies facilitated more accurate authorship attribution and allowed for the investigation of large corpora beyond the capacities of manual analysis. However, findings indicated that while AI could identify patterns and linguistic regularities, it lacked the ability to interpret deeper cultural, emotional, and symbolic meanings embedded in literary texts. Conclusions: the integration of AI into philological research offers valuable computational tools that expand analytical possibilities without displacing the interpretive role of the human scholar. AI technologies serve as a methodological extension, enhancing the precision and scope of critical analysis. Ultimately, the use of AI enriches the study of literature by uncovering patterns inaccessible to traditional methods, while preserving the necessity of human insight for contextual and interpretative depth. Journal: LatIA Pages: 295 Volume: 3 Year: 2025 DOI: 10.62486/latia2025295 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:295:id:1062486latia2025295 Template-Type: ReDIF-Article 1.0 Author-Name: Ivan Bakhov Author-Name-First: Ivan Author-Name-Last: Bakhov Author-Name: Nataliia Ishchuk Author-Name-First: Nataliia Author-Name-Last: Ishchuk Author-Name: Iryna Hrachova Author-Name-First: Iryna Author-Name-Last: Hrachova Author-Name: Liliana Dzhydzhora Author-Name-First: Liliana Author-Name-Last: Dzhydzhora Author-Name: Iryna Strashko Author-Name-First: Iryna Author-Name-Last: Strashko Title: Artificial intelligence tools for automating philological text research Abstract: Introduction: in recent years, artificial intelligence (AI) has significantly advanced across various fields, including linguistics, particularly in translation. While AI offers substantial opportunities for translation automation, scholarly debates continue regarding its reliability and impact on the translation process. Purpose: This study aims to analyze the role of artificial intelligence in automating linguistic research and to provide a comprehensive evaluation of its main advantages and limitations compared to human translation. Method: The study employs a systematic literature review based on scientific articles from Google Scholar, ResearchGate, and Scopus. A total of 55 scientific articles were analyzed, 25 specifically focused on automated translation's characteristics and challenges. Results: The findings indicate that collaboration between human translators and artificial intelligence is the most effective approach. AI is an auxiliary tool that enhances translation efficiency but cannot fully replace human translators due to their unique ability to convey emotions, cultural subtleties, and linguistic nuances. AI has great potential in overcoming language barriers but remains limited in comprehending cultural context and stylistic intricacies. Conclusion: The optimal use of AI in translation is in cooperation with human translators. While AI can significantly augment translation efficiency, it cannot entirely replace human expertise, particularly in literary and academic texts. Journal: LatIA Pages: 293 Volume: 3 Year: 2025 DOI: 10.62486/latia2025293 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:293:id:1062486latia2025293 Template-Type: ReDIF-Article 1.0 Author-Name: SUN Yongtai Author-Name-First: SUN Author-Name-Last: Yongtai Author-Name: Sastra Laoakka Author-Name-First: Sastra Author-Name-Last: Laoakka Title: AI Empowers Chinese Shaolin Kung Fu Movies: The Transformation Path from “Visual Spectacle” to “Cultural Creation Abstract: Introduction:In the dual context of globalization and digitalization, Shaolin kung fu films are facing unprecedented opportunities for transformation and challenges. As one of the most culturally distinctive film genres representing China's excellent traditional culture, Shaolin kung fu films have embodied the essence of Eastern philosophy—the unity of Zen and martial arts—since the release of *Shaolin Temple* (1982). However, existing research has largely remained at the level of typological analysis or qualitative interpretation of cultural symbols, failing to adequately address the paradigm shift in film narrative in the digital age. With breakthrough developments in generative AI, computer vision, and other technologies, film studies urgently need to establish new theoretical frameworks to explain how technology is reshaping cultural expression. This study adopts an interdisciplinary perspective at the intersection of film semiotics and technology philosophy. As an important carrier of national culture, Chinese Shaolin kung fu films urgently need to break free from the traditional narrative constraints of “visual spectacles” and achieve a paradigm shift from technology-driven to culture-driven innovation. Methods:1) Text and symbol mining: Conduct semantic network analysis on the scripts and dialogues of classic films such as Shaolin Temple;2) Multimodal collaborative analysis: Reconstruct narrative units using generative AI (such as the GPT-4 multimodal model);3) Cross-cultural comparative research: Interpret differences in symbolic meanings and propose AI adaptation and cultural generation strategies. Results:1) The “Zen and martial arts unity” symbol exhibits centrality in the semantic network, with a significant association between slow motion and Zen-like imagery; 2) In multimodal analysis, the overlapping effects of action and soundtrack significantly enhance emotional resonance; 3) Semantic network analysis and symbolic metaphor interpretation of the film script, dialogue, and action design are conducted, with generative AI used to reconstruct narrative units. Conclusions:1) The study validated the feasibility of quantitative analysis of cultural symbols and AI adaptation strategies; 2) A localized technology ecosystem was constructed to balance technology dominance and cultural authenticity; 3) In the future, further attention should be paid to balancing technological ethics and cultural sovereignty to ensure that AI truly serves the in-depth dissemination of cultural values. Journal: LatIA Pages: 290 Volume: 3 Year: 2025 DOI: 10.62486/latia2025290 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:290:id:1062486latia2025290 Template-Type: ReDIF-Article 1.0 Author-Name: Bryan Smith Contreras-Yupanqui Author-Name-First: Bryan Smith Author-Name-Last: Contreras-Yupanqui Title: Implementation of Artificial Intelligence Systems in Public Administration in Latin America: Impacts and Challenges Abstract: Introduction: an analysis of scientific production on the implementation of artificial intelligence in public administration in Latin America during the 2020-2024 period provides insight into the trends and areas of greatest impact. This field has experienced significant growth in 2021 and 2023, reflecting the academic and public interest in the challenges and opportunities that AI presents in the public sector. Methods: a bibliometric review of scientific publications related to artificial intelligence in public administration was conducted, considering the temporal, geographic, and thematic distribution of articles indexed in international academic databases. Results: Brazil (18 publications), Mexico (12 publications), and Colombia (10 publications) are the leading countries in Latin America regarding AI implementation research. The most frequent topics (accounting for 62% of publications) address operational efficiency, digital governance, transparency, and citizen engagement. Qualitative findings indicate that AI adoption improves decision-making and process automation but faces persistent challenges, including ethical considerations (reported in 45% of studies), data privacy issues (38%), and limited technical capacity (33%). Conclusions: the overview highlights the complexity and diversity of approaches adopted to study artificial intelligence in the public sector. It also highlights the need to strengthen research in Latin America to consolidate its own capabilities and respond to the ethical, technical, and social challenges posed by the adoption of AI in government management. Journal: LatIA Pages: 288 Volume: 3 Year: 2025 DOI: 10.62486/latia2025288 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:288:id:1062486latia2025288 Template-Type: ReDIF-Article 1.0 Author-Name: Anna Khomyk Author-Name-First: Anna Author-Name-Last: Khomyk Author-Name: Tetyana Dniprovska Author-Name-First: Tetyana Author-Name-Last: Dniprovska Author-Name: Olga Kondrashova Author-Name-First: Olga Author-Name-Last: Kondrashova Author-Name: Antonina Varlakova Author-Name-First: Antonina Author-Name-Last: Varlakova Author-Name: Oksana Ivasiuk Author-Name-First: Oksana Author-Name-Last: Ivasiuk Title: Trends in The Use of Artificial Intelligence for Automating Assessment in English Language Teaching Abstract: This study aims to analyze the potential of using artificial intelligence (AI) for automating assessment and English language teaching, as well as its impact on personalizing the learning process. A systematic literature review was conducted using a targeted search strategy across relevant academic databases, including Google Scholar, ResearchGate, and Scopus. As a result of this review, 52 academic papers dedicated to the use of AI for automating assessment and foreign language teaching processes were selected. The findings revealed that AI demonstrates significant potential in personalized learning, skill development, assessment automation, and enhancing teachers' professional development. The possibility of using AI to automate the knowledge assessment process, thereby reducing the workload for educators and increasing the objectivity of evaluation, was explored. The article explores the potential applications of technologies like ChatGPT4 and virtual reality in automating educational processes and assessments, emphasizing their capacity to enhance access to education without limitations of time and space. However, the paper acknowledges the challenges of integrating AI systems into education, encompassing ethical, pedagogical, and technical considerations. Ultimately, the article examines strategies to optimize the benefits of AI in higher education and anticipates the future trajectory of AI development within the educational context. Journal: LatIA Pages: 286 Volume: 3 Year: 2025 DOI: 10.62486/latia2025286 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:286:id:1062486latia2025286 Template-Type: ReDIF-Article 1.0 Author-Name: Yuliia Rud Author-Name-First: Yuliia Author-Name-Last: Rud Author-Name: Nataliia Sapotnitska Author-Name-First: Nataliia Author-Name-Last: Sapotnitska Author-Name: Natalia Chaplynska Author-Name-First: Natalia Author-Name-Last: Chaplynska Author-Name: Mykola Palasevych Author-Name-First: Mykola Author-Name-Last: Palasevych Author-Name: Oksana Kudriavtseva Author-Name-First: Oksana Author-Name-Last: Kudriavtseva Title: Analysis of the use of artificial intelligence in the management of logistics processes: Approaches and benefits Abstract: This study explores artificial intelligence (AI) applications to improve logistics within the Belt and Road Initiative’s Middle Corridor, focusing on Ukraine amid geopolitical disruptions. Its objective is to quantify AI's impact on delivery times, inventory costs, fuel consumption, and network resilience in this complex region. Employing a mixed-method approach, the research combines Social Network Analysis (SNA) to identify key nodes and bottlenecks with simulation modelling based on empirical data from 2019 to 2023, including GPS tracking and port metrics. Results show AI integration reduces delivery times by 11,7 %, cuts inventory holding costs by 16,3 %, and lowers fuel consumption by 9,2 %. SNA reveals enhanced connectivity and efficiency at critical hubs such as Kyiv and Odesa, strengthening Ukraine’s strategic logistics role. The study concludes that AI-driven optimizations significantly boost corridor efficiency, resilience, and sustainability. It recommends future work on real-time data integration and broader AI applications to support adaptive, greener supply chains across politically sensitive trade routes. Journal: LatIA Pages: 279 Volume: 3 Year: 2025 DOI: 10.62486/latia2025279 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:279:id:1062486latia2025279 Template-Type: ReDIF-Article 1.0 Author-Name: Editorial Team Title: A Journal: LatIA Volume: 2 Year: 2024 DOI: 10.62486/latia2024139 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p::id:1062486latia2024139 Template-Type: ReDIF-Article 1.0 Author-Name: Chaofan Ji Author-Name-First: Chaofan Author-Name-Last: Ji Author-Name: Mengya Dong Author-Name-First: Mengya Author-Name-Last: Dong Author-Name: Dong Li Author-Name-First: Dong Author-Name-Last: Li Author-Name: Leigang Wang Author-Name-First: Leigang Author-Name-Last: Wang Author-Name: Junkai Hou Author-Name-First: Junkai Author-Name-Last: Hou Author-Name: Yitong Niu Author-Name-First: Yitong Author-Name-Last: Niu Title: Research on the path to improve the teaching ability of college teachers based on artificial intelligence Abstract: With the rapid development of artificial intelligence technology, its application in the field of education has provided a new path for improving the teaching ability of college teachers. This paper explores the role of artificial intelligence in improving the teaching ability of college teachers in terms of teaching design, classroom management, and student evaluation through literature analysis, case studies, questionnaires, and interviews. The study found that artificial intelligence technology can significantly optimize teachers' teaching design ability, classroom management ability, and student evaluation ability, but it also faces challenges such as data security and technology dependence. This paper proposes suggestions for optimizing the path of artificial intelligence to improve the teaching ability of college teachers, in order to provide a reference for the reform of higher education. Journal: LatIA Pages: 135 Volume: 3 Year: 2025 DOI: 10.62486/latia2025135 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:135:id:1062486latia2025135 Template-Type: ReDIF-Article 1.0 Author-Name: Salma Abdel Wahed Author-Name-First: Salma Author-Name-Last: Abdel Wahed Author-Name: Mutaz Abdel Wahed Author-Name-First: Mutaz Author-Name-Last: Abdel Wahed Title: AI-Driven Digital Well-being: Developing Machine Learning Model to Predict and Mitigate Internet Addiction Abstract: Background: Internet addiction has become a major public health issue due to the increased dependence on digital technology, affecting mental health and overall well-being. Artificial intelligence (AI) offers innovative approaches to predicting and mitigating excessive internet use. Objective: This study aims to develop and evaluate AI-driven machine learning models for predicting and mitigating internet addiction by analyzing behavioral patterns and psychological indicators. Methods: Open-access datasets from “Kaggle”, such as “Smartphone Usage Data” and “Social Media Usage and Mental Health”, were analyzed using machine learning and deep learning models, including Random Forest, XGBoost, Neural Networks, and Natural Language Processing (NLP) techniques. Model performance was assessed based on accuracy, precision, recall, F1-score, and AUC-ROC. Results: Neural Networks and XGBoost achieved the highest accuracy (91% and 90%, respectively), surpassing traditional models like Logistic Regression and SVM. Clustering and anomaly detection techniques provided further insights into user behavior, while NLP revealed emotional and thematic patterns associated with addiction. Conclusion: AI-driven models effectively predict and classify internet addiction, offering scalable and personalized interventions to promote digital well-being. Future research should focus on addressing ethical concerns and improving real-time deployment of these models. Journal: LatIA Pages: 134 Volume: 3 Year: 2025 DOI: 10.62486/latia2025134 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:134:id:1062486latia2025134 Template-Type: ReDIF-Article 1.0 Author-Name: Salma Abdel Wahed Abdel Wahed Author-Name-First: Salma Abdel Wahed Author-Name-Last: Abdel Wahed Author-Name: Rama Shdefat Shdefat Author-Name-First: Rama Shdefat Author-Name-Last: Shdefat Author-Name: Mutaz Abdel Wahed Author-Name-First: Mutaz Abdel Author-Name-Last: Wahed Title: A Machine Learning Model for Diagnosis and Differentiation of Schizophrenia, Bipolar Disorder and Borderline Personality Disorder Abstract: Schizophrenia, bipolar disorder, and borderline personality disorder present overlapping symptoms, complicating accurate diagnosis. Misdiagnosis leads to inappropriate treatment, increased patient distress, and higher healthcare burdens. This study develops a machine learning model integrating clinical, neuroimaging, and behavioral data to improve diagnostic accuracy. The model utilizes Convolutional Neural Networks (CNNs) for neuroimaging, Gradient Boosting Machines (GBMs) for structured clinical and behavioral data, and Recurrent Neural Networks (RNNs) for speech analysis. The combined model demonstrated superior accuracy (94.1%) compared to individual models. SHAP analysis identified key diagnostic features, including specific brain regions, cognitive measures, and speech patterns. External validation confirmed robustness, highlighting the model’s potential as a clinical decision-support tool. Future research should focus on enhancing model interpretability and real-time diagnostic support. Journal: LatIA Pages: 133 Volume: 3 Year: 2025 DOI: 10.62486/latia2025133 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:133:id:1062486latia2025133 Template-Type: ReDIF-Article 1.0 Author-Name: Mrutyunjay Padhiary Author-Name-First: Mrutyunjay Author-Name-Last: Padhiary Author-Name: Gajendra Prasad Author-Name-First: Gajendra Author-Name-Last: Prasad Author-Name: Azmirul Hoque Author-Name-First: Azmirul Author-Name-Last: Hoque Author-Name: Kundan Kumar Author-Name-First: Kundan Author-Name-Last: Kumar Author-Name: Bhabashankar Sahu Author-Name-First: Bhabashankar Author-Name-Last: Sahu Title: Advances in Vertical Farming: The Role of Artificial Intelligence and Automation in Sustainable Agriculture Abstract: Vertical farming has emerged as a sustainable agricultural method, resolving the issues of land scarcity, environmental consequences, and food security in urban and highly populated areas. The inclusion of artificial intelligence (AI) and automation into vertical farming systems improves their efficiency, production, and adaptability. The study highlights recent breakthroughs in AI-driven systems, spanning data analytics, predictive modeling, and autonomous control, which enhance critical parameters such as light, temperature, humidity, and nutrient delivery. Significant advancements in agricultural automation, including robotic technologies for planting, monitoring, and harvesting, are emphasized for their capacity to decrease labor expenses and enhance yield accuracy. Further, research evaluates the environmental effect, scalability, and practicality of automated vertical farming systems, examining the contribution of renewable energy and optimal use of resources to the development of resilient food production methods. This discussion addresses future directions and issues seeking to shed light on how AI and automation are shifting vertical farming into an important aspect of sustainable agriculture. Journal: LatIA Pages: 131 Volume: 3 Year: 2025 DOI: 10.62486/latia2025131 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:131:id:1062486latia2025131 Template-Type: ReDIF-Article 1.0 Author-Name: Jahiro Samar Andrade Preciado Author-Name-First: Jahiro Samar Author-Name-Last: Andrade Preciado Author-Name: Héctor Javier Sánchez Ramírez Author-Name-First: Héctor Javier Author-Name-Last: Sánchez Ramírez Author-Name: Cristian Gabriela Gallego Real Author-Name-First: Cristian Gabriela Author-Name-Last: Gallego Real Title: Integration of ChatGPT in the Translation and Post-Editing of Specialized Texts: A Study on its Application Abstract: This study examines the impact of artificial intelligence (AI) on specialized translation, using ChatGPT as the primary tool. Employing an empirical-exploratory and mixed-methods approach, it analyzes the strategies and translation skills of 15 advanced students from the Translation Bachelor’s Program at UABC while translating specialized texts from legal, medical, and scientific fields. It also describes the post-editing process and techniques used to enhance terminological accuracy and cultural appropriateness. Participants translated and post-edited three specialized texts, complementing the process with the creation of terminological glossaries. A specialized rubric was used to evaluate translation quality, while Translog-II software measured efficiency and time spent on each task. The objectives were to analyze the translation process and strategies employed with ChatGPT in translating specialized texts, and to describe the post-editing techniques used by students in their final academic stage. Preliminary results show a significant improvement in the efficiency and quality of translations due to the use of AI tools, highlighting the positive impact of ChatGPT on the development of specific translation competencies. Students expressed a favorable perception of the experience, emphasizing the usefulness of these tools in facilitating and optimizing the translation process Journal: LatIA Pages: 129 Volume: 3 Year: 2025 DOI: 10.62486/latia2025129 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:129:id:1062486latia2025129 Template-Type: ReDIF-Article 1.0 Author-Name: Xavier Brito Alvarado Author-Name-First: Xavier Author-Name-Last: Brito Alvarado Author-Name: Paulina Tamayo Rodríguez Author-Name-First: Paulina Author-Name-Last: Tamayo Rodríguez Author-Name: Nelly Guamán Guadalima Author-Name-First: Nelly Author-Name-Last: Guamán Guadalima Title: The construction of the enemy: from telenovela to Tik Tok, Latin American melodramatic telepolitics Abstract: Wide and diverse criteria have been planned on melodrama, from its overvaluation as a cultural reference, to its marginalization in contemporary academic debates. However, it constitutes one of the most representative discourses in Latin America, on which various types of cultural industries and forms of social coexistence converge. This paper proposes a position that identifies melodrama not only as a cultural scenario, but also as a political one. The objective is to reflect on how these discourses have been narratives appropriated by politicians to seduce the population with promises of social change and how they have shifted from traditional media to social networks. The question that articulates this essay is: In what way has melodrama become the prevailing political discourse in the region, and which have assumed the stories of soap operas as a platform to conquer the votes of the population, and which have been massified thanks to social networks? Journal: LatIA Pages: 128 Volume: 3 Year: 2025 DOI: 10.62486/latia2025128 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:128:id:1062486latia2025128 Template-Type: ReDIF-Article 1.0 Author-Name: Alok Kumar Anand Author-Name-First: Alok Author-Name-Last: Kumar Anand Author-Name: Rajesh Kumar Mahto Author-Name-First: Rajesh Author-Name-Last: Kumar Mahto Author-Name: Awadesh Prasad Author-Name-First: Awadesh Author-Name-Last: Prasad Title: Analysis of Cyberbullying Behaviors Using Machine Learning:A Study on Text Classification Abstract: Introduction:Cyberbullying is a significant concern in today's digital age, affecting individuals across various demographics. Objective: This study aims to analyze and classify instances of cyberbullying using a dataset sourced from Kaggle, containing text data labeled for different types of bullying behaviors. Method: Our approach to tackling these challenges involves several key steps, starting with data preprocessing and feature extraction to identify patterns and improve detection methods, enhancing our understanding of how cyberbullying manifests in online communications. Result: The dataset provides a valuable resource for developing and evaluating machine learning models aimed at detecting sexist and racist content in tweets. Conclusion: This study advances the current understanding of the complexities involved in detecting cyberbullying and paves the way for future breakthroughs in this domain. The binary classification enabled by the 'oh_label' column streamlines the analysis process, making it particularly compatible with binary classification models Journal: LatIA Pages: 126 Volume: 3 Year: 2025 DOI: 10.62486/latia2023126 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:126:id:1062486latia2023126 Template-Type: ReDIF-Article 1.0 Author-Name: Tanweer Alam Author-Name-First: Tanweer Author-Name-Last: Alam Author-Name: Awadesh Prasad Author-Name-First: Awadesh Author-Name-Last: Prasad Title: Artificial Intelligence in Perovskite-Based Materials for Energy Applications Abstract: Introduction; Perovskite-based materials have gained significant attention in energy applications due to their remarkable optoelectronic properties and versatile composition. These materials, characterized by their ABX₃ crystal structure, have demonstrated high efficiencies in solar cells, light-emitting diodes (LEDs), and potential in energy storage systems. Objective; Perovskite solar cells (PSCs) have achieved efficiencies comparable to silicon-based cells, with advantages in cost and fabrication flexibility. Method; A literature review was conducted, including original articles, reviews, and bibliometric studies. The research focused on AI in Perovskite-Based Materials for Energy Applications. Result; AI is driving significant advancements in the field of perovskite-based materials for energy applications. Conclusion; Perovskite LEDs offer high color purity and tunable emission, making them ideal for display technologies. Despite challenges like stability and scalability, ongoing research aims to enhance their performance, positioning perovskites as key materials in sustainable energy technologies. By accelerating material discovery, optimizing manufacturing processes, enhancing stability and performance, and promoting sustainability Journal: LatIA Pages: 125 Volume: 3 Year: 2025 DOI: 10.62486/latia2025125 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:125:id:1062486latia2025125 Template-Type: ReDIF-Article 1.0 Author-Name: Maryna О. Dei Author-Name-First: Maryna О. Author-Name-Last: Dei Title: The impact of AI on the adaptation of educational materials and teaching methods to the needs of each student Abstract: Artificial intelligence (AI) is significantly transforming the educational process, offering new opportunities to adapt learning materials and teaching methods to the individual needs of each student. This article explores the impact of AI on education, in particular, how innovative technologies can help achieve personalized learning. It analyzes the main advantages and challenges of AI implementation in education, as well as practical examples and prospects for the development of this technology. The study shows that the introduction of AI contributes to the efficiency of the educational process, improves student engagement, and allows teachers to more accurately take into account individual differences in students' abilities and learning styles. The study results emphasize the need for further development and implementation of AI technologies in education to ensure a more inclusive and efficient learning environment Journal: LatIA Pages: 124 Volume: 3 Year: 2025 DOI: 10.62486/latia2025124 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:124:id:1062486latia2025124 Template-Type: ReDIF-Article 1.0 Author-Name: Herasym Dei Author-Name-First: Herasym Author-Name-Last: Dei Title: The use of AI in the organization of local government work Abstract: The article discusses the use of AI in organizing the work of local governments. The competencies required for employees in the management field were identified to enable them to effectively interact with automated systems based on AI algorithms. The most popular types of AI are identified, which are already used in the work of municipalities in Ukraine and abroad. Promising areas for the use of AI in public administration were identified. The results of the current study made it possible to identify a wide practice of using AI in the field of municipal management and describe new opportunities for its implementation in the practice of local self-government bodies in Ukraine. The practical result of the study is a set of recommendations for improving the effectiveness of the use of AI in the organization of the work of local self-government bodies. The theoretical result was the identification of the main regularities in the use of AI algorithms and automated systems created on their basis in the professional activities of specialists in the field of municipal management. It is emphasized that the transition to the use of modern technologies and means of implementing managerial decisions at a qualitatively new level has already become one of the necessary tools for the effective work of local authorities Journal: LatIA Pages: 123 Volume: 3 Year: 2025 DOI: 10.62486/latia2025123 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:123:id:1062486latia2025123 Template-Type: ReDIF-Article 1.0 Author-Name: Richard Injante Author-Name-First: Richard Author-Name-Last: Injante Author-Name: Sergio Sánchez-Isuiza Author-Name-First: Sergio Author-Name-Last: Sánchez-Isuiza Title: Detection of citrus diseases using artificial intelligence: A systematic review Abstract: Early detection of citrus diseases is important for the global agricultural industry, facing threats such as Huanglongbing and canker. This study reviews the current status of the use of artificial intelligence to improve detection accuracy and speed. A systematic literature review was conducted from 2019 to 2023, using databases such as Scopus, IEEE Xplore and ACM, focusing on identifying the fruits studied, prevalent diseases, AI algorithms used and their accuracies, as well as technical challenges in implementing AI systems. The results highlight that oranges, lemons and mandarins are the most investigated fruits, with Huanglongbing, black spot and canker as the most studied diseases. AI algorithms such as Deep Neural Networks (DNN) and Adaboost show high accuracies, essential to improve disease detection. However, challenges include lack of labeled data, adaptation to different agricultural conditions, and effective integration in dynamic agricultural environments. This study reveals the need to advance data quality and algorithm adaptability to strengthen sustainability and efficiency in disease detection in citrus crops Journal: LatIA Pages: 122 Volume: 3 Year: 2025 DOI: 10.62486/latia2025122 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:122:id:1062486latia2025122 Template-Type: ReDIF-Article 1.0 Author-Name: Danish Anwar Author-Name-First: Danish Author-Name-Last: Anwar Author-Name: Md Faizanuddin Author-Name-First: Md Author-Name-Last: Faizanuddin Author-Name: Soofia Fatima Author-Name-First: Soofia Author-Name-Last: Fatima Author-Name: Rajeshwar Dayal Author-Name-First: Rajeshwar Author-Name-Last: Dayal Title: Transforming Supply Chain Finance with AI and IoT for Greater Inclusivity, Efficiency, and Intelligence Abstract: The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) is revolutionizing supply chain finance by making it more inclusive, efficient, and intelligent. AI-driven algorithms automate critical financial processes such as credit analysis, risk management, and fraud detection, while IoT-enabled devices provide real-time visibility into inventory and asset tracking. These technologies streamline operations, enhance transparency, and enable dynamic, data-driven decision-making. Additionally, AI and IoT solutions democratize access to financing, particularly for small and medium enterprises (SMEs), by leveraging real-time data to assess creditworthiness. This paper explores how the fusion of AI and IoT is transforming supply chain finance, offering innovative strategies for improved efficiency, risk reduction, and financial inclusion. Journal: LatIA Pages: 121 Volume: 3 Year: 2025 DOI: 10.62486/latia2025121 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:121:id:1062486latia2025121 Template-Type: ReDIF-Article 1.0 Author-Name: Haya Alhadramy Author-Name-First: Haya Author-Name-Last: Alhadramy Author-Name: Alzyoud Mazen Author-Name-First: Alzyoud Author-Name-Last: Mazen Author-Name: Mohammad Subhi Al-Batah Author-Name-First: Mohammad Subhi Author-Name-Last: Al-Batah Author-Name: Najah Al-shanableh Author-Name-First: Najah Author-Name-Last: Al-shanableh Title: Robust Face Tracking Under Challenging Conditions Using Linear Regression and YOLO algorithm Abstract: Face detection and tracking play a crucial role in various computer vision applications, including surveillance, fault face detection systems, artificial intelligence, etc. The objective of this paper is to enhance the precision of face detection and tracking through the introduction of an innovative approach centered on the linear regression algorithm. The effectiveness of the proposed method was compared to the traditional Kalman filter approach. Additionally, the study explored the integration of the YOLO algorithm for face detection with the linear regression tracking algorithm to further enhance accuracy. The proposed algorithm's performance is assessed through comprehensive experiments on annotated images and video sequences affected by occlusions or other issues such as poor lighting conditions and motion blur. These experiments utilize the COCO dataset, operating at a speed of 60 FPS. The experimental results show that the proposed method can accurately track the human face in different facial positions Journal: LatIA Pages: 120 Volume: 3 Year: 2025 DOI: 10.62486/latia2025120 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:120:id:1062486latia2025120 Template-Type: ReDIF-Article 1.0 Author-Name: Danish Anwar Author-Name-First: Danish Author-Name-Last: Anwar Author-Name: Faizan Uddin Author-Name-First: Faizan Author-Name-Last: Uddin Author-Name: Soofia Fatima Author-Name-First: Soofia Author-Name-Last: Fatima Author-Name: Shams Raza Author-Name-First: Shams Author-Name-Last: Raza Author-Name: Rajeshwar Dayal Author-Name-First: Rajeshwar Author-Name-Last: Dayal Title: Understanding AI's Role in the Banking Industry: A Conceptual Review Abstract: This study delves into the shifting role of Artificial Intelligence (AI) within the banking industry, with a focus on its transformative effects on service quality, operational effectiveness, and customer interaction. The research underscores significant developments in AI and its integration, highlighting its pivotal role in updating traditional banking practices and tackling modern-day challenges. It offers a comprehensive analysis of the potential of AI to enhance banking services, while also addressing obstacles such as technical difficulties and regulatory concerns. The outlook section predicts ongoing AI expansion in the banking sector, particularly its capacity to further tailor banking services and improve risk management. The goal of this research is to provide a comprehensive understanding of AI's integration into Indian banking, shedding light on the evolving relationship between technological innovation and the financial sector Journal: LatIA Pages: 119 Volume: 2 Year: 2024 DOI: 10.62486/latia2024119 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:119:id:1062486latia2024119 Template-Type: ReDIF-Article 1.0 Author-Name: Khushwant Singh Author-Name-First: Khushwant Author-Name-Last: Singh Author-Name: Mohit Yadav Author-Name-First: Mohit Author-Name-Last: Yadav Title: Design of AI in leadership Abstract: The present research aims to demonstrate the dominance of AI-based technologies over the Leadership sector in Industry 4.0 by combining the two main industries, such as "artificial intelligence" and "leadership." Artificial Intelligence (AI) has had a notable impact on the technical and social working environment due to the growing use of AI-supported technology. In particular, to recognise and address the needs and difficulties faced by leaders in the majority of organisations. The current essay emphasises how crucial leadership is to the adoption and use of AI in business. It has been thought that a thorough examination of the literature studies now in existence would demonstrate the need for AI-supported leadership techniques in businesses. The research divided leadership into four categories: the Process of Strategic Transformation, Competencies and Qualification, Culture, and the Interaction of Human-AI. This division was made based on the analysis of the literature review. The study's findings provide potential paths for further research and growth, as well as a thorough view. Journal: LatIA Pages: 118 Volume: 3 Year: 2025 DOI: 10.62486/latia2025118 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:118:id:1062486latia2025118 Template-Type: ReDIF-Article 1.0 Author-Name: Mutaz Abdel Wahed Author-Name-First: Mutaz Abdel Author-Name-Last: Wahed 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 Title: Evaluating AI and Machine Learning Models in Breast Cancer Detection: A Review of Convolutional Neural Networks (CNN) and Global Research Trends Abstract: Numerous studies have highlighted the significance of artificial intelligence (AI) in breast cancer diagnosis. However, systematic reviews of AI applications in this field often lack cohesion, with each study adopting a unique approach. The aim of this study is to provide a detailed examination of AI's role in breast cancer diagnosis through citation analysis, helping to categorize the key areas that attract academic attention. It also includes a thematic analysis to identify the specific research topics within each category. A total of 30,200 studies related to breast cancer and AI, published between 2015 and 2024, were sourced from databases such as IEEE, Scopus, PubMed, Springer, and Google Scholar. After applying inclusion and exclusion criteria, 32 relevant studies were identified. Most of these studies utilized classification models for breast cancer prediction, with high accuracy being the most commonly reported performance metric. Convolutional Neural Networks (CNN) emerged as the preferred model in many studies. The findings indicate that both the quantity and quality of AI-based algorithms in breast cancer diagnosis are increases in the given years. AI is increasingly seen as a complement to healthcare sector and clinical expertise, with the target of enhancing the accessibility and affordability of quality healthcare worldwide. Journal: LatIA Pages: 117 Volume: 3 Year: 2025 DOI: 10.62486/latia2025117 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:117:id:1062486latia2025117 Template-Type: ReDIF-Article 1.0 Author-Name: Dipak Roy Author-Name-First: Dipak Author-Name-Last: Roy Author-Name: Mrutyunjay Padhiary Author-Name-First: Mrutyunjay Author-Name-Last: Padhiary Author-Name: Pankaj Roy Author-Name-First: Pankaj Author-Name-Last: Roy Author-Name: Javed Akhtar Barbhuiya Author-Name-First: Javed Akhtar Author-Name-Last: Barbhuiya Title: Artificial Intelligence-Driven Smart Aquaculture: Revolutionizing Sustainability through Automation and Machine Learning Abstract: AI incorporation in aquaculture has transformed the industry completely, making crucial processes automated, maximizing productivity, and promoting sustainability. AI, specifically machine learning, refers to the application of modern smart aquaculture systems for tasks such as fish species classification, health monitoring, feed regulation, and management of water quality. It thereby sets inefficiency issues right while reducing impacts on the environment through real-time data-driven decision-making. This article deals with very recent developments in the applications of AI and machine learning in aquaculture, pointing out their importance in increasing production as well as eco-friendly management of aquatic environments Journal: LatIA Pages: 116 Volume: 2 Year: 2024 DOI: 10.62486/latia2024116 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:116:id:1062486latia2024116 Template-Type: ReDIF-Article 1.0 Author-Name: Nabat Erdogan Author-Name-First: Nabat Author-Name-Last: Erdogan Author-Name: Kitson Christina Author-Name-First: Kitson Author-Name-Last: Christina Title: Integrating AI in Language Learning: Boosting Pragmatic Competence for Young English Learners Abstract: This article explores the role of artificial intelligence (AI) tools in enhancing pragmatic language skills of young English learners (ELs). It defines terms such as interlanguage pragmatics, pragmatic competence, and intercultural communicative competence, and discusses key concepts in pragmatics, including maxims of discourse, implicatures, presuppositions, and speech acts. The article emphasizes the importance of sociocultural context and interaction in promoting ELs’ pragmatic skills in the second language (L2). It also explores different ways AI can be utilized to teach essential pragmatic skills, including understanding implicatures, making inferences, interpreting presuppositions, applying speech acts properly, and adhering to the maxims of discourse for effective communication in the target language – specifically, English. By creating immersive and interactive learning environments, AI chatbots, dialogue systems, and platforms facilitate contextual learning that engages ELs and promotes practical language use. The article concludes by discussing the limitations and challenges related to teaching pragmatics to language learners, advocating for targeted research efforts to enhance our understanding of pragmatic development among young ELs and the role of AI tools in this process Journal: LatIA Pages: 115 Volume: 3 Year: 2025 DOI: 10.62486/latia2025115 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:115:id:1062486latia2025115 Template-Type: ReDIF-Article 1.0 Author-Name: Anshu A Author-Name-First: Anshu Author-Name-Last: A Title: Leveraging Internet of Things (IoT) to Enhance Accessibility and Independence for People with Disabilities Abstract: Any physical or mental ailment (impairment) that makes it harder for the affected individual to engage in particular activities (activity limitation) or engage with their environment (participation restrictions) is considered a disability. According to the 2011 Census, 2.68 Cr people (2.21% of India's 121 crore total population) are classified as "Disabled." One way to combat disability is to develop intelligent prosthesis and assistive technology. The usability and functionality of these devices are improved by real-time monitoring, feedback, and control made possible by advanced sensors and networking. We have attempted to use some of the Internet of Technologies' (IOT) solutions to address the limits in this research. People with disabilities could live far better lives because to the Internet of Things (IoT), which offers creative solutions to a range of problems they could encounter. IoT integration offers a revolutionary chance to address disability and create a more inclusive society in many areas of life. IoT is transforming how we meet the requirements of people with disabilities, from smart assistive devices and home automation to health monitoring, communication aids, and accessible transportation. In this research paper, we have proposed some IoT based models to tackle few constraints of disabled people which can help the disabled people with their day-to- day problems. Journal: LatIA Pages: 114 Volume: 3 Year: 2025 DOI: 10.62486/latia2025114 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:114:id:1062486latia2025114 Template-Type: ReDIF-Article 1.0 Author-Name: Zahra Jabeen Author-Name-First: Zahra Author-Name-Last: Jabeen Author-Name: Khushboo Mishra Author-Name-First: Khushboo Author-Name-Last: Mishra Author-Name: Rajeshwar Dayal Author-Name-First: Rajeshwar Author-Name-Last: Dayal Author-Name: Binay Kumar Mishra Author-Name-First: Binay Author-Name-Last: Kumar Mishra Title: Transforming Education in the World of Artificial Intelligence Abstract: Introduction: Artificial Intelligence (AI) and Machine Learning (ML) are key drivers of innovation and growth among all industries, and the education sector is no exemption. AI is considered as a powerful tool to facilitate new examples for technological development, instructional design and educational research that are otherwise not possible to develop in the traditional education techniques. With the development in information processing and computing techniques, artificial intelligence has been widely applied in educational practices (Artificial Intelligence in Education; AIEd), such as teaching robots, human-computer interactions intelligent tutoring systems, learning analytics dashboards and adaptive learning systems. It has the ability to maximize both teaching and learning, helping the education sector to evolve for better thus benefitting students and teachers both Journal: LatIA Pages: 113 Volume: 2 Year: 2024 DOI: 10.62486/latia2024113 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:113:id:1062486latia2024113 Template-Type: ReDIF-Article 1.0 Author-Name: Richard Injante Author-Name-First: Richard Author-Name-Last: Injante Author-Name: Marck Julca Author-Name-First: Marck Author-Name-Last: Julca Title: Detection of diabetic retinopathy using artificial intelligence: an exploratory systematic review Abstract: Diabetic retinopathy is a disease that can lead to vision loss and blindness in people with diabetes, so its early detection is important to prevent ocular complications. The aim of this study was to analyze the usefulness of artificial intelligence in the detection of diabetic retinopathy. For this purpose, an exploratory systematic review was performed, collecting 77 empirical articles from the Scopus, IEEE, ACM, SciELO and NIH databases. The results indicate that the most commonly used factors for the detection of diabetic retinopathy include changes in retinal vascularization, macular edema and microaneurysms. Among the most commonly applied algorithms for early detection are ResNet 101, CNN and IDx-DR. In addition, some artificial intelligence models are reported to have an accuracy ranging from 90% to 95%, although models with accuracies below 80% have also been identified. It is concluded that artificial intelligence, and in particular deep learning, has been shown to be effective in the early detection of diabetic retinopathy, facilitating timely treatment and improving clinical outcomes. However, ethical and legal concerns arise, such as privacy and security of patient data, liability in case of diagnostic errors, algorithmic bias, informed consent, and transparency in the use of artificial intelligence. Journal: LatIA Pages: 112 Volume: 2 Year: 2024 DOI: 10.62486/latia2024112 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:112:id:1062486latia2024112 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: Vugar Abdullayev Author-Name-First: Vugar Author-Name-Last: Abdullayev Author-Name: Khushwant Singh Author-Name-First: Khushwant Author-Name-Last: Singh Title: Artificial neural networks with better analysis reliability in data mining Abstract: If there are relatively few cases, semi-supervised learning approaches make advantage of a large amount of unlabeled data to assist develop a better classifier. To expand the labeled training set and update the classifier, a fundamental method is to select and label the unlabeled instances for which the current classifier has higher classification confidence. This approach is primarily used in two distinct semi-supervised learning paradigms: co-training and self-training. However, compared to self-labeled examples that would be tagged by a classifier, the real labeled instances will be more trustworthy. Incorrect label assignment to unlabeled occurrences might potentially compromise the classifier's accuracy in classification. This research presents a novel instance selection method based on actual labeled data. This will take into account the classifier's current performance on unlabeled data in addition to its performance on actual labeled data alone. This uses the accuracy changes in the newly trained classifier over the original labeled data as a criterion in each iteration to determine whether or not the selected most confident unlabeled examples would be accepted by a subsequent iteration. Naïve Bayes (NB) will be used as the basic classifier in the co-training and self-training studies. The findings indicate that the accuracy and categorization of self-training and co-training will be greatly enhanced by SIS. As compared to semi-supervised classification methods, it will enhance accuracy, precision, recall, and F1 score, according to the findings. Journal: LatIA Pages: 111 Volume: 2 Year: 2024 DOI: 10.62486/latia2024111 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:111:id:1062486latia2024111 Template-Type: ReDIF-Article 1.0 Author-Name: Daniel Roman-Acosta Author-Name-First: Daniel Author-Name-Last: Roman-Acosta Title: Potential of artificial intelligence in textual cohesion, grammatical precision, and clarity in scientific writing Abstract: Introduction: the use of artificial intelligence (AI) tools in writing has significantly increased in recent years, promising improvements in textual coherence, grammatical precision, and clarity of ideas. This study focused on evaluating the long-term impact of AI usage on these aspects of academic writing. Objective: Identify the long-term effects of AI on cohesion, grammatical precision, and clarity in academic writing, while also exploring its ethical implications. Methods: a qualitative systematic review was conducted using the SALSA method, analyzing recent studies that address the influence of AI on writing quality. The databases used included Scopus, Web of Science, SciELO, and Latindex, with results restricted to publications since 2023. Results: the findings indicate that AI can enhance cohesion, precision, and clarity in texts, especially when used as a support tool. However, the effectiveness of these improvements depends on the context of use and the appropriate integration of human intervention. Conclusions: although AI offers clear benefits in improving academic writing, its use raises ethical and legal challenges that must be addressed. It is crucial to continue researching to optimize these tools and ensure responsible use in educational settings Journal: LatIA Pages: 110 Volume: 2 Year: 2024 DOI: 10.62486/latia2024110 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:110:id:1062486latia2024110 Template-Type: ReDIF-Article 1.0 Author-Name: S. Muthusundari Author-Name-First: S. Author-Name-Last: Muthusundari Author-Name: M. Priyadharshii Author-Name-First: M. Author-Name-Last: Priyadharshii Author-Name: V. Preethi Author-Name-First: V. Author-Name-Last: Preethi Author-Name: K. Priya Author-Name-First: K. Author-Name-Last: Priya Author-Name: K. Priyadharcini Author-Name-First: K. Author-Name-Last: Priyadharcini Title: Smart watch for early heart attack detection and emergency assistance using IoT Abstract: This research introduces a Smart Watch equipped with advanced physiological monitoring capabilities for the early detection of heart attacks and automatic initiation of emergency assistance. Cardiovascular diseases, particularly heart attacks, are a leading cause of global mortality. Rapid response during a heart attack significantly improves patient outcomes, emphasizing the need for innovative solutions. The proposed Smart Watch integrates a combination of sensors, including ECG (Electrocardiogram) and PPG (Photoplethysmography), to continuously monitor the user's heart rate, rhythm, and other relevant physiological parameters. Machine learning (Time Series Analysis algorithm) is employed to analyse the collected data in real-time, identifying patterns indicative of a potential heart attack.Upon detecting abnormal cardiac activity, the Smart Watch triggers an immediate response by connecting to a dedicated mobile application. The application utilizes built-in communication features to establish a connection with emergency services, providing vital information about the user's condition, location, and medical history. Simultaneously, the Smart Watch alerts predefined emergency contacts, ensuring a swift response from friends or family members. Journal: LatIA Pages: 109 Volume: 2 Year: 2024 DOI: 10.62486/latia2024109 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:109:id:1062486latia2024109 Template-Type: ReDIF-Article 1.0 Author-Name: Amit Kumar Dinkar Author-Name-First: Amit Kumar Author-Name-Last: Dinkar Author-Name: Alimul Haque Author-Name-First: Alimul Author-Name-Last: Haque Author-Name: Alamgir Hossain Author-Name-First: Alamgir Author-Name-Last: Hossain Author-Name: Shams Raza Author-Name-First: Shams Author-Name-Last: Raza Author-Name: Moidur Rahman Author-Name-First: Moidur Author-Name-Last: Rahman Author-Name: Ajay Kumar Choudhary Author-Name-First: Ajay Kumar Author-Name-Last: Choudhary Title: Unveiling the Power of the Internet of Things: Exploring Services, Applications, and Overcoming Challenges Abstract: The Internet of Things (IoT) has transcended its futuristic perception and become an omnipresent reality. Its pervasive nature encompasses devices, sensors, clouds, big data, and business interactions. This revolutionary concept amalgamates traditional embedded systems with wireless microsensors, automation-driven control systems, and other elements to establish a vast infrastructure. The integration of wireless communication, micro electro mechanical devices, and the Internet has given rise to novel IoT applications. The IoT is essentially a network of interconnected objects accessible through the Internet, each object uniquely identifiable. The advent of IPv6, superseding IPv4, plays a pivotal role in expanding the address space for IoT development. The primary objective of IoT applications is to imbue objects with intelligence, eliminating the need for human intervention. However, the proliferation of smart nodes and the exponential data generated by each node present new challenges pertaining to data privacy, scalability, security, manageability, and other critical issues, which we delve into in this comprehensive exploration Journal: LatIA Pages: 108 Volume: 2 Year: 2024 DOI: 10.62486/latia2024108 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:108:id:1062486latia2024108 Template-Type: ReDIF-Article 1.0 Author-Name: Khushwant Singh Author-Name-First: Khushwant Author-Name-Last: Singh Author-Name: Mohit Yadav Author-Name-First: Mohit Author-Name-Last: Yadav Title: Prognosis of artificial intelligence in education Abstract: The Higher Education Institutions require emphasis on disruptive intelligent systems which includes Artificial Intelligence that challenges conventional methods with improved products and services. This study aimed to know the trend artificial intelligence in engineering education. Specifically, it aimed to know the profile of the respondents, know the level of utilization of artificial intelligence tools in engineering education, know if there is significant relationship between profile of respondents to the AI tools used in engineering education, and propose a model of artificial intelligence in engineering education. This paper used quantitative correlational methods of research. Result showed that majority of the respondents has more work experience, found that most teachers have five years or more of experience and found that in terms of educational attainment, majority of the respondents had master’s degree. Artificial intelligence tools are generally “Sometimes Utilized” in engineering education and the respondents' profiles had no significant relationship on the use of the AI technologies, which are often occasionally used in engineering education. To fully utilize AI capabilities in engineering education, the model achieved offers a number of particular actions, including institutional in-house training, awareness campaigns, research conferences, and informal information exchange. Journal: LatIA Pages: 107 Volume: 3 Year: 2025 DOI: 10.62486/latia2025107 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:107:id:1062486latia2025107 Template-Type: ReDIF-Article 1.0 Author-Name: Ruben Dario Cardenas Espinosa Author-Name-First: Ruben Dario Author-Name-Last: Cardenas Espinosa Author-Name: Julio César Caicedo-Eraso Author-Name-First: Julio César Author-Name-Last: Caicedo-Eraso Author-Name: Andres David Epifanía Huerta Author-Name-First: Andres David Author-Name-Last: Epifanía Huerta Title: Digital skills in the use of artificial intelligence tools for the formulation of formative research projects from the TECSIS Research Seminar. Abstract: Artificial intelligence (AI) has promoted a change in the way research and innovation (R&D) is performed bringing new perspectives, automating time and routine tasks contributing to the generation of knowledge. To take full advantage of the potential of AI, it is proposed to develop strategic digital skills for the use of artificial intelligence tools in the formulation of formative research projects from the TECSIS Research Seminar. The methodology corresponds to a qualitative research with a descriptive analytical approach with a cross-sectional approach in four stages: analysis, design, evaluation and dissemination. The population under study includes the students of the special programs of Computer Engineering and Systems Technology of the University of Caldas. The expected result is the characterization of strategic digital skills of use for the formulation of formative research projects. This project will contribute in the development of a methodological route that exposes in a practical and applied way the strategic digital skills of use of AI tools for the formulation of formative research projects for the special programs of the University. Journal: LatIA Pages: 106 Volume: 2 Year: 2024 DOI: 10.62486/latia2024106 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:106:id:1062486latia2024106 Template-Type: ReDIF-Article 1.0 Author-Name: Mateo Valencia Buitrago Author-Name-First: Mateo Author-Name-Last: Valencia Buitrago Author-Name: Olga Lucía Torres Vargas Author-Name-First: Olga Lucía Author-Name-Last: Torres Vargas Title: Classification of tomato ripeness in the agricultural industry using a computer vision system Abstract: Machine vision systems (SVA) occupy an important place in the field of food and agriculture, these techniques are performed in situ, are efficient, non-invasive, time-saving and more economical than traditional techniques. Tomatoes (Solanum lycopersicum) are extensively cultivated throughout the world, are essential in the agricultural and culinary fields and are recognized for their beneficial contributions to health. Lack of knowledge about fruit maturity, proper harvesting and postharvest handling are factors responsible for large postharvest losses. Therefore, the objective of this research was the construction of a VAS that allows establishing relationships between color and maturity stage of the Chonto Roble F1 tomato. The VAS built is composed of hardware and software duly synchronized through the application of computer vision algorithms in Python 3.9 software that allow it to perform the acquisition and segmentation of the image and present the user with the color coordinates in the CIEL*a*b* system. Finally, color measurements were performed on tomato samples at different stages of ripening in the VAS and a HunterLab ColorQuest XE (EHL) spectrophotometer. The results obtained indicated that there are no significant differences in both measurement systems for L* values, the changes produced in b* and a* were statistically significant for tomato samples. The results obtained indicated the potential use of the constructed VAS for the determination of the degree of maturity of tomatoes in real time, in a non-invasive and low-cost way. Journal: LatIA Pages: 105 Volume: 2 Year: 2024 DOI: 10.62486/latia2024105 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:105:id:1062486latia2024105 Template-Type: ReDIF-Article 1.0 Author-Name: Michael Alexander Florez Muñoz Author-Name-First: Michael Alexander Author-Name-Last: Florez Muñoz Author-Name: Juan Camilo Jaramillo De La Torre Author-Name-First: Juan Camilo Author-Name-Last: Jaramillo De La Torre Author-Name: Stefany Pareja López Author-Name-First: Stefany Author-Name-Last: Pareja López Author-Name: Stiven Herrera Author-Name-First: Stiven Author-Name-Last: Herrera Author-Name: Christian Andrés Candela Uribe Author-Name-First: Christian Andrés Author-Name-Last: Candela Uribe Title: Comparative Study of AI Code Generation Tools: Quality Assessment and Performance Analysis Abstract: Artificial intelligence (AI) code generation tools are crucial in software development, processing natural language to improve programming efficiency. Their increasing integration in various industries highlights their potential to transform the way programmers approach and execute software projects. The present research was conducted with the purpose of determining the accuracy and quality of code generated by artificial intelligence (AI) tools. The study began with a systematic mapping of the literature to identify applicable AI tools. Databases such as ACM, Engineering Source, Academic Search Ultimate, IEEE Xplore and Scopus were consulted, from which 621 papers were initially extracted. After applying inclusion criteria, such as English-language papers in computing areas published between 2020 and 2024, 113 resources were selected. A further screening process reduced this number to 44 papers, which identified 11 AI tools for code generation. The method used was a comparative study in which ten programming exercises of varying levels of difficulty were designed and the results obtained from 4 of them are presented. The identified tools generated code for these exercises in different programming languages. The quality of the generated code was evaluated using the SonarQube static analyzer, considering aspects such as safety, reliability and maintainability. The results showed significant variations in code quality among the AI tools. Bing as a code generation tool showed slightly superior performance compared to others, although none stood out as a noticeably superior AI. In conclusion, the research evidenced that, although AI tools for code generation are promising, they still require a pilot to reach their full potential, giving evidence that there is still a long way to go. Journal: LatIA Pages: 104 Volume: 2 Year: 2024 DOI: 10.62486/latia2024104 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:104:id:1062486latia2024104 Template-Type: ReDIF-Article 1.0 Author-Name: Olga Lucía Torres Vargas Author-Name-First: Olga Lucía Author-Name-Last: Torres Vargas Author-Name: Mateo Valencia Buitrago Author-Name-First: Mateo Author-Name-Last: Valencia Buitrago Title: Color in images: a machine vision approach to the measurement of CIEL*a*b* coordinates in bovine loins Abstract: Electronic machine vision systems bring together a set of technologies and techniques used to capture, process and analyze images to perform a specific task, such as object or measurement pattern recognition. These systems rely on image processing and machine learning algorithms to interpret visual information. Therefore, the objective of this research was the construction of an electronic machine vision system (SVA) for color analysis in bovine (longisimus dorsi) loins based on the CIEL*a*b* color space. The VAS implementation was carried out using the programming language Python 3.9 programming language and the color parameters obtained were compared with those obtained on a Minolta CR-400 colorimeter (CM). Both systems were synchronized to provide the user with information about the color coordinates in the samples of loins stored for 6 days at 4°C. The results obtained showed no significant differences. The results obtained showed no significant differences in the values of the L* parameter, while b* and a* showed significant differences during the storage time of the loins. These results are attributed to the oxidation process of the myoglobin and to factors such as breed, feeding and slaughtering process of the cattle, which affect the color of the samples. The results obtained indicate that VAS could be used for the determination of color during the storage of beef loins in real time, offering a non-invasive and low-cost solution to the actors in the meat chain. Keywords: image analysis, beef, colorimeter, artificial vision system. Journal: LatIA Pages: 103 Volume: 2 Year: 2024 DOI: 10.62486/latia2024103 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:103:id:1062486latia2024103 Template-Type: ReDIF-Article 1.0 Author-Name: Gisela Yasmín García Espinoza Author-Name-First: Gisela Yasmín Author-Name-Last: García Espinoza Title: Transforming the Salitre campus into a smart campus: proposal of smart initiatives for the Gerardo Barrios University of El Salvador Abstract: This study proposes a set of initiatives to transform the Salitre campus of the Gerardo Barrios University in San Miguel, El Salvador, into a Smart Campus inspired by Smart City concepts. Through a documentary research, a detailed diagnosis of the campus and a SWOT analysis, several proposals were defined grouped in six axes: Smart Government, Smart Environment, Smart Living, Smart Economy, Smart People and Smart Mobility. The viability of the initiatives was evaluated considering their acceptance by the authorities and stakeholders, as well as a feasibility study. The results show that the transformation of the Salitre campus into a Smart Campus may be possible and would bring multiple benefits in terms of efficiency, sustainability, innovation and quality of life for the university community. The study establishes the basis for future projects and research, promoting digital and sustainable transformation in the educational and community environment of El Salvador. Journal: LatIA Pages: 102 Volume: 2 Year: 2024 DOI: 10.62486/latia2024102 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:102:id:1062486latia2024102 Template-Type: ReDIF-Article 1.0 Author-Name: Roger David Pimienta Barros Author-Name-First: Roger David Author-Name-Last: Pimienta Barros Title: Design and implementation of an IoT monitoring system for the optimization of solar stills for water desalination Abstract: The project "Design and Implementation of an IoT Monitoring System for the Optimization of Solar Distillers in Water Desalination" sought to improve the efficiency of desalination in La Guajira, a region with critical water scarcity. The objective was to develop an IoT system to optimize solar stills, offering a sustainable solution. A prototype solar still with IoT monitoring was built. The study included the creation of circuits to integrate sensors and an HTML dashboard to visualize real-time variables, such as internal and external temperatures, humidity, and water level in the basin, facilitating the calculation of efficiency. The IoT monitoring system proved to be effective in increasing efficiency and providing valuable data for design decisions, marking a step towards water autonomy. Journal: LatIA Pages: 101 Volume: 2 Year: 2024 DOI: 10.62486/latia2024101 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:101:id:1062486latia2024101 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: Vugar Abdullayev Author-Name-First: Vugar Author-Name-Last: Abdullayev Author-Name: Khushwant Singh Author-Name-First: Khushwant Author-Name-Last: Singh Title: Improving Cleaning of Solar Systems through Machine Learning Algorithms Abstract: The study focuses on the importance of maintaining photovoltaic (PV) systems for optimal performance in sustainable energy generation. It highlights the impact of dust accumulation on reducing system efficiency and proposes a method to predict system performance, aiding in scheduling cleaning activities effectively. Two prediction models are developed: one using time-series prediction techniques (LSTM, ARIMA, SARIMAX) to forecast Performance Ratio (PR), and another employing ensemble voting classifiers (RF, Log, GBM) to predict the need for cleaning. The SARIMAX model performs best, achieving high accuracy in PR prediction (R2 = 92.12%), while the classification model accurately predicts cleaning needs (91%). The research provides valuable insights for improving maintenance strategies and enhancing the efficiency and sustainability of PV systems. Journal: LatIA Pages: 100 Volume: 2 Year: 2024 DOI: 10.62486/latia2024100 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:100:id:1062486latia2024100 Template-Type: ReDIF-Article 1.0 Author-Name: Duong Ngoc Anh Author-Name-First: Duong Ngoc Author-Name-Last: Anh Author-Name: Phan Minh Duc Author-Name-First: Phan Minh Author-Name-Last: Duc Title: Social responsibility of small and medium enterprises in Vietnam through digital transformation and application of artificial intelligence Abstract: The study on the social responsibility of small and medium enterprises (SMEs) in Vietnam through digital transformation and the application of artificial intelligence explored key aspects such as challenges faced during digital transformation, the importance of SMEs in the Vietnamese economy, and the significance of corporate social responsibility (CSR). It emphasized the need for SMEs to adapt to remain competitive and contribute more significantly to the state budget. The research highlighted the landscape of SMEs in Vietnam from 2017 to 2021, focusing on their classification, numbers, and characteristics, noting a steady increase in the number of SMEs each year. The document discussed the limited adoption of advanced technologies like artificial intelligence among Vietnamese SMEs and the need for increased support and resources for effective digital transformation, especially adopting AI technology. Additionally, it touched upon the social responsibility aspects of SMEs in the context of digital transformation, addressing opportunities and challenges related to environmental impact, labor productivity, financial transparency, and animal welfare. Through a qualitative analysis approach, the study aimed to provide insights into the evolving landscape of SMEs in Vietnam and their integration of digital technologies to enhance social responsibility practices Journal: LatIA Pages: 99 Volume: 2 Year: 2024 DOI: 10.62486/latia202499 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:99:id:1062486latia202499 Template-Type: ReDIF-Article 1.0 Author-Name: Muthusundari Author-Name-First: Muthusundari Author-Name-Last: Muthusundari Author-Name: A Velpoorani Author-Name-First: A Author-Name-Last: Velpoorani Author-Name: S Venkata Kusuma Author-Name-First: S Author-Name-Last: Venkata Kusuma Author-Name: Trisha L Author-Name-First: Trisha Author-Name-Last: L Author-Name: Om.k. Rohini Author-Name-First: Om.k. Author-Name-Last: Rohini Title: Optical character recognition system using artificial intelligence Abstract: Abstract A technique termed optical character recognition, or OCR, is used to extract text from images. An OCR the system's primary goal is to transform already present paper-based paperwork or picture data into usable papers. Character as well as word detection are the two main phases of an OCR, which is designed using many algorithms. An OCR also maintains a document's structure by focusing on sentence identification, which is a more sophisticated approach. Research has demonstrated that despite the efforts of numerous scholars, no error-free Bengali OCR has been produced. This issue is addressed by developing an OCR for the Bengali language using the latest 3.03 version of the Tesseract OCR engine for Windows. Journal: LatIA Pages: 98 Volume: 2 Year: 2024 DOI: 10.62486/latia202498 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:98:id:1062486latia202498 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: Subhi Al-Batah Mohammad Author-Name-First: Subhi Al-Batah Author-Name-Last: Mohammad Title: Optimizing Resource Discovery in Grid Computing: A Hierarchical and Weighted Approach with Behavioral Modeling Abstract: Parallel programs that require sizeable computational electricity increasingly depend on grid computing structures. Efficient, helpful resource discovery algorithms are critical for optimizing resource allocation and minimizing execution time in these environments. This look presents a unique hierarchical and weighted resource discovery algorithm designed to decorate resource distribution while decreasing communique costs among grid nodes. A behavioural modelling technique systematically establishes the weighted resource discovery algorithm's accuracy and effectiveness. The behavioural model is carried out using StarUML. At the same time, the NuSMV version checker is hired to verify essential residences along with reachability, equity, and impasse-free operation of the resource discovery procedure. Critical overall performance metrics, including the quantity of inspected nodes consistent with request and the frequency of re-discovery operations, are used to evaluate the rules' efficiency and flexibility. The weighted resource discovery algorithm also evaluates the efficiency of finding loose resources with high-bandwidth connections, optimizing overall grid resource allocation. In addition to enhancing resource localization, the observation introduces resource facts tables, which store information crucial for powerful, proper resource allocation. This study aims to develop grid computing competencies by addressing resource discovery challenges. The hierarchical shape and weighted valid resource selection decorate valid resource inspection, adaptability, and high-bandwidth utilization. Behavioural modelling and formal verification verify the algorithm's accuracy and applicability in grid environments. By using weighted resource discovery and resource information tables, this study drastically improves resource discovery's performance and effectiveness in grid computing, optimizing overall performance and proper resource allocation. Journal: LatIA Pages: 97 Volume: 3 Year: 2025 DOI: 10.62486/latia202597 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:97:id:1062486latia202597 Template-Type: ReDIF-Article 1.0 Author-Name: Jesús Alexis Aguilar-Rodríguez Author-Name-First: Jesús Alexis Author-Name-Last: Aguilar-Rodríguez Author-Name: Patricia Rivera-García Author-Name-First: Patricia Author-Name-Last: Rivera-García Author-Name: Armando Cervantes-Sandoval Author-Name-First: Armando Author-Name-Last: Cervantes-Sandoval Author-Name: Alejandro Josué Perales-Avila Author-Name-First: Alejandro Josué Author-Name-Last: Perales-Avila Title: Artificial intelligence as a resource for teaching mathematics. Integral calculus as a specific case Abstract: The use of artificial intelligence, AI, has increased in recent years, offering tools to generate content and solve problems in different areas of knowledge. Questioning their relevance in areas such as integral calculus, 11 AI tools were tested to solve from basic to advanced integrals, described in different ways such as natural language or images. The results were compared with the solutions given in the literature, analyzing precision, number of steps, clarity of explanations and ease of use. Finding that the Chat GPT-Wolfram Alpha partnership stood out for its ability to identify appropriate integration techniques and offer detailed and understandable explanations; While Copilot is more complex to understand if you do not use the LaTeX language, the rest present some problems when interpreting instructions and images. Although these tools are not designed to solve mathematical problems, they proved to be effective in most cases, promoting an interactive space to clarify doubts in real time and generate debate; however, their effectiveness depends on the use given to them, since either as support in the classroom to deepen the analysis of the problem or simply use it as a black box to obtain quick answers. Journal: LatIA Pages: 96 Volume: 2 Year: 2024 DOI: 10.62486/latia202496 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:96:id:1062486latia202496 Template-Type: ReDIF-Article 1.0 Author-Name: Ranta Butarbutar Author-Name-First: Ranta Author-Name-Last: Butarbutar Title: Virtual Socio-Cultural Collaborative Learning for EFL Speaking Skills Classes Abstract: Virtual Sociocultural Collaborative Learning (VSCL) is an innovative techno-pedagogical approach aimed at enhancing English as a Foreign Language (EFL) speaking skills in classrooms. This study investigated the impact of VSCL on EFL speaking proficiency through a quasi-experimental design, utilizing pre- and post-test time-series measurements. The Cognitive Function Scale (ACFS) was used to assess the effectiveness of the VSCL. The results indicated that VSCL significantly improved classification, perspective-taking, and verbal and auditory skills, confirming its positive influence on learners. Furthermore, VSCL was found to enhance key speaking components, including grammar, fluency, accuracy, and comprehension, with a t-count of 0.000, which was lower than the t-table value of 0.68. These findings suggest that VSCL is a valuable approach for EFL teachers to support diverse zones of proximal development (ZPD) in their classrooms. Journal: LatIA Pages: 95 Volume: 3 Year: 2025 DOI: 10.62486/latia202595 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:95:id:1062486latia202595 Template-Type: ReDIF-Article 1.0 Author-Name: Ankit Kumar Author-Name-First: Ankit Author-Name-Last: Kumar Author-Name: Khushboo Mishra Author-Name-First: Khushboo Author-Name-Last: Mishra Author-Name: Rajesh Kumar Mahto Author-Name-First: Rajesh Author-Name-Last: Kumar Mahto Author-Name: Binay Kumar Mishra Author-Name-First: Binay Author-Name-Last: Kumar Mishra Title: A Framework for Institution to Enhancing Cybersecurity in Higher Education: A Review Abstract: The increasing prevalence of cybersecurity threats has highlighted the urgent need for Higher Education Institutions (HEIs) to prioritize and enhance their cybersecurity measures. This research article presents a comprehensive framework aimed at guiding institutions in strengthening their cybersecurity posture within the higher education sector. The framework addresses the unique challenges faced by HEIs, taking into account the multifaceted nature of cybersecurity and the evolving threat landscape. The proposed framework incorporates a systematic approach that encompasses key components essential for effective cybersecurity management. These components include governance and leadership, risk assessment and management, technical controls, awareness and training, incident response, and collaboration with external stakeholders. The framework emphasizes the integration of these components to establish a robust and holistic cybersecurity strategy. The research article draws upon a thorough review of existing literature, best practices, and industry standards to provide practical insights for HEIs. The framework offers a structured approach that enables institutions to assess their current cybersecurity posture, identify gaps, and implement targeted measures to enhance their overall security resilience. By adopting this framework, institutions can proactively address cybersecurity challenges, mitigate risks, and protect sensitive data and systems. The framework serves as a valuable resource for HEI leaders, policymakers, and cybersecurity professionals seeking to enhance cybersecurity in the higher education landscape Journal: LatIA Pages: 94 Volume: 2 Year: 2024 DOI: 10.62486/latia202494 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:94:id:1062486latia202494 Template-Type: ReDIF-Article 1.0 Author-Name: Khushwant Singh Author-Name-First: Khushwant Author-Name-Last: Singh Author-Name: Mohit Yadav Author-Name-First: Mohit Author-Name-Last: Yadav Author-Name: Vugar Hacimahmud Abdullayev Author-Name-First: Vugar Author-Name-Last: Hacimahmud Abdullayev Title: Prediction of Flight Areas using Machine Learning Algorithm Abstract: Anyone who often uses the airways wants to predict when it will be best to purchase a ticket in order to get the best possible value. Aircraft firms continuously adjust ticket prices in an effort to maximize profits. When it's anticipated that demand for more income will grow, aircraft manufacturers may raise flying prices. Information analysis for a given air route, comprising the features like take-off time, entrance time, and airways during a specified period, has been gathered in order to decrease costs. To use the machine learning models, qualities are arranged based on the information that has been gathered. The machine learning approach to determine costs based on attributes is presented in the paper below. Journal: LatIA Pages: 93 Volume: 2 Year: 2024 DOI: 10.62486/latia202493 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:93:id:1062486latia202493 Template-Type: ReDIF-Article 1.0 Author-Name: Miguel Valencia-Contrera Author-Name-First: Miguel Author-Name-Last: Valencia-Contrera Author-Name: Flérida Rivera-Rojas Author-Name-First: Flérida Author-Name-Last: Rivera-Rojas Author-Name: Jenifer Villa-Velasquez Author-Name-First: Jenifer Author-Name-Last: Villa-Velasquez Author-Name: Daniella Cancino-Jiménez Author-Name-First: Daniella Author-Name-Last: Cancino-Jiménez Title: Use of artificial intelligence in nursing Abstract: Introduction: Artificial Intelligence (AI) encompasses technologies such as machine learning and neural networks, with applications across various fields. The World Health Organization recognizes its potential to enhance healthcare, yet emphasizes the need to address ethical considerations in its implementation. In nursing, AI has the potential to increase autonomy and efficiency in care, though its use remains limited and poorly understood within the profession. Objective: To analyze the use of AI in nursing by evaluating its impact on care functions, administrative tasks, educational activities, and research. Methods: A literature review was conducted, including original articles, reviews, and bibliometric studies. The research focused on AI applications across the four primary functions of nursing. Results: AI has demonstrated benefits in predictive analytics and improving patient care efficiency, as well as in administrative management and patient classification. In education, generative AI facilitates the development of educational materials, although it presents risks of bias. In research, AI serves as an assistant in data search and analysis, despite facing ethical and methodological challenges. Conclusions: AI has the potential to significantly transform nursing practice, enhancing both the quality and efficiency of care. However, its integration necessitates careful management to address its limitations and ensure a positive impact in the field. Journal: LatIA Pages: 92 Volume: 2 Year: 2024 DOI: 10.62486/latia202492 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:92:id:1062486latia202492 Template-Type: ReDIF-Article 1.0 Author-Name: Taleh Askerov Author-Name-First: Taleh Author-Name-Last: Askerov Author-Name: Vugar Abdullayev Author-Name-First: Vugar Author-Name-Last: Abdullayev Author-Name: Vusala Abuzarova Author-Name-First: Vusala Author-Name-Last: Abuzarova Author-Name: Yitong Niu Author-Name-First: Yitong Author-Name-Last: Niu Author-Name: Khushwant Singh Author-Name-First: Khushwant Author-Name-Last: Singh Title: Data processing in internet of things networks Abstract: One of the distinctive features of cloud applications in the Internet of Things (IoT) network is the real-time collection and processing of data about the objects of the physical environment. To solve this problem, with the help of sensors, data about the state of physical objects is collected to form the processing of data streams. The performance of such systems is critically dependent on the ability to securely and efficiently collect, transmit, and analyze data streams between environmental (real-world) objects and information systems. In such a case, it is relevant to study the data stream in Internet of Things networks and the operations that may occur on them. The issue of which platforms to process the operations to be carried out at the same time is the main subject of discussion. Journal: LatIA Pages: 91 Volume: 2 Year: 2024 DOI: 10.62486/latia202491 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:91:id:1062486latia202491 Template-Type: ReDIF-Article 1.0 Author-Name: Richard Injante Author-Name-First: Richard Author-Name-Last: Injante Author-Name: Karen Chamaya Author-Name-First: Karen Author-Name-Last: Chamaya Title: Use of artificial intelligence in the detection of coffee rust: An exploratory systematic review Abstract: Coffee rust, caused by the fungus Hemileia vastatrix, is a fungal disease that affects coffee production and quality, so its early detection is crucial to prevent massive outbreaks and protect production. This article analyzes the most effective factors, the algorithms used, the accuracy of the models, and the challenges in the detection of coffee rust, through an exploratory systematic review of 35 empirical articles obtained from Scopus, IEEE Xplore and SciELO. The review identifies that the most determinant factors for detection include humidity, temperature and the presence of shade. The most commonly used algorithms are Convolutional Neural Networks (CNN), Support Vector Machines (SVM) and Random Forest, highlighting CNN for its ability to process and analyze images with an accuracy of 99.57%, followed by Artificial Neural Networks (ANN) with 98% and SVM with 96%. However, it is concluded that challenges remain such as the need for high quality labeled datasets, variability in environmental conditions and implementation costs. This study provides a comprehensive overview of recent advances and areas for improvement in coffee rust detection, providing information for researchers, practitioners and decision makers in the agricultural sector. Journal: LatIA Pages: 90 Volume: 2 Year: 2024 DOI: 10.62486/latia202490 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:90:id:1062486latia202490 Template-Type: ReDIF-Article 1.0 Author-Name: Konstantinos T. Kotsis Author-Name-First: Konstantinos Author-Name-Last: T. Kotsis Title: Integrating Artificial Intelligence for Science Teaching in High School Abstract: This paper studies the potential benefits and challenges of incorporating AI into science education for secondary-level schools. It explores how AI-driven tools can enhance personalized learning, improve student engagement, and reshape teaching methodologies while addressing concerns regarding equity, accessibility, and teacher-student interactions. A literature review and analysis of AI applications in education focused on adaptive learning technologies, interactive simulations, and AI-driven feedback systems. AI technologies, including ChatGPT, facilitate personalized learning through adaptive feedback that targets individual knowledge gaps and learning preferences, promoting a more profound comprehension of intricate subjects such as physics. Findings indicate that AI enhances learning experiences by providing personalized feedback, fostering interactive and collaborative learning environments, and supporting differentiated instruction. However, challenges such as limited access to technology, teacher training, and ethical considerations regarding data privacy must be addressed to ensure equitable AI implementation in education. AI has the potential to revolutionize science education by making learning more engaging and tailored to student needs. However, successful integration requires addressing challenges related to infrastructure, teacher training, and ethical concerns. This study highlights the need for comprehensive policies and professional development programs to maximize the benefits of AI while ensuring fair and effective implementation in science education. Journal: LatIA Pages: 89 Volume: 3 Year: 2025 DOI: 10.62486/latia202589 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:89:id:1062486latia202589 Template-Type: ReDIF-Article 1.0 Author-Name: Petros Chavula Author-Name-First: Petros Author-Name-Last: Chavula Author-Name: Fredrick Kayusi Author-Name-First: Fredrick Author-Name-Last: Kayusi Author-Name: Linety Juma Author-Name-First: Linety Author-Name-Last: Juma Title: Leveraging Artificial Intelligence for Enhancing Wheat Yield Resilience Amidst Climate Change in Sub-Saharan Africa Abstract: The introduction of a deep learning-based method for non-destructive leaf area index (LAI) assessment has enhanced rapid estimation for wheat and similar crops, aiding crop growth monitoring, water, and nutrient management. Convolutional Neural Network (CNN)-based algorithms enable accurate, non-destructive quantification of seedling leaf areas and assess LAI across diverse genotypes and environments, demonstrating adaptability. Transfer learning, known for efficiency in plant phenotyping, was tested as a resource-saving approach for training the wheat LAI model. These advancements support wheat breeding, facilitate genotype selection for varied environments, accelerate genetic gains, and enhance genomic selection for LAI. By capturing diverse environments, this method can improve wheat resilience to climate change. Additionally, advances in machine learning and data science enable better prediction and distribution mapping of global wheat rust pathogens, a major agricultural challenge. Accurate risk identification allows for timely and effective control measures. Moreover, wheat lodging prediction models using CNNs can assess lodging-prone varieties, influencing selection decisions to improve yield stability. These artificial intelligence-driven techniques contribute to sustainable crop growth and yield enhancement, especially in the context of climate change and increasing global food demand. Journal: LatIA Pages: 88 Volume: 2 Year: 2024 DOI: 10.62486/latia202588 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:88:id:1062486latia202588 Template-Type: ReDIF-Article 1.0 Author-Name: Fredrick Kayusi Author-Name-First: Fredrick Author-Name-Last: Kayusi Author-Name: Petros Chavula Author-Name-First: Petros Author-Name-Last: Chavula Title: Enhancing Urban Green Spaces: AI-Driven Insights for Biodiversity Conservation and Ecosystem Services Abstract: Urban green spaces (UGS) enhance biodiversity and provide essential ecosystem services like air purification, climate regulation, water management, and recreation. Despite their importance, UGS are often overlooked in urban planning, limiting their potential for resilience and sustainability. This study examines biodiversity in UGS and their capacity to deliver ecosystem services using field surveys, GIS mapping, stakeholder interviews, and AI-driven analytics. AI-based image recognition and remote sensing automate species identification and assess vegetation health, improving biodiversity assessments. Machine learning models analyze spatial and environmental data to predict UGS contributions to mitigating heat islands, air pollution, and stormwater runoff. Findings show that UGS serve as biodiversity hotspots, hosting diverse flora and fauna. Ecosystem service provision varies based on green space type, size, and management. AI-driven insights reveal key biodiversity factors like vegetation composition, spatial configurations, and human activities, offering data-driven recommendations for urban planning. Integrating AI into urban ecology supports evidence-based decision-making, urging policymakers and communities to optimize UGS management for biodiversity and human well-being. Journal: LatIA Pages: 87 Volume: 2 Year: 2024 DOI: 10.62486/latia202587 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:87:id:1062486latia202587 Template-Type: ReDIF-Article 1.0 Author-Name: Fredrick Kayusi Author-Name-First: Fredrick Author-Name-Last: Kayusi Author-Name: James Wasike Author-Name-First: James Author-Name-Last: Wasike Author-Name: Petros Chavula Author-Name-First: Petros Author-Name-Last: Chavula Title: The Role of Mulching in Reducing Greenhouse Gas Emissions and Enhancing Soil Health Among Smallholder Farmers in Zambia, Malawi, Kenya, and Tanzania: An AI-Driven Approach Abstract: Mulching is a widely recognized conservation practice that improves soil moisture retention, enhances fertility, and reduces greenhouse gas (GHG) emissions. This study explores the effectiveness of mulching among smallholder farmers in Zambia, Malawi, Kenya, and Tanzania, focusing on its role in mitigating climate change and improving soil health. Additionally, we integrate artificial intelligence (AI) to optimize mulching practices through predictive analytics and real-time monitoring. AI-powered models, utilizing remote sensing data and machine learning algorithms, assess soil conditions, moisture levels, and carbon sequestration potential. These insights enable precision agriculture techniques, helping farmers make data-driven decisions that maximize mulching benefits while minimizing environmental impact. The study also evaluates AI-driven mobile applications and advisory systems that provide tailored recommendations based on localized climate and soil data. By leveraging AI technology, this research aims to enhance the sustainability of mulching practices, improve productivity, and contribute to climate resilience in smallholder farming systems. Journal: LatIA Pages: 75 Volume: 1 Year: 2023 DOI: 10.62486/latia202575 Handle: RePEc:dbk:rlatia:v:1:y:2023:i::p:75:id:1062486latia202575 Template-Type: ReDIF-Article 1.0 Author-Name: Fredrick Kayusi Author-Name-First: Fredrick Author-Name-Last: Kayusi Author-Name: Petros Chavula Author-Name-First: Petros Author-Name-Last: Chavula Author-Name: Linety Juma Author-Name-First: Linety Author-Name-Last: Juma Title: Role of Redox Reactions and AI-Driven Approaches in Enhancing Nutrient Availability for Plants Abstract: Empirical studies have shown that environmental variability in the field remains uncontrolled in certain cases, with research often conducted at a limited number of agricultural sites. Direct measurements of redox potential in soils have been reported, yet quantifying rapid changes in this variable across microsites proves inaccessible in situ. Existing measurements of redox potential also fail to account for variability in the identity of reduced or oxidized compounds. Additionally, methodological constraints and researcher bias, particularly in studies focusing on processes in reduced sediments, may impair interpretations of anabolic reactions resulting from oxidation.Case studies further indicate that the effects of redox potential on nitrification, net mineralization, or immobilization of other nutrients often remain unmeasured. As a result, increased denitrification might stimulate nitrification, reducing the effects of nitrogen immobilization due to increasing carbon storage in environments where reduction predominates.Given the absence of studies specifically exploring the balance between reduction and oxidation in relation to nutrient availability, assessing the magnitude and likelihood of methodological shortcomings based on prior field research remains challenging. Existing research serves as a foundation for understanding how this balance may significantly influence nutrient dynamics and availability at larger scales. Future studies manipulating redox potential in the field should consider factors that could disproportionately facilitate reductions before an eastward shift occurs in the balance between oxidation and reduction in response to organic matter addition. Addressing these gaps will enhance understanding of redox reactions and their potential role in stimulating denitrification and sulfide responses. Journal: LatIA Pages: 86 Volume: 1 Year: 2023 DOI: 10.62486/latia202586 Handle: RePEc:dbk:rlatia:v:1:y:2023:i::p:86:id:1062486latia202586 Template-Type: ReDIF-Article 1.0 Author-Name: Fredrick Kayusi Author-Name-First: Fredrick Author-Name-Last: Kayusi Author-Name: Srinivas Kasulla Author-Name-First: Srinivas Author-Name-Last: Kasulla Author-Name: S J Malik Author-Name-First: S J Author-Name-Last: Malik Author-Name: Petros Chavula Author-Name-First: Petros Author-Name-Last: Chavula Title: Article Context and Technological Integration: AI's Role in Climate Change Research Abstract: This article explores the transformative role of artificial intelligence and machine learning in tackling climate change. It highlights how advanced computational techniques enhance our understanding and response to environmental shifts. Machine learning algorithms process vast climate datasets, revealing patterns that traditional methods might overlook. Deep learning neural networks, particularly effective in climate research, analyze satellite imagery, climate sensor data, and environmental indicators with unprecedented accuracy. Key applications include predictive modeling of climate change impacts. Using convolutional and recurrent neural networks, researchers generate high-resolution projections of temperature rises, sea-level changes, and extreme weather events with remarkable precision. AI also plays a vital role in data integration, synthesizing satellite observations, ground-based measurements, and historical records to create more reliable climate models. Additionally, deep learning algorithms enable real-time environmental monitoring, tracking changes like deforestation, ice cap melting, and ecosystem shifts. The article also highlights AI-powered optimization models in mitigation efforts. These models enhance carbon reduction strategies, optimize renewable energy use, and support sustainable urban planning. By leveraging machine learning, the research demonstrates how AI-driven approaches offer data-backed solutions for climate change mitigation and adaptation. These innovations provide practical strategies to address global environmental challenges effectively. Journal: LatIA Pages: 85 Volume: 3 Year: 2025 DOI: 10.62486/latia202585 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:85:id:1062486latia202585 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: Hatim Solayman Migdadi Author-Name-First: Hatim Author-Name-Last: Solayman Migdadi Title: From Complexity to Clarity: Improving Microarray Classification with Correlation-Based Feature Selection Abstract: Gene microarray classification is yet a difficult task because of the bigness of the data and limited number of samples available. Thus, the need for efficient selection of a subset of genes is necessary to cut down on computation costs and improve classification performance. Consistently, this study employs the Correlation-based Feature Selection (CFS) algorithm to identify a subset of informative genes, thereby decreasing data dimensions and isolating discriminative features. Thereafter, three classifiers, Decision Table, JRip and OneR were used to assess the classification performance. The strategy was implemented on eleven microarray samples such that the reduced samples were compared with the complete gene set results. The observed results lead to a conclusion that CFS efficiently eliminates irrelevant, redundant, and noisy features as well. This method showed great prediction opportunities and relevant gene differentiation for datasets. JRip performed best among the Decision Table and OneR by average accuracy in all mentioned datasets. However, this approach has many advantages and enhances the classification of several classes with large numbers of genes and high time complexity. Journal: LatIA Pages: 84 Volume: 3 Year: 2025 DOI: 10.62486/latia202584 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:84:id:1062486latia202584 Template-Type: ReDIF-Article 1.0 Author-Name: Petros Chavula Author-Name-First: Petros Author-Name-Last: Chavula Author-Name: Fredrick Kayusi Author-Name-First: Fredrick Author-Name-Last: Kayusi Author-Name: Linety Juma Author-Name-First: Linety Author-Name-Last: Juma Title: Navigating the Paradox: Climate Change, Cutting-Edge Technologies, and Groundwater Sustainability Abstract: This article explores the paradoxical relationship between climate change, advanced technologies, and groundwater sustainability. It highlights how emerging technologies like artificial intelligence, blockchain, and the Internet of Things (IoT) offer innovative solutions for optimizing groundwater management while addressing climate change impacts. However, the chapter also warns of the environmental risks associated with these technologies, particularly their energy consumption and e-waste generation, which can further exacerbate climate challenges. The chapter examines practical applications such as desalination, precision farming, and water harvesting, evaluating their contributions to groundwater management and their environmental footprints. It argues that the net impact of these technologies depends largely on their design, implementation, and governance frameworks. The research identifies best practices to maximize benefits while minimizing negative environmental consequences. This work addresses key issues of water scarcity and the need for sustainable water supplies in a changing climate. It underscores the importance of fresh water for essential industries, including agriculture, energy production, and mineral processing, while acknowledging the profound effects of climate change and societal shifts on traditional water sources. The chapter also discusses the risks associated with technological investments in water management, such as toxic waste emissions, geopolitical tensions, and corruption. It emphasizes that emissions from these processes contribute significantly to rising atmospheric temperatures and water vapor levels, intensifying climate change. The chapter concludes by advocating for a holistic approach to water management, balancing the costs, benefits, and risks of emerging technologies. It highlights the potential of green engineering advancements and efficient water treatment methods, such as desalination and cleaner urban designs, to sustainably provide fresh groundwater for various uses. The chapter integrates data analytics from engineering and public health performance metrics to establish safe industry targets and calls for responsible governance to ensure technologies contribute positively to both groundwater sustainability and climate change mitigation. Journal: LatIA Pages: 83 Volume: 1 Year: 2023 DOI: 10.62486/latia202583 Handle: RePEc:dbk:rlatia:v:1:y:2023:i::p:83:id:1062486latia202583 Template-Type: ReDIF-Article 1.0 Author-Name: Fredrick Kayusi Author-Name-First: Fredrick Author-Name-Last: Kayusi Author-Name: Petros Chavula Author-Name-First: Petros Author-Name-Last: Chavula Author-Name: Linety Juma Author-Name-First: Linety Author-Name-Last: Juma Author-Name: Rashmi Mishra Author-Name-First: Rashmi Author-Name-Last: Mishra Title: Machine Learning-Based and AI Powered Satellite Imagery Processing for Global Air Traffic Surveillance Systems Abstract: The unprecedented growth of global air traffic has put immense pressure on the air traffic management systems. In light of that, global air traffic situational awareness and surveillance are indispensable, especially for satellite-based aircraft tracking systems. There has been some crucial development in the field; however, every major player in this arena relies on a single proprietary, non-transparent data feed. This is where this chapter differentiates itself. AIS data has been gaining traction recently for the same purpose and has matured considerably over the past decade; however, satellite-based communication service providers have failed to instrument significant portions of the world’s oceans. This study proposes a multimodal artificial intelligence-powered algorithm to boost the estimates of global air traffic situational awareness using the Global Air Traffic Visualization dataset. Two multimodal artificial intelligence agents categorically detect air traffic streaks in a huge collection of satellite images and notify the geospatial temporal statistical agent whenever both modalities are in concordance. A user can fine-tune the multimodal threshold hyperparameter based on the installed detection rate of datasets to get the best satellite-derived air traffic estimates. Journal: LatIA Pages: 82 Volume: 1 Year: 2023 DOI: 10.62486/latia202582 Handle: RePEc:dbk:rlatia:v:1:y:2023:i::p:82:id:1062486latia202582 Template-Type: ReDIF-Article 1.0 Author-Name: Fredrick Kayusi Author-Name-First: Fredrick Author-Name-Last: Kayusi Author-Name: Petros Chavula Author-Name-First: Petros Author-Name-Last: Chavula Title: Enhancing Wetland Restoration through Machine Learning-Based Decision Support Systems Abstract: Researchers are increasingly employing Machine Learning (ML) and Deep Learning (DL) algorithms to address complex geo-environmental challenges, particularly in predicting risk, susceptibility, and vulnerability to environmental changes. These advanced computational models have shown significant promise in various applications, ranging from natural disaster prediction to environmental monitoring. Despite their growing usage, very few studies have leveraged Machine Learning-Based Decision Support Systems (MLBDSS) to restore the health status of wetland habitats. To our knowledge, there are no comparative analyses between Machine Learning models and traditional Decision Support Systems (DSS) in this specific context. Wetlands play a crucial role in supporting biodiversity, including fish and wildlife populations, while also contributing to improved water quality and providing essential ecosystem services to nearby communities. These services include flood control, carbon sequestration, and water filtration, which are vital for both ecological and human well-being. However, over the past decades, wetland areas, particularly in coastal regions, have faced significant degradation due to anthropogenic pressures, resulting in a substantial reduction of these critical benefits. This ongoing loss poses serious ecological and socio-economic challenges that require immediate and effective intervention. Current wetland assessment and mitigation frameworks often encounter limitations in their practical implementation, despite regulatory advancements aimed at promoting wetland conservation. These shortcomings can lead to delayed project approvals, increased costs, and further loss of valuable ecosystem services. Integrating ML and DSS models into wetland management strategies could provide innovative solutions to overcome these challenges by improving predictive accuracy, optimizing restoration efforts, and enhancing decision-making processes. The development of hybrid models combining ML and DSS approaches may offer a more holistic framework for addressing wetland loss, ultimately contributing to sustainable habitat restoration and conservation efforts. Journal: LatIA Pages: 81 Volume: 1 Year: 2023 DOI: 10.62486/latia202581 Handle: RePEc:dbk:rlatia:v:1:y:2023:i::p:81:id:1062486latia202581 Template-Type: ReDIF-Article 1.0 Author-Name: Fredrick Kayusi Author-Name-First: Fredrick Author-Name-Last: Kayusi Author-Name: Petros Chavula Author-Name-First: Petros Author-Name-Last: Chavula Author-Name: Linety Juma Author-Name-First: Linety Author-Name-Last: Juma Author-Name: Rashmi Mishra Author-Name-First: Rashmi Author-Name-Last: Mishra Title: AI-Powered Satellite Imagery Processing for Global Air Traffic Surveillance Abstract: The increasing complexity of global air traffic management requires innovative surveillance solutions beyond traditional radar. This chapter explores the integration of artificial intelligence (AI) and machine learning (ML) in satellite imagery processing for enhanced air traffic surveillance. The proposed AI framework utilizes satellite remote sensing, computer vision algorithms, and geo-stamped aircraft data to improve real-time detection and classification. It addresses limitations in conventional systems, particularly in areas lacking radar coverage. The study outlines a three-phase approach: extracting radar coverage from satellite imagery, labeling data with geo-stamped aircraft locations, and applying deep learning models for classification. YOLO and Faster R-CNN models distinguish aircraft from other objects with high accuracy. Experimental trials demonstrate AI-enhanced satellite monitoring's feasibility, achieving improved detection in high-traffic zones. The system enhances situational awareness, optimizes flight planning, reduces airspace congestion, and strengthens security. It also aids disaster response by enabling rapid search-and-rescue missions. Challenges like adverse weather and nighttime monitoring remain, requiring infrared sensors and radar-based techniques. By combining big data analytics, cloud computing, and satellite monitoring, the study offers a scalable, cost-effective solution for future air traffic management. Future research will refine models and expand predictive analytics for autonomous surveillance, revolutionizing aviation safety and operational intelligence. Journal: LatIA Pages: 80 Volume: 3 Year: 2025 DOI: 10.62486/latia202580 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:80:id:1062486latia202580 Template-Type: ReDIF-Article 1.0 Author-Name: Petros Chavula Author-Name-First: Petros Author-Name-Last: Chavula Author-Name: Fredrick Kayusi Author-Name-First: Fredrick Author-Name-Last: Kayusi Author-Name: Timothy Mwewa Author-Name-First: Timothy Author-Name-Last: Mwewa Title: Assessing the Impact of Erratic Governance on Local and International NGOs in Zambia: An Exploratory Study Using Machine Learning and Artificial Intelligence Abstract: This study explores the impact of erratic governance on local and international NGOs in Zambia, using a mixed-methods approach that combines survey data, in-depth interviews, and machine learning (ML) and artificial intelligence (AI) techniques. The study finds that erratic governance practices, including funding constraints, operational challenges, and limited access to services, significantly affect the operations and effectiveness of NGOs in Zambia. Weak institutional frameworks, corruption, lack of transparency and accountability, political instability, and limited civic engagement are identified as key factors contributing to erratic governance. The study demonstrates the potential of ML and AI in analyzing and predicting the impact of erratic governance on NGOs, including predictive modeling, risk analysis, data visualization, automated reporting, and decision support systems. The findings of this study have implications for policymakers, NGO managers, and development practitioners seeking to promote more effective and sustainable development outcomes in Zambia. Journal: LatIA Pages: 79 Volume: 2 Year: 2024 DOI: 10.62486/latia202579 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:79:id:1062486latia202579 Template-Type: ReDIF-Article 1.0 Author-Name: Fredrick Kayusi Author-Name-First: Fredrick Author-Name-Last: Kayusi Author-Name: Moses Isutsa Shileche Author-Name-First: Moses Author-Name-Last: Isutsa Shileche Author-Name: Linety Juma Author-Name-First: Linety Author-Name-Last: Juma Author-Name: Rashmi Mishra Author-Name-First: Rashmi Author-Name-Last: Mishra Author-Name: Petros Chavula Author-Name-First: Petros Author-Name-Last: Chavula Title: Adoption and Usage Patterns of Referencing Services Management Software Among Postgraduate Students: A Case Study of a Select Kenyan Universities Abstract: Administration of referencing, referral services offered in university libraries across the world is a critical function in library services to its users. It impacts the way the face of the library is viewed in the eyes of the users. However, various studies show that this service is prone to numerous crisis during its provision that affect an efficient, effective, and user-friendly referencing services management system. The functions of the reference desk within the library revolve around providing a point of referral for help users seeking library services, a point of referral to other services, or to other materials within the library that cannot be accessed by the users, or to other physical spaces where library functions take place, and providing a contact point with the library. Libraries have traditionally offered in-person referencing and referral services to their users; the use of in-person referencing services has declined markedly while virtual referencing and referral services has increased, and referencing services have been integrated in library websites management systems where you see applications like Turnitin, Endote, Zotero, APA as well as AI prominently incorporated. This redefinition of referencing services has challenged librarians to identify the skills and competencies required by service staff to meet this need. Referencing services nowadays may involve much more than acting as a roving search engine. Successful referencing work requires librarians and information specialists to familiarize with a variety of databases and be comfortable with many diverse menus of technologies, as well as being familiar with good online referencing techniques. The purpose of this case study was to examine the adoption and usage of referencing services management software among postgraduate students in Kenyan universities. The study analyzed four predictors: Ease of Navigation, Institutional Resources, Training Impact, and Perceived Relevance. The study was qualitative, and the design used was multiple-case based, involving 205 postgraduate students in Kenyan universities who were purposively selected to participate in the study. Data were collected using focus group discussions, observation, and document analysis. The results revealed a strong model fit, with an R Square value of 0.770, indicating that the predictors explained 77% of the variance in adoption and productivity. Key findings highlighted the significant positive contributions of Ease of Navigation, Training Impact, and Perceived Relevance, while Institutional Resources showed no significant influence. The results underscore the importance of user-friendly design, skill development, and relevance to user needs in driving software adoption. Practical implications include the need for flexible, collaborative training programs and improvements in resource utilization. Suggestions for future research include exploring broader demographic factors and longitudinal studies. This case study can be utilized by all library users ranging from undergraduate to faculty level. Journal: LatIA Pages: 78 Volume: 3 Year: 2025 DOI: 10.62486/latia202578 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:78:id:1062486latia202578 Template-Type: ReDIF-Article 1.0 Author-Name: Petros Chavula Author-Name-First: Petros Author-Name-Last: Chavula Author-Name: Fredrick Kayusi Author-Name-First: Fredrick Author-Name-Last: Kayusi Author-Name: Gilbert Lungu Author-Name-First: Gilbert Author-Name-Last: Lungu Author-Name: Agnes Uwimbabazi Author-Name-First: Agnes Author-Name-Last: Uwimbabazi Title: The Current Landscape of Early Warning Systems and Traditional Approaches to Disaster Detection Abstract: Early warning systems (EWS) are crucial for disaster risk reduction, providing timely and reliable information to communities and authorities for proactive mitigation. Traditional methods, such as weather stations, river gauges, and seismic networks, have limitations in spatial coverage, real-time data availability, and precursor signal detection. Recent technological advancements have enhanced EWS by integrating remote sensing data from satellites, airborne platforms, and ground-based sensors, enabling real-time monitoring of phenomena like wildfires, volcanic activity, and landslides. The Internet of Things (IoT) and crowdsourced data from social media, mobile apps, and citizen reports have further improved situational awareness and response times, complementing traditional systems. Increased computational power has enabled the development of sophisticated models, such as numerical weather prediction and seismic hazard models, which predict disaster impacts more accurately. Despite these advancements, challenges remain in data interoperability, resilient communication infrastructure, and delivering clear, actionable alerts to at-risk populations. Future EWS will likely become more data-driven and interconnected, leveraging artificial intelligence, big data analytics, and IoT. Collaboration among governments, academic institutions, and local communities is essential to building robust, inclusive EWS that save lives and reduce the economic impact of disasters. Journal: LatIA Pages: 77 Volume: 3 Year: 2025 DOI: 10.62486/latia202577 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:77:id:1062486latia202577 Template-Type: ReDIF-Article 1.0 Author-Name: Petros Chavula Author-Name-First: Petros Author-Name-Last: Chavula Author-Name: Fredrick Kayusi Author-Name-First: Fredrick Author-Name-Last: Kayusi Title: Role of Artificial Intelligence in Disseminating Climate Information Services in Africa Abstract: Climate Information Services (CIS) are critical for enabling communities in Africa to make informed decisions in the face of climate variability and change. However, the dissemination of CIS in Africa faces significant challenges, including limited access to data, inadequate infrastructure, and language and cultural barriers. This paper explores the role of Artificial Intelligence (AI) in enhancing the dissemination of CIS across the continent. AI technologies, including machine learning, natural language processing (NLP), and big data analytics, offer promising solutions to these challenges by improving data collection, processing, and communication. Machine learning algorithms can enhance the accuracy of climate forecasts and provide tailored advisories for agriculture and disaster risk reduction. NLP can bridge the communication gap by translating complex climate data into local languages, making it accessible to rural communities. Big data analytics enables the integration of diverse datasets to generate comprehensive climate models and risk assessments. The paper also presents case studies from sub-Saharan Africa, demonstrating the practical implementation of AI in CIS, such as drought prediction, early warning systems, and agricultural advisories. These case studies highlight the potential of AI to improve the accuracy, timeliness, and relevance of climate information, particularly for vulnerable rural populations. The paper concludes with future directions, emphasizing the need for investment in infrastructure, capacity building, and policy frameworks to support the sustainable integration of AI in CIS. By leveraging AI, Africa can enhance its resilience to climate change and improve the livelihoods of its communities. Journal: LatIA Pages: 76 Volume: 1 Year: 2023 DOI: 10.62486/latia202576 Handle: RePEc:dbk:rlatia:v:1:y:2023:i::p:76:id:1062486latia202576 Template-Type: ReDIF-Article 1.0 Author-Name: Alqaraleh Muhyeeddin Author-Name-First: Alqaraleh Author-Name-Last: Muhyeeddin Author-Name: Salem Alzboon Mowafaq Author-Name-First: Salem Alzboon Author-Name-Last: Mowafaq Author-Name: Mohammad Subhi Al-Batah Author-Name-First: Mohammad Subhi Author-Name-Last: Al-Batah Author-Name: Abdel Wahed Mutaz Author-Name-First: Abdel Wahed Author-Name-Last: Mutaz Title: Advancing Medical Image Analysis: The Role of Adaptive Optimization Techniques in Enhancing COVID-19 Detection, Lung Infection, and Tumor Segmentation Abstract: Artificial intelligence (AI) holds significant potential to revolutionize healthcare by improving clinical practices and patient outcomes. This research explores the integration of AI in healthcare, focusing on methodologies such as machine learning, natural language processing, and computer vision, which enable the extraction of valuable insights from complex medical imaging and clinical data. Through a comprehensive literature review, the study highlights AI’s practical applications in diagnostics, treatment planning, and predicting patient outcomes. Additionally, ethical issues, data privacy, and legal frameworks are examined, emphasizing the importance of responsible AI usage in healthcare. The findings demonstrate AI’s ability to enhance diagnostic accuracy, streamline administrative tasks, and optimize resource allocation, leading to personalized treatments and more efficient healthcare management. However, challenges remain, including data quality, algorithm transparency, and ethical concerns, which must be addressed to ensure safe and effective AI deployment. Continued research, collaboration between healthcare professionals and AI experts, and the development of robust regulatory frameworks are essential for maximizing AI’s benefits while minimizing risks. This research underscores the transformative potential of AI in healthcare and stresses the need for a multidisciplinary approach to address the ethical and regulatory complexities involved in its widespread adoption Journal: LatIA Pages: 74 Volume: 2 Year: 2024 DOI: 10.62486/latia202474 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:74:id:1062486latia202474 Template-Type: ReDIF-Article 1.0 Author-Name: Khritish Swargiary Author-Name-First: Khritish Author-Name-Last: Swargiary Title: Enhancing Adaptive Learning Through Spectrum of Individuality Theory: A Neuroplasticity-Informed AI Approach to Dynamic Behavioral Modeling in Education Abstract: This study investigates the efficacy of integrating the Spectrum of Individuality Theory (SIT)—a dynamic, neuroplasticity-informed framework—into artificial intelligence (AI) systems for adaptive learning. Traditional AI models, rooted in static personality frameworks like the Five-Factor Model (FFM), often fail to capture real-time behavioral variability, limiting their adaptability. In a mixed-methods experiment, 120 undergraduate students were stratified into SIT-driven (n=60) and FFM-based (n=60) AI learning groups. The SIT system utilized real-time EEG and eye-tracking data to adjust content delivery, while the FFM system relied on fixed trait categorizations. Results demonstrated that the SIT group outperformed the FFM group in cognitive retention (mean post-test scores: 25.3 vs. 22.7; p < 0.01, Cohen’s d = 0.86) and exhibited progressive engagement improvements (Session 8 UES: 4.30 vs. 3.70; p < 0.001). Neurophysiological data revealed reduced stress biomarkers (theta/beta ratios: 3.15 vs. 3.75; p < 0.001), correlating with enhanced emotional regulation. However, ethical concerns, particularly data privacy (SIT: 4.10 vs. FFM: 3.20; d = 0.98), were heightened in the SIT group. These findings validate SIT’s potential to advance context-aware AI but underscore ethical risks tied to granular behavioral tracking. The study bridges psychological theory with AI design, advocating for interdisciplinary collaboration to balance adaptability with responsible innovation. Journal: LatIA Pages: 72 Volume: 3 Year: 2025 DOI: 10.62486/latia202572 Handle: RePEc:dbk:rlatia:v:3:y:2025:i::p:72:id:1062486latia202572 Template-Type: ReDIF-Article 1.0 Author-Name: Volodymyr Kozub Author-Name-First: Volodymyr Author-Name-Last: Kozub Title: Code optimization opportunities in the JavaScript ecosystem with Rust Abstract: This paper explores the potential of optimizing node.js applications by integrating rust. In particular, in processing cpu-intensive tasks where javascript faces performance limitations due to its single-threaded architecture. Rust's memory safety and parallelism model, which eliminates the need for a garbage collector, makes it an attractive alternative to traditional c/c++ modules for extending the capabilities of node.js. This study explores the performance gains achieved by integrating rust, both through native bindings and WebAssembly, demonstrating significant improvements in computational efficiency, especially in parallel processing scenarios. Rust's ability to efficiently handle computation-intensive workloads with work interception algorithms is emphasized as a key factor in overcoming javascript bottlenecks. The study includes a detailed performance evaluation that compares synchronous and asynchronous modules in node.js with rust implementations. Tests demonstrate how rust optimizations outperform javascript by up to ten times in certain computational tasks. The study also evaluates cross-compiled rust modules using WebAssembly in the browser environment, which once again illustrates the advantages of rust in providing near-native performance. The results emphasize the potential of rust to enhance node.js applications by making them more scalable, reliable, and efficient for high-performance web applications Journal: LatIA Pages: 68 Volume: 2 Year: 2024 DOI: 10.62486/latia202468 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:68:id:1062486latia202468 Template-Type: ReDIF-Article 1.0 Author-Name: Elisa Oliva Author-Name-First: Elisa Author-Name-Last: Oliva Author-Name: Mathias Díaz Author-Name-First: Mathias Author-Name-Last: Díaz Title: Exploration of regularities in bipartite graphs using GEOGEBRA software Abstract: A classroom proposal is presented to integrate contents of Graph Theory and Linear Algebra in complete bipartite graphs, linking adjacency and Laplacian matrices, the eigenvalues of graphs will be determined, applicable to connectivity concepts. Students will be given exploration activities working with GeoGebra software, starting from several particular cases, with table works and questionnaires to be completed, in order to determine patterns on the eigenvalues of adjacency and Laplacian matrices of complete bipartite graphs. The work with patterns will lead to the generalization process, to abstract properties from observation and experimentation on examples. This learning experience builds bridges between the concrete and the symbolic, and the student is initiated in research Journal: LatIA Pages: 51 Volume: 2 Year: 2024 DOI: 10.62486/latia202451 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:51:id:1062486latia202451 Template-Type: ReDIF-Article 1.0 Author-Name: Lipsary Águila Ramírez Author-Name-First: Lipsary Author-Name-Last: Águila Ramírez Title: Artificial Intelligence in Psychological Diagnosis and Intervention Abstract: The integration of artificial intelligence (AI) in the field of psychology is significantly transforming the diagnosis and intervention of mental disorders. Deep learning techniques enable the analysis of large volumes of data, such as neuroimages and electroencephalograms (EEG), to identify and diagnose psychiatric conditions with greater precision. These technologies also facilitate early detection of risk factors by analyzing data from social networks and electronic medical records, enabling personalized interventions. AI-based chatbots and mobile applications democratize access to psychological therapy, providing real-time support and improving the management of conditions such as anxiety and depression. Additionally, AI optimizes administrative tasks and enhances the training of new clinicians through learning platforms and virtual simulators, contributing to better preparedness and efficiency in the mental healthcare system. These innovations not only improve the quality of diagnosis and treatment but also enable more proactive and patient-centered care Journal: LatIA Pages: 26 Volume: 1 Year: 2024 DOI: 10.62486/latia202326 Handle: RePEc:dbk:rlatia:v:1:y:2024:i::p:26:id:1062486latia202326 Template-Type: ReDIF-Article 1.0 Author-Name: Diana Valencia Sinisterra Author-Name-First: Diana Author-Name-Last: Valencia Sinisterra Author-Name: Kelly Johana Barrientos Author-Name-First: Kelly Johana Author-Name-Last: Barrientos Author-Name: Maria Angelica Llanes Villota Author-Name-First: Maria Angelica Author-Name-Last: Llanes Villota Title: Benefits and challenges of artificial intelligence in the Colombian health system Abstract: This study explored the impact of artificial intelligence (AI) on the Colombian healthcare system, focusing on its potential to improve diagnosis, treatment, and resource management, the methodology included a literature review and case study analysis in rural and urban areas, findings revealed that AI can enhance the accuracy and speed of clinical decision-making, address the lack of specialist access in remote areas, and personalize medical treatments. However, significant challenges were also identified, such as insufficient technological infrastructure, the need for adequate health personnel training, and ethical and data protection concerns. It was concluded that to maximize the benefits of AI and minimize its risks, careful planning, adequate investments in infrastructure and continuous staff training, as well as robust ethical and legal regulation, are essential. Additionally, the importance of designing AI implementation policies that consider and address existing inequalities in access to healthcare services was emphasized Journal: LatIA Pages: 25 Volume: 1 Year: 2024 DOI: 10.62486/latia202325 Handle: RePEc:dbk:rlatia:v:1:y:2024:i::p:25:id:1062486latia202325 Template-Type: ReDIF-Article 1.0 Author-Name: Gabrielle González Someillán Author-Name-First: Gabrielle Author-Name-Last: González Someillán Title: E-government and Environmental Governance: Case Study Cuba Abstract: E-government has emerged as a key component in the evolution of public administration into the digital age. This paper examines the intersection between e-government, governance, and the environment. It explores the ways in which information and communication technologies (ICTs) can strengthen environmental governance and facilitate informed decision-making in environmental public policy. In addition, the case study Cuba, where the National Environmental Information System was developed to facilitate the collection, analysis and dissemination of environmental data in real time, with the objective of informing citizens and decision makers, is analyzed. This specific case illustrates how e-government can improve transparency, citizen participation and collaboration between governmental and non-governmental actors by promoting efficient, sustainable environmental initiatives with lasting impact Journal: LatIA Pages: 24 Volume: 1 Year: 2024 DOI: 10.62486/latia202324 Handle: RePEc:dbk:rlatia:v:1:y:2024:i::p:24:id:1062486latia202324 Template-Type: ReDIF-Article 1.0 Author-Name: Goh Ying Soon Author-Name-First: Goh Author-Name-Last: Ying Soon Author-Name: Nurul Ain Chua Binti Abdullah Author-Name-First: Nurul Ain Chua Author-Name-Last: Binti Abdullah Author-Name: Nurul Ajleaaç binti Abdul Rahman Author-Name-First: Nurul Ajleaaç Author-Name-Last: binti Abdul Rahman Author-Name: Zhang Suyan Author-Name-First: Zhang Author-Name-Last: Suyan Author-Name: Chen Yiming Author-Name-First: Chen Author-Name-Last: Yiming Title: Integrating AI Chatbots in ESL and CFL Instruction: Revolutionizing Language Learning with Artificial Intelligence Abstract: The integration of artificial intelligence (AI) in language teaching has emerged as a transformative approach, particularly in the realms of English as a Second Language (ESL) and Chinese as a Foreign Language (CFL). This article explores the potential of AI chatbots as effective tools for enhancing language acquisition. By examining the current landscape of AI in language education, we identify the unique benefits that chatbots bring to the learning process, including personalized interaction, immediate feedback, and continuous engagement. The article delves into the design and implementation of AI chatbot systems tailored for ESL and CFL contexts, highlighting their role in vocabulary development, grammar practice, and conversational skills. Furthermore, it addresses the challenges and limitations of using chatbots in language teaching, proposing strategies for overcoming these obstacles. Through case studies and empirical data, the article demonstrates how AI chatbots can be harnessed to create a dynamic and interactive learning environment that caters to the diverse needs of language learners. Ultimately, this work advocates for the thoughtful integration of AI chatbots to complement traditional teaching methods, thereby paving the way for more effective and accessible language education Journal: LatIA Pages: 23 Volume: 2 Year: 2024 DOI: 10.62486/latia202423 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:23:id:1062486latia202423 Template-Type: ReDIF-Article 1.0 Author-Name: Osmel Bell Águila Author-Name-First: Osmel Author-Name-Last: Bell Águila Title: The principle of transparency and Electronic Government in Cuba Abstract: The Electronic Government includes better ways of interaction between Government, companies and society through Information Technology and Communications. Access to visible, available and quality public information quickly and expeditiously improves the perception of the functioning of the institutions in pursuit of Cuba's efforts to advance in the digital transformation. The effectiveness of the principle of transparency from the constitutional mandate obliges the Public Administration to generate updated, reliable and accessible information according to the activity carried out. It is a prerequisite for good administration that involves citizens in decision-making processes as well as balancing their position vis-à-vis the State. It promotes good governance derived from the establishment and management of effective, efficient and accountable institutions. Journal: LatIA Pages: 22 Volume: 1 Year: 2024 DOI: 10.62486/latia202322 Handle: RePEc:dbk:rlatia:v:1:y:2024:i::p:22:id:1062486latia202322 Template-Type: ReDIF-Article 1.0 Author-Name: Esteban Rodríguez Torres Author-Name-First: Esteban Author-Name-Last: Rodríguez Torres Author-Name: Raúl Comas Rodríguez Author-Name-First: Raúl Author-Name-Last: Comas Rodríguez Author-Name: Edwin Tovar Briñez Author-Name-First: Edwin Author-Name-Last: Tovar Briñez Title: Use of AI to improve the teaching-learning process in children with special abilities Abstract: Through adaptive and assistive technologies, AI enables deep personalization of learning, as well as adjusting content and pacing based on each student's individual needs. These systems not only optimize the delivery of educational material, but also offer new forms of interaction and accessibility for students with physical, visual and hearing disabilities. The research was conducted with the purpose of exploring how artificial intelligence (AI) has revolutionized special education. The results indicate that the implementation of tools such as speech recognition, brain-computer interfaces and text-to-speech software significantly improves student autonomy and participation in the classroom. However, the data also highlights the importance of addressing ethical and accessibility issues, ensuring that these technological advances benefit all students equitably and without compromising their security or privacy. The inquiry concluded that, while AI presents transformative opportunities for special education, its integration requires thoughtful approaches that prioritize inclusion and equity. Journal: LatIA Pages: 21 Volume: 1 Year: 2023 DOI: 10.62486/latia202321 Handle: RePEc:dbk:rlatia:v:1:y:2023:i::p:21:id:1062486latia202321 Template-Type: ReDIF-Article 1.0 Author-Name: Yasniel Sánchez Suárez Author-Name-First: Yasniel Author-Name-Last: Sánchez Suárez Author-Name: Abdulmuneem Mohammed Alawi Author-Name-First: Abdulmuneem Mohammed Author-Name-Last: Alawi Author-Name: Sonia Emilia Leyva Ricardo Author-Name-First: Sonia Emilia Author-Name-Last: Leyva Ricardo Title: Hospital processes optimization based on artificial intelligence Abstract: Artificial intelligence is revolutionizing hospital management by optimizing critical processes to improve operational efficiency. The automation of administrative tasks allows reducing errors and streamlining the flow of patients and work, which translates into lower costs and better use of hospital resources. The objective is to analyze research related to the optimization of hospital processes based on artificial intelligence. The research paradigm was qualitative-quantitative, the focus of this research was based on a bibliometric analysis, which was complemented with a documentary review in databases of high international and Latin American impact in the period from 2010 to 2024. The trend of the research was towards an increase, where research in the area of medicine and computer sciences predominated. A keyword co-occurrence and citation analysis were carried out to identify possible lines of research. It was identified that monitoring and predictive analytics technologies based on artificial intelligence enable proactive management of patients' health, preventing complications and optimizing resource allocation. These tools also facilitate the personalization of care, adjusting treatments according to the specific needs of each patient. The implementation of artificial intelligence in hospital processes is a crucial tool for improving operational efficiency and reducing costs through the automation of administrative tasks, resulting in a smoother and more effective operation Journal: LatIA Pages: 19 Volume: 1 Year: 2023 DOI: 10.62486/latia202319 Handle: RePEc:dbk:rlatia:v:1:y:2023:i::p:19:id:1062486latia202319 Template-Type: ReDIF-Article 1.0 Author-Name: Mayra Alejandra Gaviria Alvarado Author-Name-First: Mayra Alejandra Author-Name-Last: Gaviria Alvarado Title: IA´ Tools for the development of investigative skills Abstract: This article explores how the artificial intelligence (IA) it is transforming the education in natural sciences by means of strategies pedagogic innovators. The IA allows the learning personalization, adjusting the content and the rhythm to the individual necessities of the students, what improves the understanding and retention of complex concepts significantly. Also, the use of simulations and virtual models believe interactive and visual learning environments, enriching the educational experience. These tools also foment the development of critical and creative skills, promoting a more active and collaborative approach in the resolution of scientific problems. On the whole, these strategies not only improve the effectiveness of learning, but rather they also prepare the students to face the challenges of the XXI century with a solid base in science and technology. Journal: LatIA Pages: 17 Volume: 1 Year: 2023 DOI: 10.62486/latia202317 Handle: RePEc:dbk:rlatia:v:1:y:2023:i::p:17:id:1062486latia202317 Template-Type: ReDIF-Article 1.0 Author-Name: Chris Nathalie Aristizábal Valbuena Author-Name-First: Chris Nathalie Author-Name-Last: Aristizábal Valbuena Title: Tools for AI-driven Development of Research Competencies Abstract: Artificial intelligence (AI) tools are transforming scientific research by enabling the analysis of large volumes of data and the generation of new hypotheses and theoretical models. In 2024, there is an expected proliferation of smaller and more efficient AI models that can run on accessible hardware, facilitating the democratization of access to this technology. This will allow academic institutions and small businesses to implement and optimize AI models without the need for expensive infrastructures. The ability of AI to handle and analyze large datasets has been particularly useful in fields such as biomedicine, where it has accelerated the discovery of new treatments and therapies. Furthermore, the integration of AI models into local devices addresses critical concerns regarding data privacy and security, enabling the secure processing of sensitive information. These tools not only enhance the efficiency and accuracy of research but also foster innovation by expanding the frontiers of knowledge in diverse disciplines. Journal: LatIA Pages: 16 Volume: 1 Year: 2023 DOI: 10.62486/latia202316 Handle: RePEc:dbk:rlatia:v:1:y:2023:i::p:16:id:1062486latia202316 Template-Type: ReDIF-Article 1.0 Author-Name: Brian Andreé Meneses Claudio Author-Name-First: Brian Andreé Author-Name-Last: Meneses Claudio Title: Development of an Image Recognition System Based on Neural Networks for the Classification of Plant Species in the Amazon Rainforest, Peru, 2024 Abstract: Introduction: The recognition and classification of plant species in the Amazon Rainforest is crucial for biodiversity conservation and ecological research. This study presents the development of an image recognition system based on neural networks for the classification of plant species in the Amazon Rainforest, Peru, 2024. Objective: Create an efficient model that can identify and classify various plant species from images, thus improving current methods of cataloging and studying Amazonian flora. Methodology: The methodology includes collecting a large dataset of plant images, followed by rigorous preprocessing to normalize and augment the data. A convolutional neural network (CNN) was designed and trained using advanced machine learning techniques, and its performance was evaluated using metrics such as precision, recall and F1-score. Results: The results show that the developed model achieves an accuracy of 92%, surpassing traditional methods and some previous models in the literature. This high precision suggests that the system can be a valuable tool for researchers and conservationists in the Amazon Rainforest. Conclusion: This study demonstrates the effectiveness of neural networks in the classification of plant species and highlights their potential to contribute significantly to the conservation and study of biodiversity in the Amazon region. Journal: LatIA Pages: 15 Volume: 2 Year: 2024 DOI: 10.62486/latia202415 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:15:id:1062486latia202415 Template-Type: ReDIF-Article 1.0 Author-Name: Brian Andreé Meneses Claudio Author-Name-First: Brian Andreé Author-Name-Last: Meneses Claudio Title: Application of Data Mining for the Prediction of Academic Performance in University Engineering Students at the National Autonomous University of Mexico, 2022 Abstract: Introduction: In the present study, data mining is applied to predict the academic performance of university Engineering students at the National Autonomous University of Mexico during the year 2022. The introduction addresses the importance of understanding and anticipating academic performance as a means to implement more effective and personalized educational strategies. Objective: Develop a predictive model capable of identifying determining factors in the academic performance of students and predicting their future performance. Methodology: The methodology used includes the collection of academic and sociodemographic data from students, as well as the use of data mining techniques such as cluster analysis, decision trees and neural networks. The data was preprocessed to ensure quality and divided into training and test sets to validate the predictive model. Results: The results show that the developed model has a high accuracy in predicting academic performance, identifying key variables such as class attendance, participation in extracurricular activities and performance in previous exams. These variables were essential to build a robust and reliable model. Conclusion: the application of data mining has proven to be an effective tool to predict the academic performance of engineering students. This model not only provides a valuable tool for administrators and educators in decision making, but also opens new avenues for future research in the field of personalized education and improving academic performance. Journal: LatIA Pages: 14 Volume: 2 Year: 2024 DOI: 10.62486/latia202414 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:14:id:1062486latia202414 Template-Type: ReDIF-Article 1.0 Author-Name: Brian Andreé Meneses Claudio Author-Name-First: Brian Andreé Author-Name-Last: Meneses Claudio Title: Implementation of a Machine Learning Algorithm for the Detection of Cardiovascular Diseases in Adult Patients in Public Hospitals of Lima, Peru, 2023 Abstract: Introduction: Cardiovascular diseases are one of the leading causes of death worldwide. In Lima, Peru, public hospitals face significant challenges in the early and accurate diagnosis of these diseases due to limited resources and trained personnel. The implementation of machine learning (ML) algorithms offers a promising solution to improve the detection and management of cardiovascular diseases. Objective: The objective of this study is to implement and evaluate a machine learning algorithm for the detection of cardiovascular diseases in adult patients attended to in public hospitals of Lima, Peru, in the year 2023. Methodology: Medical data from 10,000 adult patients were collected, including medical histories, laboratory test results, and electrocardiogram (ECG) records from various public hospitals in Lima. The data were cleaned and normalized to ensure their quality and consistency. A classification algorithm based on deep neural networks was selected. The model was trained using 80% of the data and validated with the remaining 20%. Metrics of accuracy, sensitivity, and specificity were used to evaluate the model's performance. Results: The model achieved an accuracy of 92% in detecting cardiovascular diseases. The sensitivity was 89%, indicating that the model correctly identified 89% of positive cases. The specificity reached 94%, meaning the model correctly identified 94% of negative cases. Conclusion: The implementation of the machine learning algorithm proved effective for the detection of cardiovascular diseases in adult patients in public hospitals in Lima, Peru. With high accuracy, sensitivity, and specificity, this approach has the potential to significantly improve medical care and patient outcomes in resource-limited settings. Integrating this system into clinical processes is recommended to maximize its positive impact on public health. Journal: LatIA Pages: 13 Volume: 1 Year: 2023 DOI: 10.62486/latia202313 Handle: RePEc:dbk:rlatia:v:1:y:2023:i::p:13:id:1062486latia202313 Template-Type: ReDIF-Article 1.0 Author-Name: Guillermo Alfredo Jiménez Pérez Author-Name-First: Guillermo Alfredo Author-Name-Last: Jiménez Pérez Author-Name: José Manuel Hernández de la Cruz Author-Name-First: José Manuel Author-Name-Last: Hernández de la Cruz Title: Applications of Artificial Intelligence in Contemporary Sociology Journal: LatIA Pages: 12 Volume: 1 Year: 2024 DOI: 10.62486/latia202412 Handle: RePEc:dbk:rlatia:v:1:y:2024:i::p:12:id:1062486latia202412 Template-Type: ReDIF-Article 1.0 Author-Name: Marena de la C. Hernández-Lugo Author-Name-First: Marena de la C. Author-Name-Last: Hernández-Lugo Title: Artificial Intelligence as a tool for analysis in Social Sciences: methods and applications Abstract: Artificial Intelligence (AI) transforms the social sciences by providing new methodologies and tools for data analysis. This article was based on a comprehensive literature review that analyzed the role of artificial intelligence as an analytical tool in the social sciences. It was observed that the ability of AI to process text, images, and audio in an integrated manner allows researchers to address complex problems with greater accuracy and efficiency. Multimodal tools facilitate the analysis of large volumes of data, the interpretation of financial documents, and the evaluation of facial expressions, which improves decision making in social research. Specialized databases offer access to a wide range of AI tools that optimize tasks such as literature review, data collection and visualization of results. In addition, safety and ethics in the use of AI are key priorities, with the creation of alliances and regulatory frameworks that ensure responsible and safe development of these technologies. Initiatives such as the AI Safety Alliance and the European Union's Artificial Intelligence Act set global standards for the ethical and safe use of AI, safeguarding both individuals and society at large. Journal: LatIA Pages: 11 Volume: 2 Year: 2024 DOI: 10.62486/latia202411 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:11:id:1062486latia202411 Template-Type: ReDIF-Article 1.0 Author-Name: Deepa Sonal Author-Name-First: Deepa Author-Name-Last: Sonal Author-Name: Khushboo Mishra Author-Name-First: Khushboo Author-Name-Last: Mishra Author-Name: Alimul Haque Author-Name-First: Alimul Author-Name-Last: Haque Author-Name: Faizan Uddin Author-Name-First: Faizan Author-Name-Last: Uddin Title: A Practical Approach to Increase Crop Production Using Wireless Sensor Technology Abstract: Introduction; The global demand for food production continues to rise due to the growing population and changing consumption patterns. Traditional agricultural practices often fail to meet this demand efficiently, leading to the exploration of innovative technologies to enhance crop productivity. Wireless sensor technology (WST) has emerged as a promising tool to monitor and optimize agricultural practices, providing real-time data on various environmental parameters crucial for crop growth. Objective; This study aims to evaluate the effectiveness of wireless sensor technology in increasing crop production. By integrating WST into conventional farming practices, we seek to optimize resource usage, reduce waste, and improve crop yields. Methods; We have proposed an IoT-enabled soil nutrient classification and crop recommendation model to recommend crops. By incorporating machine learning, artificial intelligence (AI), the cloud, sensors, and other automated equipment into the decision-assisting system, farmers will be able to take decisive actions without relying entirely on regional farming offices. Results; The analysis showed that the plot using wireless sensor technology exhibited a significant increase in crop yield compared to the traditional plot. Soil moisture levels were maintained within optimal ranges, leading to better water usage efficiency. Additionally, the automated system adjusted fertilizer application based on real-time soil nutrient data, resulting in improved plant health and productivity. Conclusions; The integration of wireless sensor technology in agriculture presents a practical and effective approach to increase crop production. This technology enables precise monitoring and management of critical growth parameters, resulting in higher yields and more efficient resource use. Adopting WST can significantly contribute to meeting the global food demand while promoting sustainable farming practices. Journal: LatIA Pages: 10 Volume: 2 Year: 2024 DOI: 10.62486/latia202410 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:10:id:1062486latia202410 Template-Type: ReDIF-Article 1.0 Author-Name: Amit Kumar Dinkar Author-Name-First: Amit Author-Name-Last: Kumar Dinkar Author-Name: Md Alimul Haque Author-Name-First: Md Author-Name-Last: Alimul Haque Author-Name: Ajay Kumar Choudhary Author-Name-First: Ajay Author-Name-Last: Kumar Choudhary Title: Enhancing IoT Data Analysis with Machine Learning: A Comprehensive Overview Abstract: Machine learning techniques are essential for processing the vast volume of IoT data efficiently, improving performance, and managing IoT applications effectively. Machine learning algorithms play a crucial role in detecting malicious attacks and anomalies in real-time IoT data analysis, thereby enhancing the security of IoT devices. The integration of big data analytics methods with machine learning techniques can further enhance IoT data analysis, improving the performance of IoT applications and overcoming related challenges. Real-time data collection using sensors like DHT11 and Gas level sensors, coupled with machine learning algorithms, enables efficient analysis of IoT data, aiding in the identification of anomalies and attacks. The comprehensive overview of enhancing IoT data analysis with machine learning provides insights for future research, including exploring advanced machine learning algorithms and optimizing data preprocessing techniques to enhance IoT data analysis capabilities. Journal: LatIA Pages: 9 Volume: 2 Year: 2024 DOI: 10.62486/latia20249 Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:9:id:1062486latia20249 Template-Type: ReDIF-Article 1.0 Author-Name: Dalía Rodríguez Cairo Author-Name-First: Dalía Author-Name-Last: Rodríguez Cairo Author-Name: Yisel Ramírez Echavarría Author-Name-First: Yisel Author-Name-Last: Ramírez Echavarría Title: Smart Tutors: improving the quality of higher education through AI Abstract: Intelligent Tutoring Systems (ITS) are revolutionizing higher education through artificial intelligence (AI), offering personalized and adaptive learning experiences. In this sense, the study aimed to analyze the impact of ITS on the quality of higher education based on AI. For this purpose, a bibliographic review was carried out that explored the main trends around the current topic. Among the findings, it was recognized that ITS use advanced algorithms, such as data mining and Bayesian networks, which allow educational content to be dynamically adjusted to meet the individual needs of students, improving learning effectiveness and keeping students more engaged and motivated. . This integration was shown to significantly improve knowledge retention and reduce dropout rates through real-time, personalized interventions. In addition, a focus on the sustainability and scalability of these systems was evident, integrating sustainable design principles. These developments made it possible to ensure that intelligent tutors can be widely implemented in various educational institutions without losing their effectiveness, thus improving the quality of higher education in a sustainable and expansive manner. Journal: LatIA Pages: 8 Volume: 1 Year: 2023 DOI: 10.62486/latia20238 Handle: RePEc:dbk:rlatia:v:1:y:2023:i::p:8:id:1062486latia20238 Template-Type: ReDIF-Article 1.0 Author-Name: Ginna Tovar Cardozo Author-Name-First: Ginna Author-Name-Last: Tovar Cardozo Title: Approach to global regulations around AI Abstract: Regulation of artificial intelligence (AI) varies significantly globally, reflecting different approaches and priorities. These trends underscore the need to balance technological innovation with rights protection and security. The purpose of this article is to examine the main trends and challenges in the regulation of AI, with a comprehensive view of how the governments of the European Union, China and the United States address this complex and crucial issue due to their involvement as great government powers. . at the economic and social pyolytic level. The study was based on a bibliographic review whose search was intentional towards publications from journals indexed in electronic databases such as Scopus, Web of Science and Google Scholar. The findings demonstrate that the European Union has established a comprehensive framework with the AI ​​Law, imposing specific restrictions and requiring transparency to establish a global standard similar to the GDPR. China, for its part, is transitioning from a fragmented approach to more unified regulation. The introduction of a holistic AI law and the creation of a national AI office indicate an effort to consolidate its regulatory framework, improving consistency and efficiency in risk management. In the United States, regulation remains gradual and decentralized, with initiatives at both the federal and state levels. Although efforts like the AI ​​Bill of Rights are significant, the lack of a unified framework poses coherence and applicability challenges. Journal: LatIA Pages: 7 Volume: 1 Year: 2023 DOI: 10.62486/latia20237 Handle: RePEc:dbk:rlatia:v:1:y:2023:i::p:7:id:1062486latia20237 Template-Type: ReDIF-Article 1.0 Author-Name: Yasniel Sánchez Suárez Author-Name-First: Yasniel Author-Name-Last: Sánchez Suárez Author-Name: Naylet Sangroni Laguardia Author-Name-First: Naylet Author-Name-Last: Sangroni Laguardia Title: Trends in research on the implementation of artificial intelligence in supply chain management Abstract: Supply chains play a critical role in the functioning of the global economy. The integration of information systems and emerging technologies, such as artificial intelligence and the Internet of Things, improves visibility, decision making and responsiveness throughout the supply chain. The objective of the research is to analyze research trends on the implementation of artificial intelligence to supply chain management. The research paradigm was quantitative, based on a descriptive, retrospective and bibliometric study, in the SCOPUS database, during the period from 2019 to 2024, without language restriction. The trend of research was positive and towards increase with a maximum peak in the year 2023 of 214 researches, research articles in the area of computer science predominated. The top producing country was the United Kingdom with 127 research papers and four lines of scientific research were identified around the implementation of artificial intelligence in supply chain management. In the business environment, the ability of supply chains to adapt to change is crucial; their management includes planning and coordination, logistics process management and customer relationship management. The integration of information systems and emerging technologies, such as artificial intelligence, has had a great impact on the improvement of all the processes involved in management. Journal: LatIA Pages: 6 Volume: 1 Year: 2023 DOI: 10.62486/latia20236 Handle: RePEc:dbk:rlatia:v:1:y:2023:i::p:6:id:1062486latia20236 Template-Type: ReDIF-Article 1.0 Author-Name: Alfredo Javier Pérez Gamboa Author-Name-First: Alfredo Javier Author-Name-Last: Pérez Gamboa Author-Name: Diego D. Díaz-Guerra Author-Name-First: Diego D. Author-Name-Last: Díaz-Guerra Title: Artificial Intelligence for the development of qualitative studies Abstract: The integration of Artificial Intelligence (AI) is revolutionizing qualitative research by optimizing data collection and analysis. Tools such as machine learning and natural language processing enable the analysis of large volumes of information with precision and speed, facilitating the identification of patterns and trends. The adoption of virtual research methods, such as online focus groups and video interviews, has overcome geographical barriers, enabling the participation of diverse and representative samples, in addition to being more cost-effective and allowing real-time data acquisition. The incorporation of advanced biometric techniques, such as eye tracking, facial expression analysis, and neuroimaging, provides a more holistic and accurate understanding of consumers' emotional and subconscious responses. These innovations allow companies to adapt their marketing strategies and product designs more effectively, enhancing personalization and emotional resonance of the experiences offered. Journal: LatIA Pages: 4 Volume: 1 Year: 2023 DOI: 10.62486/latia20234 Handle: RePEc:dbk:rlatia:v:1:y:2023:i::p:4:id:1062486latia20234 Template-Type: ReDIF-Article 1.0 Author-Name: Ana María Chavez-Cano Author-Name-First: Ana María Author-Name-Last: Chavez-Cano Title: Artificial Intelligence Applied to Telemedicine: opportunities for healthcare delivery in rural areas Abstract: The integration of artificial intelligence (AI) in telemedicine is revolutionizing the provision of healthcare services, especially in rural areas. These technologies enable the overcoming of geographical and resource barriers, facilitating precise diagnoses, personalized recommendations, and continuous monitoring through portable devices. AI systems analyze patient data and suggest the most appropriate care options based on their health profile, thus optimizing the efficiency of the healthcare system and improving patient satisfaction. In addition, the automation of administrative tasks through AI frees up time for healthcare professionals to concentrate on direct care. To ensure trust and effectiveness in these technologies, it is essential to implement clinically validated and unbiased algorithms, while fostering transparency and collaboration among developers, healthcare professionals, and regulators. Therefore, AI applied to telemedicine offers a revolutionary opportunity to improve the accessibility and quality of healthcare in rural areas by promoting more equitable and efficient care. Journal: LatIA Pages: 3 Volume: 1 Year: 2023 DOI: 10.62486/latia20233 Handle: RePEc:dbk:rlatia:v:1:y:2023:i::p:3:id:1062486latia20233 Template-Type: ReDIF-Article 1.0 Author-Name: Carlos Alberto Gómez Cano Author-Name-First: Carlos Alberto Author-Name-Last: Gómez Cano Author-Name: Ana Lucía Colala Troya Author-Name-First: Ana Lucía Author-Name-Last: Colala Troya Title: Artificial Intelligence applied to teaching and learning processes Abstract: Artificial Intelligence (AI) transforms teaching and learning processes by personalizing educational content according to individual students' needs, thus enhancing their performance and motivation. Tools like SlidesAI and Tome facilitate the creation of efficient educational resources, although the quality and privacy of generated data need to be addressed. AI also enables interactive and immersive learning environments, such as simulations and educational games, that adapt in real-time to students' actions. These environments provide richer and more practical experiences. Additionally, the creation of multilingual videos with avatars enhances accessibility and customization of learning. However, ensuring equitable access to these technologies is crucial to avoid educational inequalities. As demonstrated, AI offers multiple benefits for education but requires careful implementation to maximize its advantages and mitigate potential risks. Journal: LatIA Pages: 2 Volume: 1 Year: 2023 DOI: 10.62486/latia20232 Handle: RePEc:dbk:rlatia:v:1:y:2023:i::p:2:id:1062486latia20232 Template-Type: ReDIF-Article 1.0 Author-Name: Verenice Sánchez Castillo Author-Name-First: Verenice Author-Name-Last: Sánchez Castillo Title: Analysis of the scientific production on the implementation of artificial intelligence in precision agriculture Abstract: The implementation of artificial intelligence is having a transformative impact on precision agriculture by optimizing agricultural resources and minimizing environmental impact, with a focus on sustainable development. The objective of the research is to analyze the scientific production on the implementation of artificial intelligence in precision agriculture. The research was conducted under the quantitative paradigm, using a descriptive and retrospective approach, and its implementation was carried out through a bibliometric study. It was conducted in SCOPUS database in the period 2014 - 2024 without language restriction. The behavior of the research was positive with a maximum peak of 112 researches where research articles in the area of computer science predominated. The most productive country was India with 79 research papers, while the most productive affiliation with 18 research papers was the University of Florida in the United States. Four lines of research and the periods with the highest number of citations in the subject were identified, where it was evidenced that the greatest boom was from 2019. Precision agriculture is an agricultural management tool that integrates a group of advanced technologies such as global positioning systems, geographic information systems, remote sensors, drones, internet of things and artificial intelligence, with an impact on optimizing agricultural resources and minimizing environmental impact in terms of territorial development and the fulfillment of sustainable development objectives. Journal: LatIA Pages: 1 Volume: 1 Year: 2023 DOI: 10.62486/latia20231 Handle: RePEc:dbk:rlatia:v:1:y:2023:i::p:1:id:1062486latia20231