Template-Type: ReDIF-Book 1.0
Editor-Name: Daniel Román-Acosta
Editor-Name: Guillermo Alejandro Zaragoza Alvarado
Title: Artificial Intelligence for Operational and Predictive Optimization
Provider-Name: AG Editor
Abstract: This volume gathers a set of studies analyzing the role of Artificial Intelligence (AI) in operational and predictive optimization across multiple industrial, technological, and social domains. Through research on smart logistics, recurrent neural networks for predictive maintenance, AI-assisted structural design, automated clinical processes, and applications in dentistry, it demonstrates how intelligent technologies are redefining management, analysis, and decision-making strategies. The chapters reveal the convergence of deep learning models, genetic algorithms, expert systems, and hybrid architectures in real-world environments. Beyond technical innovation, the book emphasizes the importance of ethical and sustainable AI adoption aimed at efficiency, resilience, and human development. With an interdisciplinary and applied approach, Artificial Intelligence for Operational and Predictive Optimization serves as a comprehensive reference for researchers, engineers, policy-makers, and academics seeking to understand how AI is transforming the logic of optimization, prediction, and strategic decision-making in the twenty-first century.
Keywords: Artificial Intelligence, Operational Optimization, Machine Learning, Neural Networks, Predictive Systems
ISBN: 978-9915-9851-1-4
DOI: 10.62486/978-9915-9851-1-4
Year: 2024
File-URL: https://doi.org/10.62486/978-9915-9851-1-4
File-Format: text/html
Handle: RePEc:dbk:siseri:2024v1