Artificial Intelligence for Operational and Predictive Optimization

Edited by Daniel Román-Acosta and Guillermo Alejandro Zaragoza Alvarado

in Superintelligence Series from AG Editor (Uruguay)

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 (search for similar items in EconPapers)
Date: 2024
ISBN: 978-9915-9851-1-4
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:siseri:2024v1

DOI: 10.62486/978-9915-9851-1-4

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