Superintelligence Series Volume 1: Artificial Intelligence for Operational and Predictive Optimization

Authors

Daniel Román-Acosta
Plataforma de Acción, Gestión e Investigación Social S.A.S. (PLAGCIS). Colombia
https://orcid.org/0000-0002-4300-9174
Guillermo Alejandro Zaragoza Alvarado
Universidad Virtual del Estado de Guanajuato Guanajuato. México
https://orcid.org/0009-0006-5466-7486

Keywords:

Artificial Intelligence, Operational Optimization, Machine Learning, Neural Networks, Predictive Systems

Synopsis

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.

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Published

January 1, 2024

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Details about this monograph

ISBN-13 (15)

978-9915-9851-1-4

How to Cite

1.
Román-Acosta D, Zaragoza Alvarado GA, editors. Superintelligence Series Volume 1: Artificial Intelligence for Operational and Predictive Optimization [Internet]. AG Editor; 2024 [cited 2025 Dec. 29]. Available from: https://books.southam.pub/index.php/books/catalog/book/978-9915-9851-1-4