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Название: Optimization and Computing using Intelligent Data-Driven Approaches for Decision-Making: Artificial Intelligence Applications
Автор: Irfan Ali, Umar Muhammad Modibbo, Asaju La’aro Bolaji, Harish Garg
Издательство: CRC Press
Год: 2025
Страниц: 228
Язык: английский
Формат: pdf (true), epub
Размер: 13.0 MB
This book comprehensively discusses nature-inspired algorithms, Deep Learning methods, applications of mathematical programming and Artificial Intelligence techniques. It will further cover important topic such as linking green supply chain management practices with competitiveness, industry 4.0, and social responsibility. In today’s hyper‑connected digital landscape, the demand for seamless and efficient computing resources has skyrocketed, driven by the proliferation of Internet of Things (IoT) devices, edge computing, and data‑intensive applications. The convergence of fog and cloud computing has emerged as a promising solution to meet these escalating computational requirements. Fog computing, characterized by its proximity to edge devices, brings computation and data storage closer to the point of data generation, reducing latency and improving real‑time processing. Machine Learning techniques, particularly Deep Learning and Reinforcement Learning, have demonstrated remarkable capabilities in handling complex and dynamic scenarios. When combined with multi‑objective optimization approaches, they can significantly enhance load balancing in integrated fog‑cloud environments. This integration enables decision‑making processes that consider various objectives simultaneously, such as minimizing latency, maximizing energy efficiency, and ensuring resource availability.
Автор: Irfan Ali, Umar Muhammad Modibbo, Asaju La’aro Bolaji, Harish Garg
Издательство: CRC Press
Год: 2025
Страниц: 228
Язык: английский
Формат: pdf (true), epub
Размер: 13.0 MB
This book comprehensively discusses nature-inspired algorithms, Deep Learning methods, applications of mathematical programming and Artificial Intelligence techniques. It will further cover important topic such as linking green supply chain management practices with competitiveness, industry 4.0, and social responsibility. In today’s hyper‑connected digital landscape, the demand for seamless and efficient computing resources has skyrocketed, driven by the proliferation of Internet of Things (IoT) devices, edge computing, and data‑intensive applications. The convergence of fog and cloud computing has emerged as a promising solution to meet these escalating computational requirements. Fog computing, characterized by its proximity to edge devices, brings computation and data storage closer to the point of data generation, reducing latency and improving real‑time processing. Machine Learning techniques, particularly Deep Learning and Reinforcement Learning, have demonstrated remarkable capabilities in handling complex and dynamic scenarios. When combined with multi‑objective optimization approaches, they can significantly enhance load balancing in integrated fog‑cloud environments. This integration enables decision‑making processes that consider various objectives simultaneously, such as minimizing latency, maximizing energy efficiency, and ensuring resource availability.