Optimization and Computing using Intelligent Data-Driven Approaches for Decision-Making: Artificial Intelligence ApplicationsКНИГИ » ПРОГРАММИНГ
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. However, the integration of fog and cloud computing also poses new challenges, and one of the most critical ones is load balancing.
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.
This book:
Addresses solving practical problems such as supply chain management, take-off, and healthcare analytics using intelligent computing. Presents a comparative analysis of machine learning algorithms for power consumption prediction. Discusses a machine learning-based multi-objective optimization technique for load balancing in an integrated fog cloud environment. Illustrates a data-driven optimization concept for modeling environmental and economic sustainability. Explains the use of heuristics and metaheuristics in supply chain networks and the use of fuzzy optimization in sustainable development goals.
The text is primarily written for graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, mathematics and statistics, Computer Science and engineering.
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Optimization and Computing using Intelligent Data-Driven Approaches for Decision-Making: Artificial Intelligence Applications 
