Ali I. Optimization and Computing using Intelligent Data-Driven Approaches..2025
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- Textbook in PDF format This book comprehensively discusses nature‑inspired algorithms, deep learning methods, applications of mathematical programming, and Artificial Intelligence techniques. It further covers important topics such as the use of Machine Learning and the Internet of Things and multi‑objective optimization under Fermatean hesitant fuzzy and uncertain environment. Data Science has risen as a comprehensive field that utilizes statistical methods, data analysis, and related techniques to comprehend and scrutinize phenomena through data. Sophisticated analytics techniques, inclusive of Machine Learning models, are utilized to derive actionable intelligence or profound understanding from data. This procedure of converting raw data into significant insights is recognized as data‑driven decision‑making (DDDM). DDDM is a methodology that entails gathering data in line with a firm’s key performance indicators (KPIs) and converting this data into insights that can be acted upon. During this procedure, business intelligence (BI) reporting instruments are frequently employed to streamline the process of data collection and visualization. These tools democratize data analytics, making it accessible to individuals without extensive technical expertise. DDDM is a concept within the realm of decision‑making that encompasses a broad spectrum of activities. These activities include the collection of data, the extraction of patterns and facts from this data, and the application of these insights to decision‑making processes. DDDM is characterized by an organizational decision‑making process that is grounded in empirical data. This book: Addresses solving practical problems such as supply chain management, smart manufacturing, and healthcare analytics using intelligent computing and discusses solving the fuzzy inference system in ant colony optimization for traveling salesman problem. Presents an overview of artificial intelligence (AI) and explainable AI decision‑making (XAIDM) and illustrates a data‑driven optimization concept for modeling environmental and economic sustainability. Discusses machine learning‑based multi‑objective optimization technique for load balancing in integrated fog‑cloud environment. Explains the use of heuristics and metaheuristics in supply chain networks and the use of fuzzy optimization in sustainable development goals. Discusses sustainable transit of hazardous waste, green fractional transportation system, perishable inventory, M‑estimation of functional regression operator, and intuitionistic fuzzy sets applications. The text is primarily written for graduate students and academic researchers in diverse fields, including operations research, mathematics, statistics, Computer Science, information and communication technology, and industrial engineering
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