Intelligent Data-Driven Systems and Artificial Intelligence- Fuzzy Sets and Triangular Norms

The book focuses on decision-aided intelligent systems, showing readers how fuzzy sets and t-norms enhance decision-making amidst uncertainty and incomplete information. It further presents a decision support model for medical diagnosis and treatment planning and evaluation of smart mega cities under a Pythagorean fuzzy environment. This book aims to serve as a comprehensive resource that equips readers with the knowledge and practical skills needed to navigate the intricacies of fuzzy set theory, t-norms, and their integration into decision-aided intelligent systems. It provides a comprehensive understanding of aggregation operators and their role in data fusion, risk analysis, and expert opinion aggregation. New aggregation operators, entropy measures, t-norm, and t-conorm structures are developed across multiple fuzzy set extensions to better model uncertainty and hesitation in decision-making. A wide range of real-world applications, including, tourism planning, smart cities, urban mobility, water security, smart campus automation, energy facility siting, and firefighting helicopter selection, are addressed using advanced multi-criteria decision making methods. The chapters collectively emphasize sustainable, data-driven, and uncertainty-aware decision support, contributing solutions in areas such as environmental protection, resource optimization, public services, and technological infrastructure. Innovative techniques, such as Lambert W–based aggregation operators, Choquet integral–based entropy, confidence-level aggregation, and fuzzy–machine learning hybrid models, improve the representation of interaction, ambiguity, and complexity in multi-criteria decision problems. The text is primarily written for senior undergraduates, graduate students, and academic…