Soft Computing and Artificial Intelligence: A Fuzzy and Neutrosophic Perspective on the Real World

Abstract

This book provides an in-depth and interdisciplinary exploration of Soft Computing and Artificial Intelligence (AI) with a particular focus on fuzzy logic and neutrosophic theory as powerful tools for modeling and solving real-world problems characterized by uncertainty, imprecision, vagueness, and incomplete information. Unlike traditional hard computing approaches, soft computing techniques offer flexible, human-like reasoning capabilities that are better suited to complex and dynamic environments. The book systematically examines the theoretical foundations and practical applications of fuzzy systems, intuitionistic fuzzy sets, neutrosophic sets, and hybrid AI models, demonstrating how these approaches enhance decision-making, pattern recognition, optimization, and intelligent control systems. A key contribution of this book lies in its emphasis on real-world problem solving, where uncertainty and indeterminacy are inherent. Through conceptual models, methodological frameworks, and application-oriented discussions, the book illustrates how fuzzy and neutrosophic perspectives can improve robustness, interpretability, and adaptability in intelligent systems across diverse domains. By bridging mathematical theory with practical implementation, this book offers novel insights into the role of soft computing in advancing intelligent systems. It serves as a valuable reference for researchers and practitioners seeking innovative solutions beyond conventional AI methodologies. This book is intended for researchers, scholars, academicians, practitioners, engineers, data scientists, instructors, and graduate students working in artificial intelligence, computational intelligence, decision sciences, and applied mathematics.