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Full Description
This book explains the foundational principles of fuzzy algebra, including fuzzy sets, fuzzy operations, and fuzzy relations. It shows how fuzzy algebra can be applied in artificial intelligence, particularly in areas such as decision-making, pattern recognition, and machine learning, where uncertainty and imprecision are common. It demonstrates the use of fuzzy algebra in engineering disciplines, such as control systems, cybersecurity, signal processing, and robotics, where fuzzy logic can be used to model and solve complex and real-world problems. This makes the book valuable not just for process engineers, but also for researchers in multiple domains.
This book is designed to familiarize data science, machine learning and other engineering professionals, in a relatively simple and easy to understand fashion, with decision making skills founded on calculations and case study based quantitative analysis.
Contents
Chapter 1: Fundamental Concepts of Fuzzy Sets and Rough Sets Chapter 2: WASPAS Approach based on Complex Fuzzy Rough Aggregation Operators and their Application to Cybersecurity Chapter 3: Integrating Complex Fuzzy Rough Yager Aggregation Operators with Neural Networks for improved AI predictive models Chapter 4: Innovative Approaches to Prioritizing Financial Tools for Business Growth and Sustainability through Complex Fuzzy Rough Structure Chapter 5: A Framework for Prioritizing Tools and Techniques in Electrical Engineering Design and Analysis: An application of complex fuzzy rough sets Chapter 6: Innovative AI Solutions through the Utilization of Complex Fuzzy Rough Set Theory Chapter 7: Advanced-Data Science Techniques with Complex Fuzzy Rough Sets; Applications and insights Chapter 8: Tackling the Challenges of Environmental Complexity: A Complex Fuzzy Rough Set Approach to Ecosystem Management Chapter 9: Empowering Robotic Decision-Making with Complex Fuzzy Rough Sets: Towards Autonomous Industrial Applications
