Decision Systems

Decision Systems: Integrating Machine Learning, Fuzzy Logic, and Artificial Neural Networks provides readers with a comprehensive understanding of the principal techniques used to build effective decision-making systems. This book covers the fundamental principles and concepts of machine learning, fuzzy logic, and artificial neural networks, and explains how these techniques can be used to build intelligent decision-making systems that can learn from data, reason, and make accurate predictions. The book also presents a wide range of applications of machine learning, fuzzy logic, and artificial neural networks in various domains, such as engineering, medicine, finance, and robotics. The book also provides practical guidance on how to design and implement effective decision-making systems using these techniques and discusses the potential challenges and limitations of machine learning, fuzzy logic, and artificial neural networks, and how to overcome them. The book provides a stepwise approach to provide readers with the knowledge and tools they need to build intelligent decision-making systems, including a robust introduction to the mathematical concepts and principles necessary to understand the concepts and applications of Decision Systems and Machine Learning algorithms. Next, the book provides readers with an in-depth explanation and demonstration of two of the major machine learning techniques - Fuzzy Logic/Fuzzy Set Theory and Artificial Neural Networks - followed by an in-depth look at more advanced topics that play essential roles in making machine learning algorithms more useful in practice, including creating full-fledged Recurrent Networks and their mathematical foundations, Associative Memories, and Deep Learning networks such as Convolutional Neural Networks, Generative Adversarial Networks, Radial Basis Function Networks, Multilayer Perceptrons, and Self-Organizing Maps. The lynchpin of the book provides readers with an understanding of how the various types of techniques can be integrated to create dynamic Decision Systems. The book wraps up with coverage of challenges and opportunities in Decision Systems along with real-world applications of Decision Systems with case studies in healthcare, finance, education, social media, and agriculture.

Dr. Pallavi Vijay Chavan is Associate Professor in the Department of Information Technology, RAIT, D. Y. Patil Deemed to be University, NERUL, Navi Mumbai, India. During her 20-year carrier, she has worked on a variety of research topics, including Visual Cryptography, Image Processing, Intelligent Systems, Machine Learning and Analytics. She has taught core subjects at the undergrad level including DBMS, Theory of Computation, Artificial Neural Networks, and Soft Computing. Dr. Chavan is the author of dozens of research papers in international journals and conferences, including Springer, Elsevier, Inderscience and IEEE. Dr. Chavan is recipient of research grants from Mumbai University and is a member of ACM and ISTE. Dr. Chavan is the author of Automata Theory and Formal Languages from Elsevier Academic Press.