Decision Systems

Integrating Machine Learning, Fuzzy Logic, and Artificial Neural Networks
Erscheinungsjahr 2025
ISBN: 978-0-443-33728-4
Verlag: Elsevier Science

Buch, Englisch, 270 Seiten, Format (B × H): 191 mm x 235 mm

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.

Autoren/Hrsg.

Vijay Chavan, Pallavi

Balani, Nisha

Mangrulkar, Ramchandra

Chaudhari, Sangita Santosh

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Fuzzy-Systeme

Inhaltsverzeichnis

1. Introduction to Decision Systems
2. Foundations of Machine Learning
3. Fuzzy Logic and Fuzzy Set Theory
4. Artificial Neural Networks
5. Recurrent Networks
6. Associative Memories
7. Deep Learning
8. Integration of Machine Learning, Fuzzy Logic, and Artificial Neural Networks
9. Challenges and Opportunities in Decision Systems
10. Real World Applications of Decision Systems

Über Autor(innen)

Balani, Nisha
Dr. Nisha Balani serves as the Head of the Department of Computer Science and Engineering, specializing in Artificial Intelligence and Machine Learning, at Jhulelal Institute of Technology in Nagpur, Maharashtra, India. With a tenure spanning 15 years in academia, she has acquired expertise in the fields of data structures, algorithms, network security, artificial intelligence, machine learning, data science, and blockchain technology. Throughout her academic tenure, she has contributed significantly to her field, publishing research papers in esteemed journals indexed by SCI, Web of Science, Scopus, and UGC Care. Her academic qualifications include a Doctor of Philosophy (Ph.D.) in Computer Engineering from Ramrao Adik College of Engineering, Mumbai, a Master of Technology (M.Tech) in Computer Science and Engineering obtained in 2012, and a Bachelor of Engineering (B.E) in Information Technology in 2008 from Shri Ramdeobaba College of Engineering, Nagpur. Guided by the belief that knowledge transforms possibilities, she is dedicated to igniting the spark of discovery and fostering a culture of continuous learning.

Chaudhari, Sangita Santosh
Dr. Sangita Santosh Chaudhari obtained her Master of Computer Engineering from Mumbai University in 2008 and Ph. D. in GIS and Remote Sensing from Indian Institute of Technology Bombay in 2016. Currently, she is working as a Professor and Head of Department of Information Technology at Ramrao Adik Institute of Technology, D. Y. Patil Deemed to be University, Nerul, Navi Mumbai.

Dr. Chaudhari has over 100 research papers to her credit published in peer reviewed and referred National and International Journals and Conferences. She is the co-editor of Advances in Scalable and Intelligent Geospatial Analytics: Challenges and Applications from Taylor & Francis/CRC Press, Intelligent Solutions for Cognitive Disorders from IGI Global, and Computational Intelligence in Image and Video Processing from Taylor & Francis/Chapman & Hall/CRC Press. She is an IEEE senior member and active member of IEEE GRSS and IEEE Women in Engineering. Her research interests include Image processing, Information security, Data Analytics, Geographical Information Systems, and Remote sensing.

Mangrulkar, Ramchandra
Dr. Mangrulkar is an Assistant Professor of Computer Engineering in the is Dwarkadas Sanghvi College of Engineering and has 20 years of teaching experience in the field of intelligent systems and security. He completed his M.Sc. in Computer Science and Engineering from NIT Rourkela. He completed his Ph.D. in the Information Security from SGBAU, Amravati. Dr. Mangrulkar is the recipient of grants from UGC as well as AICTE.

Vijay Chavan, Pallavi
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.