Decision Systems
Integrating Machine Learning, Fuzzy Logic, and Artificial Neural Networks
- 1st Edition - July 28, 2025
- Authors: Pallavi Vijay Chavan, Nisha Balani, Ramchandra Mangrulkar, Sangita Santosh Chaudhari
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 3 7 2 8 - 4
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 3 7 2 9 - 1
Decision-making is a fundamental process that influences outcomes across a wide range of domains, including business, healthcare, scientific research, and automation. With the… Read more
Decision-making is a fundamental process that influences outcomes across a wide range of domains, including business, healthcare, scientific research, and automation. With the increasing availability of data and the growing computational power of modern systems, decision-making models have become more sophisticated and capable of providing highly accurate and efficient solutions. The ability to develop, analyze, and implement these models has become crucial for professionals and researchers working in fields that rely on data-driven decision-making.
This book explores the evolution and significance of decision systems, covering both foundational theories and advanced methodologies. It introduces readers to the essential principles of decision-making models, illustrating their applications through practical case studies and real-world scenarios. The discussion begins with a focus on traditional decision-making techniques and gradually progresses to more advanced topics, including machine learning-based approaches, the integration of artificial intelligence, and the role of fuzzy logic in decision support systems. Furthermore, ethical considerations in decision-making and strategies for mitigating bias are examined, ensuring that models remain fair and transparent.
Throughout this book, each chapter builds on the previous one, providing a structured and comprehensive learning experience. By the time readers complete this book, they will have gained an in-depth understanding of decision-making frameworks, their applications, and the future directions of research in this dynamic field. Whether one is a student, a researcher, or an industry professional, this book serves as a valuable guide to mastering the complexities of decision systems and applying them effectively in various domains.
This book explores the evolution and significance of decision systems, covering both foundational theories and advanced methodologies. It introduces readers to the essential principles of decision-making models, illustrating their applications through practical case studies and real-world scenarios. The discussion begins with a focus on traditional decision-making techniques and gradually progresses to more advanced topics, including machine learning-based approaches, the integration of artificial intelligence, and the role of fuzzy logic in decision support systems. Furthermore, ethical considerations in decision-making and strategies for mitigating bias are examined, ensuring that models remain fair and transparent.
Throughout this book, each chapter builds on the previous one, providing a structured and comprehensive learning experience. By the time readers complete this book, they will have gained an in-depth understanding of decision-making frameworks, their applications, and the future directions of research in this dynamic field. Whether one is a student, a researcher, or an industry professional, this book serves as a valuable guide to mastering the complexities of decision systems and applying them effectively in various domains.
- Covers foundational concepts, advanced theories, and real-world applications, ensuring readers gain a thorough understanding of Decision Systems
- Presents the foundational mathematics behind the various techniques covered, including stepwise mathematical formula development, R and Python code syntax listings for the worked examples, and stepwise methods and procedures for application algorithms
- Illustrates how fuzzy logic and neural networks can be integrated with other disciplines like machine learning, optimization, and data science to create powerful hybrid solutions
Computer Science researchers, artificial intelligence researchers, machine learning researchers, deep learning researchers, and software developers that have immediate and direct responsibilities related to developing and implementing Decision Systems in their work. The primary audience also includes data scientists, software engineers, as well as researchers and professionals across the fields of science and engineering
CHAPTER 1: Introduction to Decision Systems
1.1 Types of decision systems
1.1.1 Machine Learning
1.1.2 Fuzzy Logic
1.1.3 Artificial Neural Network
1.1.4 Hybrid Systems
1.2 Limitations and challenges of decision systems
1.3 Components
1.4 Importance of Decision Systems in Modern Society
1.5 Key Concepts and Terminology
1.6 Application domains of Decision systems
1.6.1 Recommender systems
1.6.2 fraud detection systems
1.6.3 supply chain management systems
CHAPTER 2: Foundations of Machine Learning
2.1 Introduction to Machine Learning
2.2 Supervised Learning
2.3 Unsupervised Learning
2.4 Reinforcement Learning
2.5 When to use what ?
2.6 Regression Techniques
2.6.1 Simple Linear Regression
2.6.2 Multiple Linear Regression
2.6.3 Logistic Regression
2.6.4 Practical Examples
2.7 Classification Techniques
2.7.1 Attribute Selection Measures
2.7.2 Decision Tree Classifier
2.7.3 Bayesian Classifier
2.7.4 K Nearest Neighbour
2.7.5 Support Vector Machine
2.7.6 Measures to evaluate classifier performance
2.7.7 Practical Examples
2.8 Clustering Techniques
2.8.1 Introduction 2.8.2 Centroid-based Clustering
2.8.3 Density-based Clustering
2.8.4 Distribution-based Clustering
2.8.5 K- means Clustering
2.8.6 K- Medoid Clustering
2.8.7 Hierarchical Clustering
CHAPTER 3: Fuzzy Logic and Fuzzy Set Theory
3.1 Fuzzy Logic Background
3.2 Crisp Set theory
3.3 Crisp vs Fuzzy
3.4 Fuzzy Sets
3.4.1 Properties of Fuzzy sets
3.4.2 Operations of Fuzzy sets
3.4.3 Fuzzy Relations
3.4.4 Projection
3.4.5 Cylindrical Extension
3.5 Introduction Fuzzy Logic
3.6 Fuzzy Membership Functions
3.7 α Cuts
3.8 Fuzzification
3.9 Defuzzification
3.10 Fuzzy Inference Systems
3.11 Fuzzy Logic Applications
3.12 Practical Examples
CHAPTER 4: Artificial Neural Networks
4.1 Biological Neuron
4.2 Introduction to Artificial Neural Networks
4.3 MP-Neuron Model
4.3.1 Working Examples
4.3.2 Logic Gates using MP Neuron
4.4 Neural Network Architectures
4.4.1 Feed forward Architecture
4.4.2 Feedback Architecture
4.5 Learning
4.6 Learning Rules
4.6.1 Hebbian Learning
4.6.2 Perceptron Learning
4.6.3 Delta Rule Learning
4.6.4 Winner Take All Learning
4.6.5 Outstar Learning
4.6.6 Widrow Hoff Learning
4.7 Single Layer Perceptron Network
4.7.1 Discriminant Function
4.7.2 Linear Machines
4.7.3 Minimum Distance Classification
4.7.4 Training and Classification using Discrete Perceptron
4.8 Multi-layer Perceptron Learning
4.8.1 Error Back Propagation
CHAPTER 5: Recurrent Networks
5.1 Introduction
5.2 Mathematical Foundations of recurrent network
5.3 Discrete Time Hopfield Network
5.4 Solving recurrent network using discrete time Hopfield neural network
CHAPTER 6: Associative Memories
6.1 Introduction
6.2 Static Associative Memory
6.3 Dynamic Associative Memory
6.4 Auto Associative Memory
6.5 Bi-directional Associative Memory
CHAPTER 7: Deep Learning
7.1 Introduction
7.2 Components of Deep Learning
7.3 Deep Learning Networks
7.3.1 Convolutional Neural Networks
7.3.2 Long Short-Term Memory Networks
7.3.3 Recurrent Neural Networks
7.3.4 Generative Adversarial Networks
7.3.5 Radial Basis Function Networks
7.3.6 Multilayer Perceptrons
7.3.7 Self Organizing Maps
CHAPTER 8: Integration of Machine Learning, Fuzzy Logic, and Artificial Neural Networks
8.1 Introduction to Hybrid Systems
8.2 Neuro-Fuzzy Systems
8.3 Fuzzy Neural Networks
8.4 Deep Reinforcement Learning
8.5 Applications of Integrated Systems
CHAPTER 9: Challenges and Opportunities in Decision Systems
9.1 Ethical and Social Implications
9.2 Bias and Fairness
9.3 Interpretability and Explainability
9.4 Scalability and Performance
9.5 Future Directions
CHAPTER 10: Real World Applications of Decision Systems
10.1 Broad Coverage of Application domains
10.2 Decision systems in finance
10.3 Decision systems in healthcare
10.4 Decision systems in education
10.5 Decision systems on social media platform
10.6 Decision systems in Agriculture
1.1 Types of decision systems
1.1.1 Machine Learning
1.1.2 Fuzzy Logic
1.1.3 Artificial Neural Network
1.1.4 Hybrid Systems
1.2 Limitations and challenges of decision systems
1.3 Components
1.4 Importance of Decision Systems in Modern Society
1.5 Key Concepts and Terminology
1.6 Application domains of Decision systems
1.6.1 Recommender systems
1.6.2 fraud detection systems
1.6.3 supply chain management systems
CHAPTER 2: Foundations of Machine Learning
2.1 Introduction to Machine Learning
2.2 Supervised Learning
2.3 Unsupervised Learning
2.4 Reinforcement Learning
2.5 When to use what ?
2.6 Regression Techniques
2.6.1 Simple Linear Regression
2.6.2 Multiple Linear Regression
2.6.3 Logistic Regression
2.6.4 Practical Examples
2.7 Classification Techniques
2.7.1 Attribute Selection Measures
2.7.2 Decision Tree Classifier
2.7.3 Bayesian Classifier
2.7.4 K Nearest Neighbour
2.7.5 Support Vector Machine
2.7.6 Measures to evaluate classifier performance
2.7.7 Practical Examples
2.8 Clustering Techniques
2.8.1 Introduction 2.8.2 Centroid-based Clustering
2.8.3 Density-based Clustering
2.8.4 Distribution-based Clustering
2.8.5 K- means Clustering
2.8.6 K- Medoid Clustering
2.8.7 Hierarchical Clustering
CHAPTER 3: Fuzzy Logic and Fuzzy Set Theory
3.1 Fuzzy Logic Background
3.2 Crisp Set theory
3.3 Crisp vs Fuzzy
3.4 Fuzzy Sets
3.4.1 Properties of Fuzzy sets
3.4.2 Operations of Fuzzy sets
3.4.3 Fuzzy Relations
3.4.4 Projection
3.4.5 Cylindrical Extension
3.5 Introduction Fuzzy Logic
3.6 Fuzzy Membership Functions
3.7 α Cuts
3.8 Fuzzification
3.9 Defuzzification
3.10 Fuzzy Inference Systems
3.11 Fuzzy Logic Applications
3.12 Practical Examples
CHAPTER 4: Artificial Neural Networks
4.1 Biological Neuron
4.2 Introduction to Artificial Neural Networks
4.3 MP-Neuron Model
4.3.1 Working Examples
4.3.2 Logic Gates using MP Neuron
4.4 Neural Network Architectures
4.4.1 Feed forward Architecture
4.4.2 Feedback Architecture
4.5 Learning
4.6 Learning Rules
4.6.1 Hebbian Learning
4.6.2 Perceptron Learning
4.6.3 Delta Rule Learning
4.6.4 Winner Take All Learning
4.6.5 Outstar Learning
4.6.6 Widrow Hoff Learning
4.7 Single Layer Perceptron Network
4.7.1 Discriminant Function
4.7.2 Linear Machines
4.7.3 Minimum Distance Classification
4.7.4 Training and Classification using Discrete Perceptron
4.8 Multi-layer Perceptron Learning
4.8.1 Error Back Propagation
CHAPTER 5: Recurrent Networks
5.1 Introduction
5.2 Mathematical Foundations of recurrent network
5.3 Discrete Time Hopfield Network
5.4 Solving recurrent network using discrete time Hopfield neural network
CHAPTER 6: Associative Memories
6.1 Introduction
6.2 Static Associative Memory
6.3 Dynamic Associative Memory
6.4 Auto Associative Memory
6.5 Bi-directional Associative Memory
CHAPTER 7: Deep Learning
7.1 Introduction
7.2 Components of Deep Learning
7.3 Deep Learning Networks
7.3.1 Convolutional Neural Networks
7.3.2 Long Short-Term Memory Networks
7.3.3 Recurrent Neural Networks
7.3.4 Generative Adversarial Networks
7.3.5 Radial Basis Function Networks
7.3.6 Multilayer Perceptrons
7.3.7 Self Organizing Maps
CHAPTER 8: Integration of Machine Learning, Fuzzy Logic, and Artificial Neural Networks
8.1 Introduction to Hybrid Systems
8.2 Neuro-Fuzzy Systems
8.3 Fuzzy Neural Networks
8.4 Deep Reinforcement Learning
8.5 Applications of Integrated Systems
CHAPTER 9: Challenges and Opportunities in Decision Systems
9.1 Ethical and Social Implications
9.2 Bias and Fairness
9.3 Interpretability and Explainability
9.4 Scalability and Performance
9.5 Future Directions
CHAPTER 10: Real World Applications of Decision Systems
10.1 Broad Coverage of Application domains
10.2 Decision systems in finance
10.3 Decision systems in healthcare
10.4 Decision systems in education
10.5 Decision systems on social media platform
10.6 Decision systems in Agriculture
- Edition: 1
- Published: July 28, 2025
- Language: English
PV
Pallavi Vijay Chavan
Dr. Pallavi Vijay Chavan is a Professor and Head in the Department of Information Technology, RAIT, D. Y. Patil Deemed to be University, NERUL, Navi Mumbai, India. During her 20-year career, 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.
Affiliations and expertise
Professor & Head – Information Technology Ramrao Adik Institute of Technology, D Y Patil deemed to be University, India.
Affiliations and expertise
Associate Professor – Information Technology Ramrao Adik Institute of Technology, D Y Patil deemed to be University, IndiaNB
Nisha Balani
Dr. Nisha Balani serves is an Assistant Professor in the Department of Computer Science and Engineering at Jhulelal Institute of Technology in Nagpur, Maharashtra, India and Executive Director at First Impression Technologies, Nagpur. With a tenure spanning 17 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.
Affiliations and expertise
1. Assistant Professor, Department of Computer Science and Engineering, Jhulelal Institute of Technology, Nagpur, India.
2. Executive Director, First Impression Technologies, Nagpur, India.
Affiliations and expertise
Head of the Department of Computer Science and Engineering, specializing in Artificial Intelligence and Machine Learning, Jhulelal Institute of Technology, IndiaRM
Ramchandra Mangrulkar
Dr. Mangrulkar is a Professor of Information Technology at 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 Information Security from SGBAU, Amravati. Dr. Mangrulkar is the recipient of grants from UGC as well as AICTE.
Affiliations and expertise
Professor, Department of Information Technology, Dwarkadas Sanghvi College of Engineering, India.
Affiliations and expertise
Assistant Professor – Computer Engineering Dwarkadas Sanghvi College of Engineering, IndiaSC
Sangita Santosh Chaudhari
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.
Affiliations and expertise
Professor and Head, Department of Computer Science and Engineering, Ramrao Adik Institute of Technology, D. Y. Patil Deemed to be University, India.
Affiliations and expertise
Professor and Head of Department of Information Technology at Ramrao Adik Institute of Technology, D. Y. Patil Deemed to be University, IndiaRead Decision Systems on ScienceDirect