Decision Systems

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