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Soft Computing: Fundamentals & Practical Approaches 

Author Name : Rupam Kumar Sharma and Gypsy Nandi

Features

  • Publisher : Studium Press (India) Pvt. Ltd.
  • Edition : Ist
  • ISBN 13 : 978-93-85046-53-7
  • Page no : 372
  • Publication Year : 2019
$1995

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  • Description
  • Table Of Content
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This book introduces the fundamental concepts of different soft computing techniques that are widely used in a variety of optimization and classification problems. Topics covered include Fuzzy computing, Neural Network, Deep learning, Population based algorithms, Rough sets, Soft computing techniques in Intrusion Detection Systems and Soft Computing techniques in Data Mining. The content discusses the fundamentals together with how the soft computing techniques can be implemented using various standard libraries and tools of the Python programming language and the ROSE tool. Readers with previous knowledge of python programming will find easy to understand the program examples presented in the chapters. Each chapter contains numerous examples and case study that explains the important concepts. Appropriate number of questions is presented at the end of each chapter for self assessing the conceptual understanding. The references presented at the end of every chapter will help the readers to explore more on a given topic.
Table of Content "About the Authors – Acknowledgment – Preface – Key Features of the Book – 1. Fundamentals of Soft Computing 1.1. Introduction to Soft Computing 1.2. Soft computing versus Hard Computing 1.3. Soft computing Characteristics 1.4. Components of Soft Computing 1.4.1. Fuzzy Computing 1.4.2. Neural Network 1.4.3. Evolutionary Computing 1.4.4. Machine Learning 1.4.5. Other Techniques of Soft Computing; References; Exercises – 2. Fuzzy Computing 2.1. Fuzzy Set 2.2. Fuzzy Set Operations 2.3. Fuzzy Set Properties 2.4. Fuzzy Membership Functions 2.5. Defuzzification 2.6. The Butterfly Classification Problem 2.7. Fuzzy c-Means 2.8. Applications of Fuzzy Logic on Different Product Development 2.8.1. Control of a Model Car-Like Vehicle 2.8.2. Driving a Car-Like Vehicle 2.8.3. Fuzzy Logic in Washing Machine 2.9. Practical Approach of Fuzzy Using Python; Exercises; References – 3. Artificial Neural Networks 3.1. Introduction to Artificial Neural Network 3.1.1. McCulloh-Pitts Neuron Model 3.1.2. The Perceptron 3.1.3. Types of Transfer Function 3.1.3.1. Hard Limit Transfer Function 3.1.3.2. Linear Transfer Function 3.1.3.3. Log-Simoid Transfer Function 3.1.3.4. Different Other Transfer Functions 3.1.4. Perceptron Learning and Learning Rate 3.1.5. Perceptron Algorithm 3.1.6. Pattern Classification 3.1.7. Gradient Descent Rule 3.2. Multilayer Perceptron 3.2.1. Preliminaries of Multilayer Neural Network 3.2.2. Multilayer Perceptron Algorithm 3.2.3. Backpropagation Training 3.2.3.1. Design Procedure of the Algorithm 3.2.3.2. Batch Learning 3.2.3.3. Online Learning 3.2.4. Cross Validation and Generalization 3.2.5. Generalization 3.3. Self-Organizing Map 3.4. ANN Implementation in Python 3.4.1. Significance of Bias 3.4.2. Neural Network Design using Python; References; Exercises – 4. Deep Learning 4.1. Introduction to Deep Learning 4.2. Deep Learning Primitives 4.2.1. Soft max Function 4.2.2. Sigmoid, Tanh and ReLU Neurons 4.2.3. Functions and Gradient Descent 4.2.4. Linear/Logistic Regression 4.3. Feedforward Network 4.4. Convolutional Neural Network 4.5. Recurrent Neural Network; Exercises; References – 5. Population Based Algorithms 5.1. Introduction to Genetic Algorithm 5.2. Five Phases of Genetic Algorithm 5.2.1. Population Initialization 5.2.2. Fitness Function Calculation (Evaluation) 5.2.3. Parent Selection 5.2.4. Crossover 5.2.5. Mutation 5.3. How Genetic Algorithm Works? 5.4. Application Areas of Genetic Algorithm (GA) 5.4.1. Using GA in Travelling Salesman Problem 5.4.2. Using GA in Vehicle Routing Problem 5.5. Python Code for Implementing a Simple GA 5.6. Introduction to Swarm Intelligence 5.7. Few Important Aspects of Swarm Intelligence 5.7.1. Collective Sorting 5.7.2. Foraging Behaviour 5.7.3. Stigmergy 5.7.4. Division of Labour 5.7.5. Collective Transport 5.7.6. Self-Organization 5.8. Swarm Intelligence Techniques 5.8.1. Ant Colony Optimization (ACO) 5.8.1.1. How ACO Technique Works? 5.8.1.2. Applying ACO to Optimization Problems 5.8.1.3 Using ACO in Travelling Salesman Problem (TSP) 5.8.1.4 Python Code for Implementing ACO in TSP 5.8.2. Particle Swarm Optimization (PSO) 5.8.2.1. How PSO Technique Works? 5.8.2.2. Applying PSO to Optimization Problems 5.8.2.3. Using PSO in Job-Shop Scheduling Problem 5.8.2.4. Python Code for Implementing PSO; Exercises; References – Rough Sets 6.1. The Pawlak Rough Set Model 6.1.1 Basic Terms in Pawlak Rough Set Model 6.1.2 Measures of Rough Set Approximations 6.2. Using Rough Sets for Information System 6.3. Decision Rules and Decision Tables 6.3.1. Parameters of Decision Tables 6.3.1.1. Consistency Factor 6.3.1.2. Support and Strength 6.3.1.3. Certainty Factor 6.3.1.4. Coverage Factor 6.3.2. Probabilistic Properties of Decision Tables 6.4. Application Areas of Rough Set Theory 6.4.1. Classification 6.4.2. Clustering 6.4.3. Medical Diagnosis 6.4.4. Image Processing 6.4.5. Speech Analysis 6.5. Using ROSE Tool for RST Operations 6.5.1. Attribute Discretization 6.5.2. Finding Lower and Upper Approximations; Exercises; References – 7. Soft Computing Techniques and IDS 7.1. Genetic Algorithm and Intrusion Detection Systems (IDS) 7.2. Deep Learning and IDS 7.3. Recurrent Neural Network and IDS 7.4. Network Intrusion Detection using Rough Sets and KNN 7.5. Fuzzy Logic and IDS 7.6. IDS with Genetic Algorithm and Fuzzy Logic 7.7. Feature Selection using Ant Colony Optimization 7.8. Decision Tree and IDS 7.9 Basic Python Code Programs for Performing Simple Network Stuffs; Exercises; References – 8. Soft Computing Techniques in Data Mining 8.1. Feature Selection using Rough Set Theory 8.2. Rule Induction using Rough Set Theory 8.3. Data Preprocessing for Data Mining 8.4. Hierarchical Clustering 8.5. Decision Tree Classification 8.6. KNN Classification 8.7. Decision Tree Regression 8.8. Random Forest Regression; Exercises; References – Subject Index – Appendix.
"
Series No
Title Soft Computing Fundamentals and Practical Approaches 
Author's Name Rupam Kumar Sharma and Gypsy Nandi
Publisher Studium Press (India) Pvt. Ltd.
Page No. 372
Year Of Publication 2019
ISBN 10 No
ISBN 13 978-93-85046-53-7
Book size width -
book size(hei) -
Edition Ist
Book Size(len) -
Binding type Hard bound
About The Book This book introduces the fundamental concepts of different soft computing techniques that are widely used in a variety of optimization and classification problems. Topics covered include Fuzzy computing, Neural Network, Deep learning, Population based algorithms, Rough sets, Soft computing techniques in Intrusion Detection Systems and Soft Computing techniques in Data Mining. The content discusses the fundamentals together with how the soft computing techniques can be implemented using various standard libraries and tools of the Python programming language and the ROSE tool. Readers with previous knowledge of python programming will find easy to understand the program examples presented in the chapters. Each chapter contains numerous examples and case study that explains the important concepts. Appropriate number of questions is presented at the end of each chapter for self assessing the conceptual understanding. The references presented at the end of every chapter will help the readers to explore more on a given topic.

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