{"product_id":"computational-intelligence-theory-and-applications-hardback-9781394214228","title":"Computational Intelligence; Theory and Applications (Hardback) 9781394214228","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eComputational Intelligence\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eTheory and Applications\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eT. Ananth Kumar (Edited by), TA Kumar (Author), E. Golden Julie (Edited by), Venkata Raghuveer Burugadda (Edited by), Abhishek Kumar (Edited by), Puneet Kumar (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394214228, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 1 November 2024\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e416 pages\u003cbr\u003e22.9 x 15.2 x 2.6 cm, 0.794 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eThis book provides a comprehensive exploration of computational intelligence techniques and their applications, offering valuable insights into advanced information processing, machine learning concepts, and their impact on agile manufacturing systems.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eComputational Intelligence\u003c\/i\u003e presents a new concept for advanced information processing. Computational Intelligence (CI) is the principle, architecture, implementation, and growth of machine learning concepts that are physiologically and semantically inspired. Computational Intelligence methods aim to develop an approach to evaluating and creating flexible processing of human information, such as sensing, understanding, learning, recognizing, and thinking. The Artificial Neural Network simulates the human nervous system’s physiological characteristics and has been implemented numerically for non-linear mapping. Fuzzy Logic Systems simulate the human brain’s psychological characteristics and have been used for linguistic translation through membership functions and bioinformatics. The Genetic Algorithm simulates computer evolution and has been applied to solve problems with optimization algorithms for improvements in diagnostic and treatment technologies for various diseases. To expand the agility and learning capacity of manufacturing systems, these methods play essential roles. This book will express the computer vision techniques that make manufacturing systems more flexible, efficient, robust, adaptive, and productive by examining many applications and research into computational intelligence techniques concerning the main problems in design, making plans, and manufacturing goods in agile manufacturing systems.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eIntroduction xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Computational Intelligence Theory: An Orientation Technique 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eS. Jaisiva, C. Kumar, S. Sakthiya Ram, C. Sakthi Gokul Rajan and P. Praveen Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Computational Intelligence 2\u003c\/p\u003e \u003cp\u003e1.2 Application Fields for Computational Intelligence 4\u003c\/p\u003e \u003cp\u003e1.2.1 Neural Networks 4\u003c\/p\u003e \u003cp\u003e1.2.1.1 Classification 4\u003c\/p\u003e \u003cp\u003e1.2.1.2 Clustering or Compression 5\u003c\/p\u003e \u003cp\u003e1.2.1.3 Generation of Sequences or Patterns 5\u003c\/p\u003e \u003cp\u003e1.2.1.4 Control Systems 5\u003c\/p\u003e \u003cp\u003e1.2.1.5 Evolutionary Computation 6\u003c\/p\u003e \u003cp\u003e1.2.2 Fuzzy Logic 6\u003c\/p\u003e \u003cp\u003e1.2.2.1 Fuzzy Control Systems 6\u003c\/p\u003e \u003cp\u003e1.2.2.2 Fuzzy Systems 6\u003c\/p\u003e \u003cp\u003e1.2.2.3 Behavioral Motivations for Fuzzy Logic 7\u003c\/p\u003e \u003cp\u003e1.3 Computational Intelligence Paradigms 7\u003c\/p\u003e \u003cp\u003e1.3.1 Artificial Neural Networks 7\u003c\/p\u003e \u003cp\u003e1.3.2 Evolutionary Computation (EC) 10\u003c\/p\u003e \u003cp\u003e1.3.3 Optimization Method 11\u003c\/p\u003e \u003cp\u003e1.3.3.1 Optimization 11\u003c\/p\u003e \u003cp\u003e1.4 Architecture Assortment 12\u003c\/p\u003e \u003cp\u003e1.4.1 Swarm Intelligence 14\u003c\/p\u003e \u003cp\u003e1.4.2 Artificial Immune Systems 14\u003c\/p\u003e \u003cp\u003e1.5 Myths About Computational Intelligence 15\u003c\/p\u003e \u003cp\u003e1.6 Supervised Learning in Computational Intelligence 16\u003c\/p\u003e \u003cp\u003e1.6.1 Performance Measures 17\u003c\/p\u003e \u003cp\u003e1.6.1.1 Accuracy 17\u003c\/p\u003e \u003cp\u003e1.6.1.2 Complexity 18\u003c\/p\u003e \u003cp\u003e1.6.1.3 Convergence 19\u003c\/p\u003e \u003cp\u003e1.6.2 Performance Factors 19\u003c\/p\u003e \u003cp\u003e1.6.2.1 Data Preparation 19\u003c\/p\u003e \u003cp\u003e1.6.2.2 Scaling and Normalization 19\u003c\/p\u003e \u003cp\u003e1.6.2.3 Learning Rate and Momentum 20\u003c\/p\u003e \u003cp\u003e1.6.2.4 Learning Rate 20\u003c\/p\u003e \u003cp\u003e1.6.2.5 Noise Injection 20\u003c\/p\u003e \u003cp\u003e1.7 Training Set Manipulation 21\u003c\/p\u003e \u003cp\u003e1.8 Conclusion 21\u003c\/p\u003e \u003cp\u003eReferences 21\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Nature-Inspired Algorithms for Computational Intelligence Theory—A State-of-the-Art Review 25\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eB. Akoramurthy, K. Dhivya and B. Surendiran\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 25\u003c\/p\u003e \u003cp\u003e2.2 Related Works 27\u003c\/p\u003e \u003cp\u003e2.3 Optimization and Its Algorithms 28\u003c\/p\u003e \u003cp\u003e2.3.1 Definition 28\u003c\/p\u003e \u003cp\u003e2.3.2 Mathematical Notations 28\u003c\/p\u003e \u003cp\u003e2.3.3 Gradient-Based Algorithms 29\u003c\/p\u003e \u003cp\u003e2.3.4 Gradient-Free Optimizers or Algorithms 31\u003c\/p\u003e \u003cp\u003e2.4 Metaheuristic Optimization Methods 32\u003c\/p\u003e \u003cp\u003e2.4.1 Ant Colony Algorithm 32\u003c\/p\u003e \u003cp\u003e2.4.1.1 Ant Colony Optimization Algorithm 32\u003c\/p\u003e \u003cp\u003e2.4.2 Flower Pollination Algorithm 34\u003c\/p\u003e \u003cp\u003e2.4.3 Genetic Algorithms 35\u003c\/p\u003e \u003cp\u003e2.4.4 Evolutionary Algorithm 36\u003c\/p\u003e \u003cp\u003e2.4.5 Method Based on Bats 37\u003c\/p\u003e \u003cp\u003e2.4.6 Cuckoo Searching Method 38\u003c\/p\u003e \u003cp\u003e2.4.7 Firefly Algorithm 39\u003c\/p\u003e \u003cp\u003e2.4.8 Particle Swarm Optimization Algorithm 41\u003c\/p\u003e \u003cp\u003e2.4.9 Krill Herd Algorithm 42\u003c\/p\u003e \u003cp\u003e2.4.10 Artificial Bee Colony (ABC) 43\u003c\/p\u003e \u003cp\u003e2.5 Computational and Autonomous Systems 44\u003c\/p\u003e \u003cp\u003e2.5.1 Computational Features of Nature-Inspired Computing 44\u003c\/p\u003e \u003cp\u003e2.5.2 Comparison with Legacy Algorithms 45\u003c\/p\u003e \u003cp\u003e2.5.3 Autonomous Criticality Systems 46\u003c\/p\u003e \u003cp\u003e2.6 Unresolved Issues for Continued Study 47\u003c\/p\u003e \u003cp\u003eReferences 49\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 AI-Based Computational Intelligence Theory 53\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJana Selvaganesan, S. Arunmozhiselvi, E. Preethi and S. Thangam\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Computational Intelligence 54\u003c\/p\u003e \u003cp\u003e3.2 Designing Expert Systems 55\u003c\/p\u003e \u003cp\u003e3.2.1 Characteristics 56\u003c\/p\u003e \u003cp\u003e3.3 Core of Computational Intelligence 56\u003c\/p\u003e \u003cp\u003e3.3.1 Artificial Intelligence (AI) 56\u003c\/p\u003e \u003cp\u003e3.3.2 Machine Learning (ML) 57\u003c\/p\u003e \u003cp\u003e3.3.3 Neural Networks 57\u003c\/p\u003e \u003cp\u003e3.3.4 Evolutionary Computation 58\u003c\/p\u003e \u003cp\u003e3.3.5 Fuzzy Systems 58\u003c\/p\u003e \u003cp\u003e3.3.6 Swarm Intelligence 59\u003c\/p\u003e \u003cp\u003e3.3.7 Bayesian Networks 60\u003c\/p\u003e \u003cp\u003e3.3.8 Optimization Techniques 60\u003c\/p\u003e \u003cp\u003e3.3.9 Data Mining and Pattern Recognition 60\u003c\/p\u003e \u003cp\u003e3.3.10 Decision Support Systems 61\u003c\/p\u003e \u003cp\u003e3.3.11 Hybrid Approaches 61\u003c\/p\u003e \u003cp\u003e3.4 Research and Development 62\u003c\/p\u003e \u003cp\u003e3.4.1 Government Plans in Enriching AI-Based Computational Intelligence Theory 62\u003c\/p\u003e \u003cp\u003e3.4.1.1 Funding and Research Initiatives 62\u003c\/p\u003e \u003cp\u003e3.4.1.2 Policy and Regulation 62\u003c\/p\u003e \u003cp\u003e3.4.1.3 Standards and Interoperability 63\u003c\/p\u003e \u003cp\u003e3.4.1.4 Education and Workforce Development 63\u003c\/p\u003e \u003cp\u003e3.4.1.5 Industry Collaboration and Partnerships 63\u003c\/p\u003e \u003cp\u003e3.4.1.6 Ethical Guidelines and Responsible AI 63\u003c\/p\u003e \u003cp\u003e3.4.1.7 International Collaboration and Governance 64\u003c\/p\u003e \u003cp\u003e3.5 New Opportunities and Challenges 64\u003c\/p\u003e \u003cp\u003e3.5.1 Explainable AI (XAI) 64\u003c\/p\u003e \u003cp\u003e3.5.2 Adversarial Machine Learning 65\u003c\/p\u003e \u003cp\u003e3.5.3 AI for Edge Computing 65\u003c\/p\u003e \u003cp\u003e3.5.4 Continual Learning 67\u003c\/p\u003e \u003cp\u003e3.5.5 Meta-Learning 68\u003c\/p\u003e \u003cp\u003e3.5.6 AI for Cybersecurity 69\u003c\/p\u003e \u003cp\u003e3.5.7 AI for Healthcare 70\u003c\/p\u003e \u003cp\u003e3.5.7.1 AI for Healthcare-Based Recommendation System 72\u003c\/p\u003e \u003cp\u003e3.5.8 Responsible AI 72\u003c\/p\u003e \u003cp\u003e3.5.9 AI and Robotics Integration 73\u003c\/p\u003e \u003cp\u003e3.5.10 AI for Sustainability and Climate Change 74\u003c\/p\u003e \u003cp\u003e3.5.11 Quantum Computing and AI 75\u003c\/p\u003e \u003cp\u003e3.5.12 Human–AI Collaboration 76\u003c\/p\u003e \u003cp\u003e3.6 Applications 77\u003c\/p\u003e \u003cp\u003e3.6.1 Google-Waymo Car 77\u003c\/p\u003e \u003cp\u003e3.6.2 ChatGPT 79\u003c\/p\u003e \u003cp\u003e3.6.3 Boston Dynamics’ Atlas 80\u003c\/p\u003e \u003cp\u003e3.6.4 Netflix 81\u003c\/p\u003e \u003cp\u003e3.6.5 Trinetra 82\u003c\/p\u003e \u003cp\u003e3.6.6 Voice-Activated Backpack 83\u003c\/p\u003e \u003cp\u003e3.7 Case Study: YOLO v7 for Object Detection in TensorFlow 84\u003c\/p\u003e \u003cp\u003e3.7.1 Yolo V 7 84\u003c\/p\u003e \u003cp\u003e3.7.2 Working and Its Features 85\u003c\/p\u003e \u003cp\u003e3.7.3 Configuration to Deploy YOLO V 7 87\u003c\/p\u003e \u003cp\u003e3.8 Results 88\u003c\/p\u003e \u003cp\u003e3.9 Performance Analysis 89\u003c\/p\u003e \u003cp\u003e3.10 Challenges in Automation 91\u003c\/p\u003e \u003cp\u003e3.10.1 Marching Towards Solution 92\u003c\/p\u003e \u003cp\u003e3.11 Conclusion 93\u003c\/p\u003e \u003cp\u003eReferences 93\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Information Processing, Learning, and Its Artificial Intelligence 97\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eP. Praveenkumar, Pragati M., Prathiba S., Mirthulaa G., Supriya P., Jayashree B. and Jayasri R.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction—Artificial Intelligence 98\u003c\/p\u003e \u003cp\u003e4.2 Artificial Intelligence and Its Learning 99\u003c\/p\u003e \u003cp\u003e4.3 Artificial Intelligence’s Effects on IT 100\u003c\/p\u003e \u003cp\u003e4.4 Examples of Artificial Intelligence 101\u003c\/p\u003e \u003cp\u003e4.4.1 Smart Learning Content 101\u003c\/p\u003e \u003cp\u003e4.4.2 Intelligent Tutorial System Future 103\u003c\/p\u003e \u003cp\u003e4.4.3 Virtual Facilitators and Learning Environment 104\u003c\/p\u003e \u003cp\u003e4.4.4 Content Analytics 105\u003c\/p\u003e \u003cp\u003e4.5 Data Processing and AI in Human-Centered Manufacturing 106\u003c\/p\u003e \u003cp\u003e4.6 Information Learning 107\u003c\/p\u003e \u003cp\u003e4.6.1 Information Learning Through AI—Chatbots 107\u003c\/p\u003e \u003cp\u003e4.6.2 Information Learning Through AI—Virtual Reality (vr) 108\u003c\/p\u003e \u003cp\u003e4.6.3 Information Learning Through AI—Management of Learning (LMS) 110\u003c\/p\u003e \u003cp\u003e4.6.4 Information Learning Through AI—Robotics 111\u003c\/p\u003e \u003cp\u003e4.6.5 AI Invoice Processing is Not Fantastical— It is Fantastic 113\u003c\/p\u003e \u003cp\u003e4.7 Results 113\u003c\/p\u003e \u003cp\u003e4.8 Conclusion 114\u003c\/p\u003e \u003cp\u003eReferences 114\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Computational Intelligence Approach for Exploration of Spatial Co-Location Patterns 117\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eS. LourduMarie Sophie, S. Siva Sathya, S. Sharmiladevi and J. Dhakshayani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 118\u003c\/p\u003e \u003cp\u003e5.2 Spatial Data Mining 120\u003c\/p\u003e \u003cp\u003e5.2.1 Spatial Co-Location Pattern Mining 120\u003c\/p\u003e \u003cp\u003e5.3 Preliminaries 123\u003c\/p\u003e \u003cp\u003e5.3.1 Basic Concepts 123\u003c\/p\u003e \u003cp\u003e5.3.1.1 Feature Instance 124\u003c\/p\u003e \u003cp\u003e5.3.1.2 Participation Ratio (PR) 124\u003c\/p\u003e \u003cp\u003e5.3.1.3 Participation Index (PI) 125\u003c\/p\u003e \u003cp\u003e5.3.1.4 Neighbor Relation 125\u003c\/p\u003e \u003cp\u003e5.3.1.5 Conditional Neighborhood 126\u003c\/p\u003e \u003cp\u003e5.3.2 Apache Hadoop—MapReduce 126\u003c\/p\u003e \u003cp\u003e5.3.3 Related Work 128\u003c\/p\u003e \u003cp\u003e5.4 Proposed Grid-Conditional Neighborhood Algorithm 130\u003c\/p\u003e \u003cp\u003e5.4.1 Module Description 131\u003c\/p\u003e \u003cp\u003e5.4.1.1 Search Neighbor 131\u003c\/p\u003e \u003cp\u003e5.4.1.2 Group Neighbors 132\u003c\/p\u003e \u003cp\u003e5.4.1.3 Pattern Search 133\u003c\/p\u003e \u003cp\u003e5.4.1.4 Top K Pattern Generation 133\u003c\/p\u003e \u003cp\u003e5.5 Experimental Setup and Analysis 134\u003c\/p\u003e \u003cp\u003e5.5.1 Dataset Used 134\u003c\/p\u003e \u003cp\u003e5.5.2 Performance Analysis 136\u003c\/p\u003e \u003cp\u003e5.6 Discussion and Conclusion 138\u003c\/p\u003e \u003cp\u003eReferences 140\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Computational Intelligence-Based Optimal Feature Selection Techniques for Detecting Plant Diseases 145\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKarthickmanoj R., S. Aasha Nandhini and T. Sasilatha\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 145\u003c\/p\u003e \u003cp\u003e6.2 Literature Survey 146\u003c\/p\u003e \u003cp\u003e6.3 Proposed Framework 151\u003c\/p\u003e \u003cp\u003e6.4 Simulation Results 152\u003c\/p\u003e \u003cp\u003e6.5 Summary 156\u003c\/p\u003e \u003cp\u003eReferences 156\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Protein Structure Prediction Using Convolutional Neural Networks Augmented with Cellular Automata 159\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePokkuluri Kiran Sree, Prasun Chakrabarti, Martin Margala and SSSN Usha Devi N.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 160\u003c\/p\u003e \u003cp\u003e7.2 Methods 162\u003c\/p\u003e \u003cp\u003e7.3 Design of the Model 164\u003c\/p\u003e \u003cp\u003e7.4 Results and Comparisons 167\u003c\/p\u003e \u003cp\u003e7.5 Conclusion 172\u003c\/p\u003e \u003cp\u003eReferences 172\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Modeling and Approximating Renewable Energy Systems Using Computational Intelligence 175\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eB. Balaji, P. Hemalatha, T. Rampradesh, G. Anbarasi and A. Eswari\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 176\u003c\/p\u003e \u003cp\u003e8.2 Expert System 178\u003c\/p\u003e \u003cp\u003e8.3 Artificial Neural Networks 179\u003c\/p\u003e \u003cp\u003e8.4 ANN in Renewable Energy Systems 182\u003c\/p\u003e \u003cp\u003e8.5 Conclusion 185\u003c\/p\u003e \u003cp\u003eReferences 186\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Computational Intelligence and Deep Learning in Health Informatics: An Introductory Perspective 189\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJ. Naskath, R. Rajakumari, Hamza Aldabbas and Zaid Mustafa\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 190\u003c\/p\u003e \u003cp\u003e9.2 Mobile Application in Health Informatics Using Deep Learning 191\u003c\/p\u003e \u003cp\u003e9.3 Health Informatics Wearables Using Deep Learning 197\u003c\/p\u003e \u003cp\u003e9.4 Electroencephalogram 202\u003c\/p\u003e \u003cp\u003e9.5 Conclusion 203\u003c\/p\u003e \u003cp\u003eReferences 207\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Computational Intelligence for Human Activity Recognition (HAR) 213\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eThangapriya and Nancy Jasmine Goldena\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 214\u003c\/p\u003e \u003cp\u003e10.2 Fuzzy Logic in Human Judgment and Decision-Making 215\u003c\/p\u003e \u003cp\u003e10.2.1 FL Algorithm 216\u003c\/p\u003e \u003cp\u003e10.2.2 Applications of FL 217\u003c\/p\u003e \u003cp\u003e10.2.3 Advantages of FL 217\u003c\/p\u003e \u003cp\u003e10.2.4 Disadvantages of FL 218\u003c\/p\u003e \u003cp\u003e10.2.5 Utilizing FLS and FIS in HAR Research and Health Monitoring 218\u003c\/p\u003e \u003cp\u003e10.3 Artificial Neural Networks: From Perceptrons to Modern Applications 219\u003c\/p\u003e \u003cp\u003e10.3.1 ANN Algorithm 221\u003c\/p\u003e \u003cp\u003e10.3.2 Applications of ANN 222\u003c\/p\u003e \u003cp\u003e10.3.3 Advantages of ANN 222\u003c\/p\u003e \u003cp\u003e10.3.4 Disadvantages of ANN 222\u003c\/p\u003e \u003cp\u003e10.3.5 Artificial Neural Networks in HAR Research 223\u003c\/p\u003e \u003cp\u003e10.4 Swarm Intelligence 223\u003c\/p\u003e \u003cp\u003e10.4.1 SI Algorithm 224\u003c\/p\u003e \u003cp\u003e10.4.2 Applications of SI 224\u003c\/p\u003e \u003cp\u003e10.4.3 Advantages of SI 225\u003c\/p\u003e \u003cp\u003e10.4.4 Disadvantages of SI 225\u003c\/p\u003e \u003cp\u003e10.4.5 Swarm Intelligence Techniques in HAR Research 225\u003c\/p\u003e \u003cp\u003e10.5 Evolutionary Computing 226\u003c\/p\u003e \u003cp\u003e10.5.1 EC Algorithm 226\u003c\/p\u003e \u003cp\u003e10.5.2 Applications of EC 227\u003c\/p\u003e \u003cp\u003e10.5.3 Advantages of EC 228\u003c\/p\u003e \u003cp\u003e10.5.4 Disadvantages of EC 228\u003c\/p\u003e \u003cp\u003e10.5.5 Harnessing Evolutionary Computation for HAR Research 228\u003c\/p\u003e \u003cp\u003e10.6 Artificial Immune System 228\u003c\/p\u003e \u003cp\u003e10.6.1 AIS Algorithm 229\u003c\/p\u003e \u003cp\u003e10.6.2 Applications of AIS 230\u003c\/p\u003e \u003cp\u003e10.6.3 Advantages of AIS 230\u003c\/p\u003e \u003cp\u003e10.6.4 Disadvantages of AIS 230\u003c\/p\u003e \u003cp\u003e10.6.5 Harnessing AIS for Preventive Measures 231\u003c\/p\u003e \u003cp\u003e10.7 Conclusion 231\u003c\/p\u003e \u003cp\u003eReferences 232\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Computational Intelligence for Multimodal Analysis of High-Dimensional Image Processing in Clinical Settings 235\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eB. Balaji, P. Pugazhendiran, N. Sivanantham, N. Velammal and P. Vimala\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Basics of Machine Learning 236\u003c\/p\u003e \u003cp\u003e11.2 Feature Extraction 237\u003c\/p\u003e \u003cp\u003e11.3 Selection of Features 238\u003c\/p\u003e \u003cp\u003e11.4 Statistical Classifiers 239\u003c\/p\u003e \u003cp\u003e11.5 Neural Networks 242\u003c\/p\u003e \u003cp\u003e11.6 Biometric Analysis 244\u003c\/p\u003e \u003cp\u003e11.7 Data from High-Resolution Medical Imaging 251\u003c\/p\u003e \u003cp\u003e11.8 Computational Architectures 255\u003c\/p\u003e \u003cp\u003e11.9 Timing and Uncertainty 256\u003c\/p\u003e \u003cp\u003e11.10 AI and Risk of Harm 258\u003c\/p\u003e \u003cp\u003e11.11 Conclusion 259\u003c\/p\u003e \u003cp\u003eReferences 259\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 A Review of Computational Intelligence-Based Biometric Recognition Methods 263\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eT. IlamParithi, K. Antony Sudha and D. Jessintha\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 263\u003c\/p\u003e \u003cp\u003e12.1.1 Objective 264\u003c\/p\u003e \u003cp\u003e12.2 Computational Intelligence 264\u003c\/p\u003e \u003cp\u003e12.3 CI-Based Biometric Recognition 266\u003c\/p\u003e \u003cp\u003e12.3.1 Acquisition 266\u003c\/p\u003e \u003cp\u003e12.3.2 Segmentation 266\u003c\/p\u003e \u003cp\u003e12.3.3 Quality Assessment 269\u003c\/p\u003e \u003cp\u003e12.3.4 Enhancement 270\u003c\/p\u003e \u003cp\u003e12.3.5 Feature Extraction 270\u003c\/p\u003e \u003cp\u003e12.3.6 Matching 271\u003c\/p\u003e \u003cp\u003e12.3.7 Classification 272\u003c\/p\u003e \u003cp\u003e12.3.8 Score Normalization 272\u003c\/p\u003e \u003cp\u003e12.3.9 Anti-Spoofing 272\u003c\/p\u003e \u003cp\u003e12.3.10 Privacy 273\u003c\/p\u003e \u003cp\u003e12.4 Applications 273\u003c\/p\u003e \u003cp\u003e12.4.1 Business 273\u003c\/p\u003e \u003cp\u003e12.4.2 Education 274\u003c\/p\u003e \u003cp\u003e12.4.3 Military 275\u003c\/p\u003e \u003cp\u003e12.4.4 Health Care 276\u003c\/p\u003e \u003cp\u003e12.4.5 Banking 276\u003c\/p\u003e \u003cp\u003e12.5 Conclusion 277\u003c\/p\u003e \u003cp\u003eReferences 277\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Seeing the Unseen: An Automated Early Breast Cancer Detection Using Hyperspectral Imaging 281\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSravan Kumar Sikhakolli, Suresh Aala, Sunil Chinnadurai and Inbarasan Muniraj\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 282\u003c\/p\u003e \u003cp\u003e13.1.1 Conventional Imaging Methods for Detecting BC 283\u003c\/p\u003e \u003cp\u003e13.1.2 Optical Imaging Techniques to Detect BC 284\u003c\/p\u003e \u003cp\u003e13.2 Hyperspectral Imaging (HSI) 285\u003c\/p\u003e \u003cp\u003e13.2.1 How Does HSI Setup Look Like? 286\u003c\/p\u003e \u003cp\u003e13.3 State-of-the-Art Techniques for BC Detection 287\u003c\/p\u003e \u003cp\u003e13.3.1 Breast Cancer Ex Vivo Analysis 287\u003c\/p\u003e \u003cp\u003e13.3.2 Breast Cancer In Vivo Analysis 290\u003c\/p\u003e \u003cp\u003e13.4 Artificial Intelligence in BC Detection Using HSI 291\u003c\/p\u003e \u003cp\u003e13.4.1 Deep Learning in HSI 291\u003c\/p\u003e \u003cp\u003e13.4.2 Convolutional Neural Networks 292\u003c\/p\u003e \u003cp\u003e13.4.3 Deep Belief Networks Using HSI 293\u003c\/p\u003e \u003cp\u003e13.4.4 Residual Networks 293\u003c\/p\u003e \u003cp\u003e13.5 Discussion and Conclusion 293\u003c\/p\u003e \u003cp\u003eReferences 294\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Shedding Light into the Dark: Early Oral Cancer Detection Using Hyperspectral Imaging 301\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSuresh Aala, Sravan Kumar Sikhakolli, Inbarasan Muniraj and Sunil Chinnadurai\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 302\u003c\/p\u003e \u003cp\u003e14.2 HSI in HNC Detection 305\u003c\/p\u003e \u003cp\u003e14.3 Deep Learning in In Vivo HSI 313\u003c\/p\u003e \u003cp\u003e14.3.1 Endoscopic 313\u003c\/p\u003e \u003cp\u003e14.4 Conclusion and Future Research Directions 315\u003c\/p\u003e \u003cp\u003eReferences 316\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Machine Learning Techniques for Glaucoma Screening Using Optic Disc Detection 321\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eV. Subha, S. Niraja P. Rayen and Manivanna Boopathi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 322\u003c\/p\u003e \u003cp\u003e15.1.1 Ophthalmic Process 324\u003c\/p\u003e \u003cp\u003e15.1.2 Digital Imaging 324\u003c\/p\u003e \u003cp\u003e15.1.2.1 Image Processing 325\u003c\/p\u003e \u003cp\u003e15.1.3 Eye and Its Parts 326\u003c\/p\u003e \u003cp\u003e15.1.3.1 Optic Disc 327\u003c\/p\u003e \u003cp\u003e15.1.3.2 Aqueous Humor 327\u003c\/p\u003e \u003cp\u003e15.1.3.3 Choroid 327\u003c\/p\u003e \u003cp\u003e15.1.3.4 Ciliary Body 327\u003c\/p\u003e \u003cp\u003e15.1.3.5 Ciliary Muscle 327\u003c\/p\u003e \u003cp\u003e15.1.3.6 Iris 328\u003c\/p\u003e \u003cp\u003e15.1.3.7 Pupil 328\u003c\/p\u003e \u003cp\u003e15.1.3.8 Retina 328\u003c\/p\u003e \u003cp\u003e15.1.3.9 Photoreceptor Cells 328\u003c\/p\u003e \u003cp\u003e15.1.3.10 Retinal Blood Vessels 328\u003c\/p\u003e \u003cp\u003e15.1.3.11 Sclera 329\u003c\/p\u003e \u003cp\u003e15.1.3.12 Uvea 329\u003c\/p\u003e \u003cp\u003e15.1.3.13 Visual Axis 329\u003c\/p\u003e \u003cp\u003e15.1.3.14 Visual Cortex 329\u003c\/p\u003e \u003cp\u003e15.1.3.15 Visual Fields 329\u003c\/p\u003e \u003cp\u003e15.1.3.16 Vitreous 329\u003c\/p\u003e \u003cp\u003e15.1.3.17 Zonules 330\u003c\/p\u003e \u003cp\u003e15.1.3.18 Macula (Yellow Spot) 330\u003c\/p\u003e \u003cp\u003e15.1.3.19 Optic Nerve 330\u003c\/p\u003e \u003cp\u003e15.1.4 Eye Diseases 330\u003c\/p\u003e \u003cp\u003e15.1.4.1 Myopia 330\u003c\/p\u003e \u003cp\u003e15.1.4.2 Hyperopia 330\u003c\/p\u003e \u003cp\u003e15.1.4.3 Astigmatism 330\u003c\/p\u003e \u003cp\u003e15.1.4.4 Presbyopia 331\u003c\/p\u003e \u003cp\u003e15.1.4.5 Strabismus 331\u003c\/p\u003e \u003cp\u003e15.1.4.6 Amblyopia 331\u003c\/p\u003e \u003cp\u003e15.1.4.7 Cataracts 331\u003c\/p\u003e \u003cp\u003e15.1.4.8 Glaucoma 332\u003c\/p\u003e \u003cp\u003e15.1.5 Indications of Glaucoma 332\u003c\/p\u003e \u003cp\u003e15.1.6 Causes of Glaucoma 332\u003c\/p\u003e \u003cp\u003e15.1.6.1 Dietary 332\u003c\/p\u003e \u003cp\u003e15.1.6.2 Ethnicity and Gender 332\u003c\/p\u003e \u003cp\u003e15.1.6.3 Genetics 333\u003c\/p\u003e \u003cp\u003e15.1.7 Analytical Methods of Glaucoma 333\u003c\/p\u003e \u003cp\u003e15.2 Glaucoma Screening with Optic Disc and Classification 334\u003c\/p\u003e \u003cp\u003e15.2.1 Optic Disc Detection 335\u003c\/p\u003e \u003cp\u003e15.2.2 Cropping ROI 337\u003c\/p\u003e \u003cp\u003e15.2.3 Optic Disc Segmentation 338\u003c\/p\u003e \u003cp\u003e15.2.4 Optic Cup Segmentation 338\u003c\/p\u003e \u003cp\u003e15.2.5 Post-Processing 340\u003c\/p\u003e \u003cp\u003e15.2.5.1 Cup–Disc Ratio 340\u003c\/p\u003e \u003cp\u003e15.2.5.2 Evaluation of the NRR Area in the ISNT Quadrants 341\u003c\/p\u003e \u003cp\u003e15.2.5.3 Superpixel Method 341\u003c\/p\u003e \u003cp\u003e15.2.5.4 Level Set Method 342\u003c\/p\u003e \u003cp\u003e15.3 Experimental Section 342\u003c\/p\u003e \u003cp\u003e15.3.1 Dataset Description 342\u003c\/p\u003e \u003cp\u003e15.3.2 Experimental Images 343\u003c\/p\u003e \u003cp\u003e15.3.3 Experimental Testing Phase 343\u003c\/p\u003e \u003cp\u003e15.3.4 Performance Analysis 344\u003c\/p\u003e \u003cp\u003e15.4 Conclusion 345\u003c\/p\u003e \u003cp\u003eReferences 346\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Role of Artificial Intelligence in Marketing 349\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eG. Muruganantham and R.S. Aswanth\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 350\u003c\/p\u003e \u003cp\u003e16.1.1 Impact of AI in Marketing 351\u003c\/p\u003e \u003cp\u003e16.1.2 Benefits of AI in Marketing 352\u003c\/p\u003e \u003cp\u003e16.1.3 AI in Marketing Functions 354\u003c\/p\u003e \u003cp\u003e16.1.4 Applications of AI in Marketing 354\u003c\/p\u003e \u003cp\u003e16.1.5 Challenges of AI in Marketing 356\u003c\/p\u003e \u003cp\u003e16.1.6 Future of AI in Marketing 357\u003c\/p\u003e \u003cp\u003e16.2 New Trends of AI in Marketing 358\u003c\/p\u003e \u003cp\u003e16.2.1 Companies Using AI in Marketing 359\u003c\/p\u003e \u003cp\u003e16.3 Aspects of AI in Marketing across Different Industries 362\u003c\/p\u003e \u003cp\u003e16.4 Conclusion 364\u003c\/p\u003e \u003cp\u003eReferences 365\u003c\/p\u003e \u003cp\u003eAbout the Editors 369\u003c\/p\u003e \u003cp\u003eIndex 371\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433207886104,"sku":"9781394214228","price":165.29,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394214228.jpg?v=1784851839","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/computational-intelligence-theory-and-applications-hardback-9781394214228","provider":"Freshly Printed Books","version":"1.0","type":"link"}