Introduction xvii 1 Computational Intelligence Theory: An Orientation Technique 1 S. Jaisiva, C. Kumar, S. Sakthiya Ram, C. Sakthi Gokul Rajan and P. Praveen Kumar 1.1 Computational Intelligence 2 1.2 Application Fields for Computational Intelligence 4 1.

2.1 Neural Networks 4 1.2.1.1 Classification 4 1.2.1.2 Clustering or Compression 5 1.

2.1.3 Generation of Sequences or Patterns 5 1.2.1.4 Control Systems 5 1.2.1.

5 Evolutionary Computation 6 1.2.2 Fuzzy Logic 6 1.2.2.1 Fuzzy Control Systems 6 1.2.2.

2 Fuzzy Systems 6 1.2.2.3 Behavioral Motivations for Fuzzy Logic 7 1.3 Computational Intelligence Paradigms 7 1.3.1 Artificial Neural Networks 7 1.3.

2 Evolutionary Computation (EC) 10 1.3.3 Optimization Method 11 1.3.3.1 Optimization 11 1.4 Architecture Assortment 12 1.4.

1 Swarm Intelligence 14 1.4.2 Artificial Immune Systems 14 1.5 Myths About Computational Intelligence 15 1.6 Supervised Learning in Computational Intelligence 16 1.6.1 Performance Measures 17 1.6.

1.1 Accuracy 17 1.6.1.2 Complexity 18 1.6.1.3 Convergence 19 1.

6.2 Performance Factors 19 1.6.2.1 Data Preparation 19 1.6.2.2 Scaling and Normalization 19 1.

6.2.3 Learning Rate and Momentum 20 1.6.2.4 Learning Rate 20 1.6.2.

5 Noise Injection 20 1.7 Training Set Manipulation 21 1.8 Conclusion 21 References 21 2 Nature-Inspired Algorithms for Computational Intelligence Theory--A State-of-the-Art Review 25 B. Akoramurthy, K. Dhivya and B. Surendiran 2.1 Introduction 25 2.2 Related Works 27 2.

3 Optimization and Its Algorithms 28 2.3.1 Definition 28 2.3.2 Mathematical Notations 28 2.3.3 Gradient-Based Algorithms 29 2.3.

4 Gradient-Free Optimizers or Algorithms 31 2.4 Metaheuristic Optimization Methods 32 2.4.1 Ant Colony Algorithm 32 2.4.1.1 Ant Colony Optimization Algorithm 32 2.4.

2 Flower Pollination Algorithm 34 2.4.3 Genetic Algorithms 35 2.4.4 Evolutionary Algorithm 36 2.4.5 Method Based on Bats 37 2.4.

6 Cuckoo Searching Method 38 2.4.7 Firefly Algorithm 39 2.4.8 Particle Swarm Optimization Algorithm 41 2.4.9 Krill Herd Algorithm 42 2.4.

10 Artificial Bee Colony (ABC) 43 2.5 Computational and Autonomous Systems 44 2.5.1 Computational Features of Nature-Inspired Computing 44 2.5.2 Comparison with Legacy Algorithms 45 2.5.3 Autonomous Criticality Systems 46 2.

6 Unresolved Issues for Continued Study 47 References 49 3 AI-Based Computational Intelligence Theory 53 Jana Selvaganesan, S. Arunmozhiselvi, E. Preethi and S. Thangam 3.1 Computational Intelligence 54 3.2 Designing Expert Systems 55 3.2.1 Characteristics 56 3.

3 Core of Computational Intelligence 56 3.3.1 Artificial Intelligence (AI) 56 3.3.2 Machine Learning (ML) 57 3.3.3 Neural Networks 57 3.3.

4 Evolutionary Computation 58 3.3.5 Fuzzy Systems 58 3.3.6 Swarm Intelligence 59 3.3.7 Bayesian Networks 60 3.3.

8 Optimization Techniques 60 3.3.9 Data Mining and Pattern Recognition 60 3.3.10 Decision Support Systems 61 3.3.11 Hybrid Approaches 61 3.4 Research and Development 62 3.

4.1 Government Plans in Enriching AI-Based Computational Intelligence Theory 62 3.4.1.1 Funding and Research Initiatives 62 3.4.1.2 Policy and Regulation 62 3.

4.1.3 Standards and Interoperability 63 3.4.1.4 Education and Workforce Development 63 3.4.1.

5 Industry Collaboration and Partnerships 63 3.4.1.6 Ethical Guidelines and Responsible AI 63 3.4.1.7 International Collaboration and Governance 64 3.5 New Opportunities and Challenges 64 3.

5.1 Explainable AI (XAI) 64 3.5.2 Adversarial Machine Learning 65 3.5.3 AI for Edge Computing 65 3.5.4 Continual Learning 67 3.

5.5 Meta-Learning 68 3.5.6 AI for Cybersecurity 69 3.5.7 AI for Healthcare 70 3.5.7.

1 AI for Healthcare-Based Recommendation System 72 3.5.8 Responsible AI 72 3.5.9 AI and Robotics Integration 73 3.5.10 AI for Sustainability and Climate Change 74 3.5.

11 Quantum Computing and AI 75 3.5.12 Human-AI Collaboration 76 3.6 Applications 77 3.6.1 Google-Waymo Car 77 3.6.2 ChatGPT 79 3.

6.3 Boston Dynamics'' Atlas 80 3.6.4 Netflix 81 3.6.5 Trinetra 82 3.6.6 Voice-Activated Backpack 83 3.

7 Case Study: YOLO v7 for Object Detection in TensorFlow 84 3.7.1 Yolo V 7 84 3.7.2 Working and Its Features 85 3.7.3 Configuration to Deploy YOLO V 7 87 3.8 Results 88 3.

9 Performance Analysis 89 3.10 Challenges in Automation 91 3.10.1 Marching Towards Solution 92 3.11 Conclusion 93 References 93 4 Information Processing, Learning, and Its Artificial Intelligence 97 P. Praveenkumar, Pragati M., Prathiba S., Mirthulaa G.

, Supriya P., Jayashree B. and Jayasri R. 4.1 Introduction--Artificial Intelligence 98 4.2 Artificial Intelligence and Its Learning 99 4.3 Artificial Intelligence''s Effects on IT 100 4.4 Examples of Artificial Intelligence 101 4.

4.1 Smart Learning Content 101 4.4.2 Intelligent Tutorial System Future 103 4.4.3 Virtual Facilitators and Learning Environment 104 4.4.4 Content Analytics 105 4.

5 Data Processing and AI in Human-Centered Manufacturing 106 4.6 Information Learning 107 4.6.1 Information Learning Through AI--Chatbots 107 4.6.2 Information Learning Through AI--Virtual Reality (vr) 108 4.6.3 Information Learning Through AI--Management of Learning (LMS) 110 4.

6.4 Information Learning Through AI--Robotics 111 4.6.5 AI Invoice Processing is Not Fantastical-- It is Fantastic 113 4.7 Results 113 4.8 Conclusion 114 References 114 5 Computational Intelligence Approach for Exploration of Spatial Co-Location Patterns 117 S. LourduMarie Sophie, S. Siva Sathya, S.

Sharmiladevi and J. Dhakshayani 5.1 Introduction 118 5.2 Spatial Data Mining 120 5.2.1 Spatial Co-Location Pattern Mining 120 5.3 Preliminaries 123 5.3.

1 Basic Concepts 123 5.3.1.1 Feature Instance 124 5.3.1.2 Participation Ratio (PR) 124 5.3.

1.3 Participation Index (PI) 125 5.3.1.4 Neighbor Relation 125 5.3.1.5 Conditional Neighborhood 126 5.

3.2 Apache Hadoop--MapReduce 126 5.3.3 Related Work 128 5.4 Proposed Grid-Conditional Neighborhood Algorithm 130 5.4.1 Module Description 131 5.4.

1.1 Search Neighbor 131 5.4.1.2 Group Neighbors 132 5.4.1.3 Pattern Search 133 5.

4.1.4 Top K Pattern Generation 133 5.5 Experimental Setup and Analysis 134 5.5.1 Dataset Used 134 5.5.2 Performance Analysis 136 5.

6 Discussion and Conclusion 138 References 140 6 Computational Intelligence-Based Optimal Feature Selection Techniques for Detecting Plant Diseases 145 Karthickmanoj R., S. Aasha Nandhini and T. Sasilatha 6.1 Introduction 145 6.2 Literature Survey 146 6.3 Proposed Framework 151 6.4 Simulation Results 152 6.

5 Summary 156 References 156 7 Protein Structure Prediction Using Convolutional Neural Networks Augmented with Cellular Automata 159 Pokkuluri Kiran Sree, Prasun Chakrabarti, Martin Margala and SSSN Usha Devi N. 7.1 Introduction 160 7.2 Methods 162 7.3 Design of the Model 164 7.4 Results and Comparisons 167 7.5 Conclusion 172 References 172 8 Modeling and Approximating Renewable Energy Systems Using Computational Intelligence 175 B. Balaji, P.

Hemalatha, T. Rampradesh, G. Anbarasi and A. Eswari 8.1 Introduction 176 8.2 Expert System 178 8.3 Artificial Neural Networks 179 8.4 ANN in Renewable Energy Systems 182 8.

5 Conclusion 185 References 186 9 Computational Intelligence and Deep Learning in Health Informatics: An Introductory Perspective 189 J. Naskath, R. Rajakumari, Hamza Aldabbas and Zaid Mustafa 9.1 Introduction 190 9.2 Mobile Application in Health Informatics Using Deep Learning 191 9.3 Health Informatics Wearables Using Deep Learning 197 9.4 Electroencephalogram 202 9.5 Conclusion 203 References 207 10 Computational Intelligence for Human Activity Recognition (HAR) 213 Thangapriya and Nancy Jasmine Goldena 10.

1 Introduction 214 10.2 Fuzzy Logic in Human Judgment and Decision-Making 215 10.2.1 FL Algorithm 216 10.2.2 Applications of FL 217 10.2.3 Advantages of FL 217 10.

2.4 Disadvantages of FL 218 10.2.5 Utilizing FLS and FIS in HAR Research and Health Monitoring 218 10.3 Artificial Neural Networks: From Perceptrons to Modern Applications 219 10.3.1 ANN Algorithm 221 10.3.

2 Applications of ANN 222 10.3.3 Advantages of ANN 222 10.3.4 Disadvantages of ANN 222 10.3.5 Artificial Neural Networks in HAR Research 223 10.4 Swarm Intelligence 223 10.

4.1 SI Algorithm 224 10.4.2 Applications of SI 224 10.4.3 Advantages of SI 225 10.4.4 Disadvantages of SI 225 10.

4.5 Swarm Intelligence Techniques in HAR Research 225 10.5 Evolutionary Computing 226 10.5.1 EC Algorithm 226 10.5.2 Applications of E.