This 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. Computational Intelligence 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.

Table of Contents:
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 EC 227 10.5.3 Advantages of EC 228 10.5.4 Disadvantages of EC 228 10.5.5 Harnessing Evolutionary Computation for HAR Research 228 10.6 Artificial Immune System 228 10.6.1 AIS Algorithm 229 10.6.2 Applications of AIS 230 10.6.3 Advantages of AIS 230 10.6.4 Disadvantages of AIS 230 10.6.5 Harnessing AIS for Preventive Measures 231 10.7 Conclusion 231 References 232 11 Computational Intelligence for Multimodal Analysis of High-Dimensional Image Processing in Clinical Settings 235 B. Balaji, P. Pugazhendiran, N. Sivanantham, N. Velammal and P. Vimala 11.1 Basics of Machine Learning 236 11.2 Feature Extraction 237 11.3 Selection of Features 238 11.4 Statistical Classifiers 239 11.5 Neural Networks 242 11.6 Biometric Analysis 244 11.7 Data from High-Resolution Medical Imaging 251 11.8 Computational Architectures 255 11.9 Timing and Uncertainty 256 11.10 AI and Risk of Harm 258 11.11 Conclusion 259 References 259 12 A Review of Computational Intelligence-Based Biometric Recognition Methods 263 T. IlamParithi, K. Antony Sudha and D. Jessintha 12.1 Introduction 263 12.1.1 Objective 264 12.2 Computational Intelligence 264 12.3 CI-Based Biometric Recognition 266 12.3.1 Acquisition 266 12.3.2 Segmentation 266 12.3.3 Quality Assessment 269 12.3.4 Enhancement 270 12.3.5 Feature Extraction 270 12.3.6 Matching 271 12.3.7 Classification 272 12.3.8 Score Normalization 272 12.3.9 Anti-Spoofing 272 12.3.10 Privacy 273 12.4 Applications 273 12.4.1 Business 273 12.4.2 Education 274 12.4.3 Military 275 12.4.4 Health Care 276 12.4.5 Banking 276 12.5 Conclusion 277 References 277 13 Seeing the Unseen: An Automated Early Breast Cancer Detection Using Hyperspectral Imaging 281 Sravan Kumar Sikhakolli, Suresh Aala, Sunil Chinnadurai and Inbarasan Muniraj 13.1 Introduction 282 13.1.1 Conventional Imaging Methods for Detecting BC 283 13.1.2 Optical Imaging Techniques to Detect BC 284 13.2 Hyperspectral Imaging (HSI) 285 13.2.1 How Does HSI Setup Look Like? 286 13.3 State-of-the-Art Techniques for BC Detection 287 13.3.1 Breast Cancer Ex Vivo Analysis 287 13.3.2 Breast Cancer In Vivo Analysis 290 13.4 Artificial Intelligence in BC Detection Using HSI 291 13.4.1 Deep Learning in HSI 291 13.4.2 Convolutional Neural Networks 292 13.4.3 Deep Belief Networks Using HSI 293 13.4.4 Residual Networks 293 13.5 Discussion and Conclusion 293 References 294 14 Shedding Light into the Dark: Early Oral Cancer Detection Using Hyperspectral Imaging 301 Suresh Aala, Sravan Kumar Sikhakolli, Inbarasan Muniraj and Sunil Chinnadurai 14.1 Introduction 302 14.2 HSI in HNC Detection 305 14.3 Deep Learning in In Vivo HSI 313 14.3.1 Endoscopic 313 14.4 Conclusion and Future Research Directions 315 References 316 15 Machine Learning Techniques for Glaucoma Screening Using Optic Disc Detection 321 V. Subha, S. Niraja P. Rayen and Manivanna Boopathi 15.1 Introduction 322 15.1.1 Ophthalmic Process 324 15.1.2 Digital Imaging 324 15.1.2.1 Image Processing 325 15.1.3 Eye and Its Parts 326 15.1.3.1 Optic Disc 327 15.1.3.2 Aqueous Humor 327 15.1.3.3 Choroid 327 15.1.3.4 Ciliary Body 327 15.1.3.5 Ciliary Muscle 327 15.1.3.6 Iris 328 15.1.3.7 Pupil 328 15.1.3.8 Retina 328 15.1.3.9 Photoreceptor Cells 328 15.1.3.10 Retinal Blood Vessels 328 15.1.3.11 Sclera 329 15.1.3.12 Uvea 329 15.1.3.13 Visual Axis 329 15.1.3.14 Visual Cortex 329 15.1.3.15 Visual Fields 329 15.1.3.16 Vitreous 329 15.1.3.17 Zonules 330 15.1.3.18 Macula (Yellow Spot) 330 15.1.3.19 Optic Nerve 330 15.1.4 Eye Diseases 330 15.1.4.1 Myopia 330 15.1.4.2 Hyperopia 330 15.1.4.3 Astigmatism 330 15.1.4.4 Presbyopia 331 15.1.4.5 Strabismus 331 15.1.4.6 Amblyopia 331 15.1.4.7 Cataracts 331 15.1.4.8 Glaucoma 332 15.1.5 Indications of Glaucoma 332 15.1.6 Causes of Glaucoma 332 15.1.6.1 Dietary 332 15.1.6.2 Ethnicity and Gender 332 15.1.6.3 Genetics 333 15.1.7 Analytical Methods of Glaucoma 333 15.2 Glaucoma Screening with Optic Disc and Classification 334 15.2.1 Optic Disc Detection 335 15.2.2 Cropping ROI 337 15.2.3 Optic Disc Segmentation 338 15.2.4 Optic Cup Segmentation 338 15.2.5 Post-Processing 340 15.2.5.1 Cup–Disc Ratio 340 15.2.5.2 Evaluation of the NRR Area in the ISNT Quadrants 341 15.2.5.3 Superpixel Method 341 15.2.5.4 Level Set Method 342 15.3 Experimental Section 342 15.3.1 Dataset Description 342 15.3.2 Experimental Images 343 15.3.3 Experimental Testing Phase 343 15.3.4 Performance Analysis 344 15.4 Conclusion 345 References 346 16 Role of Artificial Intelligence in Marketing 349 G. Muruganantham and R.S. Aswanth 16.1 Introduction 350 16.1.1 Impact of AI in Marketing 351 16.1.2 Benefits of AI in Marketing 352 16.1.3 AI in Marketing Functions 354 16.1.4 Applications of AI in Marketing 354 16.1.5 Challenges of AI in Marketing 356 16.1.6 Future of AI in Marketing 357 16.2 New Trends of AI in Marketing 358 16.2.1 Companies Using AI in Marketing 359 16.3 Aspects of AI in Marketing across Different Industries 362 16.4 Conclusion 364 References 365 About the Editors 369 Index 371