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Computational Intelligence

Theory and Applications
Edited by T. Ananth Kumar, E. Golden Julie, Venkata Raghuveer Burugadda, Abhishek Kumar, and Puneet Kumar
Copyright: 2025   |   Status: Published
ISBN: 9781394214228  |  Hardcover  |  

Price: $225 USD
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One Line Description
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.

Audience
Professionals, educators, undergraduate, graduate, and post-graduate students, and policymakers involved in manufacturing industries, remote healthcare, and modern information systems

Description
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 systems physiological characteristics and has been implemented numerically to non-linear mapping. Fuzzy Logic Systems simulate the human brains 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.

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Author / Editor Details
Ananth Kumar, PhD is an associate professor in the Indo French Educational Trust College of Engineering, Anna University, Chennai, India. He has presented papers in various national and international conferences and journals, as well as may book chapters. Additionally, he is a lifetime member of the Indian Society of Technical Education and has many patents spanning across various domains.

E. Golden Julie, PhD is currently working as a Senior Assistant Professor in the department of Computer Science and Engineering, Anna University, Tirunelveli, India. She has more than 12 years of experience in teaching, has published more than 34 papers in various international journals, presented more than 20 papers in both national and international conferences, and written ten book chapters. She has given many guest lectures on various subjects such as multicore architecture, operating systems, and compiler design in premier institutions. Additionally, she is an active lifetime member in Indian Society of Technical Education.

Venkata Raghuveer Burugudda is a senior solution architect and technology leader with over 16 years of experience across the United States and India in product delivery and technology transformation, driving growth for a global network in remittances and payments. He modernized a point of sale solution with new and organizational common architecture to deliver a fast and efficient user experience and allow easy integration of new capabilities through the adoption of cloud migration, devops, test driven principles, and user-centric solutions.

Abhishek Kumar, PhD is currently working as an associate professor and the assistant director of the Computer Science & Engineering Department at Chandigarh University, Punjab, India. He more than 11 years of teaching experience and has been a session chair and keynote speaker for many international conferences and webinars. In addition to his experience, he has more than 100 publications in reputed, peer reviewed national and international journals, books, and conferences, has authored or co-authored seven books published, and edited and additional 27 books.

Puneet Kumar, PhD is a professor and the head of the Department of Computer Science & Engineering at the University Institute of Engineering, Chandigarh University, Mohali, India. He has more than 18 years of teaching, research, and industrial experience and has published various research papers and articles in national and international journals, many of which are widely cited across the world. He is also the recipient of several software copyrights from the Ministry of Human Resource and Development, Government of India.

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Table of Contents
Preface
1. Computational Intelligence Theory: An Orientation Technique
S. Jaisiva, C. Kumar, S. Sakthiya Ram, C. Sakthi Gokul Rajan and P. Praveen Kumar
1.1 Computational Intelligence
1.2 Application Fields for Computational Intelligence
1.2.1 Neural Networks
1.2.1.1 Classification
1.2.1.2 Clustering or Compression
1.2.1.3 Generation of Sequences or Patterns
1.2.1.4 Control Systems
1.2.1.5 Evolutionary Computation
1.2.2 Fuzzy Logic
1.2.2.1 Fuzzy Control Systems
1.2.2.2 Fuzzy Systems
1.2.2.3 Behavioral Motivations for Fuzzy Logic
1.3 Computational Intelligence Paradigms
1.3.1 Artificial Neural Networks
1.3.2 Evolutionary Computation (EC)
1.3.3 Optimization Method
1.3.3.1 Optimization
1.4 Architecture Assortment
1.4.1 Swarm Intelligence
1.4.2 Artificial Immune Systems
1.5 Myths About Computational Intelligence
1.6 Supervised Learning in Computational Intelligence
1.6.1 Performance Measures
1.6.1.1 Accuracy
1.6.1.2 Complexity
1.6.1.3 Convergence
1.6.2 Performance Factors
1.6.2.1 Data Preparation
1.6.2.2 Scaling and Normalization
1.6.2.3 Learning Rate and Momentum
1.6.2.4 Learning Rate
1.6.2.5 Noise Injection
1.7 Training Set Manipulation
1.8 Conclusion
References
2. Nature-Inspired Algorithms for Computational Intelligence Theory—A State-of-the-Art Review
B. Akoramurthy, K. Dhivya and B. Surendiran
2.1 Introduction
2.2 Related Works
2.3 Optimization and Its Algorithms
2.3.1 Definition
2.3.2 Mathematical Notations
2.3.3 Gradient-Based Algorithms
2.3.4 Gradient-Free Optimizers or Algorithms
2.4 Metaheuristic Optimization Methods
2.4.1 Ant Colony Algorithm
2.4.2 Flower Pollination Algorithm
2.4.3 Genetic Algorithms
2.4.4 Evolutionary Algorithm
2.4.5 Method Based on Bats
2.4.6 Cuckoo Searching Method
2.4.7 Firefly Algorithm
2.4.8 Particle Swarm Optimization Algorithm
2.4.9 Krill Herd Algorithm
2.4.10 Artificial Bee Colony (ABC)
2.5 Computational and Autonomous Systems
2.5.1 Computational Features of Nature-Inspired Computing
2.5.2 Comparison with Legacy Algorithms
2.5.3 Autonomous Criticality Systems
2.6 Unresolved Issues for Continued Study
References
3. AI-Based Computational Intelligence Theory
Jana Selvaganesan, S. Arunmozhiselvi, E. Preethi and S. Thangam
3.1 Computational Intelligence
3.2 Designing Expert Systems
3.2.1 Characteristics
3.3 Core of Computational Intelligence
3.3.1 Artificial Intelligence (AI)
3.3.2 Machine Learning (ML)
3.3.3 Neural Networks
3.3.4 Evolutionary Computation
3.3.5 Fuzzy Systems
3.3.6 Swarm Intelligence
3.3.7 Bayesian Networks
3.3.8 Optimization Techniques
3.3.9 Data Mining and Pattern Recognition
3.3.10 Decision Support Systems
3.3.11 Hybrid Approaches
3.4 Research and Development
3.4.1 Government Plans in Enriching AI-Based Computational Intelligence Theory
3.4.1.1 Funding and Research Initiatives
3.4.1.2 Policy and Regulation
3.4.1.3 Standards and Interoperability
3.4.1.4 Education and Workforce Development
3.4.1.5 Industry Collaboration and Partnerships
3.4.1.6 Ethical Guidelines and Responsible AI
3.4.1.7 International Collaboration and Governance
3.5 New Opportunities and Challenges
3.5.1 Explainable AI (XAI)
3.5.2 Adversarial Machine Learning
3.5.3 AI for Edge Computing
3.5.4 Continual Learning
3.5.5 Meta-Learning
3.5.6 AI for Cybersecurity
3.5.7 AI for Healthcare
3.5.7.1 AI for Healthcare-Based Recommendation System
3.5.8 Responsible AI
3.5.9 AI and Robotics Integration
3.5.10 AI for Sustainability and Climate Change
3.5.11 Quantum Computing and AI
3.5.12 Human–AI Collaboration
3.6 Applications
3.6.1 Google-Waymo Car
3.6.2 ChatGPT
3.6.3 Boston Dynamics’ Atlas
3.6.4 Netflix
3.6.5 Trinetra
3.6.6 Voice-Activated Backpack
3.7 Case Study: YOLO v7 for Object Detection in TensorFlow
3.7.1 YOLO v7
3.7.2 Working and Its Features
3.7.3 Configuration to Deploy YOLO V7
3.8 Results
3.9 Performance Analysis
3.10 Challenges in Automation
3.10.1 Marching Towards Solution
3.11 Conclusion
References
4. Information Processing, Learning, and Its Artificial Intelligence
P. Praveenkumar, Pragati M., Prathiba S., Mirthulaa G., Supriya P., Jayashree B. and Jayasri R.
4.1 Introduction—Artificial Intelligence
4.2 Artificial Intelligence and Its Learning
4.3 Artificial Intelligence’s Effects on IT
4.4 Examples of Artificial Intelligence
4.4.1 Smart Learning Content
4.4.2 Intelligent Tutorial System Future
4.4.3 Virtual Facilitators and Learning Environment
4.4.4 Content Analytics
4.5 Data Processing and AI in Human-Centered Manufacturing
4.6 Information Learning
4.6.1 Information Learning Through AI—Chatbots
4.6.2 Information Learning Through AI—Virtual Reality (VR)
4.6.3 Information Learning Through AI—Management of Learning (LMS)
4.6.4 Information Learning Through AI—Robotics
4.6.5 AI Invoice Processing is Not Fantastical—It is Fantastic
4.7 Results
4.8 Conclusion
References
5. Computational Intelligence Approach for Exploration of Spatial Co-Location Patterns
S. LourduMarie Sophie, S. Siva Sathya, S. Sharmiladevi and J. Dhakshayani
5.1 Introduction
5.2 Spatial Data Mining
5.2.1 Spatial Co-Location Pattern Mining
5.3 Preliminaries
5.3.1 Basic Concepts
5.3.1.1 Feature Instance
5.3.1.2 Participation Ratio (PR)
5.3.1.3 Participation Index (PI)
5.3.1.4 Neighbor Relation
5.3.1.5 Conditional Neighborhood
5.3.2 Apache Hadoop—MapReduce
5.3.3 Related Work
5.4 Proposed Grid-Conditional Neighborhood Algorithm
5.4.1 Module Description
5.4.1.1 Search Neighbor
5.4.1.2 Group Neighbors
5.4.1.3 Pattern Search
5.4.1.4 Top K Pattern Generation
5.5 Experimental Setup and Analysis
5.5.1 Dataset Used
5.5.2 Performance Analysis
5.6 Discussion and Conclusion
References
6. Computational Intelligence-Based Optimize Feature Selection Techniques for Detecting Plant Diseases
Karthickmanoj R., S. Aasha Nandhini and T. Sasilatha
6.1 Introduction
6.2 Literature Survey
6.3 Proposed Framework
6.4 Simulation Results
6.5 Summary
References
7. Protein Structure Prediction Using Convolutional Neural Networks Augmented with Cellular Automata
Pokkuluri Kiran Sree, Prasun Chakrabarti, Martin Margala and SSSN Usha Devi N.
7.1 Introduction
7.2 Methods
7.3 Design of the Model
7.4 Results and Comparisons
7.5 Conclusion
References
8. Modeling and Approximating Renewable Energy Systems Using Computational Intelligence
B. Balaji, P. Hemalatha, T. Rampradesh, G. Anbarasi and A. Eswari
8.1 Introduction
8.2 Expert System
8.3 Artificial Neural Networks
8.4 ANN in Renewable Energy Systems
8.5 Conclusion
References
9. Computational Intelligence and Deep Learning in Health Informatics: An Introductory Perspective
J. Naskath, R. Rajakumari, Hamza Aldabbas and Zaid Mustafa
9.1 Introduction
9.2 Mobile Application in Health Informatics Using Deep Learning
9.3 Health Informatics Wearables Using Deep Learning
9.4 Electroencephalogram
9.5 Conclusion
References
10. Computational Intelligence for Human Activity Recognition (HAR)
Thangapriya and Nancy Jasmine Goldena
10.1 Introduction
10.2 Fuzzy Logic in Human Judgment and Decision-Making
10.2.1 FL algorithm
10.2.2 Applications of FL
10.2.3 Advantages of FL
10.2.4 Disadvantages of FL
10.2.5 Utilizing FLS and FIS in HAR Research and Health Monitoring
10.3 Artificial Neural Networks: From Perceptrons to Modern Applications
10.3.1 ANN Algorithm
10.3.2 Applications of ANN
10.3.3 Advantages of ANN
10.3.4 Disadvantages of ANN
10.3.5 Artificial Neural Networks in HAR Research
10.4 Swarm Intelligence
10.4.1 SI Algorithm
10.4.2 Applications of SI
10.4.3 Advantages of SI
10.4.4 Disadvantages of SI
10.4.5 Swarm Intelligence Techniques in HAR Research
10.5 Evolutionary Computing
10.5.1 EC Algorithm
10.5.2 Applications of EC
10.5.3 Advantages of EC
10.5.4 Disadvantages of EC
10.5.5 Harnessing Evolutionary Computation for HAR Research
10.6 Artificial Immune System
10.6.1 AIS Algorithm
10.6.2 Applications of AIS
10.6.3 Advantages of AIS
10.6.4 Disadvantages of AIS
10.6.5 Harnessing AIS for Preventive Measures
10.7 Conclusion
References
11. Computational Intelligence for Multimodal Analysis of High-Dimensional Image Processing in Clinical Settings
B. Balaji, P. Pugazhendiran, N. Sivanantham, N. Velammal and P. Vimala
11.1 Basics of Machine Learning
11.2 Feature Extraction
11.3 Selection of Features
11.4 Statistical Classifiers
11.5 Neural Networks
11.6 Biometric Analysis
11.7 Data from High-Resolution Medical Imaging
11.8 Computational Architectures
11.9 Timing and Uncertainty
11.10 AI and Risk of Harm
11.11 Conclusion
References
12. A Review of Computational Intelligence-Based Biometric Recognition Methods
T. IlamParithi, K. Antony Sudha and D. Jessintha
12.1 Introduction
12.1.1 Objective
12.2 Computational Intelligence
12.3 CI-Based Biometric Recognition
12.3.1 Acquisition
12.3.2 Segmentation
12.3.3 Quality Assessment
12.3.4 Enhancement
12.3.5 Feature Extraction
12.3.6 Matching
12.3.7 Classification
12.3.8 Score Normalization
12.3.9 Anti-Spoofing
12.3.10 Privacy
12.4 Applications
12.4.1 Business
12.4.2 Education
12.4.3 Military
12.4.4 Health Care
12.4.5 Banking
12.5 Conclusion
References
13. Seeing the Unseen: An Automated Early Breast Cancer Detection Using Hyperspectral Imaging
Sravan Kumar Sikhakolli, Suresh Aala, Sunil Chinnadurai and Inbarasan Muniraj
13.1 Introduction
13.1.1 Conventional Imaging Methods for Detecting BC
13.1.2 Optical Imaging Techniques to Detect BC
13.2 Hyperspectral Imaging (HSI)
13.2.1 How Does HSI Setup Look Like?
13.3 State-of-the-Art Techniques for BC Detection
13.3.1 Breast Cancer Ex Vivo Analysis
13.3.2 Breast Cancer In Vivo Analysis
13.4 Artificial Intelligence in BC Detection Using HSI
13.4.1 Deep Learning in HSI
13.4.2 Convolutional Neural Networks
13.4.3 Deep Belief Networks Using HSI
13.4.4 Residual Networks
13.5 Discussion and Conclusion
References
14. Shedding Light into the Dark: Early Oral Cancer Detection Using Hyperspectral Imaging
Suresh Aala, Sravan Kumar Sikhakolli, Inbarasan Muniraj and Sunil Chinnadurai
14.1 Introduction
14.2 HSI in HNC Detection
14.3 Deep Learning in In Vivo HSI
14.3.1 Endoscopic
14.4 Conclusion and Future Research Directions
References
15. Machine Learning Techniques for Glaucoma Screening Using Optic Disc Detection
V. Subha, S. Niraja P. Rayen and Manivanna Boopathi
15.1 Introduction
15.1.1 Ophthalmic Process
15.1.2 Digital Imaging
15.1.2.1 Image Processing
15.1.3 Eye and Its Parts
15.1.3.1 Optic Disc
15.1.3.2 Aqueous Humor
15.1.3.3 Choroid
15.1.3.4 Ciliary Body
15.1.3.5 Ciliary Muscle
15.1.3.6 Iris
15.1.3.7 Pupil
15.1.3.8 Retina
15.1.3.9 Photoreceptor Cells
15.1.3.10 Retinal Blood Vessels
15.1.3.11 Sclera
15.1.3.12 Uvea
15.1.3.13 Visual Axis
15.1.3.14 Visual Cortex
15.1.3.15 Visual Fields
15.1.3.16 Vitreous
15.1.3.17 Zonules
15.1.3.18 Macula (yellow spot)
15.1.3.19 Optic Nerve
15.1.4 Eye Diseases
15.1.4.1 Myopia
15.1.4.2 Hyperopia
15.1.4.3 Astigmatism
15.1.4.4 Presbyopia
15.1.4.5 Strabismus
15.1.4.6 Amblyopia
15.1.4.7 Cataracts
15.1.4.8 Glaucoma
15.1.5 Indications of Glaucoma
15.1.6 Causes of Glaucoma
15.1.6.1 Dietary
15.1.6.2 Ethnicity and Gender
15.1.6.3 Genetics
15.1.7 Analytical Methods of Glaucoma
15.2 Glaucoma Screening with Optic Disc and Classification
15.2.1 Optic Disc Detection
15.2.2 Cropping ROI
15.2.3 Optic Disc Segmentation
15.2.4 Optic Cup Segmentation
15.2.5 Post-Processing
15.2.5.1 Cup–Disc Ratio
15.2.5.2 Evaluation of the NRR Area in the ISNT Quadrants
15.2.5.3 Superpixel Method
15.2.5.4 Level Set Method
15.3 Experimental Section
15.3.1 Dataset Description
15.3.2 Experimental Images
15.3.3 Experimental Testing Phase
15.3.4 Performance Analysis
15.4 Conclusion
References
16. Role of Artificial Intelligence in Marketing
G. Muruganantham and R.S. Aswanth
16.1 Introduction
16.1.1 Impact of AI in Marketing
16.1.2 Benefits of AI in Marketing
16.1.3 AI in Marketing Functions
16.1.4 Applications of AI in Marketing
16.1.5 Challenges of AI in Marketing
16.1.6 Future of AI in Marketing
16.2 New Trends of AI in Marketing
16.2.1 Companies Using AI in Marketing
16.3 Aspects of AI in Marketing across Different Industries
16.4 Conclusion
References
About the Editors
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