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Integrating Metaheuristics in Computer Vision for Real-World Optimization Problems

Edited by Shubham Mahajan, Kapil Joshi, Amit Kant Pandit and Nitish Pathak
Copyright: 2024   |   Status: Published
ISBN: 9781394230921  |  Hardcover  |  
361 pages
Price: $225 USD
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One Line Description
A comprehensive book providing high-quality research addressing challenges
in theoretical and application aspects of soft computing and machine
learning in image processing and computer vision.

Audience
The book has been designed for researchers, engineers, graduate, and post-graduate students wanting to learn more about the theoretical and application aspects of soft computing and machine learning in image processing and computer vision.

Description
Researchers are working to create new algorithms that combine the methods provided by CI approaches to solve the problems of image processing and computer vision such as image size, noise, illumination, and security. The 19 chapters in this book examine computational intelligence (CI) approaches as alternative solutions for automatic computer vision and image processing systems in a wide range of applications, using machine learning and soft computing.
Applications highlighted in the book include:
•diagnostic and therapeutic techniques for ischemic stroke, object detection, tracking face detection and recognition;
•computational-based strategies for drug repositioning and improving performance with feature selection, extraction, and learning;
•methods capable of retrieving photometric and geometric transformed images;
•concepts of trading the cryptocurrency market based on smart price action strategies; comparative evaluation and prediction of exoplanets using machine learning methods; the risk of using failure rate with the help of MTTF and MTBF to calculate reliability; a detailed description of various techniques using edge detection algorithms;
•machine learning in smart houses; the strengths and limitations of swarm intelligence and computation; how to use bidirectional LSTM for heart arrhythmia detection;
•a comprehensive study of content-based image-retrieval techniques for feature extraction;
•machine learning approaches to understanding angiogenesis;
•handwritten image enhancement based on neutroscopic-fuzzy.

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Author / Editor Details
Kapil Joshi, PhD, is an assistant professor in the Computer Science & Engineering Department, Uttaranchal Institute of Technology in Dehradun, India. His doctorate was on image quality enhancement using fusion techniques. He has 8 years of academic experience and has published patents, research papers, and two books. In 2021, he was awarded the ‘Best Young Researcher’ Award in Global Education and Corporate Leadership received by Life Way Tech India Pvt. Ltd.

Shubham Mahajan, PhD, is an assistant professor in the School of Engineering at Ajeekya D Y Patil University, Pune, Maharashtra, India. He has eight Indian, one Australian, and one German patent to his credit in artificial intelligence and image processing. He has authored/co-authored more than 50 publications including peer-reviewed journals and conferences. His main research interests include image processing, video compression, image segmentation, fuzzy entropy, nature-inspired computing methods with applications in optimization, data mining, machine learning, robotics, and optical communication.

Amit Kant Pandit, PhD, is an associate professor in the School of Electronics & Communication Engineering Shri Mata Vaishno Devi University, India. He has authored/co-authored more than 60 publications including peer-reviewed journals and conferences. He has two Indian and one Australian patent to his credit in artificial intelligence and image processing. His main research interests are image processing, video compression, image segmentation, fuzzy entropy, and nature-inspired computing methods with applications in optimization.

Nitish Pathak, PhD, is an associate professor in the Department of Information Technology, Bhagwan Parshuram Institute of Technology, New Delhi, India. He has 17 years of engineering education experience and has published more than 80 journal articles, in peer-reviewed journals as well as book chapters, patents, and conference papers. His research areas include intelligent computing techniques, empirical software engineering, and artificial intelligence.

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Table of Contents
Preface
1. Advancement in Diagnostic and Therapeutic Techniques for Ischemic Stroke
Mukul Jain, Divya Patil, Shubham Gupta and Shubham Mahajan
1.1 Introduction
1.2 Diagnostic Tools of Ischemic Stroke
1.2.1 Preimaging
1.2.2 Imaging
1.2.2.1 Computed Tomography Scan
1.2.2.2 Magnetic Resonance Imaging
1.2.2.3 Electromyography
1.2.2.4 Electroencephalography (EEG)
1.2.2.5 Positron Emission Tomography (PET)
1.3 Artificial Intelligence–Based Diagnostic Tools
1.4 Blood-Based Protein Biomarker for Stroke
1.5 Markers for Endothelial Damage
1.6 Markers of Brain Injury
1.7 Therapeutic Advances in Ischemic Stroke
1.7.1 Ligand-Mediated Active Targeting
1.7.2 Nanomedicines That Provide Oxygen to Ischemic Brain Tissue
1.7.3 Reducing Oxidative Stress With Nanomedicines
1.7.4 Multiple Abnormalities are Controlled by Nanomedicine
1.8 Nanoparticles
1.8.1 Carbon Nanotubes
1.8.2 Dendrimers
1.8.3 Metal Nanoparticles
1.9 Conclusion
Future Perspectives
References
2. Object Detection and Tracking Face Detection and Recognition
Varsha K. Patil, Pawan Nawade, Rudra Nagarkar and Paresh Kadale
2.1 Introduction
2.2 Motivation
2.3 The Basics of Computer Vision
2.3.1 Computer Vision
2.3.2 Implementation of Computer Vision
2.3.3 Applications of Computer Vision
2.3.3.1 Image Processing Technique
2.3.3.2 Feature Extraction and Feature Selection Technique
2.3.3.3 Object Recognition Algorithm
2.4 Face Detection
2.4.1 What is Face Detection
2.4.2 Techniques for Face Detection
2.5 Facial Expression
2.5.1 Facial Recognition
2.5.2 Information About Face
2.5.3 Algorithms
2.6 Object Detection
2.6.1 Object Tracking
2.6.2 Algorithms Used in Object Detection
2.7 Face Detection and Identification in Practical Situations
2.7.1 Face Detection
2.7.2 Face Detection and Identification in Real-World Situations
2.8 Future Direction in Object Detection and Tracking
2.8.1 Future Plans for Object Tracking and Detection
2.8.1.1 Multiobject Tracking
2.8.2 3D Object Tracking and Detection
2.8.3 Real-Time Performance
2.9 Conclusion
References
3. Printing Organs with 3D Technology
Shaik Aminabee
3.1 Introduction
3.2 Bioprinting in Three Dimensions (3D)
3.3 3D Printing Types
3.3.1 Inkjet Bioprinting
3.3.2 Microextrusion Bioprinting
3.3.3 Laser-Assisted Printing
3.3.4 Stereolithography
3.3.5 3D Bioprinting Materials and Cells
3.4 Applications for 3D Printing in Cells
3.4.1 Blood Vessels
3.4.2 Liver
3.4.3 Cartilage
3.4.4 Muscle
3.4.5 Bone
3.4.6 Skin
3.4.7 Neutralization of Neurons
3.4.8 Pancreas
3.5 New Developments
3.6 Progress in India
3.7 Limitation
3.8 A Future Point of View
3.9 Conclusion
References
4. Comparative Evaluation of Machine Learning Algorithms for Bank Fraud
Detection
Kiran Jot Singh, Divneet Singh Kapoor, Kunal Ranjan Singh, Chirag Kalucha, Gatik Alagh, Khushal Thakur and Anshul Sharma
4.1 Introduction
4.2 Proposed Framework
4.3 Results
4.4 Concluding Remarks and Future Scope
References
5. An Overview of Computational-Based Strategies for Drug Repositioning
Shalu Verma, Nidhi Nainwal, Alka Singh, Gauree Kukreti and Kiran Dobhal
5.1 Introduction
5.2 Drug Repositioning
5.2.1 Computational Strategies for Drug Repositioning
5.2.1.1 IoT in Drug Repositioning
5.2.1.2 AI and ML in Drug Repositioning
5.2.1.3 Digital Twin in Drug Repurposing
5.2.1.4 Cloud Computing in Drug Repositioning
5.2.1.5 Big Data in Drug Repositioning
5.3 Challenges and Opportunities for Drug Repurposing
5.4 Conclusion
References
6. Improving Performance With Feature Selection, Extraction, and Learning
Varsha K. Patil, Vrinda Shinde, Ritika Singh and Vipul Singh
6.1 Introduction
6.2 Feature Selection
6.2.1 Filter Methods
6.2.1.1 Procedure
6.2.1.2 Advantages
6.2.1.3 Disadvantages
6.2.2 Wrapper Method
6.2.2.1 Procedure
6.2.2.2 Advantages and Disadvantages
6.2.2.3 Forward Selection Algorithm
6.2.2.4 Backward Selection Algorithm
6.2.3 Embedded Method
6.2.3.1 Least Absolute Shrinkage and Selection Operator
6.2.3.2 Advantages
6.2.3.3 Disadvantages
6.3 Feature Extraction
6.3.1 Principal Component Analysis
6.3.1.1 Procedure
6.3.1.2 Implementation
6.3.1.3 Advantages
6.3.1.4 Disadvantages
6.3.2 Linear Discriminant Analysis
6.3.2.1 Concept
6.3.2.2 Implementation
6.3.2.3 Advantages
6.3.2.4 Disadvantages
6.4 Feature Learning
6.4.1 Supervised Learning
6.4.2 Unsupervised Learning
6.4.2.1 Procedure
6.4.2.2 Advantages
6.4.2.3 Disadvantages
6.4.3 Deep Learning
6.4.3.1 Neural Network Architecture
6.4.3.2 Training Process
6.4.3.3 Advantages
6.4.3.4 Disadvantages
6.4.4 Machine Learning and Deep Learning
6.5 Future Research and Development
6.6 Future Scope
6.7 Conclusion
References
7. Fusion of Phase and Local Features for CBIR
Pooja Sharma
7.1 Introduction
7.2 Overview of the Proposed System
7.3 Proposed Hybrid-Shape Descriptors
7.3.1 Global Feature Extraction Using ZMs
7.3.1.1 Recurrence Relation for Radial Polynomials Rpq(r)
7.3.1.2 Recurrence Relation for Trigonometric Functions
7.3.2 Local Feature Extraction Using Hough Transform
7.3.3 Features Dimension
7.3.4 Effectiveness of the Proposed Descriptors
7.4 Similarity Measurement
7.5 Experimental Study and Performance Evaluation
7.5.1 Precision and Recall (P − R)
7.5.2 Database Construction
7.5.3 Experimental Study
7.5.3.1 Evaluation of Image Retrieval Performance on Subject Databases
7.5.3.2 Evaluation of Image Retrieval Performance on Geometric and Photometric Transformed Databases
7.5.3.3 Evaluation of Scalability and Time Complexity
7.6 Conclusions
References
8. Trading Bot for Cryptocurrency Market Based on Smart Price Action Strategies
Divneet Singh Kapoor, Kiran Jot Singh, Anshoom Jain, Rhythm Chauhan, Khushal Thakur and Anshul Sharma
8.1 Introduction
8.2 Background
8.3 Proposed Framework
8.4 Results
8.5 Conclusion and Future Scope
References
9. Comparative Evaluation and Prediction of Exoplanets Using Machine Learning Methods
Divneet Singh Kapoor, Kiran Jot Singh, Ashirvad Singh, Benarji Mulakala, Karan Singh, Prashant, Ramanjeet Singh and Shubham Mahajan
9.1 Introduction
9.2 Background
9.3 Proposed Framework
9.4 Results
9.5 Conclusion and Future Scope
References
10. The Risk of Using Failure Rate With the Help of MTTF and MTBF to Calculate Reliability
Harpreet Kaur and Shiv Kumar Sharma
10.1 Introduction
10.2 Failure
10.2.1 Failure Rate
10.2.2 Mean Time Between Failure
10.2.3 Mean Time to Failure
10.2.4 Reliability
10.2.5 Fault Tree Analysis
10.2.6 Fault Tree Symbols Logic Entrance
10.2.6.1 OR-Gate
10.2.6.2 AND-Gate
10.2.7 Regulations for Fault Tree Structure
10.2.7.1 Illustrate the Fault Actions
10.2.7.2 Estimate the Fault Events
10.2.7.3 Inclusive the Gates
10.3 Conclusion
References
11. A Detailed Description on Various Techniques of Edge Detection Algorithms
Pritha A. and G. Fathima
11.1 Introduction
11.2 Edge Detection Techniques
11.2.1 Steps in Edge Detection
11.2.2 Gradient-Based Techniques
11.2.2.1 Sobel Edge Detected Operator
11.2.2.2 Prewitt Edge Detected Operator
11.2.2.3 Robert Cross Edge Detection Operator
11.2.3 Gaussian Based Technique
11.2.3.1 Canny Edge Detector
11.2.3.2 Canny Operator Architecture
11.3 Experimental Results
11.4 Comparative Results
11.5 Conclusion
11.6 Future Work
References
12. Advancement of ML in Smart House
Gokula Udhayan V., K. Mahaeshwari and N. Vinoth Kumar
12.1 Objective
12.2 Introduction
12.3 Smart House System With IoT
12.3.1 Elements of Smart Home
12.3.2 Smart Home Application Framework
12.3.2.1 Cloud Computing in IoT
12.3.2.2 Smart House System
12.3.3 LPG Detecting System
12.3.3.1 Materials Description
12.3.3.2 Circuit Diagram
12.3.3.3 Power Consumption
12.3.3.4 Components Required
12.3.4 Materials Description
12.3.4.1 NodeMCU 8266
12.3.5 Online Switch
12.3.5.1 Components Required
12.3.5.2 Circuit Diagram
12.3.5.3 Materials Description
12.3.5.4 Projects in Smart House Systems
12.3.6 Introducing Image Processing
12.3.6.1 Image Processing
12.3.6.2 Machine Learning in Automation
12.3.6.3 Online Switch
12.3.6.4 Machine Learning Module
12.3.7 Plants Health Monitoring
12.3.7.1 Components Required
12.3.7.2 Working of the System
12.4 Future Scope
12.5 Conclusion
References
13. Multi-Robot Navigation: A Biologically Inspired Framework
Imran Mir and Faiza Gul
13.1 Introduction
13.1.1 Motivation
13.2 Optimization Algorithms
13.2.1 Mathematical Formulation
13.2.2 Gradient-Based Approaches
13.2.3 Gradient-Free Algorithm
13.2.4 Nature-Inspired Optimization Algorithms
13.2.5 Genetic Algorithms
13.2.6 Particle Swarm Optimization
13.2.7 Ant Colony Optimization
13.2.8 Grey Wolf Algorithm
13.2.9 Arithmetic Algorithm
13.2.10 Aquila Optimization Algorithm
13.2.11 Different Algorithms
13.3 Algorithms and Self-Organization
13.3.1 Algorithmic Attributes
13.3.2 Comparison With Classical Optimization Techniques
13.3.3 Self-Organized Systems
13.4 Future Research Directions
13.5 Conclusion
References
14. Bidirectional LSTM for Heart Arrhythmia Detection
Nikhil M. Agrawal, H. D. Bhanu Cheitanya, Abhishek Kumar Rai and Shubham Mahajan
14.1 Introduction
14.2 About the Dataset
14.3 Flow of the Model
14.4 Results
14.5 Conclusion
References
15. Study on Content-Based Image Retrieval
Thanga Subha Devi M., R. Suji Pramila and Tibbie Pon Symon
15.1 Introduction
15.2 Related Works
15.2.1 Conventional-Indexing Techniques
15.2.2 Dimensionality’s Curse
15.2.2.1 Parallel Architecture
15.2.2.2 Hashing
15.2.2.3 Reduction of Size
15.2.2.4 Bag-of-Features
15.3 Extraction of Features
15.3.1 Color
15.3.1.1 Color Space
15.3.1.2 Method of Representation
15.3.2 Texture
15.3.2.1 Method of Depiction
15.3.3 Shape
15.3.3.1 Methods of Representation
15.4 User Interactions for CBIR System
15.4.1 Query Description
15.4.2 Result Visualization
15.4.2.1 Performance Metrics
15.4.3 Relevance Feedback
15.5 Conclusions
References
16. Machine Learning and Angiogenesis in Cancer
Dharambir Kashyap, Riya Sharma, Neelam Goel and Vivek Kumar Garg
16.1 Introduction
16.2 History of Angiogenesis Discovery
16.3 Overview of Angiogenesis
16.4 Angiogenesis in Carcinogenesis
16.5 Molecular Mechanisms of Angiogenesis Formation
16.6 Angiogenesis as a Target in Cancer Therapy
16.7 Machine Learning Approaches in Angiogenesis
16.8 Conclusion
References
17. Handwritten Image Enhancement Based on Neutroscopic-Fuzzy and K-Mean Clustering
Jaspreet Kaur, Divya Gupta, Simarjeet Kaur and Amrinder Singh
17.1 Introduction
17.1.1 Image Acquisition
17.1.2 Image Segmentation
17.1.3 Image Filtering
17.1.4 Image Enhancement
17.1.5 Clustering
17.1.6 Image Restoration
17.1.7 Data Compression for Image
17.1.8 Color Image Processing
17.2 Application of Image Processing
17.2.1 In Healthcare
17.2.2 In Robotics
17.2.3 In Defence
17.2.4 In Agriculture
17.2.5 In Manufacturing
17.2.6 In Entertainment
17.2.7 Object Recognition
17.3 Enhancement of Handwritten Document
17.3.1 Local Thresholding Technique
17.4 Clustering Techniques
17.4.1 K-Means Clustering Technique
17.4.2 Combination of Neutroscopic and Fuzzy Type 1 Technique
17.5 Performance Parameters
17.5.1 Mean Square Error
17.5.2 Root Mean Square Error
17.5.3 Peak Signal to Noise Ratio
17.5.4 Normalized Coefficient
17.5.5 Distance Reciprocal Distortion Metric
17.6 Results and Discussion
17.6.1 Peak Signal-to-Noise Ratio
17.6.2 Normalized Coefficient
17.6.3 Distance Reciprocal Distortion Metric
17.7 Conclusion
References
18. A Texture Classification System Based on an Adaptive Histogram Equalized
Shearlet Transform
K. Gopalakrishnan, V. Karthikeyan and P.T. Vanathi
18.1 Introduction
18.1.1 Texture Analysis and Texture Classification
18.1.2 Texture Analysis Methods
18.2 Literature Survey
18.3 Materials and Methods
18.3.1 Continuous Shearlet
18.3.2 Discrete Shearlet
18.3.3 Shearlet Decomposition
18.3.4 Feature Extraction
18.3.5 Classifiers
18.3.5.1 Minimum Distance Classifier
18.3.5.2 SVM
18.4 Proposed Methodology
18.4.1 Adaptive Histogram Equalization Method
18.4.2 AHE Algorithm
18.5 Result and Discussion
18.5.1 Experimental Dataset
18.5.2 Experiment 1
18.5.3 Experiment 2
18.6 Conclusion
References
19. A Thyroid Nodule Detection Using L1-Norm Inception Deep Neural Network
Saranya G.
19.1 Introduction
19.2 Related Work
19.3 Methodology
19.3.1 Preprocessing
19.3.2 Noise Removal With Lee Filter
19.3.3 Hybrid Pyramid Fusion Algorithm
19.3.4 L1-Norm Inception Deep Neural Network Classification
19.4 Results and Discussion
19.4.1 Classification Performance Evaluation
19.4.2 Recall
19.4.3 Precision
19.4.4 Accuracy
19.4.5 Specificity
19.4.6 F1 Score
19.5 Conclusion
References
Index

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