Integrating Metaheuristics in Computer Vision for Real-World Optimization Problems

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. Researchers are working to create new algorithms that combine the methods provided by CI approaches to sol...

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Detalles Bibliográficos
Otros Autores:	Joshi, Kapil, editor (editor)
Formato:	Libro electrónico
Idioma:	Inglés
Publicado:	Hoboken, NJ : World Scientific [2024]
Edición:	First edition
Materias:	Image processing > Digital techniques.
Ver en Biblioteca Universitat Ramon Llull:	https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009843331306719

Tabla de Contenidos:

Cover
Series Page
Title Page
Coyright Page
Contents
Preface
Chapter 1 Advancement in Diagnostic and Therapeutic Techniques for Ischemic Stroke
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
Chapter 2 Object Detection and Tracking Face Detection and Recognition
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
Chapter 3 Printing Organs with 3D Technology
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
Chapter 4 Comparative Evaluation of Machine Learning Algorithms for Bank Fraud Detection
4.1 Introduction
4.2 Proposed Framework
4.3 Results
4.4 Concluding Remarks and Future Scope
References
Chapter 5 An Overview of Computational-Based Strategies for Drug Repositioning
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
Chapter 6 Improving Performance With Feature Selection, Extraction, and Learning
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
Chapter 7 Fusion of Phase and Local Features for CBIR
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
Chapter 8 Trading Bot for Cryptocurrency Market Based on Smart Price Action Strategies
8.1 Introduction
8.2 Background
8.3 Proposed Framework
8.4 Results
8.5 Conclusion and Future Scope
References
Chapter 9 Comparative Evaluation and Prediction of Exoplanets Using Machine Learning Methods
9.1 Introduction
9.2 Background
9.3 Proposed Framework
9.4 Results
9.5 Conclusion and Future Scope
References
Chapter 10 The Risk of Using Failure Rate With the Help of MTTF and MTBF to Calculate Reliability
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
Chapter 11 A Detailed Description on Various Techniques of Edge Detection Algorithms
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
Chapter 12 Advancement of ML in Smart House
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
Chapter 13 Multi-Robot Navigation: A Biologically Inspired Framework
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
Chapter 14 Bidirectional LSTM for Heart Arrhythmia Detection
14.1 Introduction
14.2 About the Dataset
14.3 Flow of the Model
14.4 Results
14.5 Conclusion
References
Chapter 15 Study on Content-Based Image Retrieval
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.

Integrating Metaheuristics in Computer Vision for Real-World Optimization Problems

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