Integrating Metaheuristics in Computer Vision for Real-World Optimization Problems / edited by Kapil Joshi [and three others].

Format:

Book

Contributor:

Joshi, Kapil, editor.

Language:

English

Subjects (All):

Image processing--Digital techniques.

Image processing.

Physical Description:

1 online resource (362 pages)

Edition:

First edition.

Place of Publication:

Hoboken, NJ : World Scientific, [2024]

Summary:

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 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. 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.

Contents:

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

Includes bibliographical references and index.

ISBN:

1-394-23095-8

1-394-23094-X

OCLC:

1449546684