Machine Learning Hybridization and Optimization for Intelligent Applications

Tanvir Habib Sardar, Bishwajeet Kumar Pandey

• Publisher: CRC Press
• Year: 2024
• eBook ISBN: 9781040130391
• Edition: 0
• Topic: Computer Science
• Index: Computer Science

This book discusses state-of-the-art reviews of the existing machine learning techniques and algorithms including hybridizations and optimizations. It covers applications of machine learning via artificial intelligence (AI) prediction tools, discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, pattern recognition approaches to functional magnetic resonance imaging, image and speech recognition, automatic language translation, medical diagnostic, stock market prediction, traffic prediction, and product automation.

Features:

• Focuses on hybridization and optimization of machine learning techniques
• Reviews supervised, unsupervised, and reinforcement learning using case study-based applications
• Covers the latest machine learning applications in as diverse domains as the Internet of Things, data science, cloud computing, and distributed and parallel computing
• Explains computing models using real-world examples and dataset-based experiments
• Includes case study-based explanations and usage for machine learning technologies and applications

This book is aimed at graduate students and researchers in machine learning, artificial intelligence, and electrical engineering.

Table of contents

• Cover
• Half-Title
• Series
• Title
• Copyright
• Contents
• Preface
• About the Editors
• List of Contributors
• List of Abbreviations
• Glossary
• Chapter 1 Big Data Computing: Transforming From Cloud Computing to Edge Scheduling Perspectives Review
• Chapter 2 Decision Making in the Field of Unmanned Aerial Vehicles: State-of-the-Art
• Chapter 3 A Brief Study on Understanding and Handling COVID-19: Test Bed for Forecasting with Deep Learning and Machine Learning Algorithms
• Chapter 4 AgTech: Using Sensors and Machine Learning to Revolutionize Farming Practices (IoT)
• Chapter 5 Developing an AI-based Multi-Task Transfer Learning Framework for Automating Judicial Contracts
• Chapter 6 Analysis of Deep Learning Methodologies for Handling Non-Medical Big Data and Very Limited Medical Data with Feature Extraction and Annotation Techniques
• Chapter 7 Introduction to Virtualization Security and Cloud Security
• Chapter 8 Security Breaches in IoT Applications: An Extensive Study
• Chapter 9 An Efficient and Accurate Classification Algorithm for ECG Signals Using PNN and KNN
• Chapter 10 Big Data Analytics: The Classification of Remote Sensing Images Using Machine Learning Techniques
• Chapter 11 Segmentation of Transmission Tower Components Based on Machine Learning
• Chapter 12 A Systematic Analysis of Robot Path Planning and Optimization Techniques
• Chapter 13 Pneumonia Prediction Model Using Deep Learning on Docker
• Chapter 14 A Sequential Deep Learning Model Approach to OCR-Based Handwritten Digit Recognition for Physically Impaired People
• Chapter 15 A Deep Learning Strategy for Sign Language Classification and Recognition for Hearing-Impaired People
• Chapter 16 Non-fungible Tokens (NFTs): The Design and Development of the “Obstacle Assault” Game and “Turtle Sidestep” Game
• Chapter 17 Design and Development of 2D Space Shooter Game and Arcade Game Using Unity
• Chapter 18 An Ensemble Technique Using Genetic Algorithm and Deep Learning for the Prediction of Rice Diseases
• Chapter 19 History of Machine Learning
• Chapter 20 Internet of Things Start-Ups: An Overview of the Privacy and Security in IoT Start-Ups
• Index