Machine Learning Hybridization and Optimization for Intelligent Applications
1st Edition
Published on 28. October 2024
366 pages
978-1-040-13035-3 (ISBN)
Description
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.
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.
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Illustrations
81 Line drawings, black and white; 30 Halftones, black and white; 111 Illustrations, black and white
ISBN-13
978-1-040-13035-3 (9781040130353)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Tanvir Habib Sardar | Bishwajeet Kumar Pandey
Machine Learning Hybridization and Optimization for Intelligent Applications
Book
approx. 06/2026
1st Edition
CRC Press
€77.50
Not yet published
Tanvir Habib Sardar | Bishwajeet Kumar Pandey
Machine Learning Hybridization and Optimization for Intelligent Applications
Book
10/2024
1st Edition
CRC Press
€208.50
Shipment within 10-20 days
Persons
Tanvir Habib Sardar is an Assistant Professor in the department of CSE at GITAM University, Bengaluru campus. He has more than fifteen years of experience in industry and academia. His research domain is big data, machine learning, fuzzy logic, and distributed computing using MapReduce.
Bishwajeet Kumar Pandey is a Professor at Department of Intelligent System and Cyber Security, Astana IT University Kazaksthan. He is also a visiting professor at Eurasian National University, Astana, Kazaksthan (QS World Rank 355) and UCSI University, Kuala Lumpur, Malaysia (QS World Rank 300). He has interest in Green Computing, High-Performance Computing, Cyber-Physical Systems, Machine Learning, and Cyber Security.
Bishwajeet Kumar Pandey is a Professor at Department of Intelligent System and Cyber Security, Astana IT University Kazaksthan. He is also a visiting professor at Eurasian National University, Astana, Kazaksthan (QS World Rank 355) and UCSI University, Kuala Lumpur, Malaysia (QS World Rank 300). He has interest in Green Computing, High-Performance Computing, Cyber-Physical Systems, Machine Learning, and Cyber Security.
Content
1. Big Data Computing: Transforming From Cloud Computing to Edge Scheduling Perspectives Review. 2. Decision Making in the Field of Unmanned Aerial Vehicles: State-of-the-Art. 3. A Brief Study on Understanding and Handling COVID-19: Test Bed for Forecasting with Deep Learning and Machine Learning Algorithms. 4. AgTech: Using Sensors and Machine Learning to Revolutionize Farming Practices (IoT). 5. Developing an AI-based Multi-Task Transfer Learning Framework for Automating Judicial Contracts. 6. Analysis of Deep Learning Methodologies for Handling Non-Medical Big Data and Very Limited Medical Data with Feature Extraction and Annotation Techniques. 7. Introduction to Virtualization Security and Cloud Security. 8.Security Breaches in IoT Applications: An Extensive Study. 9.An Efficient and Accurate Classifcation Algorithm for ECG Signals Using PNN and KNN. 10. Big Data Analytics: The Classification of Remote Sensing Images Using Machine Learning Techniques. 11. Segmentation of Transmission Tower Components Based on Machine Learning. 12. A Systematic Analysis of Robot Path Planning and Optimization Techniques. 13.Pneumonia Prediction Model Using Deep Learning on Docker. 14. A Sequential Deep Learning Model Approach to OCR-Based Handwritten Digit Recognition for Physically Impaired People. 15. A Deep Learning Strategy for Sign Language Classification and Recognition for Hearing-Impaired People. 16. Non-fungible Tokens (NFT): The Design and Development of the "Obstacle Assault" Game and "Turtle Sidestep" Game. 17. Design and Development of 2D Space Shooter Game and Arcade Game Using Unity. 18. An Ensemble Technique Using Genetic Algorithm and Deep Learning for the Prediction of Rice Diseases. 19. History of Machine Learning. 20. Internet of Things Start-Ups: An Overview of the Privacy and Security in IoT Start-Ups.
