This book discusses state-of-the-art reviews of the existing machine-learning techniques and algorithms including hybridizations and optimizations. It is aimed at graduate students and researchers in machine learning, artificial intelligence, and electrical engineering.
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.
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.
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.
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