Machine Learning Hybridization and Optimization for Intelligent Applications

Tanvir Habib Sardar · Bishwajeet Kumar Pandey

2024-10 · CRC Press

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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.

Apie autorių

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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