Название: Machine Learning Hybridization and Optimization for Intelligent Applications
Автор: Tanvir Habib Sardar, Bishwajeet Kumar Pandey
Издательство: CRC Press
Серия: Computational Intelligence Techniques
Год: 2025
Страниц: 367
Язык: английский
Формат: pdf (true), epub
Размер: 33.6 MB

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, the 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.

In recent years, the fusion of Machine Learning methodologies with optimization techniques has significantly advanced the realm of intelligent applications across diverse domains. This book delves into the symbiotic relationship between Machine Learning hybridization and optimization, presenting a comprehensive exploration of their integration to empower intelligent systems. The amalgamation of Machine Learning and optimization has revolutionized the way we approach problem-solving, enabling us to create sophisticated applications that adapt, evolve, and enhance their performance over time. This synergy has paved the way for innovation in various fields, from healthcare and finance to manufacturing and beyond.

Within these pages, readers will embark on a journey through the principles, methodologies, and practical implementations of hybridized Machine Learning and optimization techniques. We aim to equip both novices and experts in the field with a thorough understanding of these methodologies and their application in creating intelligent systems that can learn, predict, and optimize outcomes.

Features:
• Focuses on hybridization and optimization of Machine Learning techniques.
• Reviews supervised, unsupervised and reinforcement learning using case study based applications.
• Covers latest machine learning applications in diverse domains as IoT, data science, cloud computing, 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.