Advanced Machine Learning with Evolutionary and Metaheuristic Techniques / edited by Jayaraman Valadi, Krishna Pratap Singh, Muneendra Ojha, Patrick Siarry.

Format:

Book

Contributor:

Valadi, Jayaraman., Editor.

Singh, Krishna Pratap., Editor.

Ojha, Muneendra., Editor.

Siarry, Patrick, Editor.

Series:

Computational Intelligence Methods and Applications, 2510-1773

Language:

English

Subjects (All):

Machine learning.

Medical informatics.

Machine Learning.

Health Informatics.

Local Subjects:

Machine Learning.

Health Informatics.

Physical Description:

1 online resource (X, 362 p. 1 illus.)

Edition:

1st ed. 2024.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2024.

Summary:

This book delves into practical implementation of evolutionary and metaheuristic algorithms to advance the capacity of machine learning. The readers can gain insight into the capabilities of data-driven evolutionary optimization in materials mechanics, and optimize your learning algorithms for maximum efficiency. Or unlock the strategies behind hyperparameter optimization to enhance your transfer learning algorithms, yielding remarkable outcomes. Or embark on an illuminating journey through evolutionary techniques designed for constructing deep-learning frameworks. The book also introduces an intelligent RPL attack detection system tailored for IoT networks. Explore a promising avenue of optimization by fusing Particle Swarm Optimization with Reinforcement Learning. It uncovers the indispensable role of metaheuristics in supervised machine learning algorithms. Ultimately, this book bridges the realms of evolutionary dynamic optimization and machine learning, paving the way for pioneering innovations in the field.

Contents:

Chapter 1. From Evolution to Intelligence: Exploring the Synergy of Optimization and Machine Learning

Chapter 2. Metaheuristic and Evolutionary Algorithms in Ex-plainable Artificial Intelligence

Chapter 3. Evolutionary Dynamic Optimization and Machine Learning

Chapter 4. Evolutionary Techniques in making Efficient Deep-Learning Framework: A Review

Chapter 5. Integrating Particle Swarm Optimization with Reinforcement Learning: A Promising Approach to Optimization

Chapter 6. Synergies between Natural Language Processing and Swarm Intelligence Optimization: A Comprehensive Overview

Chapter 7. Heuristics-based Hyperparameter Tuning for Transfer Learning Algorithms

Chapter 8. Machine Learning Applications of Evolutionary and Metaheuristic Algorithms

Chapter 9. Machine Learning Assisted Metaheuristic Based Optimization of Mixed Suspension Mixed Product Removal Process

Chapter 10. Machine Learning based Intelligent RPL Attack Detection System for IoT Networks

Chapter 11. Shallow and Deep Evolutionary Neural Networks applications in Solid Mechanics

Chapter 12. Polymer and nanocomposite Informatics: Recent Applications of Artificial Intelligence and Data Repositories

Chapter 13. Synergistic combination of machine learning and evolutionary and heuristic algorithms for handling imbalance in biological and biomedical datasets.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9789819997183

9819997186

OCLC:

1431881893