04647nam a22005535i 4500 978-981-99-9718-3 DE-He213 20251030112007.0 cr nn 008mamaa 240422s2024 si | s |||| 0|eng d 9789819997183 978-981-99-9718-3 10.1007/978-981-99-9718-3 doi Q325.5-.7 UYQM bicssc MAT029000 bisacsh UYQM thema Advanced Machine Learning with Evolutionary and Metaheuristic Techniques [electronic resource] / edited by Jayaraman Valadi, Krishna Pratap Singh, Muneendra Ojha, Patrick Siarry. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. X, 362 p. 1 illus. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Computational Intelligence Methods and Applications, 2510-1773 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. 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. Machine learning. 55059 Medical informatics. 60693 Machine Learning. 55059 Health Informatics. Valadi, Jayaraman. editor. edt http://id.loc.gov/vocabulary/relators/edt 75630 Singh, Krishna Pratap. editor. edt http://id.loc.gov/vocabulary/relators/edt 75631 Ojha, Muneendra. editor. edt http://id.loc.gov/vocabulary/relators/edt 75632 Siarry, Patrick. editor. edt http://id.loc.gov/vocabulary/relators/edt 75633 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819997176 Printed edition: 9789819997190 Printed edition: 9789819997206 Computational Intelligence Methods and Applications, 2510-1773 70583 https://doi.org/10.1007/978-981-99-9718-3 ZDB-2-SCS ZDB-2-SXCS 59325 59325