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082 0 4 _a006.31
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245 1 0 _aAdvanced Machine Learning with Evolutionary and Metaheuristic Techniques
_h[electronic resource] /
_cedited by Jayaraman Valadi, Krishna Pratap Singh, Muneendra Ojha, Patrick Siarry.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aX, 362 p. 1 illus.
_bonline resource.
336 _atext
_btxt
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337 _acomputer
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_2rdamedia
338 _aonline resource
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347 _atext file
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490 1 _aComputational Intelligence Methods and Applications,
_x2510-1773
505 0 _aChapter 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.
520 _aThis 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.
650 0 _aMachine learning.
_91831
650 0 _aMedical informatics.
_94729
650 1 4 _aMachine Learning.
_91831
650 2 4 _aHealth Informatics.
_931799
700 1 _aValadi, Jayaraman.
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700 1 _aSingh, Krishna Pratap.
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700 1 _aOjha, Muneendra.
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700 1 _aSiarry, Patrick.
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710 2 _aSpringerLink (Online service)
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773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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830 0 _aComputational Intelligence Methods and Applications,
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_9100891
856 4 0 _uhttps://doi.org/10.1007/978-981-99-9718-3
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