Table of contents
About this book
This book reviews the latest developments in nature-inspired computation, with a focus on the cross-disciplinary applications in data mining and machine learning. Data mining, machine learning and nature-inspired computation are current hot research topics due to their importance in both theory and practical applications. Adopting an application-focused approach, each chapter introduces a specific topic, with detailed descriptions of relevant algorithms, extensive literature reviews and implementation details.
Covering topics such as nature-inspired algorithms, swarm intelligence, classification, clustering, feature selection, cybersecurity, learning algorithms over cloud, extreme learning machines, object categorization, particle swarm optimization, flower pollination and firefly algorithms, and neural networks, it also presents case studies and applications, including classifications of crisis-related tweets, extraction of named entities in the Tamil language, performance-based prediction of diseases, and healthcare services. This book is both a valuable a reference resource and a practical guide for students, researchers and professionals in computer science, data and management sciences, artificial intelligence and machine learning.
Nature-inspired Algorithm Computational Intelligence Bio-inspired Computation Bioinformatics Data Mining Evolutionary Computing Machine Learning Data Modeling Heuristics Pattern Recognition
Editors and affiliations
- DOI https://doi.org/10.1007/978-3-030-28553-1
- Copyright Information Springer Nature Switzerland AG 2020
- Publisher Name Springer, Cham
- eBook Packages Intelligent Technologies and Robotics
- Print ISBN 978-3-030-28552-4
- Online ISBN 978-3-030-28553-1
- Series Print ISSN 1860-949X
- Series Online ISSN 1860-9503
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