Buch, Englisch, Band 855, 273 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 600 g
Buch, Englisch, Band 855, 273 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 600 g
Reihe: Studies in Computational Intelligence
ISBN: 978-3-030-28552-4
Verlag: Springer International Publishing
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Adaptive Improved Flower Pollination Algorithm for Global Optimization.- Algorithms for Optimization and Machine Learning over Cloud.- Implementation of Machine Learning and Data Mining to Improve Cybersecurity and Limit Vulnerabilities to Cyber Attacks.- Comparative analysis of different classi?ers on crisis-related tweets: An elaborate study.- An Improved Extreme Learning Machine Tuning by Flower Pollination Algorithm.- Prospects of Machine and Deep Learning in Analysis of Vital Signs for the Improvement of Healthcare Services.
