Nature-Inspired Computation in Data Mining and Machine Learning

Author:   Xin-She Yang ,  Xing-Shi He
Publisher:   Springer Nature Switzerland AG
Edition:   1st ed. 2020
Volume:   855
ISBN:  

9783030285555


Pages:   273
Publication Date:   16 September 2020
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Nature-Inspired Computation in Data Mining and Machine Learning


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Overview

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.

Full Product Details

Author:   Xin-She Yang ,  Xing-Shi He
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2020
Volume:   855
Weight:   0.454kg
ISBN:  

9783030285555


ISBN 10:   3030285553
Pages:   273
Publication Date:   16 September 2020
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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 classifiers 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.

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