Nature-inspired computation in data mining and machine learning / Xin-She Yang, Xing-Shi He, editors.
Yang, Xin-She, editor.; He, Xing-Shi, editor.
2020
QA76.9.N37
Available Online

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Title Nature-inspired computation in data mining and machine learning / Xin-She Yang, Xing-Shi He, editors.
ISBN 9783030285531 (electronic book)
3030285537 (electronic book)
9783030285524
DOI https://doi.org/10.1007/978-3-030-28553-1
https://doi.org/10.1007/978-3-030-28
Published Cham, Switzerland : Springer, [2020]
Language English
Description 1 online resource (xi, 273 pages) : illustrations.
Item Number 10.1007/978-3-030-28553-1 doi
10.1007/978-3-030-28
Call Number QA76.9.N37
Dewey Decimal Classification 006.38
Summary 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.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed September 9, 2019).
Added Author Yang, Xin-She, editor.
He, Xing-Shi, editor.
Series Studies in computational intelligence ; v. 855.
Available in Other Form 3030285529
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
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
A Comprehensive Review and Performance Analysis of Firefly Algorithm for Artificial Neural Networks
3D Object Categorization in Cluttered Scene Using Deep Belief Network Architectures
Performance-Based Prediction of Chronic Kidney Disease Using Machine Learning for High-Risk Cardiovascular Disease Patients
Extraction of Named Entities from Social Media Text in Tamil Language Using N-Gram Embedding for Disaster Management
Classification and Clustering Algorithms of Machine Learning with their Applications
Hybrid Binary Particle Swarm Optimization and Flower Pollination Algorithm Based on Rough Set Approach for Feature Selection Problem.

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