國立虎尾科技大學 |

Nature-Inspired Computation in Data Mining and Machine Learning

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Nature-Inspired Computation in Data Mining and Machine Learning/ edited by Xin-She Yang, Xing-Shi He.
other author:	Yang, Xin-She.
Description:	XI, 273 p. 87 illus., 66 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computational intelligence. -
Online resource:	https://doi.org/10.1007/978-3-030-28553-1
ISBN:	9783030285531

Nature-Inspired Computation in Data Mining and Machine Learning
Nature-Inspired Computation in Data Mining and Machine Learning[electronic resource] /edited by Xin-She Yang, Xing-Shi He. - 1st ed. 2020. - XI, 273 p. 87 illus., 66 illus. in color.online resource. - Studies in Computational Intelligence,8551860-949X ;. - Studies in Computational Intelligence,564.

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.

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.

ISBN: 9783030285531

Standard No.: 10.1007/978-3-030-28553-1doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Nature-Inspired Computation in Data Mining and Machine Learning
LDR:03276nam a22004095i 4500 001 1020845
003 DE-He213
005 20200706040907.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030285531 $9 978-3-030-28553-1
024 7 $a 10.1007/978-3-030-28553-1 $2 doi
035 $a 978-3-030-28553-1
050 4 $a Q342
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
245 1 0 $a Nature-Inspired Computation in Data Mining and Machine Learning $h [electronic resource] / $c edited by Xin-She Yang, Xing-Shi He.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XI, 273 p. 87 illus., 66 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Studies in Computational Intelligence, $x 1860-949X ; $v 855
505 0 $a 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.
520 $a 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.
650 0 $a Computational intelligence. $3 568984
650 0 $a Artificial intelligence. $3 559380
650 0 $a Data mining. $3 528622
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
700 1 $a Yang, Xin-She. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 786470
700 1 $a He, Xing-Shi. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1227420
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030285524
776 0 8 $i Printed edition: $z 9783030285548
776 0 8 $i Printed edition: $z 9783030285555
830 0 $a Studies in Computational Intelligence, $x 1860-949X ; $v 564 $3 1253640
856 4 0 $u https://doi.org/10.1007/978-3-030-28553-1
912 $a ZDB-2-INR
912 $a ZDB-2-SXIT
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)
950 $a Intelligent Technologies and Robotics (R0) (SpringerNature-43728)