000			03965cam a2200529Ma 4500
001			9781351720250
003			FlBoTFG
005			20220711212532.0
006			m o d
007			cr |n|||||||||
008			170811s2017 enka o 000 0 eng d
040			_aOCoLC-P _beng _epn _cOCoLC-P
020			_a1351720252 _q(ebk)
020			_a9781351720250
020			_z1498761011
020			_z1351720244
020			_z1351720236
020			_a9781498761024
020			_a149876102X
020			_z9781498761017
024	7		_a10.1201/9781315180748 _2doi
035			_a(OCoLC)992484108
035			_a(OCoLC-P)992484108
050		4	_aHC79.I55
072		7	_aCOM021000 _2bisacsh
072		7	_aCOM021030 _2bisacsh
072		7	_aCOM032000 _2bisacsh
082	0	4	_a658.4 _223
100	1		_aSugumaran, Vijayan. _917397
245	1	0	_aComputational Intelligence Applications in Business Intelligence and Big Data Analytics.
260			_aLondon : _bCRC Press, _c2017.
300			_a1 online resource (348 pages) : _billustrations
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
520	2		_a"There are a number of books on computational intelligence (CI), but they tend to cover a broad range of CI paradigms and algorithms rather than provide an in-depth exploration in learning and adaptive mechanisms. This book sets its focus on CI based architectures, modeling, case studies and applications in big data analytics, and business intelligence. The intended audiences of this book are scientists, professionals, researchers, and academicians who deal with the new challenges and advances in the specific areas mentioned above. Designers and developers of applications in these areas can learn from other experts and colleagues through this book."--Provided by publisher.
505	0		_aCover -- Half Title -- Title Page -- Copyright Page -- Contents -- Editors -- Contributors -- PART I: INTRODUCTION -- 1 Computational Intelligence Paradigms in Business Intelligence and Analytics -- PART II: COMPUTATIONAL INTELLIGENCE IN BUSINESS INTELLIGENCE AND ANALYTICS -- 2 Conditional Value at Risk-Based Portfolio Optimization Using Metaheuristic Approaches -- 3 Big Data Analysis and Application for Video Surveillance Systems -- 4 Trends in Mining Biological Big Data -- 5 Computational Challenges in Group Membership Prediction of Highly Imbalanced Big Data Sets -- PART III: DATA ANALYTICS AND PREDICTION MODELS -- 6 A New Paradigm in Fraud Detection Modeling Using Predictive Models, Fuzzy Expert Systems, Social Network Analysis, and Unstructured Data -- 7 Speedy Data Analytics through Automatic Balancing of Big Data in MongoDB Sharded Clusters -- 8 Smart Metering as a Service Using Hadoop (SMAASH) -- 9 Service-Oriented Architecture for Big Data and Business Intelligence Analytics in the Cloud -- PART IV: APPLICATIONS OF COMPUTATIONAL INTELLIGENCE -- 10 Rough Set and Neighborhood Systems in Big Data Analysis -- 11 An Investigation of Fuzzy Techniques in Clustering of Big Data -- 12 A Survey on Learning Models with Respect to Human Behavior Analysis for Large-Scale Surveillance Videos -- 13 Mining Unstructured Big Data for Competitive Intelligence and Business Intelligence -- Index.
588			_aOCLC-licensed vendor bibliographic record.
650		0	_aInformation technology _xManagement. _95368
650		0	_aComputational intelligence. _97716
650		0	_aBig data. _94174
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781351720250 _qapplication/PDF _zDistributed by publisher. Purchase or institutional license may be required for access.
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781315180748 _zClick here to view.
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
938			_aTaylor & Francis _bTAFR _n9781315180748
942			_cEBK
999			_c71515 _d71515