000			03386cam a2200493Mu 4500
001			9781003119838
003			FlBoTFG
005			20240213122824.0
006			m o d
007			cr cnu---unuuu
008			210911s2021 xx o ||| 0 eng d
040			_aOCoLC-P _beng _cOCoLC-P
020			_a9781000461367
020			_a100046136X
020			_a9781003119838 _q(electronic bk.)
020			_a1003119832 _q(electronic bk.)
020			_a9781000461350 _q(electronic bk. : PDF)
020			_a1000461351 _q(electronic bk. : PDF)
020			_z0367635941
020			_z9780367635947
035			_a(OCoLC)1267762820
035			_a(OCoLC-P)1267762820
050		4	_aQ325.5
072		7	_aCOM _x004000 _2bisacsh
072		7	_aCOM _x044000 _2bisacsh
072		7	_aUT _2bicssc
082	0	4	_a006.31 _223
245	0	0	_aApplied Learning Algorithms for Intelligent IoT _h[electronic resource].
260			_aMilton : _bAuerbach Publishers, Incorporated, _c2021.
300			_a1 online resource (369 p.)
500			_aDescription based upon print version of record.
520			_aThis book vividly illustrates all the promising and potential machine learning (ML) and deep learning (DL) algorithms through a host of real-world and real-time business use cases. Machines and devices can be empowered to self-learn and exhibit intelligent behavior. Also, Big Data combined with real-time and runtime data can lead to personalized, prognostic, predictive, and prescriptive insights. This book examines the following topics: Cognitive machines and devices Cyber physical systems (CPS) The Internet of Things (IoT) and industrial use cases Industry4.0 for smarter manufacturing Predictive and prescriptive insights for smarter systems Machine vision and intelligence Natural interfaces K-means clustering algorithm Support vector machine (SVM) algorithm A priori algorithms Linear and logistic regression Applied Learning Algorithms for Intelligent IoT clearly articulates ML and DL algorithms that can be used to unearth predictive and prescriptive insights out of Big Data. Transforming raw data into information and relevant knowledge is gaining prominence with the availability of data processing and mining, analytics algorithms, platforms, frameworks, and other accelerators discussed in the book. Now, with the emergence of machine learning algorithms, the field of data analytics is bound to reach new heights. This book will serve as a comprehensive guide for AI researchers, faculty members, and IT professionals. Every chapter will discuss one ML algorithm, its origin, challenges, and benefits, as well as a sample industry use case for explaining the algorithm in detail. The book's detailed and deeper dive into ML and DL algorithms using a practical use case can foster innovative research.
588			_aOCLC-licensed vendor bibliographic record.
650		0	_aMachine learning.
650		0	_aAlgorithms.
650		0	_aInternet of things.
650		7	_aCOMPUTERS / Artificial Intelligence _2bisacsh
650		7	_aCOMPUTERS / Neural Networks _2bisacsh
700	1		_aChelliah, Pethuru Raj.
700	1		_aSakthivel, Usha.
700	1		_aNagarajan, Susila.
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781003119838
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999			_c4810 _d4810