000			03622cam a2200505Ii 4500
001			9781003139041
003			FlBoTFG
005			20220531132500.0
006			m o d
007			cr cnu|||unuuu
008			210415s2021 flua ob 001 0 eng d
040			_aOCoLC-P _beng _erda _epn _cOCoLC-P
020			_a9781003139041 _q(electronic bk.)
020			_a1003139043 _q(electronic bk.)
020			_a9781000337860 _q(electronic bk. : EPUB)
020			_a1000337863 _q(electronic bk. : EPUB)
020			_a9781000337822 _q(electronic bk. : PDF)
020			_a1000337820 _q(electronic bk. : PDF)
020			_z9780367686314
020			_a9781000337846 _q(electronic bk. : Mobipocket)
020			_a1000337847 _q(electronic bk. : Mobipocket)
020			_z9780367687823
035			_a(OCoLC)1246250279
035			_a(OCoLC-P)1246250279
050		4	_aTK5105.8857
082	0	4	_a004.67/8 _223
100	1		_aPerros, Harry G., _eauthor.
245	1	3	_aAn introduction to IoT analytics / _cHarry G. Perros.
250			_aFirst edition.
264		1	_aBoca Raton, FL : _bCRC Press, _c2021.
300			_a1 online resource (xvii, 354 pages) : _billustrations (some color).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aChapman & Hall/CRC data science series
505	0		_aReview of probability theory -- Simulation techniques -- Hypothesis testing -- Multivariable linear regression -- Time series forecasting -- Dimensionality reduction -- Clustering techniques -- Classification techniques -- Artificial neural networks -- Support vector machines -- Hidden Markov models.
520			_a"An Introduction to IoT Analytics covers techniques that can be used to analyze data from IoT sensors and also addresses questions regarding the performance of an IoT system. It strikes a balance between practice and theory so that one can learn how to apply these tools in practice with a good understanding of their inner workings. It is an introductory book for readers that have no familiarity with these techniques. The techniques presented in the book come from the areas of Machine Learning, Statistics, and Operations Research. Machine Learning techniques are described that can be used to analyze IoT data generated from sensors for clustering, classification, and regression. The statistical techniques described can be used to carry out regression and forecasting of IoT sensor data, and dimensionality reduction of data sets. Operations Research is concerned with the performance of an IoT system by constructing a model of a system under study, and then carry out what-if analysis. The book also describes simulation techniques. Key features: IoT analytics is not just Machine Learning but it also involves other tools, such as, forecasting and simulation techniques. Many diagrams and examples are given throughout the book to better explain the material presented. At the end of each chapter, there is a project designed to help the reader to better understand the techniques described in the chapter. The material is this book has been class tested over several semesters"-- _cProvided by publisher.
588			_aOCLC-licensed vendor bibliographic record.
650		0	_aInternet of things.
650		0	_aSystem analysis.
650		0	_aSystem analysis _xStatistical methods.
650		0	_aOperations research.
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781003139041
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999			_c72934 _d72934