| 000 | 03675cam a2200517Ii 4500 | ||
|---|---|---|---|
| 001 | 9781003139041 | ||
| 003 | FlBoTFG | ||
| 005 | 20230516170547.0 | ||
| 006 | m o d | ||
| 007 | cr cnu|||unuuu | ||
| 008 | 210415s2021 flua ob 001 0 eng d | ||
| 040 |
_aOCoLC-P _beng _erda _epn _cOCoLC-P |
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| 020 |
_a9781003139041 _q(electronic bk.) |
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| 020 |
_a1003139043 _q(electronic bk.) |
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| 020 |
_a9781000337860 _q(electronic bk. : EPUB) |
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| 020 |
_a1000337863 _q(electronic bk. : EPUB) |
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| 020 |
_a9781000337822 _q(electronic bk. : PDF) |
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| 020 |
_a1000337820 _q(electronic bk. : PDF) |
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| 020 | _z9780367686314 | ||
| 020 |
_a9781000337846 _q(electronic bk. : Mobipocket) |
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| 020 |
_a1000337847 _q(electronic bk. : Mobipocket) |
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| 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. _971515 |
|
| 245 | 1 | 3 |
_aAn introduction to IoT analytics / _cHarry G. Perros. |
| 250 | _aFirst edition. | ||
| 264 | 1 |
_aBoca Raton, FL : _bCRC Press, _c2021. |
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| 300 |
_a1 online resource (xvii, 354 pages) : _billustrations (some color). |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 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. |
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| 588 | _aOCLC-licensed vendor bibliographic record. | ||
| 650 | 0 |
_aInternet of things. _94027 |
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| 650 | 0 |
_aSystem analysis. _93242 |
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| 650 | 0 |
_aSystem analysis _xStatistical methods. _971516 |
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| 650 | 0 |
_aOperations research. _912218 |
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| 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 |
| 942 | _cEBK | ||
| 999 |
_c83049 _d83049 |
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