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211103 ||| eng |
| 020 |
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|a 9789811659362
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| 100 |
1 |
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|a Samanta, Debabrata
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| 245 |
0 |
0 |
|a Computationally Intensive Statistics for Intelligent IoT
|h Elektronische Ressource
|c by Debabrata Samanta, Amit Banerjee
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| 250 |
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|a 1st ed. 2021
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| 260 |
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|a Singapore
|b Springer Nature Singapore
|c 2021, 2021
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| 300 |
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|a XX, 218 p. 53 illus., 11 illus. in color
|b online resource
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| 505 |
0 |
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|a Introduction to Intelligent IoT -- ML and Information Advancement platform in Intelligent IoT -- Machine Intelligence and Data Science for Intelligent IoT -- Approaches of Data Analytics in Intelligent Medicare utilizing IoT -- Trends and Applications of Intelligent IoT in Agriculture -- Transformation of Intelligent IoT in the Energy Sector -- Abnormality Diagnosis from Ambient Data: Intelligent IoT Data Sequences in Real Time -- Future of Intelligent IoT.
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| 653 |
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|a Data Analysis and Big Data
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| 653 |
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|a Health Informatics
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| 653 |
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|a Internet of things
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| 653 |
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|a Computational intelligence
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| 653 |
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|a Medical informatics
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| 653 |
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|a Quantitative research
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| 653 |
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|a Computational Intelligence
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| 653 |
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|a Internet of Things
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| 653 |
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|a Mathematical statistics / Data processing
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| 653 |
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|a Statistics and Computing
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| 700 |
1 |
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|a Banerjee, Amit
|e [author]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
0 |
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|a Studies in Autonomic, Data-driven and Industrial Computing
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| 028 |
5 |
0 |
|a 10.1007/978-981-16-5936-2
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-981-16-5936-2?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 006.3
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| 520 |
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|a The book covers computational statistics, its methodologies and applications for IoT device. It includes the details in the areas of computational arithmetic and its influence on computational statistics, numerical algorithms in statistical application software, basics of computer systems, statistical techniques, linear algebra and its role in optimization techniques, evolution of optimization techniques, optimal utilization of computer resources, and statistical graphics role in data analysis. It also explores computational inferencing and computer model's role in design of experiments, Bayesian analysis, survival analysis and data mining in computational statistics
|