Computationally Intensive Statistics for Intelligent IoT [electronic resource] /

By:

Samanta, Debabrata [author.]

Contributor(s):

Material type: Text

TextSeries: Studies in Autonomic, Data-driven and Industrial ComputingPublisher: Singapore : Springer Nature Singapore : Imprint: Springer, 2021Edition: 1st ed. 2021Description: XX, 218 p. 53 illus., 11 illus. in color. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9789811659362

Subject(s):

Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification:

006.3 23

LOC classification:

Q342

Online resources:

Click here to access online

Contents:

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.

In: Springer Nature eBookSummary: 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.

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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.

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