Название: Computationally Intensive Statistics for Intelligent IoTАвтор: Debabrata Samanta, Amit Banerjee
Издательство: Springer
Год: 2021
Страниц: 233
Язык: английский
Формат: pdf (true), epub
Размер: 18.4 MB
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.
Introduction to intelligent IoT is dealt with in Chap. 1. Modern computer systems, data centers, and digital strategies generate terabytes of data every day via numerous means in order to handle the growing number of internet-connected devices. Despite the vast amounts of data available, gathering information and making decisions from it requires a tremendous deal of effort on many levels. As a result, advanced analytics has gone to the top of the priority list for research and development. The statistical analysis of data from the Internet of Things is the subject of this article, which looks at cutting-edge research (IoT). The primary purpose of this paper is to call attention to the potential ramifications of Big Data issues, as well as research efforts focused on Internet of Things statistical analysis and the technologies that go with it, in this context. As a result, this article recommends tools for evaluating big data at various phases and better comprehending the information we may get from it, opening up new opportunities for scientists to deliver discoveries based on open research subjects and themes.
Chapter 2 deep dives with ML and information advancement platform in intelligent IoT. The lack of adaptive learning systems is a disincentive to the proliferation of smart IoT structures, given the prominence of IoT applications. Nothing is looked at as to how computers will exchange knowledge regardless of where the information comes from. As a result, this section proposes a model for how nodes would independently share information, develop new information, and adaptively comprehend from knowledge in order to be relevant through diverse applications. Individuals’ acquisition and categorization of information inform the structure. To begin with, this study examines the various methodologies used in IoT smart big data analysis.
Chapter 3 intends various applications of machine intelligence and data science for intelligent IoT. The Internet of Things has spawned an abundance of applications, resulting in a massive amount of data that requires clever data translation.
Contents:
1. Introduction to Intelligent IoT
2. ML and Information Advancement Platform in Intelligent IoT
3. Application of Machine Intelligence and Data Science for Intelligent IoT
4. Approaches of Data Analytics in Intelligent Medicare Utilizing IoT
5. Trends and Applications of Intelligent IoT in Agriculture
6. Transformation of Intelligent IoT in the Energy Sector
7. Abnormality Diagnosis from Ambient dаta: IoT Data Sequences in Real Time
8. Future of Intelligent IoT
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