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Title	Machine Learning And Big Data In Cyber-Physical System: Methods, Applications And Challenges
ID_Doc	35882
Authors	Nayak J.; Suresh Kumar P.; Reddy D.K.K.; Naik B.; Pelusi D.
Year	2021
Published	Cognitive Engineering for Next Generation Computing: A Practical Analytical Approach
DOI	http://dx.doi.org/10.1002/9781119711308.ch2
Abstract	One of the most considerable and emerging methodologies, defined by the integration of physical and computational processes as cyber-physical systems. These systems are emphasized to supervise the data processing and synchronize it among the cyber computational processes and physically connected systems. Machine learning crafts the intelligent control process by instilling astute self-learning processes with automated and indicative capabilities. The main point of this literature is to give overall survey of cyber-physical system challenges and to accomplish the ideal degree of integration in automation and intelligence in diverse domains for the progress of emerging systems. This chapter aims to provide the advances and challenges for significant research and development of existing and future cyber physical-based technologies using machine learning methods. The research challenges are primarily summarized as i) large-scale data collection, in the integration of systems from IoT, smart cities as well as industries, ii) the synchronization of interconnectivity and intelligence of these frameworks requires a shared perspectives model with decision making, iii) control technique, management service, model-based design, network security, and resource allocation, iv) safety, security, robustness, and reliability is a vital challenge because of errors, security attacks, improbability in the environment, security, and protection of physical systems. © 2021 Scrivener Publishing LLC.
Author Keywords	big-data; cyber-physical system; IoT; machine learning

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