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Issue title: Special Section: Intelligent Data Aggregation Inspired Paradigm and Approaches in IoT Applications
Guest editors: Xiaohui Yuan and Mohamed Elhoseny
Article type: Research Article
Authors: Tian, Shuichenga | Tang, Kaia; b; * | Yang, Pengfeia | Jia, Aifanga | Melvin, Haileyc; *
Affiliations: [a] College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an, China | [b] Department of Electric Power, Xi’an Electric Power College, Xi’an, China | [c] The Colorado Community College System, USA
Correspondence: [*] Corresponding author. Kai Tang, College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an, 710054, China. E-mail: kaitang1@foxmail.com and Melvin, Hailey, The Colorado Community College System, USA. E-mail: hmelvin@student.cccs.edu.
Abstract: To build a safety behavior evaluation system in network management, the cloud computing model is introduced in this study for parametric study. First, the development model of cloud computing management is expounded, a parametric model of safety behavior coefficient is constructed by using a cloud computing platform. Then, an optimization and update scheme is put forward from the algorithm flow and evaluation model, and an adequate evaluation model is established by effectively evaluating the state of security management. Finally, the parameter data is refined through USES data mining algorithm to ensure the reasonable calculation of the data. In the test of the parameter model, the simulation is carried out by adopting the cloud computing platform. The test results prove that the application of the parameter model can guarantee the reasonable management of safety behavior, the error in the process of safety behavior management can be reduced, and the accuracy of control can be enhanced. The parameter model is worthy of further application and extension.
Keywords: Cloud computing, safety management, safety behavior coefficient, optimization decision
DOI: 10.3233/JIFS-179060
Journal: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 27-34, 2019
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