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Stochastic Approach to Investigate Protected Access to Information Resources in Combined E-Learning Environment

Author

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  • Radi Romansky

    (Department of Informatics, Faculty of Applied Mathematics and Informatics, Technical University of Sofia, 1000 Sofia, Bulgaria)

Abstract

The digital era expands the scope and application of information technologies, which also affects the forms of e-learning, motivating the development of combined systems with heterogeneous resources and services, including in the cloud. In this vein, the present article investigates the implementation of a set of procedures for maintaining regulated access to resources (identification, authentication, authorization, etc.) in a combined e-learning environment, with the main goal to confirm their effectiveness and correctness. The study was conducted through analytical modelling using stochastic tools from the theory of Petri nets and Markov chains with additional statistical analysis. The application of such a combined approach allows increased research efficiency and better adequacy of the obtained estimates.

Suggested Citation

  • Radi Romansky, 2022. "Stochastic Approach to Investigate Protected Access to Information Resources in Combined E-Learning Environment," Mathematics, MDPI, vol. 10(16), pages 1-12, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2909-:d:887120
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    References listed on IDEAS

    as
    1. Richa Manocha & Taranjeet Duggal & Neeti Rana, 2022. "Exploring DSS for Personality Assessment: Influence of Personality on Citizenship," International Journal of Human Capital and Information Technology Professionals (IJHCITP), IGI Global, vol. 13(1), pages 1-16, January.
    2. Chang Wook Kang & Muhammad Imran & Muhammad Omair & Waqas Ahmed & Misbah Ullah & Biswajit Sarkar, 2019. "Stochastic-Petri Net Modeling and Optimization for Outdoor Patients in Building Sustainable Healthcare System Considering Staff Absenteeism," Mathematics, MDPI, vol. 7(6), pages 1-26, June.
    3. Raices Cruz, Ivette & Lindström, Johan & Troffaes, Matthias C.M. & Sahlin, Ullrika, 2022. "Iterative importance sampling with Markov chain Monte Carlo sampling in robust Bayesian analysis," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).
    Full references (including those not matched with items on IDEAS)

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