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On Soft Pre-Rough Approximation Space with Applications in Decision Making

M. El Sayed1,*, Wadia Faid Hassan Al-shameri1, M. A. El Safty2

1 Department of Mathematics, College of Science and Arts, Najran University, Najran, 66445, Saudi Arabia
2 Department of Mathematics and Statistics, College of Science, Taif University, Taif, 21944, Saudi Arabia

* Corresponding Author: M. El Sayed. Email: email

(This article belongs to the Special Issue: Extension, Modeling and Applications of Fuzzy Set Theory in Engineering and Science)

Computer Modeling in Engineering & Sciences 2022, 132(3), 865-879. https://doi.org/10.32604/cmes.2022.020066

Abstract

A soft, rough set model is a distinctive mathematical model that can be used to relate a variety of real-life data. In the present work, we introduce new concepts of rough set based on soft pre-lower and soft pre-upper approximation space. These concepts are soft pre-rough equality, soft pre-rough inclusion, soft pre-rough belonging, soft predefinability, soft pre-internal lower, and soft pre-external lower. We study the properties of these concepts. Finally, we use the soft pre-rough approximation to illustrate the importance of our method in decision-making for Chikungunya medical illnesses. In reality, the impact factors of Chikungunya’s medical infection were determined. Moreover, we develop two new algorithms to address Chikungunya virus issues. Our proposed approach is sensible and effective.

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Cite This Article

APA Style
Sayed, M.E., Al-shameri, W.F.H., Safty, M.A.E. (2022). On soft pre-rough approximation space with applications in decision making. Computer Modeling in Engineering & Sciences, 132(3), 865-879. https://doi.org/10.32604/cmes.2022.020066
Vancouver Style
Sayed ME, Al-shameri WFH, Safty MAE. On soft pre-rough approximation space with applications in decision making. Comput Model Eng Sci. 2022;132(3):865-879 https://doi.org/10.32604/cmes.2022.020066
IEEE Style
M.E. Sayed, W.F.H. Al-shameri, and M.A.E. Safty "On Soft Pre-Rough Approximation Space with Applications in Decision Making," Comput. Model. Eng. Sci., vol. 132, no. 3, pp. 865-879. 2022. https://doi.org/10.32604/cmes.2022.020066



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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