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Soft -Rough Set and Its Applications in Decision Making of Coronavirus

M. A. El Safty1,*, Samirah Al Zahrani1, M. K. El-Bably2, M. El Sayed3

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

* Corresponding Author: M. A. El Safty. Email: email

(This article belongs to this Special Issue: Application of Artificial Intelligence, Internet of Things, and Learning Approach for Learning Process in COVID-19/Industrial Revolution 4.0)

Computers, Materials & Continua 2022, 70(1), 267-285. https://doi.org/10.32604/cmc.2022.019345

Abstract

In this paper, we present a proposed method for generating a soft rough approximation as a modification and generalization of Zhaowen et al. approach. Comparisons were obtained between our approach and the previous study and also. Eventually, an application on Coronavirus (COVID-19) has been presented, illustrated using our proposed concept, and some influencing results for symptoms of Coronavirus patients have been deduced. Moreover, following these concepts, we construct an algorithm and apply it to a decision-making problem to demonstrate the applicability of our proposed approach. Finally, a proposed approach that competes with others has been obtained, as well as realistic results for patients with Coronavirus. Moreover, we used MATLAB programming to obtain the results; these results are consistent with those of the World Health Organization and an accurate proposal competing with the method of Zhaowen et al. has been studied. Therefore, it is recommended that our proposed concept be used in future decision making.

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Cite This Article

M. A. El Safty, S. Al Zahrani, M. K. El-Bably and M. El Sayed, "Soft -rough set and its applications in decision making of coronavirus," Computers, Materials & Continua, vol. 70, no.1, pp. 267–285, 2022. https://doi.org/10.32604/cmc.2022.019345



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