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Mathematical Biosciences and Engineering

2020, Volume 17, Issue 5: 6045-6063. doi: 10.3934/mbe.2020321
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Soft pre-rough sets and its applications in decision making

  • 1.
    Department of Mathematics, College of Science and Arts, Najran University, Kingdom of Saudi Arabia
  • 2.
    Department of Mathematics, Faculty of Science, Tanta University, Egypt
  • Received: 05 July 2020 Accepted: 30 July 2020 Published: 14 September 2020

  • Soft rough set model represents a different mathematical model to which many real-life data can be connected. In fact, this theory represents a link between soft set and rough set theories. The main goal of the present paper is to introduce a new approach to modify and generalize soft rough sets. We are discussing and exploring the basic properties for these approaches. In addition, we use the suggested approaches as a mathematical modeling for an uncertain data and deal with the ambiguity. Comparisons among the proposed methods and the previous one are obtained. Finally, a medical application of the suggested approximations in decision making of diagnosis of COVID-19 is illustrated. Moreover, we develop an algorithm following these concepts and apply it to a decision making problem to demonstrate the applicability of the proposed methods.

    Citation: M. El Sayed, Abdul Gawad A. Q. Al Qubati, M. K. El-Bably. Soft pre-rough sets and its applications in decision making[J]. Mathematical Biosciences and Engineering, 2020, 17(5): 6045-6063. doi: 10.3934/mbe.2020321

    Related Papers:

  • Soft rough set model represents a different mathematical model to which many real-life data can be connected. In fact, this theory represents a link between soft set and rough set theories. The main goal of the present paper is to introduce a new approach to modify and generalize soft rough sets. We are discussing and exploring the basic properties for these approaches. In addition, we use the suggested approaches as a mathematical modeling for an uncertain data and deal with the ambiguity. Comparisons among the proposed methods and the previous one are obtained. Finally, a medical application of the suggested approximations in decision making of diagnosis of COVID-19 is illustrated. Moreover, we develop an algorithm following these concepts and apply it to a decision making problem to demonstrate the applicability of the proposed methods.


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  • © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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