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AIMS Mathematics

2022, Volume 7, Issue 2: 2479-2497. doi: 10.3934/math.2022139
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Research article

θβ-ideal approximation spaces and their applications

  • 1.
    Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Menoufia, Egypt
  • 2.
    Department of Mathematics, Faculty of Science, Helwan University, Helwan, Egypt
  • 3.
    Department of Mathematics, Faculty of Science, Tanta University, Tanta, Egypt
  • 4.
    Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt
  • Commentary on: AIMS Mathematics 7: 2479-2497
  • Received: 16 July 2021 Accepted: 28 October 2021 Published: 12 November 2021

  • MSC : 54A05, 54C10

  • The essential aim of the current work is to enhance the application aspects of Pawlak rough sets. Using the notion of a j-neighborhood space and the related concept of θβ-open sets, different methods for generalizing Pawlak rough sets are proposed and their characteristics will be examined. Moreover, in the context of ideal notion, novel generalizations of Pawlak's models and some of their generalizations are presented. Comparisons between the suggested methods and the previous approximations are calculated. Finally, an application from real-life problems is proposed to explain the importance of our decision-making methods.

    Citation: Ashraf S. Nawar, Mostafa A. El-Gayar, Mostafa K. El-Bably, Rodyna A. Hosny. θβ-ideal approximation spaces and their applications[J]. AIMS Mathematics, 2022, 7(2): 2479-2497. doi: 10.3934/math.2022139

    Related Papers:

  • The essential aim of the current work is to enhance the application aspects of Pawlak rough sets. Using the notion of a j-neighborhood space and the related concept of θβ-open sets, different methods for generalizing Pawlak rough sets are proposed and their characteristics will be examined. Moreover, in the context of ideal notion, novel generalizations of Pawlak's models and some of their generalizations are presented. Comparisons between the suggested methods and the previous approximations are calculated. Finally, an application from real-life problems is proposed to explain the importance of our decision-making methods.



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