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Approximations by Ideal Minimal Structure with Chemical Application

Rodyna A. Hosny1, Radwan Abu-Gdairi2, Mostafa K. El-Bably3,*

1 Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519, Egypt
2 Department of Mathematics, Faculty of Science, Zarqa University, Zarqa, 13132, Jordan
3 Department of Mathematics, Faculty of Science, Tanta University, Tanta, 31527, Egypt

* Corresponding Author: Mostafa K. El-Bably. Email: email

Intelligent Automation & Soft Computing 2023, 36(3), 3073-3085. https://doi.org/10.32604/iasc.2023.034234

Abstract

The theory of rough set represents a non-statistical methodology for analyzing ambiguity and imprecise information. It can be characterized by two crisp sets, named the upper and lower approximations that are used to determine the boundary region and accurate measure of any subset. This article endeavors to achieve the best approximation and the highest accuracy degree by using the minimal structure approximation space via ideal . The novel approach (indicated by ) modifies the approximation space to diminish the boundary region and enhance the measure of accuracy. The suggested method is more accurate than Pawlak’s and EL-Sharkasy techniques. Via illustrated examples, several remarkable results using these notions are obtained and some of their properties are established. Several sorts of near open (resp. closed) sets based on are studied. Furthermore, the connections between these assorted kinds of near-open sets in are deduced. The advantages and disadvantages of the proposed approach compared to previous ones are examined. An algorithm using MATLAB and a framework for decision-making problems are verified. Finally, the chemical application for the classification of amino acids (AAs) is treated to highlight the significance of applying the suggested approximation.

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Cite This Article

R. A. Hosny, R. Abu-Gdairi and M. K. El-Bably, "Approximations by ideal minimal structure with chemical application," Intelligent Automation & Soft Computing, vol. 36, no.3, pp. 3073–3085, 2023. https://doi.org/10.32604/iasc.2023.034234



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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