Özet:

Soft rough set theory has been presented as a basic mathematical model for decision-making for many real-life data. However, soft rough sets are based on a possible fusion of rough sets and soft sets which were proposed by Feng et al. [20]. The main contribution of the present article is to introduce a modification and a generalization for Feng's approximations, namely, soft $\beta$-rough approximations, and some of their properties will be studied. A comparison between the suggested approximations and the previous one [20] will be discussed. Some examples are prepared to display the validness of these proposals. Finally, we put an actual example of the infections of coronavirus (COVID-19) based on soft $\beta$-rough sets. This application aims to know the persons most likely to be infected with COVID-19 via soft $\beta$-rough approximations and soft $\beta$-rough topologies.

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