فهرست نشریات علمی دسترسی آزاد ایران

A $\beta$-soft fuzzy complement neighborhood is the initial concept proposed by Zhang and Zhan (International Journal of Machine Learning and Cybernetics 10(2019) 1487-1502), which we will refer to in this study as $\circledast$-$\beta$- soft fuzzy complement neighborhood. In the present paper, we first introduce three new approximation types based on  $\beta$-soft fuzzy covering via $\circledast$-$\beta$-soft fuzzy complement neighborhood, along with several essential features and examples. The new approximation types are significant because they satisfy the inclusion property (i.e., the  upper approximation contains the lower approximation, which is one of the essential features of rough set models). In  addition, we update some algorithms to a very easy-to-understand state. As a result, improved decision-making  procedures will be observable and trustworthy in order to arrive at the optimal choice. Second, we offer a novel idea of  $\circledcirc$-$\beta$-soft fuzzy complement neighborhood and then present three other new approximation types  based on $\beta$-soft fuzzy covering via $\circledcirc$-$\beta$ soft fuzzy complement neighborhood, besides outlining  some of its key characteristics and giving some examples. On the theoretical side, by using three other new  approximations via $\circledcirc$-$\beta$-FCN${\mathscr A},$ we also study some basic fuzzy topology properties. Third,  we use $\circledcirc$-$\beta$-FCN${\mathscr A}$ to construct a new method that can be applied to the MADM field. We  illustrate this method using a real-world problem: a candidate seeking employment in a company. Finally, to demonstrate  the advantages of the proposed work, we will compare our proposed method with published $\beta$-soft  fuzzy MADM methods.