Abstract
Intuitionistic Fuzzy set (IFS) theory plays an important role in real life and engineering problems. There are many model involving fuzzy matrices to deal with different complicated aspects. Intuitionistic fuzzy set (IFS) is useful in providing a flexible model for developing the uncertainty and vagueness involved in making decisions where the theories of uncertainty are very useful to treat with mathematics that needs to address. In other words, the application of intuitionistic fuzzy sets instead of fuzzy sets means the introduction of another degree of freedom into a set description. Intuitionistic fuzzy set (IFS) called the generalization of fuzzy sets was proposed in K. T. Atanassov. So, we can use it in decision making. We examined the definition of IFS and puts new definitions of IFS (Intuitionistic fuzzy set) in this paper and suggested its implementation in the Corona Covid-19. For several similar real-life cases the suggested approach can be applied.
Highlights
IntroductionT. Atanassov [9] proposed a generalization of fuzzy sets [7] as intuitionistic fuzzy sets (IFS) incorporating the degree of hesitation called the hesitation margin (And is defined as 1 minus the total number of degrees of membership and non-membership)
De et al [14] presented an intuitive fuzzy approach in medical diagnosis using three measures such as: symptom determination, the formulation of medical information based on intuitive fuzzy relationships, and diagnosis determination based on the composition of intuitionistic fuzzy relationships
Szmidt [2] we have proven that the only proper way to measure the most commonly used distances for intuitive fuzzy sets is to take all three parameters into account: membership function, non-membership function and hesitation margin
Summary
T. Atanassov [9] proposed a generalization of fuzzy sets [7] as intuitionistic fuzzy sets (IFS) incorporating the degree of hesitation called the hesitation margin (And is defined as 1 minus the total number of degrees of membership and non-membership). The knowledge and semantic representation of intuitionistic fuzzy set becomes more meaningful, resourceful and applicable because it includes the degree of belonging, the degree of non-belonging and the margin of hesitation K. E. Szmidt et al [4] showed that intuitionistic fuzzy sets are very useful in situations where a linguistic attribute represents a problem which it just seems to be too rough given in terms of membership features. We show a novel application of intuitionistic fuzzy set in a more challenging area of decision-making (i.e., choice of department [17]).
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