The characteristic function of a crisp set assigns a value either 1 or 0 to each object in a set and thereby distinguishesthe members and non-members of crisp set under consideration. The function can be generalized such that the values assigned to the elements of a set fall within a specific range from 0 to 1 and indicate the membership grade of these elements in the set in question. Larger values denote higher degree of set membership. Such function is called a membership function, and the set defined by it is known as fuzzy set. In short, it can be opinedthat the crisp set and logic divide the world of yes or no, true or false but nothing in between. On the other hand, fuzzy sets and logic deal with objects that are of degree with all possible grades between yes or no.Thus, fuzzy set represents the vague or ill-defined (not well-defined) concept like good , very good , poor , intelligent , large , and medium large etc., and hence, it can be extensively applied in a wide range of area. Zadeh developed this novel concept of fuzzy sets that created a new branch of Mathematics which is used tocharacterize the uncertainty.A lot of significant developments have been made by the researchers in the last five decades and applied it in a large variety of fields. It is observed that in fuzzy set theory (FST) the non-membership function is the complement of the membership function. But in many situations, complement of membership function may not reflect the exact non-membership grades of an element to a set. Later, Atanassov defined both the membership function and nonmembership function which also characterized some hesitation degree between them. This newly defined set is called Intuitionistic fuzzy set (IFS). As IFS can represents the incomplete/ ill-defined information in a more specific manner than FST, therefore, IF
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