Generalized dice similarity measures for q-rung orthopair fuzzy sets with applications

Abstract

Recently, Yager has established that the notion of q-rung orthopair fuzzy set (q-ROFS) is more accomplished than pythagorean fuzzy set (PyFS) and intuitionistic fuzzy set (IFS) to cope with awkward and complicated information in real decision theory. This notion works with yes-, no- and refusal-type fuzzy information. The constraint of q-ROFS is that the sum of n-power of the truth grade and the n-power of the falsity grade is bounded to unit interval. Generalized dice similarity measures are complimentary concepts quantifying the difference and closeness of q-ROFSs. In this paper, we suggested a number of novel dice similarity measures (DSMs) in the surroundings of the q-ROFS, and we examined some prevailing dice similarity measures and their limitations. In addition, we took the DSMs broad view to some globalized dice similarity measures (GDSMs), and we examined some of their particular cases. We employed the novel suggested GDSMs to the best selections of items on identification problems, and we analyzed their acquired consequences. There is a development of novel work in which many situations are evaluated, and from this perspective, the suggested work is changed into already prevailing work. This study also examines the merits of novel DSMs and the limitations for DSMs of IFSs and PyFSs. The comparison between established measures with existing measures is explored and their graphical interpretations are also discussed to show the reliability and effectiveness of the explored measures.

Generalized complex q-rung orthopair fuzzy Einstein averaging aggregation operators and their application in multi-attribute decision making

“…In the future, we will extend the present work to consider (1) aggregation operators for different FSs, e.g., the linguistic neutrosophic set [44], probabilistic linguistic information [45], linguistic D number [46], interval type-2 FS [47], T-spherical FS [48,49], and others [50], and (2) MADM methods [51][52][53][54][55][56][57][58][59], which will be more flexible for future directs.…”

A Novel Similarity-Based Multi-Attribute Group Decision-Making Method in a Probabilistic Hesitant Fuzzy Environment

“…As a method to quantify the relationship between elements, similarities have been researched extensively in intuitionistic fuzzy sets [30], [31], interval-valued fuzzy sets [32], type-2 fuzzy sets [33], [34], spherical fuzzy sets [35], [36], [37], q-rung orthopair fuzzy sets [38], [39], pythagorean fuzzy sets [40], [41], picture fuzzy sets [42], [43], [44], fuzzy soft sets [45], [46], bipolar complex fuzzy sets [47], and hesitant fuzzy sets [48], [49]. With the increasing amount of research on extended forms of hesitant fuzzy sets in recent years, the similarity of other extended hesitant fuzzy sets has also become a research hotspot, such as dual hesitant fuzzy sets [50], [51], [52], [53], hesitant fuzzy linguistic term sets [54], hesitant interval-valued fuzzy sets [55], [56], typical hesitant fuzzy sets [57], cubic hesitant fuzzy sets [58], higher-order hesitant fuzzy sets [59], and complex hesitant fuzzy sets [60], [61].…”

Directional correlation coefficient measures for Pythagorean fuzzy sets: their applications to medical diagnosis and cluster analysis

“…The decision making refers to the human activities that rank the alternatives and select the optimal ones [26,38,39,55]. It is extremely common in our daily life and national industrial development [10,13,23]. For example, there exist some startup companies that need to look for the venture capital.…”