Abstract
The polling system has a considerable role in the democratic nation. The uncertainty of the people’s participation in polling generally affects the electoral-based system. Therefore, PFS (picture fuzzy set) is the furthermost efficient and useful extension of IFS (intuitionistic fuzzy set) in a fuzzy system capable of precisely handling human perception in the decision-making system. The PFS structure involves the different degrees, i.e., membership, nonmembership, neutral, and hesitancy which are comprehensively applied to such types of complex practical problems in the real-life scenario. This advantage of PFS motivates the author to propose PFSs centred novel entropy measure via this communication, which is comparatively more generalized, reliable, and simplified in place of the existing uncertain measures. The practicability of the proposed present research work is to deal with real-world problems pertaining to the relative importance of the attributes. Therefore, certainly, the proposed novel entropy developed a different approach to handle the uncertainty more precisely as a part of the existing approaches. The validation proof of the proposed entropy measure is proved in an organized manner and practically employed in the perspective of the polling data outcomes about the people’s opinions with the VIKOR-TOPSIS approach.
Highlights
In the modern era, fuzzy sets and their various generalized extensions are predominantly utilized by researchers and decision-makers in the multiple domains of the research field
The author was inspired [5] with the concept of nonmembership function (]) and reshaped the existing structure of Fss and added on one more parameter π: 0 ≤ μ + ] + π ≤ 1 and IFS comes to light
The authors [9] were motivated with idea of neutral membership degree and incorporated the new component η to the present structure of IFs: 0 ≤ μ + ] + η + π ≤ 1 which is known as PFs. erefore, whenever IFs restricts its scope, PFs perfectly handles the uncertainty in a better way
Summary
Fuzzy sets and their various generalized extensions are predominantly utilized by researchers and decision-makers in the multiple domains of the research field. IFs attracts the great attentions of various researchers, authors, and decision-makers across the globe because of its feature of presenting π It makes IFs more reliable and concise towards the challenges in real-life scenario. The authors [11] successively utilized PFs-based hybrid distance measure with the concept of rough set theory in selection criteria of the students In this context, the authors [12] contributed some important correlation coefficients and their application in analysis of clustering and pattern recognition in computing research, and the authors in [13] presented the hybrid form of similarity measures and biparametric distance to handle the uncertainty in medical diagnosis. Erefore, fuzzy sets are quite proficient and reliable in handling ambiguity and the dilemma approach of human mindsets in complex decision-making issues.
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