Novel pythagorean fuzzy entropy-distance measures using MCDM in the selection of face masks
Advances in Soft Computing Applications
; : 185-204, 2023.
Article
in English
| Scopus | ID: covidwho-20233231
ABSTRACT
Wearing a face mask can help reduce the spread of infection and contamination from airborne harmful germs. The requirement to wear a face mask is perhaps one of the most noticeable lifestyle changes brought on by the COVID-19 pandemic. COVID-19 transmission can be slowed down by wearing a mask, especially while in close contact with others. Choosing the best face mask is a cumbersome task from the available alternatives in India. Several multi-criteria decision-making (MCDM) techniques and approaches have been suggested to choose the optimally probable options. The purpose of this article is to deliver an entropy-distance measure for Pythagorean fuzzy sets. To validate these measures, some of the properties were also proved. A multi-criteria decision-making approach is used to rank and hence select the best face mask for wearing. The proposed research allows the ranking of face masks based on specified criteria in a Pythagorean fuzzy environment to aid in the selection process. The results suggest that the proposed model provides a realistic way to select the best mask in the pool of considered brands. A case study on the selection process and its experimental results using Pythagorean fuzzy sets are discussed. © 2023 River Publishers. All rights reserved.
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Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
Advances in Soft Computing Applications
Year:
2023
Document Type:
Article
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