Distance measures for cubic Pythagorean fuzzy sets and its applications to multicriteria decision making

Some distance measures for pythagorean cubic fuzzy sets: Application selection in optimal treatment for depression and anxiety

Pythagorean cubic fuzzy sets represent an advancement beyond conventional interval-valued Pythagorean sets, integrating the principles of Pythagorean fuzzy sets and interval-valued Pythagorean fuzzy sets. Given the critical significance of distance measures in real-world decision-making and pattern recognition tasks, it is noteworthy that there exists a notable gap in the literature regarding distance measures specifically tailored for Pythagorean cubic fuzzy sets. The objectives of this paper are:•To define novel generalized distance measures between Pythagorean cubic fuzzy sets (PCFSs) to tackle intricate decision-making challenges.•These novel distance measures are undergoing testing on a real-world scenario concerning the management of anxiety and depression to evaluate their effectiveness and practical application.•We have illustrated the boundedness and nonlinear characteristics inherent in these distance measures. In addition, we conduct comparative analyses with existing approaches to validate the proposed methodology, thereby providing insights into its advantages and potential applications.

Evolved distance measures for circular intuitionistic fuzzy sets and their exploitation in the technique for order preference by similarity to ideal solutions

Circular intuitionistic fuzzy (C-IF) sets are an up-and-coming tool for enforcing indistinct and imprecise information in variable and convoluted decision-making situations. C-IF sets, as opposed to typical intuitionistic fuzzy sets, are better suited for identifying the evaluation data with uncertainty in intricate realistic decision situations. The architecture of the technique for order preference by similarity to ideal solutions (TOPSIS) provides powerful evaluation tools to aid decision-making in intuitionistic fuzzy conditions. To address appraisal issues associated with decision analysis involving extremely convoluted information, this paper propounds a novel C-IF TOPSIS approach in the context of C-IF uncertainty. This research makes three significant contributions. First, based on the three- and four-term operating rules, this research introduces C-IF Minkowski distance measures, which are new generalized representations of distance metrics applicable to C-IF values and C-IF sets. Such general C-IF distance metrics can alleviate the constraints of established C-IF distance measures, provide usage resiliency through parameter settings, and broaden the applicability of metric analysis. Second, unlike existing C-IF TOPSIS methods, this research fully utilizes C-IF information characteristics and extends the core structure of the classic TOPSIS to C-IF contexts. With the newly developed C-IF Minkowski metrics, this study faithfully demonstrates the trade-off evaluation and compromise decision rules in the TOPSIS framework. Third, this research builds on the core strengths of the pioneered C-IF Minkowski distance measures to create innovative C-IF TOPSIS techniques utilizing four different combinations, including displaced and fixed anchoring frameworks, as well as three- and four-term representations. Such a refined C-IF TOPSIS methodology can assist decision-makers in proactively addressing increasingly sophisticated decision-making problems in practical settings. Finally, this research employs two innovative prioritization algorithms to address a site selection issue of large-scale epidemic hospitals to illustrate the superior capabilities of the C-IF TOPSIS methodology over some current related approaches.

On the similarity measures of N-cubic Pythagorean fuzzy sets using the overlapping ratio

The similarity measures are essential concepts to discuss the closeness between sets. Fuzzy similarity measures and intuitionistic fuzzy similarity measures dealt with the incomplete and inconsistent data more efficiently. With time in decision-making theory, a complex frame of the environment that occurs cannot be specified entirely by these sets. A generalization like the Pythagorean fuzzy set can handle such a situation more efficiently. The applicability of this set attracted the researchers to generalize it into N-Pythagorean, interval-valued N-Pythagorean, and N-cubic Pythagorean sets. For this purpose, first, we define the overlapping ratios of N-interval valued Pythagorean and N-Pythagorean fuzzy sets. In addition, we define similarity measures in these sets. We applied this proposed measure for comparison analysis of plagiarism software.

Multicriteria decision-making based on distance measures and knowledge measures of Fermatean fuzzy sets

Fermatean fuzzy sets are more powerful than fuzzy sets, intuitionistic fuzzy sets, and Pythagorean fuzzy sets in handling various problems involving uncertainty. The distance measures in the fuzzy and non-standard fuzzy frameworks have got their applicability in various areas such as pattern analysis, clustering, medical diagnosis, etc. Also, the fuzzy and non-standard fuzzy knowledge measures have played a vital role in computing the criteria weights in the multicriteria decision-making problems. As there is no study concerning the distance and knowledge measures of Fermatean fuzzy sets, so in this paper, we propose some novel distance measures for Fermatean fuzzy sets using t-conorms. We also discuss their various desirable properties. With the help of suggested distance measures, we introduce some knowledge measures for Fermatean fuzzy sets. Through numerical comparison and linguistic hedges, we establish the effectiveness of the suggested distance measures and knowledge measures, respectively, over the existing measures in the Pythagorean/Fermatean fuzzy setting. At last, we demonstrate the application of the suggested measures in pattern analyis and multicriteria decision-making.