A similarity measure under Pythagorean fuzzy soft environment with applications

Riaz, Muhammad; Naeem, Khalid; Afzal, Deeba

doi:10.1007/s40314-020-01321-5

Cited by 38 publications

(23 citation statements)

References 26 publications

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“…Riaz et al [35] established the TOPSIS technique for m-polar PFSSs and presented an example to solve MCGDM problems within a considered hybrid structure. ey also introduced similarity measures for PFSSs [36]. Hwang and Yoon [37] developed TOPSIS to solve decision-making problems.…”

Section: Complexitymentioning

confidence: 99%

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Development of TOPSIS Technique under Pythagorean Fuzzy Hypersoft Environment Based on Correlation Coefficient and Its Application towards the Selection of Antivirus Mask in COVID‐19 Pandemic

et al. 2021

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The correlation coefficient between two variables plays an important role in statistics. Also, the accuracy of relevance assessment depends on information from a set of discourses. The data collected from numerous statistical studies are full of exceptions. The Pythagorean fuzzy hypersoft set (PFHSS) is a parameterized family that deals with the subattributes of the parameters and an appropriate extension of the Pythagorean fuzzy soft set. It is also the generalization of the intuitionistic fuzzy hypersoft set (IFHSS), which is used to accurately assess insufficiency, anxiety, and uncertainties in decision-making. The PFHSS can accommodate more uncertainties compared to the IFHSS, and it is the most substantial methodology to describe fuzzy information in the decision-making process. The core objective of the this study is to develop the notion and features of the correlation coefficient and the weighted correlation coefficient for PFHSS and to introduce the aggregation operators such as Pythagorean fuzzy hypersoft weighted average (PFHSWA) and Pythagorean fuzzy hypersoft weighted geometric (PFHSWG) operators under the PFHSS scenario. A prioritization technique for order preference by similarity to the ideal solution (TOPSIS) under PFHSS based on correlation coefficients and weighted correlation coefficients is presented. Through the developed methodology, a technique for solving multiattribute group decision-making (MAGDM) problem is planned. Also, the importance of the developed methodology and its application in indicating multipurpose antivirus mask throughout the COVID-19 pandemic period is presented. A brief comparative analysis is described with the advantages, effectiveness, and flexibility of numerous existing studies that demonstrate the effectiveness of the proposed method.

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Section: Complexitymentioning

confidence: 99%

“…Construct the weighted decision matrix for each alternative Q (z) � (L (z) ij ) n×z by using Equation (35) given in Tables 5-8. (4) Step 4. Determine the PIA and NIA based on indices by using Equations (36) and (37):…”

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confidence: 99%

Development of TOPSIS Technique under Pythagorean Fuzzy Hypersoft Environment Based on Correlation Coefficient and Its Application towards the Selection of Antivirus Mask in COVID‐19 Pandemic

et al. 2021

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“…Riaz et al [43] established the TOPSIS technique for polar PFSS and presented an example to solve MCGDM problems within a hybrid structure. They also introduced the similarity measure for PFSS [44]. Han et al [45] developed a TOPSIS technique for PFSS and utilized their method to solve MAGDM problems.…”

Section: Introductionmentioning

confidence: 99%

TOPSIS Method Based on Correlation Coefficient under Pythagorean Fuzzy Soft Environment and Its Application towards Green Supply Chain Management

et al. 2021

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The correlation coefficient between two variables is an important aspect of statistics. The accuracy of assessments of correlation relies on information from a set of discourses. Data collected in statistical studies are often full of exceptions. Pythagorean fuzzy soft sets (PFSS) are a parametrized family of extended Pythagorean fuzzy sets (PFS). They comprise a generalization of intuitionistic fuzzy soft sets which may be used to accurately assess deficiencies and uncertainties in evaluations. PFSS can accommodate uncertainty more competently than intuitionistic fuzzy soft sets and are the most important strategy when dealing with fuzzy information in decision-making processes. Herein, the concept and characteristics of correlation coefficients and the weighted correlation coefficients in PFSS are discussed. We also introduce the Pythagorean fuzzy soft weighted average (PFSWA) and Pythagorean fuzzy soft weighted geometric (PFSWG) operators and discuss their desirable characteristics. A prioritization technique for order preference by similarity to the ideal solution (TOPSIS) under the PFSS environment based on correlation coefficients and weighted correlation coefficients will be introduced. Through the proposed methodology, a technique for decision-making is developed. Additionally, an application of the proposed TOPSIS technique is presented for green supplier selection in green supply chain management (GSCM). The practicality, efficacy, and flexibility of the proposed approach is proved through comparative analyses, drawing upon existing studies.

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“…So far, many studies have been conducted on the concept of soft sets in such fields as soft algebra [23]- [26], soft topology [27]- [31], decision-making [32]- [36], similarity measure [37]- [40], distance measure [38], [41], medical diagnosis [42], texture classification [43], and data classification [44]- [46]. Although the studies mentioned above have been carried out in a great variety of fields, these studies feature modeled problems often similar to each other and fictitious, except Fuzzy Soft Set Classifier (FSSC) [44], FussCyier [45], Fuzzy Soft Set Classification using Hamming Distance (HDFSSC) [46].…”

Section: Introductionmentioning

confidence: 99%

Numerical Data Classification via Distance-Based Similarity Measures of Fuzzy Parameterized Fuzzy Soft Matrices

2021

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In this paper, we first define eight pseudo-metrics and eight pseudo-similarities based on these pseudo-metrics over fpfs-matrices. We then propose a new classification algorithm, i.e. Fuzzy Parameterized Fuzzy Soft Euclidean Classifier (FPFS-EC), based on Euclidean pseudo-similarity. After that, we compare FPFS-EC with Support Vector Machine (SVM), Fuzzy k-nearest neighbor (Fuzzy kNN), Fuzzy Soft Set Classifier (FSSC), FussCyier, Fuzzy Soft Set Classification Using Hamming Distance (HDFSSC), and Fuzzy kNN Based on the Bonferroni Mean (BM-Fuzzy kNN) in terms of the performance criteria -namely accuracy, precision, recall, macro F-score, and micro F-score -and running time by using 18 real-world datasets in the UCI machine learning repository. The results show that FPFS-EC performs better in the occurrence of the 13 of 18 datasets in question than SVM, Fuzzy kNN, FSSC, FussCyier, HDFSSC, and BM-Fuzzy kNN.

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A similarity measure under Pythagorean fuzzy soft environment with applications

Cited by 38 publications

References 26 publications

Development of TOPSIS Technique under Pythagorean Fuzzy Hypersoft Environment Based on Correlation Coefficient and Its Application towards the Selection of Antivirus Mask in COVID‐19 Pandemic

Development of TOPSIS Technique under Pythagorean Fuzzy Hypersoft Environment Based on Correlation Coefficient and Its Application towards the Selection of Antivirus Mask in COVID‐19 Pandemic

TOPSIS Method Based on Correlation Coefficient under Pythagorean Fuzzy Soft Environment and Its Application towards Green Supply Chain Management

Numerical Data Classification via Distance-Based Similarity Measures of Fuzzy Parameterized Fuzzy Soft Matrices

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