Novel concepts of $ m $-polar spherical fuzzy sets and new correlation measures with application to pattern recognition and medical diagnosis

Riaz, Muhammad; Saba, Maryam; Khokhar, Muhammad Abdullah; Aslam, Muhammad

doi:10.3934/math.2021659

Cited by 10 publications

(9 citation statements)

References 39 publications

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“…With the establishment of T-SF theory, the correlation coefficients for T-SF information attempt a solid grounding of multiple-criteria evaluation issues in the fields of decision analysis (Guleria and Bajaj, 2021;Ullah et al, 2020a). A correlation coefficient is one of the most commonly-used statistical notions to estimate linear relationships between quantitative objects (Özlü and Karaaslan, 2022;Riaz et al, 2021), and it is often used in statistical analysis or machine learning. Correlation coefficients in statistics can be negative or positive contingent upon the direction of two objects' relationship and their values lie between −1 and 1.…”

Section: Research Gap and Motivationsmentioning

confidence: 99%

“…With two applications in pattern recognition and medically diagnostic cases, Guleria and Bajaj gave substance to the effectuality of their evolved correlation coefficients. Riaz et al (2021) exploited the statistical notions of covariances and variances to evolve a new correlation coefficient for hybrid SF and m-polar fuzzy information. Mahmood et al (2021) initiated SF cosine similarity measures and (weighted) correlation co-efficient of SF sets for tackling pattern recognition and medical diagnostic issues.…”

Section: Research Gap and Motivationsmentioning

confidence: 99%

“…Multiple-criteria choice modelling under uncertainty forms part of the intelligent decision support system and can be applied to explore an innovative advancement of intelligent decision-making approaches and models (Fernández-Martínez and Sánchez-Lozano, 2021;Jing et al, 2021;Menekse and Camgoz-Akdag, 2022;Riaz et al, 2021). Numerous multiple-criteria assessment models have flourished to evaluate predetermined choice options ascertained from (conflicting) performance criteria for finding the most suitable option (Al-Quran, 2021;Erdogan et al, 2021;Kovač et al, 2021;Naeem et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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An Uncertain Multiple-Criteria Choice Method on Grounds of T-Spherical Fuzzy Data-Driven Correlation Measures

Wang¹,

Chen²

2022

Informatica

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T-spherical fuzzy (T-SF) sets furnish a constructive and flexible manner to manifest higher-order fuzzy information in realistic decision-making contexts. The objective of this research article is to deliver an original multiple-criteria choice method that utilizes a correlation-focused approach toward computational intelligence in uncertain decision-making activities with T-spherical fuzziness. This study introduces the notion of T-SF data-driven correlation measures that are predicated on two types of the square root function and the maximum function. The purpose of these measures is to exhibit the overall desirability of choice options across all performance criteria using T-SF comprehensive correlation indices within T-SF decision environments. This study executes an application for location selection and demonstrates the effectiveness and suitability of the developed techniques in T-SF uncertain conditions. The comparative analysis and outcomes substantiate the justifiability and the strengths of the propounded methodology in pragmatic situations under T-SF uncertainties.

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Section: Research Gap and Motivationsmentioning

confidence: 99%

Section: Research Gap and Motivationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Uncertain Multiple-Criteria Choice Method on Grounds of T-Spherical Fuzzy Data-Driven Correlation Measures

Wang¹,

Chen²

2022

Informatica

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“…Multiple criteria choice analysis is regarded as a cognitive and reasoning process resulting in the assessment of possible courses of action in terms of judging criteria and the identification of a final choice among alternative options (Chen 2022b ; Riaz et al 2021 ). Using simple yet powerful methods and techniques to assist decision-makers in conducting an advisable assessment process and producing the best possible choice is crucial to improving the quality of decision-making (Akram et al 2022 ; Chen 2022a ; Cihat Onat 2022 ; Wang and Chen 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Multiple criteria decision analytic methods in management with T-spherical fuzzy information

Chen

2023

Artif Intell Rev

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With a focus on T-spherical fuzzy (T-SF) sets, the aim of this paper is to create a split-new appraisal mechanism and an innovative decision analytic method for use with multiple-criteria assessment and selection in uncertain situations. The T-SF frame is the latest recent advancement in fuzzy settings and uses four facets (consisting of membership grades of positivity, neutrality, negativity, and refusal) to elucidate complex uncertainties, thereby evidently reducing information loss, in anticipation of fully manifesting indistinct and equivocal information. This paper adds to the body of knowledge regarding multiple criteria choice modeling by raising T-SF correlation-oriented measurements connected to the fixed and displaced ideal/anti-ideal benchmarks and by creating an approachable appraisal mechanism for advancing a T-SF decision analytic methodology. Consider, in particular, the performance ratings of available options in terms of judging criteria under the T-SF type of uncertainties. This research gives correlation-oriented measurements focusing on two varieties of maximum and square root functions in T-SF situations, which serve as a solid foundation for an efficacious appraisal mechanism from two views of anchored judgments corresponding to the fixed and displaced benchmarks. The T-SF Minkowski distance index is generated to integrate the outranking and outranked identifiers relying on correlation-oriented measurements for figuring out the local outranking and outranked indices. The T-SF decision analytic procedures are constructed using a new appraisal significance index that is founded on certain valuable insights of correlation-oriented maximizing and minimizing indices as well as global outranking and outranked indices. Additionally, a concrete location selection dilemma is dealt with in this research to showcase the applicability and efficiency of the suggested T-SF decision analytic methodology. Sensitivity analyses and comparative studies are carried out to investigate substantial modifications in pertinent parameters and to confirm the robustness of the predominance relationships among the available options. The suggested approaches are adaptable, flexible, and reliable, according to the application outcomes and comparison findings. This research provides four scientific contributions: (1) the utilization of T-SF correlation coefficients as the basis for prioritization analysis involving multiple criteria assessments, (2) the evolution of the T-SF Minkowski distance index to model outranking decision-making processes, (3) the creation of a reliable appraisal mechanism based on T-SF correlation-oriented measurements for intelligent decision support, and (4) the advancement of computational tools and procedures (e.g., correlation-oriented maximizing and minimizing indices, global outranking and outranked indices, and appraisal significance indices) to perform the decision analytic procedure in T-SF settings. In terms of managerial implications, the solution findings support the employment of...

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“…SFSs have been introduced by considering the limitations of the PFS domain [66]. Due to the possibility of more accurate identification, they are widely used in work related to pattern recognition [67], the manufacturing domain [68], medical diagnostics [66,69], economics [70], management [64,71] and energy [72]. T-spherical fuzzy sets (TSFSs) have been proposed as a generalization of SFSs, free from the constraints of [67].…”

Section: Introductionmentioning

confidence: 99%

Decision Support in Selecting a Reliable Strategy for Sustainable Urban Transport Based on Laplacian Energy of T-Spherical Fuzzy Graphs

Devi

Kizielewicz²,

Guleria³

et al. 2022

Energies

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Sustainable transportation has a significant impact on factors related to urban development and economic development. Therefore, much research is being undertaken to select the best strategies to manage sustainable transportation. Transportation requires a carefully designed method to manage the development of mobility modes in terms of the pollution they produce or the use of renewable energy sources. However, due to numerous preferences of decision-makers and data uncertainty problems, it is challenging to select the optimal strategy. In this paper, we focus on creating a framework for determining the best strategy for sustainable transportation management. For this purpose, T-spherical fuzzy graphs will be used, which, together with the combination of Laplacian Energy, can accurately represent decision-makers’ preferences in an uncertain environment. Due to the lack of limitations of T-spherical fuzzy graphs and its numerous membership functions, decision-makers can decide which factor seems most important for selecting the optimal sustainable transportation strategy. Additionally, due to the applicability, the SFS TOPSIS approach has been used in this approach. The obtained results demonstrate the high performance of the proposed approach and the applicability of the approach in management and sustainable transport problems.

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Novel concepts of $ m $-polar spherical fuzzy sets and new correlation measures with application to pattern recognition and medical diagnosis

Cited by 10 publications

References 39 publications

An Uncertain Multiple-Criteria Choice Method on Grounds of T-Spherical Fuzzy Data-Driven Correlation Measures

An Uncertain Multiple-Criteria Choice Method on Grounds of T-Spherical Fuzzy Data-Driven Correlation Measures

Multiple criteria decision analytic methods in management with T-spherical fuzzy information

Decision Support in Selecting a Reliable Strategy for Sustainable Urban Transport Based on Laplacian Energy of T-Spherical Fuzzy Graphs

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