Multi-criteria decision support systems based on linguistic intuitionistic cubic fuzzy aggregation operators

Qiyas, Muhammad; Abdullah, Saleem; Liu, Yi; Naeem, Muhammad

doi:10.1007/s12652-020-02563-1

Cited by 18 publications

(7 citation statements)

References 68 publications

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“…Also, DMs have been facing various difficulties to address the uncertainties which are presented in a complex decision making problem. Therefore, a lot of researchers utilized various uncertainties to limn the data in various decision making problems, such as fuzzy set, 7,8 intuitionistic fuzzy set, 9,10 interval-valued intuitionistic fuzzy set (IVIFS), 11 type-2 intuitionistic fuzzy set (T2IFS), 2 intuitionistic cubic fuzzy set, 12 Pythagorean fuzzy set, 13 interval Pythagorean fuzzy set (IVPFS), 14 fuzzy-rough set, 15,16 and so forth. Out of which, fuzzy set (FS) theory was used frequently to tackle the uncertainty, that was initiated by Zadeh.…”

Section: Introductionmentioning

confidence: 99%

Application of Choquet integral in interval type‐2 Pythagorean fuzzy sustainable supply chain management under risk

Mondal

Roy

2021

Int J Intell Syst

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The purpose of this study is to offer requisite models for unravelling the complex issues that arise in a supplier selection-order allocation problem by soothing the risk and disturbances. To do this, a new uncertain environment, interval type-2 Pythagorean fuzzy set How to cite this article: Mondal A, Roy SK. Application of Choquet integral in interval type-2 Pythagorean fuzzy sustainable supply chain management under risk.

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Section: Introductionmentioning

confidence: 99%

Application of Choquet integral in interval type‐2 Pythagorean fuzzy sustainable supply chain management under risk

Mondal

Roy

2021

Int J Intell Syst

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“…Muhammad, Saleem, Yi and Muhammad [8] studied an advanced approach to linguistic intuitionistic fuzzy variable through application of cubic set theory, established a series of weighted aggregation operators under linguistic intuitionistic cubic fuzzy information and their fundamental properties and showed their relationship and having developed a multicriteria decision-making algorithm, they demonstrated that the algorithm performs better than pre-existing aggregation operators.…”

Section: Related Literaturementioning

confidence: 99%

Minimizing Symptom-based Diagnostic Errors Using Weighted Input Variables and Fuzzy Logic Rules in Clinical Decision Support Systems

2021

IJATCSE

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ThisstudycoversanapproachtowardsminimizingDiseasediagnosticerrorsusingweightedinputvariablesandFuzzyLogicruleswithmultiphasediagnosticengine.Theweightswereappliedbecausedifferentsymptomsmayhavedifferentdegreesofimportanceindifferentdiseases.Thisistoensurethatrecommendationsfordiseaseconfirmationbasedonsymptomsreturngoodpercentageoftruepositiveandtruenegatives.Thestudycreatesanenhanced,accurateandprecisesystemformedicaldiagnosisevenwhenonlythesymptomsareconsidered.Inordertoevaluatethemodel,fourcategoriesofdiagnoseswerecarriedoutwithoutusingthemodelatthefirstinstanceandusingthemodelatthesecondinstancewith50patientsdoneat4differentdiagnosticinstances.Thetruepositive(TP)andtheFalsenegativestatisticswereobtainedfromwherethefalsepositiverate(TPR)orsensitivityandfalsepositiverate(FPR)werederived.ThegraphofTPRvsFPRwasplottedfromwherethequalityofdiagnosescouldbegottenfromtheReceiverOperatingCharacteristics(ROC)space.Theresultshowsthatsensitivity,whichistheabilityofatesttocorrectlyidentifythosewiththediseaseorTruePositiveRate,andspecificity,whichistheabilityofthetesttocorrectlyidentifythosewithoutthediseasealsocalledTrueNegativeRateTNRstoodat87%and86%respectivelyusingthedevelopedmodelandthesameparameteryielded72%and56%respectivelywithoutusingthemodel.Theresultalsoshowsthatthefalsepositiverate(FPR)whichindicatesthedegreeoffalsealarmis19%usingthenewmodelwhileitis44%withoutusingthemodel.Thisresultshowsthatthelikelihoodofmakingwrongclinicaldiagnosticdecisionsismuchlowerwiththisapproach

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“…(2021) they used Dombi operational laws and Heronian mean operators, to develop a new concept of cubic fuzzy Heronian mean Dombi aggregation operators, they presented new rules for the cubic fuzzy numbers (CFNs) based on Dombi t-norm and t-conorm (DTT), to present a new cubic fuzzy Dombi Heronian mean operators for the aggregation of CFNs and introduced an innovative style to decision making. [7] M. Qiyas, S. Abdullah and Muneeza (2020) they discussed the basic knowledge about the fuzzy set, intuitionistic fuzzy set and linguistic cubic variables. Also they presented some operational laws for linguistic intuitionistic cubic hesitant variables and their score function.…”

Section: Introductionmentioning

confidence: 99%

On Cubic Fuzzy Groups and Cubic Fuzzy Normal Subgroups

Haso¹,

Khalaf²

2022

SJUOZ

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In this paper, the notions of cubic fuzzy groups and cubic fuzzy normal subgroups are introduced. The internal, external of cubic sets, (P-,R-) order, (P-,R-) intersection and (P-,R-) union of cubic fuzzy groups are investigated and some related properties were obtained. It is proved that a cubic fuzzy group which is both (internal, external) cubic set. Also we provide condition on cubic fuzzy group to be an internal cubic set. We show that (P-,R-) intersection and (P-,R-) union of cubic fuzzy groups are also cubic fuzzy groups. Also the (P-,R-) intersection, (P-,R-) union of cubic fuzzy normal subgroups are proved to be cubic fuzzy normal subgroup.

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Multi-criteria decision support systems based on linguistic intuitionistic cubic fuzzy aggregation operators

Cited by 18 publications

References 68 publications

Application of Choquet integral in interval type‐2 Pythagorean fuzzy sustainable supply chain management under risk

Application of Choquet integral in interval type‐2 Pythagorean fuzzy sustainable supply chain management under risk

Minimizing Symptom-based Diagnostic Errors Using Weighted Input Variables and Fuzzy Logic Rules in Clinical Decision Support Systems

On Cubic Fuzzy Groups and Cubic Fuzzy Normal Subgroups

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