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dc.contributor.authorŞahin R.
dc.contributor.authorLiu P.
dc.date.accessioned20.04.201910:49:12
dc.date.accessioned2019-04-20T21:43:18Z
dc.date.available20.04.201910:49:12
dc.date.available2019-04-20T21:43:18Z
dc.date.issued2017
dc.identifier.issn0941-0643
dc.identifier.urihttps://dx.doi.org/10.1007/s00521-015-2163-x
dc.identifier.urihttps://hdl.handle.net/20.500.12403/493
dc.description.abstractAs a combination of the hesitant fuzzy set (HFS) and the single-valued neutrosophic set (SVNS), the single-valued neutrosophic hesitant fuzzy set (SVNHFS) is an important concept to handle uncertain and vague information existing in real life, which consists of three membership functions including hesitancy, as the truth-hesitancy membership function, the indeterminacy-hesitancy membership function and the falsity-hesitancy membership function, and encompasses the fuzzy set, intuitionistic fuzzy set (IFS), HFS, dual hesitant fuzzy set (DHFS) and SVNS. Correlation and correlation coefficient have been applied widely in many research domains and practical fields. This paper, motivated by the idea of correlation coefficients derived for HFSs, IFSs, DHFSs and SVNSs, focuses on the correlation and correlation coefficient of SVNHFSs and investigates their some basic properties in detail. By using the weighted correlation coefficient information between each alternative and the optimal alternative, a decision-making method is established to handling the single-valued neutrosophic hesitant fuzzy information. Finally, an effective example is used to demonstrate the validity and applicability of the proposed approach in decision making, and the relationship between the each existing method and the developed method is given as a comparison study. © 2016, The Natural Computing Applications Forum.en_US
dc.language.isoengen_US
dc.publisherSpringer London
dc.relation.isversionof10.1007/s00521-015-2163-x
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCorrelation
dc.subjectCorrelation coefficient
dc.subjectHesitant fuzzy set
dc.subjectMultiple attribute decision making
dc.subjectSingle-valued neutrosophic hesitant fuzzy set
dc.subjectSingle-valued neutrosophic set
dc.subjectCorrelation methods
dc.subjectDecision making
dc.subjectMembership functions
dc.subjectCorrelation coefficient
dc.subjectDecision-making method
dc.subjectHesitant fuzzy sets
dc.subjectIntuitionistic fuzzy sets
dc.subjectMultiple attribute decision making
dc.subjectNeutrosophic sets
dc.subjectOptimal alternative
dc.subjectWeighted correlation coefficients
dc.subjectFuzzy sets
dc.subjectCorrelation
dc.subjectCorrelation coefficient
dc.subjectHesitant fuzzy set
dc.subjectMultiple attribute decision making
dc.subjectSingle-valued neutrosophic hesitant fuzzy set
dc.subjectSingle-valued neutrosophic set
dc.subjectCorrelation methods
dc.subjectDecision making
dc.subjectMembership functions
dc.subjectCorrelation coefficient
dc.subjectDecision-making method
dc.subjectHesitant fuzzy sets
dc.subjectIntuitionistic fuzzy sets
dc.subjectMultiple attribute decision making
dc.subjectNeutrosophic sets
dc.subjectOptimal alternative
dc.subjectWeighted correlation coefficients
dc.subjectFuzzy sets
dc.titleCorrelation coefficient of single-valued neutrosophic hesitant fuzzy sets and its applications in decision makingen_US
dc.typereviewen_US
dc.relation.journalNeural Computing and Applicationsen_US
dc.contributor.departmentBayburt Universityen_US
dc.contributor.authorID56285350800
dc.contributor.authorID35388763600
dc.identifier.volume28
dc.identifier.issue6
dc.identifier.startpage1387
dc.identifier.endpage1395
dc.relation.publicationcategoryDiğeren_US


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