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International Journal for Uncertainty Quantification

Publicou 6 edições por ano

ISSN Imprimir: 2152-5080

ISSN On-line: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

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DISTANCE AND SIMILARITY MEASURES FOR INTUITIONISTIC MULTIPLICATIVE PREFERENCE RELATION AND ITS APPLICATIONS

Volume 7, Edição 2, 2017, pp. 117-133
DOI: 10.1615/Int.J.UncertaintyQuantification.2017018981
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RESUMO

The objective of this work is to present some series of distance measures under the intuitionistic multiplicative preference relation (IMPR), instead of intuitionistic fuzzy preference relation (IFPR), for measuring the relationship between the two different intuitionistic multiplicative sets (IMSs). As IFPR deals under the conditions that the attribute values grades are symmetrical and uniformly distributed, in this manuscript, this assumption has been relaxed by distributing the attribute grades to be asymmetrical around 1 and hence based on it, a family of distance measures between the two or more IMSs has been proposed. Some of its desirable properties have also been investigated in detail. Based on these measures, a group decision-making method has been presented for ranking the alternatives. The method is illustrated by two numerical examples in pattern recognition and medical diagnosis.

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