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International Journal for Uncertainty Quantification

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ISSN Druckformat: 2152-5080

ISSN Online: 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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LINGUISTIC NEUTROSOPHIC SETS AND THEIR APPLICATION IN MULTICRITERIA DECISION-MAKING PROBLEMS

Volumen 7, Ausgabe 2, 2017, pp. 135-154
DOI: 10.1615/Int.J.UncertaintyQuantification.2017019627
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ABSTRAKT

Motivated by the reality that humans tend to convey their views using natural language, which is always indeterminate, imprecise, incomplete, and inconsistent, this paper introduces the concept of linguistic neutrosophic sets (LNSs), in which truth-membership, falsity-membership, and indeterminacy-membership are represented as linguistic terms. In order to compare any two linguistic neutrosophic numbers (LNNs), this paper defines the expected function, accuracy function, and certainty function. Subsequently, the operations for LNNs are provided based on linguistic scale functions. Then, two aggregation operators for fusing linguistic neutrosophic information are proposed, including the linguistic neutrosophic geometric Heronian mean (LNGHM) operator and the linguistic neutrosophic prioritized geometric Heronian mean (LNPGHM) operator. Moreover, this paper develops two new methods for addressing multicriteria decision-making (MCDM) problems in which the interrelationships among individual data are considered under linguistic neutrosophic environments. A practical example concerning low-carbon supplier selection is provided and the influence of different parameters is discussed. Finally, a comparison analysis is presented to verify the effectiveness and feasibility of the proposed methods.

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