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International Journal for Uncertainty Quantification

Published 6 issues per year

ISSN Print: 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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DISTANCE MEASURES BETWEEN THE COMPLEX INTUITIONISTIC FUZZY SETS AND THEIR APPLICATIONS TO THE DECISION-MAKING PROCESS

Volume 7, Issue 5, 2017, pp. 423-439
DOI: 10.1615/Int.J.UncertaintyQuantification.2017020356
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ABSTRACT

The complex intuitionistic fuzzy set (CIFS) is one of the extensions of the intuitionistic fuzzy set in which the range of the membership function is extended from the subset of the real number to the unit disc. In this environment, the main objective of the present work is to develop some series of distance measures by using Hamming, Euclidean, and Hausdorff metrics. Based on these measures, various desirable relations have been studied in detail. Further, based on these distance measures, a decision-making method has been presented for finding the best alternative under the set of the feasible one. Illustrative examples from the field of pattern recognition as well as medical diagnosis have been taken to validate the approach.

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