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International Journal for Uncertainty Quantification
Главный редактор: Habib N. Najm (open in a new tab)
Ассоциированный редакторs: Dongbin Xiu (open in a new tab) Tao Zhou (open in a new tab)
Редактор-основатель: Nicholas Zabaras (open in a new tab)

Выходит 6 номеров в год

ISSN Печать: 2152-5080

ISSN Онлайн: 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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P-SOFT ROUGH FUZZY GROUPS AND P-SOFT FUZZY ROUGH GROUPS AND CORRESPONDING DECISION-MAKING

Том 8, Выпуск 1, 2018, pp. 75-99
DOI: 10.1615/Int.J.UncertaintyQuantification.2018020594
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Краткое описание

The study of applying hybrid soft models to uncertain problems has become a hot topic. In this research study, we introduce the notions of P-soft rough fuzzy groups and P-soft fuzzy rough groups and investigate some of their basic properties. Further, we present certain notions, including P-soft rough fuzzy subgroups, P-soft rough fuzzy normal subgroups, P-soft fuzzy rough-subgroups and P-soft fuzzy rough normal, subgroups of a group. In particular, we propose two kinds of decision-making methods based on P-soft rough fuzzy sets and P-soft fuzzy rough sets in groups to illustrate the effectiveness and rationality. Finally, we present numerical experimentations of six algorithms, in which the comparison among six types of hybrid soft models are analyzed.

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