World Scientific

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems Template

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About the International Journal of Uncertainty Fuzziness and Knowledge-Based Systems format

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems is a peer-reviewed journal published by World Scientific, covering Multi-Criteria Decision Making, Fuzzy Logic and Control Systems, Fuzzy Systems and Optimization.

PublisherWorld Scientific
Reference styleSuperscript numbered (World Scientific)
Superscript — small raised numerals in the text
1. Smith, A., Jones, B. & Lee, C. A representative article title. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems 12, 45–58 (2023).

Formats any DOI in the closest standard style — International Journal of Uncertainty Fuzziness and Knowledge-Based Systems has no published style definition, so this is an approximation. No sign-up.

Publishes research inMulti-Criteria Decision Making Fuzzy Logic and Control Systems Fuzzy Systems and Optimization Rough Sets and Fuzzy Logic Neural Networks and Applications
ISSN0218-4885
Citation impact (2-yr)1.03
h-index72
i10-index708
Total citations47,259
Top institutions publishing hereThe University of Texas at El Paso
Journal websitewww.worldscinet.com
You getA submission-ready PDF and the editable LaTeX source — ready to submit.

Papers published in International Journal of Uncertainty Fuzziness and Knowledge-Based Systems per year

57
2014
65
2015
70
2016
72
2017
69
2018
62
2019
76
2020
73
2021
70
2022
73
2023
59
2024
65
2025

Citation impact of International Journal of Uncertainty Fuzziness and Knowledge-Based Systems by publication year

692
2014
707
2015
562
2016
611
2017
751
2018
402
2019
481
2020
525
2021
404
2022
244
2023
92
2024
38
2025

Citations each year’s papers have accumulated so far — the most recent years are still building up.

Most-cited papers in International Journal of Uncertainty Fuzziness and Knowledge-Based Systems

k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY

Latanya Sweeney · 1 Oct 2002

Consider a data holder, such as a hospital or a bank, that has a privately held collection of person-specific, field structured data. Suppose the data holder wants to share a version of the data with researchers. How can a data holder release a version of its private data with scientific guarantees that the individuals who…

8,519 citations Cite SaveGo to paper →
The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions

Sepp Hochreiter · 1 Apr 1998

Recurrent nets are in principle capable to store past inputs to produce the currently desired output. Because of this property recurrent nets are used in time series prediction and process control. Practical applications involve temporal dependencies spanning many time steps, e.g. between relevant inputs and desired outputs. In this case, however, gradient based learning methods…

2,947 citations Cite SaveGo to paper →
A LOGIC FOR UNCERTAIN PROBABILITIES

Audun Jøsang · 1 Jun 2001

We first describe a metric for uncertain probabilities called opinion, and subsequently a set of logical operators that can be used for logical reasoning with uncertain propositions. This framework which is called subjective logic uses elements from the Dempster-Shafer belief theory and we show that it is compatible with binary logic and probability calculus.

1,008 citations Cite SaveGo to paper →
CONSISTENCY IN THE ANALYTIC HIERARCHY PROCESS: A NEW APPROACH

José Antonio Alonso, M. Teresa Lamata · 1 Aug 2006

In this paper, we present a statistical criterion for accepting/rejecting the pairwise reciprocal comparison matrices in the analytic hierarchy process. We have studied the consistency in random matrices of different sizes. We do not agree with the traditional criterion of accepting matrices due to their inflexibility and because it is too restrictive when the size…

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems template — frequently asked questions

How do I write a paper in the International Journal of Uncertainty Fuzziness and Knowledge-Based Systems format?
In DocuGuru you write your manuscript in a normal editor — no LaTeX setup required — and select the International Journal of Uncertainty Fuzziness and Knowledge-Based Systems template. When you export, DocuGuru compiles the paper into the official World Scientific format and hands you a submission-ready PDF along with the editable LaTeX source.
What reference style does International Journal of Uncertainty Fuzziness and Knowledge-Based Systems use?
International Journal of Uncertainty Fuzziness and Knowledge-Based Systems uses Superscript numbered (World Scientific) references, shown as superscript numerals in the text. DocuGuru formats every in-text citation and the reference list in this exact style automatically. A reference appears like this: 1. Smith, A., Jones, B. & Lee, C. A representative article title. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems 12, 45–58 (2023).
Do I need to know LaTeX to submit to International Journal of Uncertainty Fuzziness and Knowledge-Based Systems?
No. DocuGuru generates the ws-ijmpa LaTeX class and compiles the PDF for you in the background, so you get a World Scientific-ready International Journal of Uncertainty Fuzziness and Knowledge-Based Systems document without writing any LaTeX. If you do want it, the LaTeX source is included in the export.
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Who publishes International Journal of Uncertainty Fuzziness and Knowledge-Based Systems?
International Journal of Uncertainty Fuzziness and Knowledge-Based Systems is a physics journal published by World Scientific. DocuGuru's International Journal of Uncertainty Fuzziness and Knowledge-Based Systems template matches World Scientific's official submission format.
Can I export a submission-ready International Journal of Uncertainty Fuzziness and Knowledge-Based Systems PDF?
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