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International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (IJUFKS)

Publisher :

World Scientific Publishing Co Pte Ltd

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Peer reviewed only
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Open Access
  • Fuzzy Systems
  • Decision-Making Systems
  • Mathematical Theory

    • +4

e-ISSN :

1793-6411

Issue Frequency :

8-issues-year

Impact Factor :

1.0

p-ISSN :

0218-4885

Est. Year :

2000

Mobile :

6564665775

DOI :

YES

Country :

Singapore

Language :

English

APC :

YES

Impact Factor Assignee :

SJR

Email :

francescoflammini@ieee.org

Journal Descriptions

The International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems is a forum for research on various methodologies for the management of imprecise, vague, uncertain or incomplete information. The aim of the journal is to promote theoretical or methodological works dealing with all kinds of methods to represent and manipulate imperfectly described pieces of knowledge, excluding results on pure mathematics or simple applications of existing theoretical results. It is published bimonthly, with worldwide distribution to researchers, engineers, decision-makers, and educators. The journal does not accept papers on direct applications of any methods to deal with uncertainty, such as applications to medicine, agriculture, teaching, cloud computing, wireless sensor, mobile ad hoc networks, image and video processing, and security. It does not either accept papers on pure fuzzy mathematics, such as fuzzy topology or fuzzy algebra.


International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (IJUFKS) is :

International, Peer-Reviewed, Open Access, Refereed, Fuzzy Systems, Decision-Making Systems, Mathematical Theory, Machine Learning, Data analysis, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE , Online or Print, 8-issues-year Journal

UGC Approved, ISSN Approved: P-ISSN - 0218-4885, E-ISSN - 1793-6411, Established in - 2000, Impact Factor - 1.0

Provide Crossref DOI

Indexed in Scopus, WoS

Not indexed in DOAJ, PubMed, UGC CARE

Indexing

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Publications of IJUFKS

Research Article
  • dott image June, 2023

Deep Learning based Improved Generative Adversarial Network for Addressing Class Imbalance Classification Problem in Breast Cancer Dataset

The breast cancer diagnosis is one of the challenging tasks of medical field. Especially, the breast cancer diagnosis among younger women (under 40 years old) is more complicated, because th...

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