Fuzzy Group Decision Making and Intelligent Systems: Recent Trends and Methodologies

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Fuzzy Sets, Systems and Decision Making".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 2367

Special Issue Editors


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Guest Editor
School of Mathematical Sciences, College of Computing, Informatics and Media, Universiti Teknologi MARA (UiTM), Shah Alam 40450, Malaysia
Interests: decision making; fuzzy set theory; consensus; social network analysis; social influence network

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Guest Editor
Department of Mathematics and Statistics, Universiti Putra Malaysia, Serdang, 43400 Selangor, Malaysia
Interests: integral transforms and special functions; generalized functions; generalized hypergeometric functions; distributions; ultra-distributions; topological semigroups, fractional integro-differential equations; fractals and fractional inequalities; fuzzy soft sets and applications in decision making
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Special Issue Information

Dear Colleagues,

Group Decision Making (GDM) is one of the most significant research areas in the social sciences, health care and medicine, and financial and businesses industries, among other areas. With the strength of fuzzy sets in handling human preferences, fuzzy-based GDM methodologies provide solutions to the ambiguity and imprecision of information. In recent years, there has been a considerable advancement in fuzzy-based GDM models, with many proposed methods used to address the consensus-reaching process, feedback mechanisms and information fusion of a variety of fuzzy GDM frameworks. The incorporation of new concepts and theories, such as Social Network Analysis (SNA) and Social Influence Network (SIN), are promising in further developing the research area of Group Decision Making. This Special Issue aims to provide a platform for scholars to disseminate their current research related to fuzzy Group Decision Making. Also welcomed are dynamic and advanced computational methodologies, including large-scale GDM, intelligent systems, decision-support systems and software, and data-driven and soft computing.

Dr. Nor Hanimah Kamis
Prof. Dr. Adem Kilicman
Guest Editors

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Keywords

  • fuzzy systems
  • large-scale group decision making
  • computational intelligence
  • consensus-reaching process
  • feedback mechanism
  • social network, influence
  • trust
  • information fusion

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Published Papers (1 paper)

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Research

25 pages, 448 KiB  
Article
Adjacent Vertex Distinguishing Coloring of Fuzzy Graphs
by Zengtai Gong and Chen Zhang
Mathematics 2023, 11(10), 2233; https://doi.org/10.3390/math11102233 - 10 May 2023
Viewed by 1695
Abstract
In this paper, we consider the adjacent vertex distinguishing proper edge coloring (for short, AVDPEC) and the adjacent vertex distinguishing total coloring (for short, AVDTC) of a fuzzy graph. Firstly, this paper describes the development process, the application areas, and the existing review [...] Read more.
In this paper, we consider the adjacent vertex distinguishing proper edge coloring (for short, AVDPEC) and the adjacent vertex distinguishing total coloring (for short, AVDTC) of a fuzzy graph. Firstly, this paper describes the development process, the application areas, and the existing review research of fuzzy graphs and adjacent vertex distinguishing coloring of crisp graphs. Secondly, we briefly introduce the coloring theory of crisp graphs and the related theoretical basis of fuzzy graphs, and add some new classes of fuzzy graphs. Then, based on the α-cuts of fuzzy graphs and distance functions, we give two definitions of the AVDPEC of fuzzy graphs, respectively. A lower bound on the chromatic number of the AVDPEC of a fuzzy graph is obtained. With examples, we show that some results of the AVDPEC of a crisp graph do not carry over to our set up; the adjacent vertex distinguishing chromatic number of the fuzzy graph is different from the general chromatic number of a fuzzy graph. We also give a simple algorithm to construct a (d,f)-extended AVDPEC for fuzzy graphs. After that, in a similar way, two definitions of the AVDTC of fuzzy graphs are discussed. Finally, the future research directions of distinguishing coloring of fuzzy graphs are given. Full article
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