Z-Hesitant fuzzy network model with reliability and transparency of information for decision systems

Abdul Malek Yaakob*, Shahira Shafie, Alexander Gegov, Siti Fatimah Abdul Rahman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Decision-making environment often encounters complexity along its processes, especially in the context of multidisciplinary scientific research. This can commonly be seen in engineering, computing, finance, astrology and other different areas. It is of great restriction in dealing with the practical problems which have diverse demands and properties. There is a growing body of literature that recognizes the importance of dealing with the complexity in decision making environment. The reliability and the transparency are the dominant feature of the integration of fuzzy network and Z-numbers. However, much of the research up to now has been descriptive in nature of the features. Hence, this proposed method is unique and novel because it offers some interesting insight of dealing with reliability and transparency of information in Z-hesitant fuzzy network decision-making environment. The fuzzy networks have the functionality under rule bases of fuzzy systems where it is recognized by its transparency and precision. The proposed method makes use of fuzzy network with the incorporation of hesitant fuzzy sets to assimilate decision information towards alternatives. For the validation and applicability purposes of the proposed method, the case study of stock evaluation assessed by a number of decision makers has been utilized as a real-world problem. The performance of the proposed method is evaluated respectively by applying the Spearman’s rho correlation. The result shows that the proposed method performs as the established method with the consideration of additional dominant features.

Original languageEnglish
Article number176
Number of pages14
JournalInternational Journal of Computational Intelligence Systems
Volume14
Issue number1
Publication statusPublished - 4 Oct 2021

Keywords

  • Decision support systems
  • Hesitant fuzzy sets
  • Interpretable model
  • Stocks selection
  • Z-numbers

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