Open Access

ARTICLE

Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

Murugan Palanikumar¹, Nasreen Kausar^2,*, Dragan Pamucar^3,4, Seifedine Kadry^5,6,7,*, Chomyong Kim⁸, Yunyoung Nam⁹

1 Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, India
2 Department of Mathematics, Faculty of Arts and Science, Yildiz Technical University, Esenler, Istanbul, 34220, Turkey
3 Department of Operations Research and Statistics, Faculty of Organizational Sciences, University of Belgrade, Belgrade, 11000, Serbia
4 College of Engineering, Yuan Ze University, Taoyuan City, 320315, Taiwan
5 Department of Applied Data Science, Noroff University College, Kristiansand, 4612, Norway
6 Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, 346, United Arab Emirates
7 Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 1102-2801, Lebanon
8 ICT Convergence Research Center, Soonchunhyang University, Asan, 31538, South Korea
9 Department of Computer Science and Engineering, Soonchunhyang University, Asan, 31538, South Korea

* Corresponding Authors: Nasreen Kausar. Email: ; Seifedine Kadry. Email:

(This article belongs to the Special Issue: Advances in Ambient Intelligence and Social Computing under uncertainty and indeterminacy: From Theory to Applications)

Computer Modeling in Engineering & Sciences 2024, 139(3), 3353-3385. https://doi.org/10.32604/cmes.2024.031439

Received 16 June 2023; Accepted 06 September 2023; Issue published 11 March 2024

Abstract

In this article, multiple attribute decision-making problems are solved using the vague normal set (VNS). It is possible to generalize the vague set (VS) and q-rung fuzzy set (FS) into the q-rung vague set (VS). A log q-rung normal vague weighted averaging (log q-rung NVWA), a log q-rung normal vague weighted geometric (log q-rung NVWG), a log generalized q-rung normal vague weighted averaging (log Gq-rung NVWA), and a log generalized q-rung normal vague weighted geometric (log Gq-rung NVWG) operator are discussed in this article. A description is provided of the scoring function, accuracy function and operational laws of the log q-rung VS. The algorithms underlying these functions are also described. A numerical example is provided to extend the Euclidean distance and the Humming distance. Additionally, idempotency, boundedness, commutativity, and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify conceptualization. We chose five anemia patients with four types of symptoms including seizures, emotional shock or hysteria, brain cause, and high fever, who had either retrograde amnesia, anterograde amnesia, transient global amnesia, post-traumatic amnesia, or infantile amnesia. Natural numbers q are used to express the results of the models. To demonstrate the effectiveness and accuracy of the models we are investigating, we compare several existing models with those that have been developed.

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Keywords

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