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A new approach to q-linear Diophantine fuzzy emergency decision support system for COVID19
Open accessAbstract
The emergency situation of COVID-19 is a very important problem for emergency decision support systems. Control of the spread of COVID-19 in emergency situations across the world is a challenge and therefore the aim of this study is to propose a q-linear Diophantine fuzzy decision-making model for the control and diagnose COVID19. Basically, the paper includes three main parts for the achievement of appropriate and accurate measures to address the situation of emergency decision-making. First, we propose a novel generalization of Pythagorean fuzzy set, q-rung orthopair fuzzy set and linear Diophantine fuzzy set, called q-linear Diophantine fuzzy set (q-LDFS) and also discussed their important properties. In addition, aggregation operators play an effective role in aggregating uncertainty in decision-making problems. Therefore, algebraic norms based on certain operating laws for q-LDFSs are established. In the second part of the paper, we propose series of averaging and geometric aggregation operators based on defined operating laws under q-LDFS. The final part of the paper consists of two ranking algorithms based on proposed aggregation operators to address the emergency situation of COVID-19 under q-linear Diophantine fuzzy information. In addition, the numerical case study of the novel carnivorous (COVID-19) situation is provided as an application for emergency decision-making based on the proposed algorithms. Results explore the effectiveness of our proposed methodologies and provide accurate emergency measures to address the global uncertainty of COVID-19.
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“As mentioned above, LDFNs and q-LDFNs do not have the I-grade, which represents neutrality, which will lead to the lack of some information.”
From T-spherical linear Diophantine fuzzy aggregation operators for multiple attribute decision-making (2023) · cites this paper as [12]
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“It is clear from Table 7 that the best alternatives obtained by using the methods of Riaz and Hashmi [11], Almagrabi et al. [12] and Riaz et al. [45] remained our proposed operators.”
From T-spherical linear Diophantine fuzzy aggregation operators for multiple attribute decision-making (2023) · cites this paper as [12]
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“Thus, the q-LDFS is a special case of the T-SLDFS and can be easily written in the form of a T-SLDFS.”
From T-spherical linear Diophantine fuzzy aggregation operators for multiple attribute decision-making (2023) · cites this paper as [12]
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Cited by
Sorted by citations
AI for Decision Support: Balancing Accuracy, Transparency, and Trust Across Sectors
Kővári, A. (2024) · Information · doi:10.3390/info15110725 · Cited by 175
Linear Diophantine Fuzzy Relations and Their Algebraic Properties with Decision Making
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Background
Metadata
- Authors
- Almagrabi, A. O., Abdullah, S., Shams, M., Al-Otaibi, Y. D., & Ashraf, S.
- Affiliations
- King Abdulaziz University (SA)
- Abdul Wali Khan University Mardan (PK)
- Corresponding author
- Shams, Maria
- Venue
- Journal of Ambient Intelligence and Humanized Computing
- Published
- 2021-04-05
- Volume
- 13
- Issue
- 4
- Pages
- 1687-1713
- ISSN
- 1868-5137 (ISSN-L)
- 1868-5145
- PMC
- PMC8019990
- OpenAlex
- W3139668460
- Cited by
- 114
- References
- 74
- Citations by year
- 2026: 16 · 2025: 15 · 2024: 23 · 2023: 33 · 2022: 21 · 2021: 6
- FWCI
- 12.62
- Type
- journal article
- Language
- en
- Update notices
- correction (2021-05-06)
- Open access
- bronze
- doi.org
- Locations
- Journal of Ambient Intelligence and Humanized Computing (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- King Abdulaziz University ((GCV19-10- 1441)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Multi-Criteria Decision Making
- Social Sciences › Decision Sciences › Management Science and Operations Research › Fuzzy and Soft Set Theory
- Physical Sciences › Mathematics › Statistics and Probability › Fuzzy Systems and Optimization
- Keywords
- emergency decision support, COVID-19 pandemic, linear Diophantine fuzzy sets, aggregation operators, ranking algorithms, uncertainty aggregation, COVID-19 diagnosis
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