The DOI matches its Crossref record; 74 of the 74 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
1 retracted
Ashraf, S., Abdullah, S., & Almagrabi, A. O. (2020) · Soft Computing · doi:10.1007/s00500-020-05287-8 · Cited by 61
Self-citationRetractedZadeh, L. A. (1965) · Information and Control · doi:10.1016/s0019-9958(65)90241-x · Cited by 67,855
Prospect Theory: An Analysis of Decision under Risk
Kahneman, D., & Tversky, A. (1979) · Econometrica · doi:10.2307/1914185 · Cited by 48,524
Zadeh, L. A. (1996) · Cited by 46,960
Prospect theory: An analysis of decision under risk
Kahneman, D. J., & Tversky, A. (1988) · Cambridge University Press eBooks · doi:10.1017/cbo9780511609220.014 · Cited by 32,720
Wu, Z., & McGoogan, J. M. (2020) · JAMA · doi:10.1001/jama.2020.2648 · Cited by 17,016
Atanassov, K. T. (1986) · Fuzzy Sets and Systems · doi:10.1016/s0165-0114(86)80034-3 · Cited by 16,465
Advances in prospect theory: Cumulative representation of uncertainty
Tversky, A., & Kahneman, D. (1992) · Journal of Risk and Uncertainty · doi:10.1007/bf00122574 · Cited by 15,467
Chan, J., Yuan, S.-F., Kok, K.-H., To, K., Chu, H., Yang, J., Xing, F., Liu, J.-L., Yip, C., Poon, R., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30154-9 · Cited by 8,450
Interval valued intuitionistic fuzzy sets
Atanassov, K. T., & Gargov, G. (1989) · Fuzzy Sets and Systems · doi:10.1016/0165-0114(89)90205-4 · Cited by 2,933
Research works printed but not linked: 3
- Atanassov K (1989) Geometrical interpretation of the elements of the intuitionistic fuzzy objects. Preprint IM-MFAIS-1-89, Sofia
- Atanassov KT, Session VIS (1984) Sofia. Sgurev, V (Ed.). Central Sci. and Techn. Library, Bulg. Academy of Sciences (June 1983 )
- Garg H (2019b) New logarithmic operational laws and their aggregation operators for Pythagorean fuzzy set and their applications. Int J Intell Syst 34(1):82–106 · DOI
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
Ayub, S., Shabir, M., Riaz, M., Aslam, M., & Chinram, R. (2021) · Symmetry · doi:10.3390/sym13060945 · Cited by 84
“Almagrabi [29] introduced a new approach to q-linear Diophantine fuzzy emergency decision support system for COVID-19.”
MethodRiaz, M., Farid, H. M. A., Aslam, M., Pamucar, D. S., & Božanić, D. I. (2021) · Symmetry · doi:10.3390/sym13071152 · Cited by 69
“LDFSs have been extended to algebraic structures and LDF-coding theory [25], LDF-relations with decision making [26], q-LDFS with emergency decision support system for COVID19 [27].”
MethodRiaz, M., & Farid, H. M. A. (2023) · Journal of Industrial Intelligence · doi:10.56578/jii010102 · Cited by 60
A new outranking method for multicriteria decision making with complex Pythagorean fuzzy information
Akram, M., Zahid, K., & Alcantud, J. C. R. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06847-1 · Cited by 53
“Recently, Almagrabi et al. [34] and Ali et al. [35] initiated new models for inexact data with a linguistic component, namely the complex linear Diophantine uncertain linguistic sets and q-linear Diophantine fuzzy sets, respectively.”
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
