Back to Search
Start Over
A new approach to q -linear Diophantine fuzzy emergency decision support system for COVID19.
- Source :
-
Journal of ambient intelligence and humanized computing [J Ambient Intell Humaniz Comput] 2022; Vol. 13 (4), pp. 1687-1713. Date of Electronic Publication: 2021 Apr 05. - Publication Year :
- 2022
-
Abstract
- 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.<br /> (© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.)
Details
- Language :
- English
- ISSN :
- 1868-5137
- Volume :
- 13
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- Journal of ambient intelligence and humanized computing
- Publication Type :
- Academic Journal
- Accession number :
- 33841585
- Full Text :
- https://doi.org/10.1007/s12652-021-03130-y