Evidence map›Paper›PMID 34603537›Full record

ArticleJournal of ambient intelligence and humanized computing2022

Emergency decision support modeling under generalized spherical fuzzy Einstein aggregation information.

Shahzaib Ashraf, Saleem Abdullah, Ronnason Chinram

Abstract read
In one paragraph

Article in Journal of ambient intelligence and humanized computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Shahzaib AshrafDepartment of Mathematics and Statistics, Bacha Khan University, Charsadda, 24420 Khyber Pakhtunkhwa, Pakistan.ORCID 0000-0002-8616-8829
Saleem AbdullahDepartment of Mathematics, Abdul Wali Khan University, Mardan, 23200 Khyber Pakhtunkhwa, Pakistan.
Ronnason ChinramDepartment of Mathematics and Statistics, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla 90110 Thailand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dominant emergency action should be adopted in the case of an emergency situation. Emergency is interpreted as limited time and information, harmfulness and uncertainty, and decision-makers are often critically bound by uncertainty and risk. This framework implements an emergency decision-making approach to address the emergency situation of COVID-19 in a spherical fuzzy environment. As the spherical fuzzy set (SFS) is a generalized framework of fuzzy structure to handle more uncertainty and ambiguity in decision-making problems (DMPs). Keeping in view the features of the SFSs, the purpose of this paper is to present some robust generalized operating laws in accordance with the Einstein norms. In addition, list of propose aggregation operators using Einstein operational laws under spherical fuzzy environment are developed. Furthermore, we design the algorithm based on the proposed aggregation operators to tackle the uncertainty in emergency decision making problems. Finally, numerical case study of COVID-19 as an emergency decision making is presented to demonstrate the applicability and validity of the proposed technique. Besides, the comparison of the existing and the proposed technique is established to show the effectiveness and validity of the established technique.

Indexed as

COVID-19Emergency decision making techniqueGeneralized Einstein aggregation operatorsSpherical fuzzy sets

Identifiers

PMID34603537
PMCPMC8475448

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.