Evidence map›Paper›PMID 37809522›Full record

ReviewHeliyon2023

Complex Fermatean fuzzy extended TOPSIS method and its applications in decision making.

Muhammad Zaman, Fazal Ghani, Asghar Khan, Saleem Abdullah, Faisal Khan

Abstract readReview
In one paragraph

Review in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
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

5 authors.

Muhammad ZamanDepartment of Mathematics, Abdul Wali Khan University, Mardan, KP, Pakistan.
Fazal GhaniDepartment of Mathematics, Abdul Wali Khan University, Mardan, KP, Pakistan.
Asghar KhanDepartment of Mathematics, Abdul Wali Khan University, Mardan, KP, Pakistan.
Saleem AbdullahDepartment of Mathematics, Abdul Wali Khan University, Mardan, KP, Pakistan.
Faisal KhanDepartment of Electrical and Electronic Engineering, College of Science and Engineering, National University of Ireland Galway, UCG, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The fuzzy set has its own limitations due to the membership function only. The fuzzy set does not describe the negative aspects of an object. The Fermatean fuzzy set covers the negative aspects of an object. The complex Fermatean fuzzy set is the most effective tool for handling ambiguous and uncertain information. The aim of this research work is to develop new techniques for complex decision-making based on complex Fermatean fuzzy numbers. First, we construct different aggregation operators for complex Fermatean fuzzy numbers, using Einstein t-norms. We define a series of aggregation operators named complex Fermatean fuzzy Einstein weighted average aggregation (CFFEWAA), complex Fermatean fuzzy Einstein ordered weighted average aggregation (CFFEOWAA), and complex Fermatean fuzzy Einstein hybrid average aggregation (CFFEHAA). The fundamental properties of the proposed aggregation operators are discussed here. The proposed aggregation operators are applied to the decision-making technique with the help of the score functions. We also construct different algorithms based on different aggregation operators. The extended TOPSIS method is described for the decision-making problem. We apply the proposed extended TOPSIS method to MAGDM problem "selection of an English language instructor". We also compare the proposed models with the existing models.

Indexed as

Complex Fermatean fuzzy Einstein hybrid average (CFFEHA) operatorComplex Fermatean fuzzy set (CFFS)Einstein aggregation operatorsMulti-attribute group decision-making (MAGDM)TOPSIS method

Identifiers

PMID37809522
PMCPMC10558321

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.