Evidence map›Paper›PMID 35808377›Full record

ArticleSensors (Basel, Switzerland)2022

The Group Decision-Making Using Pythagorean Fuzzy Entropy and the Complex Proportional Assessment.

Parul Thakur, Bartłomiej Kizielewicz, Neeraj Gandotra, Andrii Shekhovtsov, Namita Saini, Wojciech Sałabun

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

6 authors.

Parul ThakurYogananda School of AI, Computer and Data Sciences, Faculty of Engineering and Technology, Shoolini University, Solan 173229, Himachal Pradesh, India.
Bartłomiej KizielewiczResearch Team on Intelligent Decision Support Systems, Department of Artificial Intelligence and Applied Mathematics, Faculty of Computer Science and Information Technology, West Pomeranian University of Technology in Szczecin, ul. Żołnierska 49, 71-210 Szczecin, Poland.ORCID 0000-0001-5736-4014
Neeraj GandotraYogananda School of AI, Computer and Data Sciences, Faculty of Engineering and Technology, Shoolini University, Solan 173229, Himachal Pradesh, India.ORCID 0000-0001-6534-3264
Andrii ShekhovtsovResearch Team on Intelligent Decision Support Systems, Department of Artificial Intelligence and Applied Mathematics, Faculty of Computer Science and Information Technology, West Pomeranian University of Technology in Szczecin, ul. Żołnierska 49, 71-210 Szczecin, Poland.ORCID 0000-0002-0834-2019
Namita SainiYogananda School of AI, Computer and Data Sciences, Faculty of Engineering and Technology, Shoolini University, Solan 173229, Himachal Pradesh, India.
Wojciech SałabunResearch Team on Intelligent Decision Support Systems, Department of Artificial Intelligence and Applied Mathematics, Faculty of Computer Science and Information Technology, West Pomeranian University of Technology in Szczecin, ul. Żołnierska 49, 71-210 Szczecin, Poland.ORCID 0000-0001-7076-2519

Funding

National Science Center 2021/41/B/HS4/01296
6 · The paper itself

Abstract

The Pythagorean fuzzy sets conveniently capture unreliable, ambiguous, and uncertain information, especially in problems involving multiple and opposing criteria. Pythagorean fuzzy sets are one of the popular generalizations of the intuitionistic fuzzy sets. They are instrumental in expressing and managing hesitant under uncertain environments, so they have been involved extensively in a diversity of scientific fields. This paper proposes a new Pythagorean entropy for Multi-Criteria Decision-Analysis (MCDA) problems. The entropy measures the fuzziness of two fuzzy sets and has an influential position in fuzzy functions. The more comprehensive the entropy, the more inadequate the ambiguity, so the decision-making established on entropy is beneficial. The COmplex PRoportional ASsessment (COPRAS) method is used to tackle uncertainty issues in MCDA and considers the singularity of one alternative over the rest of them. This can be enforced to maximize and minimize relevant criteria in an assessment where multiple opposing criteria are considered. Using the Pythagorean sets, we represent a decisional problem solution by using the COPRAS approach and the new Entropy measure.

Indexed as

Decision MakingFuzzy LogicEntropyUncertaintycomplex proportional assessmentdecision-makingentropymultiple criteria decision analysispythagorean fuzzy sets

Identifiers

PMID35808377
PMCPMC9269554

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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.