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费马模糊软聚合算子及其在新冠肺炎对症治疗中的应用（患者识别案例研究）

Fermatean fuzzy soft aggregation operators and their application in symptomatic treatment of COVID-19 (case study of patients identification).

作者信息

Zeb Aurang, Khan Asghar, Juniad Muhammad, Izhar Muhammad

机构信息

School of Mathematics and Statistics, Central South University, Changsha, 410083 Hunan China.

Department of Mathematics, Abdul Wali Khan University, Mardan, KP 23200 Pakistan.

出版信息

J Ambient Intell Humaniz Comput. 2022 Feb 22:1-18. doi: 10.1007/s12652-022-03725-z.

DOI:10.1007/s12652-022-03725-z
PMID:35222734
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8860733/
Abstract

ABSTRACT

The main focus of this paper is the application of aggregation operators (AOs) in the environment of Fermatean fuzzy soft sets (FFSS). The unique feature of the work is its application in the symptomatic treatment of the COVID-19 disease. For this purpose, the idea of FFSS is introduced which is based on the Senapati and Yagar's Fermatean fuzzy set. Next we have defined Fermatean fuzzy soft aggregation operators (FFSAOs) like, Fermatean fuzzy soft weighted averaging (FFSWA) operator, Fermatean fuzzy soft ordered weighted averaging (FFSOWA) operator, Fermatean fuzzy soft weighted geometric (FFSWG) operator and Fermatean fuzzy soft ordered weighted geometric (FFSOWG). The prominent properties of these operators are given in details. We have also developed some approaches to solve multi-criteria decision making (MCDM) problems in Fermatean fuzzy soft (FFS) information. An introduction to the novel pandemic, safety measures, and then its possible symptomatic treatment is also provided. The developed operators are utilized in the symptomatic treatment of COVID-19 disease in order to show the practical applications and importance of these AOs as well as Fermatean fuzzy soft information. The stability of the proposed work is also proved by the comparative analysis.

摘要

摘要

本文的主要重点是聚合算子（AO）在费马模糊软集（FFSS）环境中的应用。这项工作的独特之处在于其在新冠病毒疾病对症治疗中的应用。为此，引入了基于塞纳帕蒂和亚格的费马模糊集的费马模糊软集概念。接下来，我们定义了费马模糊软聚合算子（FFSAO），如费马模糊软加权平均（FFSWA）算子、费马模糊软有序加权平均（FFSOWA）算子、费马模糊软加权几何（FFSWG）算子和费马模糊软有序加权几何（FFSOWG）。详细给出了这些算子的显著性质。我们还开发了一些方法来解决费马模糊软（FFS）信息中的多准则决策（MCDM）问题。还介绍了新型大流行病、安全措施，以及其可能的对症治疗方法。所开发的算子被用于新冠病毒疾病的对症治疗，以展示这些AO以及费马模糊软信息的实际应用和重要性。通过对比分析也证明了所提工作的稳定性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0365/8860733/695eaf444783/12652_2022_3725_Fig8_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0365/8860733/695eaf444783/12652_2022_3725_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0365/8860733/9de2116482f9/12652_2022_3725_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0365/8860733/b99a3c1b2eef/12652_2022_3725_Fig2_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0365/8860733/9be6e68bf1e8/12652_2022_3725_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0365/8860733/4b81050bbadc/12652_2022_3725_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0365/8860733/61931ae6df38/12652_2022_3725_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0365/8860733/d8d3c47bc0e3/12652_2022_3725_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0365/8860733/695eaf444783/12652_2022_3725_Fig8_HTML.jpg

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