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

被引：21
作者：
Zeb, Aurang [1 ,2 ]
Khan, Asghar [2 ]
Juniad, Muhammad [2 ]
Izhar, Muhammad [3 ]
机构：
[1] Cent South Univ, Sch Math & Stat, Changsha 410083, Hunan, Peoples R China
[2] Abdul Wali Khan Univ, Dept Math, Mardan 23200, Pakistan
[3] Govt Degree Coll Garhi Kapura, Dept Math, Mardan 23200, Pakistan
来源：
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING | 2022年 / 14卷 / 9期
关键词：
COVID-19; Fermatean fuzzy soft set; Operational laws; Fermatean fuzzy soft aggregation operators; Multiple attribute decision making problems; DECISION-MAKING; SET;
D O I：
10.1007/s12652-022-03725-z
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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. [GRAPHICS]
引用
收藏
页码：11607 / 11624
页数：18
相关论文
共 30 条
[1]   Targets of T Cell Responses to SARS-CoV-2 Coronavirus in Humans with COVID-19 Disease and Unexposed Individuals [J].
Grifoni, Alba ;
Weiskopf, Daniela ;
Ramirez, Sydney, I ;
Mateus, Jose ;
Dan, Jennifer M. ;
Moderbacher, Carolyn Rydyznski ;
Rawlings, Stephen A. ;
Sutherland, Aaron ;
Premkumar, Lakshmanane ;
Jadi, Ramesh S. ;
Marrama, Daniel ;
de Silva, Aravinda M. ;
Frazier, April ;
Carlin, Aaron F. ;
Greenbaum, Jason A. ;
Peters, Bjoern ;
Krammer, Florian ;
Smith, Davey M. ;
Crotty, Shane ;
Sette, Alessandro .
CELL, 2020, 181 (07) :1489-+
[2]   Robust aggregation operators for multi-criteria decision-making with intuitionistic fuzzy soft set environment [J].
Arora, R. ;
Garg, H. .
SCIENTIA IRANICA, 2018, 25 (02) :931-942
[3]   INTUITIONISTIC FUZZY-SETS [J].
ATANASSOV, KT .
FUZZY SETS AND SYSTEMS, 1986, 20 (01) :87-96
[4]   HANDLING MULTICRITERIA FUZZY DECISION-MAKING PROBLEMS BASED ON VAGUE SET-THEORY [J].
CHEN, SM ;
TAN, JM .
FUZZY SETS AND SYSTEMS, 1994, 67 (02) :163-172
[5]   Soft sets and soft rough sets [J].
Feng, Feng ;
Liu, Xiaoyan ;
Leoreanu-Fotea, Violeta ;
Jun, Young Bae .
INFORMATION SCIENCES, 2011, 181 (06) :1125-1137
[6]   A soft set approach for association rules mining [J].
Herawan, Tutut ;
Dens, Mustafa Mat .
KNOWLEDGE-BASED SYSTEMS, 2011, 24 (01) :186-195
[7]  
Kirisci M, 2019, ARXIV190404064
[8]  
Li DF, 2002, PATTERN RECOGN LETT, V23, P221, DOI 10.1016/S0167-8655(01)00110-6
[9]   Distance measure for Fermatean fuzzy linguistic term sets based on linguistic scale function: An illustration of the TODIM and TOPSIS methods [J].
Liu, Donghai ;
Liu, Yuanyuan ;
Wang, Lizhen .
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2019, 34 (11) :2807-2834
[10]   Fermatean fuzzy linguistic set and its application in multicriteria decision making [J].
Liu, Donghai ;
Liu, Yuanyuan ;
Chen, Xiaohong .
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2019, 34 (05) :878-894
123