A Decision‐Making Framework Using q‐Rung Orthopair Probabilistic Hesitant Fuzzy Rough Aggregation Information for the Drug Selection to Treat COVID‐19

Abstract

In our current era, a new rapidly spreading pandemic disease called coronavirus disease (COVID-19), caused by a virus identified as a novel coronavirus (SARS-CoV-2), is becoming a crucial threat for the whole world. Currently, the number of patients infected by the virus is expanding exponentially, but there is no commercially available COVID-19 medication for this pandemic. However, numerous antiviral drugs are utilized for the treatment of the COVID-19 disease. Identification of the appropriate antivirus medicine to treat the infection of COVID-19 is still a complicated and uncertain decision. This study’s key objective is to develop a novel approach called q-rung orthopair probabilistic hesitant fuzzy rough set (q-ROPHFRS), which incorporates the q-rung orthopair fuzzy set, probabilistic hesitant fuzzy set, and rough set structures. New q-ROPHFR aggregation operators have been established: the q-ROPHFR Einstein weighted averaging (q-ROPHFREWA) operator and the q-ROPHFR Einstein weighted geometric (q-ROPHFREWG) operator. In this study, we explored some basic features of the developed operators. Afterward, to demonstrate the viability and feasibility of the established decision-making approach in real-world applications, a case study related to selecting drugs for COVID-19 pandemic is addressed. Furthermore, a comprehensive comparison with the q-rung orthopair probabilistic hesitant fuzzy rough TOPSIS technique is also presented to illustrate the benefits of the new framework. The obtained results confirm the reliability and effectiveness of the proposed approach for finding uncertainty in real-world decision-making.

q-Rung Orthopair Probabilistic Hesitant Fuzzy Rough Aggregation Information and Their Application in Decision Making

“…Definition 2 [40] Let £ be the universal set then any subset ∝∈ q − RO P H F S(£×£) is said to be a q-rung probabilistic hesitant fuzzy relation. The pair (£, ∝) is called q-ROPHF approximation space.…”

Assessing indoor positioning system: A q-spherical fuzzy rough TOPSIS analysis

“…The theories of IFRS [ 44 ], PyFRS [ 45 ], and q-ROFRS [ 46 , 47 ] are well-established in the field and have been acknowledged for their contributions. However, it is essential to acknowledge that these theories encounter substantial limitations when striving to encompass all three potential grades within a given dataset: positive, neutral, and negative grades.…”

q-Rung orthopair fuzzy dynamic aggregation operators with time sequence preference for dynamic decision-making

“… Kumar & Gupta (2023) introduced some q-ROPF normal basic AOs merging with confidence level concept. Liu et al (2022) , Kumar & Chen (2022) , Attaullah et al (2022) , Garg et al (2022) , Riaz et al (2021) , Farid & Riaz (2021) and Wei, Gao & Wei (2018) proposed some extensive AOs for q-ROPFSs and their hybrid structure.…”