Open Access

ARTICLE

Einstein Hybrid Structure of q-Rung Orthopair Fuzzy Soft Set and Its Application for Diagnosis of Waterborne Infectious Disease

by Rana Muhammad Zulqarnain¹, Hafiz Khalil ur Rehman², Imran Siddique³, Hijaz Ahmad^4,5, Sameh Askar⁶, Shahid Hussain Gurmani^1,*

1 School of Mathematical Sciences, Zhejiang Normal University, Jinhua, 321004, China
2 Department of Mathematics, Government College University, Faisalabad, 38000, Pakistan
3 Department of Mathematics, University of Sargodha, Sargodha, 40100, Pakistan
4 Section of Mathematics, International Telematic University Uninettuno, Corso Vittorio Emanuele II, Roma, 3900186, Italy
5 Near East University, Operational Research Center in Healthcare, Near East Boulevard, Nicosia/Mersin 10, 99138, Turkey
6 Department of Statistics and Operations Research, College of Science, King Saud University, PO Box 2455, Riyadh, 11451, Saudi Arabia

* Corresponding Author: Shahid Hussain Gurmani. Email:

(This article belongs to the Special Issue: Advances in Ambient Intelligence and Social Computing under uncertainty and indeterminacy: From Theory to Applications)

Computer Modeling in Engineering & Sciences 2024, 139(2), 1863-1892. https://doi.org/10.32604/cmes.2023.031480

Received 20 June 2023; Accepted 12 September 2023; Issue published 29 January 2024

Abstract

This research is devoted to diagnosing water-borne infectious diseases caused by floods employing a novel diagnosis approach, the Einstein hybrid structure of q-rung orthopair fuzzy soft set. This approach integrates parts of fuzzy logic and soft set theory to develop a robust alternative for disease detection in stressful situations, especially in areas affected by floods. Compared to the traditional intuitionistic fuzzy soft set and Pythagorean fuzzy soft set, the q-rung orthopair fuzzy soft set (q-ROFSS) adequately incorporates unclear and indeterminate facts. The major objective of this investigation is to formulate the q-rung orthopair fuzzy soft Einstein hybrid weighted average (q-ROFSEHWA) operator and its specific characteristics. Moreover, our stated operator is implementing intelligent multi-criteria group decision-making (MCGDM) methodology. Floods are severe natural catastrophes that raise the risk of diseases and epidemics, particularly those caused by contaminants in the water, such as gastrointestinal diseases, respiratory infections, vector-borne diseases, skin infections, and water-borne parasites. The designed MCGDM strategy tackles the prevalence of certain conditions in flood-affected patients. A comparative investigation determined that the suggested method for detecting water-borne infectious disease due to floods is more effective and productive than conventional methods because of its logical structure.

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Keywords

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