TY  - EJOU
AU  - Zulqarnain, Rana Muhammad 
AU  - Rehman, Hafiz Khalil ur 
AU  - Siddique, Imran 
AU  - Ahmad, Hijaz 
AU  - Askar, Sameh 
AU  - Gurmani, Shahid Hussain 

TI  - Einstein Hybrid Structure of q-Rung Orthopair Fuzzy Soft Set and Its Application for Diagnosis of Waterborne Infectious Disease
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2024
VL  - 139
IS  - 2
SN  - 1526-1506

AB  - 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.
KW  - q-rung orthopair fuzzy soft set; q-ROFSEHWA operator; MCGDM; environmental disaster; water-born infection disease

DO  - 10.32604/cmes.2023.031480