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Extension of Einstein Average Aggregation Operators to Medical Diagnostic Approach Under q-Rung Orthopair Fuzzy Soft Se

dc.contributor.authorZulqarnain, Rana Muhammad
dc.contributor.authorRehman, Hafiz Khalil Ur
dc.contributor.authorAwrejcewicz, Jan
dc.contributor.authorAli, Rifaqat
dc.contributor.authorSiddique, Imran
dc.contributor.authorJarad, Fahd
dc.contributor.authorIampan, Aiyared
dc.contributor.authorID234808tr_TR
dc.date.accessioned2024-03-21T12:53:06Z
dc.date.available2024-03-21T12:53:06Z
dc.date.issued2022
dc.departmentÇankaya Üniversitesi, Fen-Edebiyat Fakültesi, Matematik Bölümüen_US
dc.description.abstractThe paradigm of the soft set (SS) was pioneered by Moldotsov in 1999 by prefixing the parametrization tool in accustomed sets, which yields general anatomy in decision-making (DM) problems. The q-rung orthopair fuzzy soft set (q-ROFSS) is an induced form of the intuitionistic fuzzy soft set (IFSS) and Pythagorean fuzzy soft set (PFSS). It is also a more significant structure to tackle complex and vague information in DM problems than IFSS and PFSS. This manuscript explores new notions based on Einstein's operational laws for q-rung orthopair fuzzy soft numbers (q-ROFSNs). Our main contribution is to investigate some average aggregation operators (AOs), such as q-rung orthopair fuzzy soft Einstein weighted average (q-ROFSEWA) and q-rung orthopair fuzzy soft Einstein ordered weighted average (q-ROFSEOWA) operators. Besides, the fundamental axioms of proposed operators are discussed. Multi-criteria group decision-making (MCGDM) is vigorous in dealing with the compactness of real-world obstacles, and still, the prevailing MCGDM methods constantly convey conflicting consequences. Based on offered AOs, a robust MCGDM approach is deliberated to accommodate the defects of the prevalent MCGDM methodologies under the q-ROFSS setting. Based on the planned MCGDM method, a medical diagnostic procedure is implemented to recognize the nature of certain infections in different patients. The protracted model estimates illustrious score values to determine patients' health compared to prevailing models, which is more helpful for healthcare experts in identifying the severity of diseases in patients. Furthermore, an inclusive comparative analysis is accomplished to ratify the pragmatism and effectiveness of the proposed technique with some formerly standing methods. The consequences gained over comparative studies display that our established method is more proficient than predominant methodologies.en_US
dc.identifier.citationZulqarnain, Rana Muhammad;...et.al. (2022). "Extension of Einstein Average Aggregation Operators to Medical Diagnostic Approach Under q-Rung Orthopair Fuzzy Soft Set", IEEE Access, Vol.10, pp.87923-87949.en_US
dc.identifier.doi10.1109/ACCESS.2022.3199069
dc.identifier.endpage87949en_US
dc.identifier.issn21693536
dc.identifier.startpage87923en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12416/7688
dc.identifier.volume10en_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Accessen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMCGDMen_US
dc.subjectQ-ROFSEOWA Operatoren_US
dc.subjectQ-ROFSEWA Operatoren_US
dc.subjectQ-Rung Orthopair Fuzzy Soft Seten_US
dc.titleExtension of Einstein Average Aggregation Operators to Medical Diagnostic Approach Under q-Rung Orthopair Fuzzy Soft Setr_TR
dc.titleExtension of Einstein Average Aggregation Operators To Medical Diagnostic Approach Under Q-Rung Orthopair Fuzzy Soft Seen_US
dc.typeArticleen_US
dspace.entity.typePublication

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