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

Rana Muhammad Zulqarnain; Hafiz Khalil Ur Rehman; Jan Awrejcewicz; Rifaqat Ali; Imran Siddique; Fahd Jarad; Aiyared Iampan

doi:10.1109/ACCESS.2022.3199069

IEEE Access (Jan 2022)

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

Rana Muhammad Zulqarnain,
Hafiz Khalil Ur Rehman,
Jan Awrejcewicz,
Rifaqat Ali,
Imran Siddique,
Fahd Jarad,
Aiyared Iampan

Affiliations

Rana Muhammad Zulqarnain: ORCiD; Department of Mathematics, University of Management and Technology Sialkot Campus, Sialkot, Pakistan
Hafiz Khalil Ur Rehman: ORCiD; Department of Mathematics, University of Management and Technology Sialkot Campus, Sialkot, Pakistan
Jan Awrejcewicz: ORCiD; Department of Automation, Biomechanics and Mechatronics, Lodz University of Technology, Lodz, Poland
Rifaqat Ali: Department of Mathematics, College of Science and Arts at Muhayil, King Khalid University, Abha, Saudi Arabia
Imran Siddique: Department of Mathematics, University of Management and Technology, Lahore, Pakistan
Fahd Jarad: ORCiD; Department of Mathematics, Çankaya University, Etimesgut, Turkey
Aiyared Iampan: Department of Mathematics, School of Science, University of Phayao, Mae Ka, Thailand

DOI: https://doi.org/10.1109/ACCESS.2022.3199069
Journal volume & issue: Vol. 10
pp. 87923 – 87949

Abstract

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The 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.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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