ArticleScientific reports2024
A comprehensive study for selecting optimal treatment modalities for blood cancer in a Fermatean fuzzy dynamic environment.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 9 citations in OpenAlex.
- Fermatean fuzzy distance and Sugeno-Weber operators-based SPC-MARCOS approach for sustainable supplier evaluation in the healthcare supply chain.Scientific reports · 2024Article
- A novel perspective on the selection of an effective approach to reduce road traffic accidents under Fermatean fuzzy settings.PloS one · 2024Article
- Selecting the foremost big data tool to optimize YouTube data in dynamic Fermatean fuzzy knowledge.PloS one · 2024Article
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Authors and funding
7 authors at 6 institutions in 4 countries.
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Abstract
Cancer is characterized by uncontrolled cell proliferation, leading to cellular damage or death. Acute lymphoblastic leukemia (ALL), a kind of blood cancer, that affects lymphoid cells and is a challenging malignancy to treat. The Fermatean fuzzy set (FFS) theory is highly effective at capturing imprecision due to its capacity to incorporate extensive problem descriptions that are unclear and periodic. Within the framework of this study, two innovative aggregation operators: The Fermatean fuzzy Dynamic Weighted Averaging (FFDWA) operator and the Fermatean fuzzy Dynamic Weighted Geometric (FFDWG) operator are presented. The important attributes of these operators, providing a comprehensive elucidation of their significant special cases has been discussed in details. Moreover, these operators are utilized in the development of a systematic approach for addressing scenarios involving multiple attribute decision-making (MADM) problems with Fermatean fuzzy (FF) data. A numerical example concerning on finding the optimal treatment approach for ALL using the proposed operators, is provided. At the end, the validity and merits of the new method to illustrate by comparing it with the existing methods.
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