To extend the application of FLe in computing the degree of causality, we employ granular fuzzy causality tactics to determine the direction of causality among related variables when there is only imperfect information. Our approach involves a hierarchy of nested inferences of interval Type-2 fuzzy sets to achieve more approximate reasoning for more workable solutions, in the sense of f-valid philosophy. To deal with intrinsic hard uncertainty in the problem architecture, we leverage expert knowledge about the problem structure. Our method involves three key steps: encoding reasons into interval Type-2 fuzzy, utilizing the interaction of concepts in forward reasoning through qualitative descriptions and allowing a certain level of uncertainty, and determining the direction of valid results based on extended fuzzy logic. Our simulation results demonstrate the reliability of our proposed method when compared with traditional paradigms of precise reasoning. Overall, this work highlights the potential of FLe in causality problems and provides a basic framework for handling causality in uncertain domains.
Sabahi, F. (2024). Analyzing Causality in Uncertain Domains using Extended Fuzzy Logic (FLe) Applied to Coronary Heart Disease Diagnosis. Scientia Iranica, (), -. doi: 10.24200/sci.2024.62780.8062
MLA
Farnaz Sabahi. "Analyzing Causality in Uncertain Domains using Extended Fuzzy Logic (FLe) Applied to Coronary Heart Disease Diagnosis". Scientia Iranica, , , 2024, -. doi: 10.24200/sci.2024.62780.8062
HARVARD
Sabahi, F. (2024). 'Analyzing Causality in Uncertain Domains using Extended Fuzzy Logic (FLe) Applied to Coronary Heart Disease Diagnosis', Scientia Iranica, (), pp. -. doi: 10.24200/sci.2024.62780.8062
VANCOUVER
Sabahi, F. Analyzing Causality in Uncertain Domains using Extended Fuzzy Logic (FLe) Applied to Coronary Heart Disease Diagnosis. Scientia Iranica, 2024; (): -. doi: 10.24200/sci.2024.62780.8062
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