The first fuzzy unit produces COVID-19 positivity as a percentage according to the respiratory rate, loss of smell, and body temperature values, and the second fuzzy unit according to the C-reactive protein, lymphocyte, and D-dimer values obtained as a result of the blood tests.
[PDF] A type-2 fuzzy rule-based model for diagnosis of COVID-19
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Nov 25, 2022 · The first fuzzy unit produces COVID-19 positivity as a percentage according to the respiratory rate, loss of smell, and body temperature values, ...
The first fuzzy unit is based on the respiratory rate, loss of smell, and body temperature values obtained in the clinical examination, and the second fuzzy ...
In this study, a type-2 fuzzy logic-based decision support system comprising clinical examination and blood test results that health professionals can use ...
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Feb 24, 2023 · This paper investigates the COVID-19 pandemic control using a fuzzy control policy based on a novel SEIAR model. The proposed S2EIAR includes ...
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An interval type-2 fuzzy system is proposed to robustly control the infected population, deal with the above randomness, and prepare an interpretation for ...
The results show that the type-2 fuzzy expert system and ANFIS models perform competitively in terms of accuracy and F-measure compared to the other system.
Jan 4, 2023 · A simplified design of the Xception model, which has three phases; entry, middle, and exit flows, respectively with four max pooling. As ...
The aim of this study is to construct a support system based on fuzzy logic for experts to help detect of COVID-19 infection risk in a timely and accurate ...
Jan 1, 2021 · Based on the input patient symptoms, the inference system initiates a set of fuzzy rules where each rule produces an output.