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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Das, Dhiraj Kumar | - |
| dc.date.accessioned | 2025-02-05T06:33:45Z | - |
| dc.date.available | 2025-02-05T06:33:45Z | - |
| dc.date.issued | 2021 | - |
| dc.identifier.uri | https://ieeexplore.ieee.org/abstract/document/9682347 | - |
| dc.identifier.uri | http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/17206 | - |
| dc.description.abstract | In this article, a Mamdani type fuzzy inference system is formulated in order to identify possible COVID-19 infected individuals based on three major clinical factors namely body-temperature, body-immunity level and vaccination efficacy. Measurements of the system's input and output parameters are considered as linguistic variable and assumed to follow trapezoidal type membership functions. The system based on total 27 fuzzy If-Then rules and called as Fuzzy Inference System (FIS) of Mamdani type. The system is analyzed using the Fuzzy Logic Toolbox of MATLAB. It has been found that with highly efficient vaccine a person with low body-immunity can escape the disease. On contrary, high body-temperature with high body-immunity power is not sufficient to exclude a person from the risk of having COVID-19. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | IEEE | en_US |
| dc.subject | Mathematics | en_US |
| dc.subject | COVID-19 model | en_US |
| dc.subject | Fuzzy logic | en_US |
| dc.subject | Fuzzy Inference System | en_US |
| dc.title | Modelling the risk of COVID-19 based on major clinical factors: A fuzzy rule approach | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | Department of Mathematics | |
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