Fuzzy Logic Inference System for Identification and Prevention of Coronavirus (COVID-19)
Nitesh Dhiman1, M.K. Sharma2

1M. K. Sharma*, Associate Professor, Department of Mathematics, Chaudhary Charan Singh University, Meerut, India.
2Nitesh Dhiman, Research Scholar, Department of Mathematics, Chaudhary Charan Singh University, Meerut, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on April 01, 2020. | Manuscript published on April 10, 2020. | PP: 1575-1580 | Volume-9 Issue-6, April 2020. | Retrieval Number: F4642049620/2020©BEIESP | DOI: 10.35940/ijitee.F4642.049620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Now a days Novel Coronavirus named COVID-19 becomes major health concern causing severe health issue in human beings and it becomes a pandemic. It’s a kind of zoonotic that means it can transmit animals to humans. It may spread via polluted hands or metals. No specific treatment is available so far for COVID-19, so initial identification and preventions for COVID-19 will be crucial to control or to break down the chain of COVID-19. For this purpose, we have proposed a fuzzy inference system to diagnose the COVID-19 disease by taking six input factor like as; Ethanol, Atmospheric Temperature (AT), Body Temperature (BT), Breath Shortness (BS), Cough and Cold and the output factor has divided into three linguistic categories which denotes the severity level of the infected patients. 
Keywords: Coronavirus (COVID-19), Gaussian Membership Function, Fuzzy Inference System, Medical Diagnosis
Scope of the Article: Fuzzy logics