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KIC Research Expert System for Corona Diagnosis (ESCD)

Sunday, 21 March 2021 / Published in Campus News

KIC Research Expert System for Corona Diagnosis (ESCD)

by

Dr. Samar Mouti

Assistant Professor, IT department.


Research Overview
Nowadays researchers are working tirelessly to find ways to better understand, treat and eventually eliminate COVID-19. This paper presents an Expert System for Corona Diagnosis (ESCD) to predict Covid-19 based on cognitive automation and production-rules.This paper gives an overview of artificial intelligence in the medical and healthcare sector and presents a system that uses artificial-intelligence methods to solve problems within a specialized domain that ordinarily requires human expertise. Cognitive Automation extracts the symptoms from the unstructured text which can be found on various online resources like blogs, forums, social media websites, etc. The knowledge base is built using facts and rules which are extracted from the expert domains.Also, this paper presents the implementation of the ESCD inference engine using python packages for the initial diagnosis of the disease. An inference engine is built using efficient procedures and a prediction process to deduct a correct diagnosis, it provides a methodology for reasoning about information in the knowledge base and for formulating conclusions. ESCD is designed to predict who may be infected with Corona by using social media tools. The success of ESCD majorly depends on the quality, completeness, and accuracy of the information stored in the knowledge base.

This system can be one of the tools to incredible progress in diagnosing Covid-19 and protect people. Characteristics of ESCD are high performance, understandable, reliable, and highly responsive. The combination of cognitive systems and cognitive theory led to make a highly intelligent application.

Find it Here:
This research was published in IOSR Journal of Computer Engineering (IOSR-JCE), Impact Factor 3.712, e-ISSN: 2278-0661, p-ISSN: 2278-8727, Volume 23, Issue 1, Ser. II (Jan. – Feb. 2021), PP 27-32 www.iosrjournals.org

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