To search, Click below search items. |
| All Published Papers Search Service |
|
|
|
Title |
Knowledge-Based Smart System for the Identification of Coronavirus (COVID-19): Battling the Pandemic with Scientific Perspectives |
|
Author |
Muhammad Saleem and Muhammad Hamid |
| Citation |
Vol. 24 No. 9 pp. 127-134 |
|
Abstract |
The acute respiratory infection known as a coronavirus (COVID-19) may present with a wide range of clinical manifestations, ranging from no symptoms at all to severe pneumonia and even death. Expert medical systems, particularly those used in the diagnostic and monitoring phases of treatment, have the potential to provide beneficial results in the fight against COVID-19. The significance of healthcare mobile technologies, as well as the advantages they provide, are quickly growing, particularly when such applications are linked to the internet of things. This research work presents a knowledge-based smart system for the primary diagnosis of COVID-19. The system uses symptoms that manifest in the patient to make an educated guess about the severity of the COVID-19 infection. The proposed inference system can assist individuals in self-diagnosing their conditions and can also assist medical professionals in identifying the ailment. The system is designed to be user-friendly and easy to use, with the goal of increasing the speed and accuracy of COVID-19 diagnosis. With the current global pandemic, early identification of COVID-19 is essential to regulate and break the cycle of transmission of the disease. The results of this research demonstrate the feasibility and effectiveness of using a knowledge-based smart system for COVID-19 diagnosis, and the system has the potential to improve the overall response to the COVID-19 pandemic. In conclusion, these sorts of knowledge-based smart technologies have the potential to be useful in preventing the deaths caused by the COVID-19 pandemic. |
|
Keywords |
Coronavirus; COVID-19; Knowledge based; pandemic; Diagnosis; Fuzzy system |
|
URL |
