| Autor: |
Ksantini, Mohamed, Kadri, Nesrine, Ellouze, Ameni, Turki, Sameh Hbaieb |
| Předmět: |
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| Zdroj: |
Ingénierie des Systèmes d'Information; Jun2020, Vol. 25 Issue 3, p319-325, 7p |
| Abstrakt: |
Since the COVID-19 pandemic surges around the world and officially entered a dangerous new phase, one of the important concerns is when to take aggressive public health measures to slow the spread of COVID-19 and to know impact of the use of protection tools. Many studies have dealt with the prediction of the evolution of cases affected by the COVID-19 virus. Given the unreliability of the data collected about the number of new cases and the uncertainties in values, the results found cannot be accurate and present a bias. In this paper, we will present a study using artificial intelligence algorithms more precisely machine and deep learning algorithms to predict the evolution of cases reached by COVID-19 in the future given the application of confinement and the use of protection tools. To improve the accuracy of the results and to take into account the uncertain aspect of the data we will apply the theory of belief functions. Among objectives of this theory is the fusion of different sources of information, given by artificial intelligence algorithms in our case, in order to obtain a global knowledge in the form of a more precise and reinforced belief function. Results shows that applying the home isolation and the use of protection tools with the rate over of 80% can reduce considerably the number of cases. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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