Objective and mehtods: Our objective is to determine the containment effect on the spread of Covid 19 in Morocco. The methodology is based on an epidemiological study whose objective is to take stock of the current situation and to estimate the future spread of Covid-19 over time in Morocco. Several conditions were considered using the SIR epidemiological model for a better reliability of the results. During the study period from the appearance of the first case until 19 June 2020, Morocco reported 9074 cases of infections, 213 deaths with a lethality of 2.35% and a mortality of 6.04 per million inhabitants.
Results and Conclusion: The results of the SIR model show a sudden increase in infections. The peak would be 4.4 million inhabitants or 12% of the total population under confined conditions. Selon ces résultats,Containment is one of the most effective methods of reducing the risk of infection.
Bangladesh Journal of Medical Science Vol.19(0) 2020 p. S 58-S 65
Student failure prediction is one of the main topics in university learning contexts, as it helps to avoid failure in higher education institutions and provides a basis to make the teaching and learning process more effective, efficient and reliable. The overall aim of this study is to identify students who are susceptible to fail a given university course. This research paper reports the implementation of an Educational Data Mining project based on the CRISP-DM methodology. The data was collected from the APOGEE system of Ibn Tofail University, a form and specifications of the tested courses. The business goal of this paper is to develop a model that can identify students who are susceptible to failure in a given academic course. Such a model helps prevent failure in higher education institutions and provides a basis for making the teaching and learning process more effective, efficient and reliable. Most common machine learning algorithms in the field of Educational Data Mining were used. The results of our research showed that the proposed method was able to achieve an overall accuracy of 97% in predicting students at potential failure.
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