Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.

The main objective of this research work is to develop the performance of education in higher schools elearning systems. This is accomplished with the aide of data mining (DM) techniques. The proposed model is applied on different students. Data is collected using online school tests, reports and quizzes. This paper applies SVM with accuracy 89%, Decision tree with accuracy 89%, M5-Rules with RMS error equal to 1.4621 and Linear Regression with RMS error equal to 2.0017, 3.0089 and 3.6057. Once getting both first and second grades, the presented results show a high predictive accuracy. Not only past student's evaluations affected in their academic achievement, but also other factors like father's and mother's jobs and absences. Briefly, student performance can be improved depending on predictive results and enhancing school systems.

Intensive care unit (ICU) admits the most seriously ill patients requiring extensive monitoring. Early mortality prediction is a crucial issue in intensive care. If a patient's likelihood of survival or mortality is predicted early enough, the patient will give proper and timely care to save the patient's life. In recent decades various severity scores and machine learning models have been developed for mortality prediction. By contrast, mortality prediction is still an open challenge. The main objective of this study is to provide a new framework for mortality prediction based on an ensemble classifier. The proposed ensemble classifier amalgamates five different classifiers: Linear discriminant analysis, decision tree, multilayer perceptron, Knearest neighbor, and logistic regression. Data is vertically divided according to expert medical opinion into six feature sets, choosing the most accurate classifier for each subset of features. Framework evaluated benchmark data from Medical Information Mart for Intensive Care (MIMIC III) database using the first 24 h collected for each patient. The performance was validated using standard metrics include Precision, Recall, F-score, Area under the roc curve. Results show F-score 91.02% with precision 92.34% recall 90.14% for MLP classifier, achieved F-score of 93.7%, precision of 96.4%, recall of 91.1%, and AUROC of 93.3% for proposed ensemble classifiers which outperforms using classical classifiers. The results show the validity of the proposed system to be an assistant system for physicians in ICU.