Globally, many research works are going on to study the infectious nature of COVID-19 and every day we learn something new about it through the flooding of the huge data that are accumulating hourly rather than daily which instantly opens hot research avenues for artificial intelligence researchers. However, the public’s concern by now is to find answers for two questions; (1) When this COVID-19 pandemic will be over? and (2) After coming to its end, will COVID-19 return again in what is known as a second rebound of the pandemic? In this work, we developed a predictive model that can estimate the expected period that the virus can be stopped and the risk of the second rebound of COVID-19 pandemic. Therefore, we have considered the SARIMA model to predict the spread of the virus on several selected countries and used it for predicting the COVID-19 pandemic life cycle and its end. The study can be applied to predict the same for other countries as the nature of the virus is the same everywhere. The proposed model investigates the statistical estimation of the slowdown period of the pandemic which is extracted based on the concept of normal distribution. The advantages of this study are that it can help governments to act and make sound decisions and plan for future so that the anxiety of the people can be minimized and prepare the mentality of people for the next phases of the pandemic. Based on the experimental results and simulation, the most striking finding is that the proposed algorithm shows the expected COVID-19 infections for the top countries of the highest number of confirmed cases will be manifested between Dec-2020 and Apr-2021. Moreover, our study forecasts that there may be a second rebound of the pandemic in a year time if the currently taken precautions are eased completely. We have to consider the uncertain nature of the current COVID-19 pandemic and the growing inter-connected and complex world, that are ultimately demanding flexibility, robustness and resilience to cope with the unexpected future events and scenarios.

COVID-19 was first discovered in Wuhan, China on December 2019. It is one of the worst pandemics in human history. The education sector is one of the sectors most affected by the COVID-19 pandemic. Educators and students worldwide were forced to shift to online learning. Students, in particular, have suffered from the psychological impacts and learning difficulties caused by the lockdowns imposed by governments to control the pandemic. In this study, we used statistics and machine learning approaches to study the impact of COVID-19 pandemic on educations systems especially on university students’ psychological health. For this purpose, a questionnaire was created,which consisted of two primary parts. The first part collects the participant’s demographic and educational characteristics, while the second part gathers information about five primary dimensions: the use of digital devices, sleep habits, social communication, emotional mental state, and academic performance. The questionnaire was distributed to university students in three Arab countries: Egypt, Jordan, and Saudi Arabia. A total of 1766 responses were returned and analyzed using statistical and machine learning approaches. The results showed an evident correlation between student’s psychological health and the use of online education during the time COVID-19. The results also showed that there is a positive relationship between digital tools used for online education and the academic performance of students during the time of COVID-19. Finally, the results highlighted the harmful impacts of COVID-19 on the education systems. The study ends by presenting suggestions and recommendations needed to improve the current online education system. Moreover, it is warranted for universities to have a role in helping students respond to the pandemic in a healthier psychological performance.

Hate speech towards a group or an individual based on their perceived identity, such as ethnicity, religion, or nationality, is widely and rapidly spreading on social media platforms. This causes harmful impacts on users of these platforms and the quality of online shared content. Fortunately, researchers have developed different machine learning algorithms to automatically detect hate speech on social media platforms. However, most of these algorithms focus on the detection of hate speech that appears in English. There is a lack of studies on the detection of hate speech in Arabic due to the language’s complex nature. This paper aims to address this issue by proposing an effective approach for detecting Arabic hate speech on social media platforms, namely Twitter. Therefore, this paper introduces the Arabic BERT-Mini Model (ABMM) to identify hate speech on social media. More specifically, the bidirectional encoder representations from transformers (BERT) model was employed to analyze data collected from Twitter and classify the results into three categories: normal, abuse, and hate speech. In order to evaluate our model and state-of-the-art approaches, we conducted a series of experiments on Twitter data. In comparison with previous works on Arabic hate-speech detection, the ABMM model shows very promising results with an accuracy score of 0.986 compared to the other models.