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The outbreak of the new coronavirus (COVID-19) had resulted in the creation of a disaster all over the world and it had become a highly acute and severe illness. The prevalence of this disease is increasing rapidly worldwide. The technology of deep learning (DL) became one of the hot topics in the computing context and it is widely implemented in a variety of the medical applications. Those techniques proved to be sufficient tools for the clinicians in automatic COVID-19 diagnosis. In the present study, a DL technology that is based on convolution neural networks (CNN) models had been suggested for the binary COVID-19 classification. In the initial step of the suggested model, COVID-19 data-set of chest X-ray (CXR) images have been obtained then preprocessed. Whereas in the second stage, a new CNN model has been built and trained for diagnosing COVID-19 data-set as (positive) infection or (negative) normal cases. The suggested architecture had a success in classifying COVID-19 with the training model accuracy that had reached 96.57% for the training data-set and 92.29% for validating data-set and could reach the target point with a minimal learning rate for training this model with promising results.

The outbreak of coronavirus disease 2019 (COVID-19) drives most higher education systems in many countries to stop face-to-face learning. Accordingly, many universities, including Jordan University of Science and Technology (JUST), changed the teaching method from face-to-face education to electronic learning from a distance. This research paper investigated the impact of the e-learning experience on the students during the spring semester of 2020 at JUST. It also explored how to predict students’ academic performances using e-learning data. Consequently, we collected students’ datasets from two resources: the center for e-learning and open educational resources and the admission and registration unit at the university. Five courses in the spring semester of 2020 were targeted. In addition, four regression machine learning algorithms had been used in this study to generate the predictions: random forest (RF), Bayesian ridge (BR), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). The results showed that the ensemble model for RF and XGBoost yielded the best performance. Finally, it is worth mentioning that among all the e-learning components and events, quiz events had a significant impact on predicting the student’s academic performance. Moreover, the paper shows that the activities between weeks 9 and 12 influenced students’ performances during the semester.

Cases of the COVID-19 virus continue to spread still needs to be considered even though we have entered the post-pandemic era. Rapid identification of COVID-19 cases is necessary to prevent the virus from spreading further. This study developed a chest X-ray-based (CXR) COVID-19 classification for COVID-19 detection using the convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) combination model and compared the CNN-BiLSTM combination model with CNN models. The CNN models used in this study are the transfer learning models, namely Resnet50, VGG19, InceptionV3, Xception, and AlexNet. This research classifies CXR into three groups: COVID-19, normal, and viral pneumonia. In comparison to other models, the Resnet50-BiLSTM model is the most accurate and hence the best. The accuracy of the Resnet50-BiLSTM model was 98.48%. The model that obtains the next highest accuracy i.e Resnet50, VGG19-BiLSTM, VGG19, InceptionV3-BiLSTM, InceptionV3, Xception-BiLSTM, Xception, AlexNet-BiLSTM, and AlexNet. In this study, precision, recall, and F1-measure are also employed to demonstrate that Resnet50-BiLSTM achieves the highest value compared to other approaches. When compared to previous studies, this study enhances classification performance results.

<p>The sudden arrival of COVID-19 called for new technologies to manage the healthcare system and to reduce the burden of patients in the hospitals. Artificial intelligence (AI) which involved using computers to model intelligent behavior became an important choice. Various AI applications helped a lot in the management of healthcare and delivering quick medical consultations and various services to a wide variety of patients. These new technological developments had significant roles in detecting the COVID-19 cases, monitoring them, and forecasting for the future. Artificial intelligence is applied to mimic the functional system of human intelligence. AI techniques and applications are also applied in proper examinations, prediction, analyzing, and tracking of the whereabouts of patients and the projected results. It also played a significant role in recognizing and proposing the generation of vaccines to prevent COVID-19. This study is therefore an attempt to understand the major role and use of AI in healthcare institutions by providing urgent decision-making techniques that greatly helped to manage and control the spread of the COVID-19 disease.</p>

In the past two years, the world witnessed the spread of the coronavirus (COVID-19) pandemic that disrupted the entire world, the only solution to this epidemic was health isolation, and with it everything stopped. When announcing the availability of a vaccine, the world was divided over the effectiveness and harms of this vaccine. This article provides an analysis of vaccinators and analysis of people's opinions of the vaccine's efficacy and whether negative or positive. Then a model is built to predict the future numbers of vaccinators and a model that predicts the number of negative opinions or tweets. The model consists of three stages: first, converting data sets into a synchronized time series, that is, the same place and time for vaccination and tweets. The second stage is building a prediction model and the third stage was descripting analysis of the prediction results. The autoregressive integrated moving averages (ARIMA) method was used after decomposing the components of ARIMA and choosing the optimal model, the best results obtained from seasonal ARIMA (SARIMA) for both predictions, the last stage is the descriptive analysis of the results and linking them together to obtain an analysis describing the change in the number of vaccinators and the number of negative tweets.

COVID-19 has caused disruptions to many aspects of everyday life. To reduce the impact of this pandemic, its spreading must be controlled via face mask wearing. Manually mask-checking for everybody is embarrassing and uncontrollable. Hence, the proposed technique is used to help for automatic mask-checking based on deep learning platforms with real-time surveillance live infra-red (IR) camera. In this paper, two recent object detection platforms, named, you only look once version 3 (YOLOv3) and TensorFlow lite are adopted to accomplish this task. The two models are trained with a dataset consisting of images of persons with/without masks. This work is simulated with Google Colab then tested in real-time on an embedded device mated with fast GPU called Raspberry Pi 4 model B, 8 GB RAM. A comparison is made between the two models to verify their performance in relation to their precision rate and processing time. The work of this paper is also succeeded to realize multiple face masks real-time detection up to 10 facemasks in a single scene with high inference speed. Temperature is also measured using IR touchless sensor for each person with sound alarming to alert fever. The presented detector is cheap, light, small, and fast, with 99% accuracy rate during training and testing.

The investigation analysed the correct use of the vaccination scheme in order to achieve efficient immunization for children and adults in the midst of the pandemic; through the use of a web application that will greatly facilitate people in their immunization, since the web application will allow them to create alerts to have reminders at times personalized by the user. Also, the web application will provide a schedule complete personalized immunization according to the personal information of the registered user, on the other hand they will also be able to view the information of the different vaccination centers to achieve a correct immunization. The methodology was Scrum, allows prepared for change and to be able to adapt, as well as allowing proper planning for efficient work. The result obtained from the investigation can be seen in the sur-vey carried out on parents; where satisfactory percentages were obtained since 98% of respondents indicated that the use of a web application for the correct immunization of people is of the utmost importance, in addition to the survey they were shown the prototype of the application and indicated that its use was efficient, educational and easy to use for everyone.

Since 2020, Indonesia has been facing the Covid-19 pandemic for 2 years. Many impacts caused by the Covid-19 pandemic, especially on the economy, have led to a recession. Regional revenues are disrupted during a recession so local governments depend on central government transfer funds. From these issues, researchers suspect that there was a change in the decentralization conditions in East Java province during the Covid-19 pandemic as it was the province with the second largest GDP and has also experienced the greatest impact in Indonesia. This research method uses multiple linear regression with cross-sectional data from 38 district/city data units in East Java province. The results of this study are that the Decentralization Fund, Civil Service Capital Expenditure and the Human Development Index did not change before and during the Covid-19 pandemic. While the vertical fiscal imbalance has a positive influence on economic growth before the Covid-19 pandemic, the vertical fiscal imbalance has no influence on economic growth. This shows that there is another shift in priority in the use of central government finances, as state money is refocused and reallocated to deal with Covid-19. This makes development programs in other health sectors postponed.

The dataset is cited in the above-mentioned author's article submitted to Global Public Health.

<div align="left"><a name="_Hlk108683337"></a><span lang="EN-US">The coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by the SARS-CoV-2 coronavirus. In breaking the transmission chain of SARS-CoV-2, the government has made it compulsory for the people to wear a mask in public places to prevent COVID-19 transmission. Hence, an automated face mask detection is crucial to facilitate the monitoring process in ensuring people to wear a face mask in public. This project aims to develop an automated face and face mask detection for multiple people by applying deep learning-based object detection algorithm you only look once version 3 (YOLOv3). YOLOv3 object detection algorithm was concatenated with different backbones including ResNet-50 and Darknet-53 to develop the face and face mask detection model. Datasets were collected from online resources including Kaggle and Github and the images were filtered and labelled accordingly. The models were trained on 4393 images and evaluated based on precision, recall, mean average precision and the detection time. In conclusion, DarkNet53_YOLOv3 was chosen as the better model compared to ResNet50_YOLOv3 model with its good performance on accuracy with a mAP of 95.94% and a fast detection speed with a detection time of 50 seconds on 776 images. </span></div>