COVID-19 syndrome has extensively escalated worldwide with the induction of the year 2020 and has resulted in the illness of millions of people. COVID-19 patients bear an elevated risk once the symptoms deteriorate. Hence, early recognition of diseased patients can facilitate early intervention and avoid disease succession. This article intends to develop a hybrid deep neural networks (HDNNs), using computed tomography (CT) and X-ray imaging, to predict the risk of the onset of disease in patients suffering from COVID-19. To be precise, the subjects were classified into 3 categories namely normal, Pneumonia, and COVID-19. Initially, the CT and chest X-ray images, denoted as ‘hybrid images’ (with resolution 1080 × 1080) were collected from different sources, including GitHub, COVID-19 radiography database, Kaggle, COVID-19 image data collection, and Actual Med COVID-19 Chest X-ray Dataset, which are open source and publicly available data repositories. The 80% hybrid images were used to train the hybrid deep neural network model and the remaining 20% were used for the testing purpose. The capability and prediction accuracy of the HDNNs were calculated using the confusion matrix. The hybrid deep neural network showed a 99% classification accuracy on the test set data.

International students are valuable resources in higher education but they often face challenges due to lack of social engagement and unfamiliarity with the new social context. The use of social network sites (SNS) such as Facebook has the potential to support international students, but limited studies have examined the actual information seeking behaviour of this group of students. Therefore, this study aims to investigate the use of Facebook groups as an example of an SNS by international students for information seeking purposes. Data were downloaded from the Facebook group of international students that belong to a public university in Malaysia, and the social network analysis technique was used to analyse the data. The result of the network graph metrics showed that 25% international students exhibit a high frequency of interaction, whereas 75% of students present low interaction. Meanwhile, the result of the vertex text attribute method identified three types of information exchange, as follows: 1) information need; 2) information source; and 3) general information during the interaction of international students. The information needs of international students differ before and after arriving in Malaysia. Thus, Facebook groups provide a platform for international students to seek information, gain knowledge, remain updated with university news, make decisions and solve problems. This study offers important implications to research and practice related to the use of social network sites to support international students.