Background As the Coronavirus Disease 2019 (COVID-19) outbreak has made a tremendous impact on medical education and healthcare institutions, we aimed to measure effects of online classes on medical students’ comprehension in comparison with attending campus classes during the COVID-19 pandemic. Methods A cross-sectional survey has been conducted between September 2020 and June 2021 in the western region of Saudi Arabia. The Convenience sampling technique was conducted to collect the data from medical students in their basic and clinical years, using a questionnaire that involved 45 multiple-choice and multiple-answer questions. Results Out of 3700 questionnaires, 922 completed the questionnaires from 11 different medical schools. Umm AL-Qura University had the highest response rate with 232 responses (25.2%), followed by King Abdulaziz University with 186 responses (20.2%). The majority of institutions preferred Blackboard and Zoom as platforms for e-learning. A total of 355 (38.5%) believed that it resulted in higher academic achievement, whereas 555 (60.2%) of students believed the limitation of clinical access was one of the biggest disadvantages of e-learning. Overall, 518 (56.2%) of students did not want to continue using e-learning on its own in the future. Whereas 668 (72.5%) wished to keep using e-learning in combination with traditional learning. Conclusion According to our findings, advantages of e-learning vary among students. Most of the students thought e-learning to be an interactive system that provides a learning opportunity. In contrast, many of the students believed that there were many disadvantages regarding online teaching methods.

The world has experienced a huge advancement in computing technology. People prefer outsourcing their confidential data for storage and processing in cloud computing because of the auspicious services provided by cloud service providers. As promising as this paradigm is, it creates issues, including everything from data security to time latency with data computation and delivery to end-users. In response to these challenges, the fog computing paradigm was proposed as an extension of cloud computing to overcome the time latency and communication overhead and to bring computing and storage resources close to both the ground and the end-users. However, fog computing inherits the same security and privacy challenges encountered by traditional cloud computing. This paper proposed a fine-grained data access control approach by integrating the ciphertext policy attribute-based encryption (CP-ABE) algorithm and blockchain technology to secure end-users’ data security against rogue fog nodes in case a compromised fog node is ousted. In this approach, we proposed federations of fog nodes that share the same attributes, such as services and locations. The fog federation concept minimizes the time latency and communication overhead between fog nodes and cloud servers. Furthermore, the blockchain idea and the CP-ABE algorithm integration allow for fog nodes within the same fog federation to conduct a distributed authorization process. Besides that, to address time latency and communication overhead issues, we equip each fog node with an off-chain database to store the most frequently accessed data files for a particular time, as well as an on-chain access control policies table (on-chain files tracking table) that must be protected from tampering by rogue fog nodes. As a result, the blockchain plays a critical role here because it is tamper-proof by nature. We assess our approach’s efficiency and feasibility by conducting a simulation and analyzing its security and performance.

SARS-CoV-2 is a recently discovered virus that poses an urgent threat to global health. The disease caused by this virus is termed COVID-19. Death tolls in different countries remain to rise, leading to continuous social distancing and lockdowns. Patients of different ages are susceptible to severe disease, in particular those who have been admitted to an ICU. Machine learning (ML) predictive models based on medical data patterns are an emerging topic in areas such as the prediction of liver diseases. Prediction models that combine several variables or features to estimate the risk of people being infected or experiencing a poor outcome from infection could assist medical staff in the treatment of patients, especially those that develop organ failure such as that of the liver. In this paper, we propose a model called the detecting model for liver damage (DMLD) that predicts the risk of liver damage in COVID-19 ICU patients. The DMLD model applies machine learning algorithms in order to assess the risk of liver failure based on patient data. To assess the DMLD model, collected data were preprocessed and used as input for several classifiers. SVM, decision tree (DT), Naïve Bayes (NB), KNN, and ANN classifiers were tested for performance. SVM and DT performed the best in terms of predicting illness severity based on laboratory testing.

Nowadays, brain tumors have become a leading cause of mortality worldwide. The brain cells in the tumor grow abnormally and badly affect the surrounding brain cells. These cells could be either cancerous or non-cancerous types, and their symptoms can vary depending on their location, size, and type. Due to its complex and varying structure, detecting and classifying the brain tumor accurately at the initial stages to avoid maximum death loss is challenging. This research proposes an improved fine-tuned model based on CNN with ResNet50 and U-Net to solve this problem. This model works on the publicly available dataset known as TCGA-LGG and TCIA. The dataset consists of 120 patients. The proposed CNN and fine-tuned ResNet50 model are used to detect and classify the tumor or no-tumor images. Furthermore, the U-Net model is integrated for the segmentation of the tumor regions correctly. The model performance evaluation metrics are accuracy, intersection over union, dice similarity coefficient, and similarity index. The results from fine-tuned ResNet50 model are IoU: 0.91, DSC: 0.95, SI: 0.95. In contrast, U-Net with ResNet50 outperforms all other models and correctly classified and segmented the tumor region.

Assistive devices for disabled people with the help of Brain-Computer Interaction (BCI) technology are becoming vital bio-medical engineering. People with physical disabilities need some assistive devices to perform their daily tasks. In these devices, higher latency factors need to be addressed appropriately. Therefore, the main goal of this research is to implement a real-time BCI architecture with minimum latency for command actuation. The proposed architecture is capable to communicate between different modules of the system by adopting an automotive, intelligent data processing and classification approach. Neuro-sky mind wave device has been used to transfer the data to our implemented server for command propulsion. Think-Net Convolutional Neural Network (TN-CNN) architecture has been proposed to recognize the brain signals and classify them into six primary mental states for data classification. Data collection and processing are the responsibility of the central integrated server for system load minimization. Testing of implemented architecture and deep learning model shows excellent results. The proposed system integrity level was the minimum data loss and the accurate commands processing mechanism. The training and testing results are 99% and 93% for custom model implementation based on TN-CNN. The proposed real-time architecture is capable of intelligent data processing unit with fewer errors, and it will benefit assistive devices working on the local server and cloud server.