Al-Biruni Earth Radius Optimization for COVID-19 Forecasting

Several instances of pneumonia with no clear etiology were recorded in Wuhan, China, on December 31, 2019. The world health organization (WHO) called it COVID-19 that stands for "Coronavirus Disease 2019," which is the second version of the previously known severe acute respiratory syndrome (SARS) Coronavirus and identified in short as (SARSCoV-2). There have been regular restrictions to avoid the infection spread in all countries, including Saudi Arabia. The prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus spread. Methodology: Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive patients are presented in this research using metaheuristic optimization and long short-term memory (LSTM). The optimization method employed for optimizing the parameters of LSTM is Al-Biruni Earth Radius (BER) algorithm. Results: To evaluate the effectiveness of the proposed methodology, a dataset is collected based on the recorded cases in Saudi Arabia between March 7th, 2020 and July 13th, 2022. In addition, six regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed approach. The achieved results show that the proposed approach could reduce the mean square error (MSE), mean absolute error (MAE), and R2 by 5.92%, 3.66%, and 39.44%, respectively, when compared with the six base models. On the other hand, a statistical analysis is performed to measure the significance of the proposed approach. Conclusions: The achieved results confirm the effectiveness, superiority, and significance of the proposed approach in predicting the infection cases of COVID-19. © 2023 CRL Publishing. All rights reserved.

Motivational factors influencing undergraduate medical students' willingness to volunteer during an infectious disease pandemic in Saudi Arabia, a cross-sectional study.

OBJECTIVE: Healthcare outbreaks, especially infectious disease pandemics, often stretch the healthcare systems to its limits. Healthcare systems have no option other than being supported by the participation of young and motivated healthcare providers (HCPs) in their undergraduate medical studies during their prevention and control internship program during the outbreak. Understanding key motivation factors influencing HCPs are vital to ensure their effective participation in such situations. SUBJECTS AND METHODS: A cross-sectional study was conducted on 410 undergraduate medical students at Qassim University in Saudi Arabia with the aim to describe the motivation factors that affect their willingness to volunteer during a pandemic. An online survey questionnaire was conducted. RESULTS: 410 participants of which 239 (58.29%) were female, 108 (26.34%) were in their third academic year and 129 (31.46%) were between 21-22 years of age. More than 70% of participants showed willingness to volunteer during a pandemic. Their willingness to volunteer was motivated by distance of workplace to home, availability of transportation, being vaccinated, access to health care for self and family if affected, and provision of specialized training. CONCLUSIONS: Healthcare administrators and policy makers need to address these factors effectively to ensure the availability of skilled and motivated healthcare providers during a pandemic.

A Framework for Mask-Wearing Recognition in Complex Scenes for Different Face Sizes

People are required to wear masks in many countries, now a days with the Covid-19 pandemic. Automated mask detection is very crucial to help identify people who do not wear masks. Other important applications is for surveillance issues to be able to detect concealed faces that might be a safety threat. However, automated mask wearing detection might be difficult in complex scenes such as hospitals and shopping malls where many people are at present. In this paper, we present analysis of several detection techniques and their performances. We are facing different face sizes and orientation, therefore, we propose one technique to detect faces of different sizes and orientations. In this research, we propose a framework to incorporate two deep learning procedures to develop a technique for mask-wearing recognition especially in complex scenes and various resolution images. A regional convolutional neural network (R-CNN) is used to detect regions of faces, which is further enhanced by introducing a different size face detection even for smaller targets. We combined that by an algorithm that can detect faces even in low resolution images. We propose a mask-wearing detection algorithms in complex situations under different resolution and face sizes. We use a convolutional neural network (CNN) to detect the presence of the mask around the detected face. Experimental results prove our process enhances the precision and recall for the combined detection algorithm. The proposed technique achieves Precision of 94.5%, and is better than other techniques under comparison.

Real Time Feature Extraction Deep-CNN for Mask Detection

COVID-19 pandemic outbreak became one of the serious threats to humans. As there is no cure yet for this virus, we have to control the spread of Coronavirus through precautions. One of the effective precautions as announced by the World Health Organization is mask wearing. Surveillance systems in crowded places can lead to detection of people wearing masks. Therefore, it is highly urgent for computerized mask detection methods that can operate in real-time. As for now, most countries demand mask-wearing in public places to avoid the spreading of this virus. In this paper, we are presenting an object detection technique using a single camera, which presents real-time mask detection in closed places. Our contributions are as follows: 1) presenting a real time feature extraction module to improve the detection computational time;2) enhancing the extracted features learned from the deep convolutional neural network models to improve small objects detection. The proposed model is a lightweight backbone CNN which ensures real time mask detection. The accuracy is also enhanced by utilizing the feature enhancement module after some of the convolution layers in the CNN. We performed extensive experiments comparing our model to the single-shot detector (SDD) and YoloV3 neural network models, which are the state-of-the-art models in the literature. The comparison shows that the result of our proposed model achieves 95.9% accuracy which is 21% higher than SSD and 17.7% higher than YoloV3 accuracy. We also conducted experiments testing the mask detection speed. It was found that our model achieves average detection time of 0.85s for images of size 1024 x 1024 pixels, which is better than the speed achieved by SSD but slightly less than the speed of YoloV3.

Cardiovascular Complications in COVID-19 Pandemic and Its Management

Pneumonia like the pandemic COVID-19 is a virus disease, first time came into light in December 2019 in the Wuhan city of China. As of today, more than two million deaths from more than 210 countries have been confirmed. This disease has modeled a great threat to human mental health, physical health, and forcefully stuck the routine life with psychosocial consequences globally. The COVID-19 disease is caused by SARS-CoV-2 which leads to acute respiratory distress. The pathogenesis of the disease started from virus entry to the host cell where it controlled the cellular system of the patient directly or indirectly. Population having cardiovascular, immunosuppressive, AIDS, and diabetes-like complications are thought to be very risky for mortality caused by the SARS-CoV-2 virus. The correlation to Noncommunicable diseases like cardiovascular disorders and diabetes has led our attention towards the management of these diseases in the corona outbreak. Thus, this review aimed to better understand the threats modeled by the disease COVID-19 to the cardiovascular system, and the medical community should share their experiences promptly. It is concluded that both published research and real-time experiences shared on social media by world experts will act as a valuable tool and will help to learn more about this disease. Several preventive and therapeutic measures including drug therapy are suggested to manage the disease in comorbidities conditions.