Female Saudi College students' e-learning experience amidst COVID-19 pandemic: An investigation and analysis.

Emergency remote teaching in the immediate wake of the COVID-19 pandemic has created a challenging situation for both students and teachers. The purpose of this research is to identify the perceptions and challenges that university students faced during online classes in a women only university in Saudi Arabia. Data was collected by circulating Google forms among students from different colleges, and a total of 542 students submitted their responses. Apart from gathering the personal information of participants, the survey also collected information on aspects such as educational, financial, internet connectivity and volunteering/donations. Chi-squared test was used to determine whether there was a significant difference in opinion between different groups of students on various questions. Stress was identified as the most prevalent issue among students. Students were found to be stressed regardless of their college of study or age. In comparison to others, younger students and students from financially disadvantaged families faced more difficulties. In terms of remote practical class satisfaction, health/medical stream students were the most dissatisfied group. They also faced more difficulties than students from other colleges. The analysis results show that problems such as stress, poor internet connectivity, the need for technical support, a lack of proper interaction with faculty, a lack of proper academic advising, a lack of proper study space at home etc. must be addressed in order to improve the effectiveness of online classes. This paper also includes recommendations for resolving the various issues that students face.

Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features.

Many health systems over the world have collapsed due to limited capacity and a dramatic increase of suspected COVID-19 cases. What has emerged is the need for finding an efficient, quick and accurate method to mitigate the overloading of radiologists' efforts to diagnose the suspected cases. This study presents the combination of deep learning of extracted features with the Q-deformed entropy handcrafted features for discriminating between COVID-19 coronavirus, pneumonia and healthy computed tomography (CT) lung scans. In this study, pre-processing is used to reduce the effect of intensity variations between CT slices. Then histogram thresholding is used to isolate the background of the CT lung scan. Each CT lung scan undergoes a feature extraction which involves deep learning and a Q-deformed entropy algorithm. The obtained features are classified using a long short-term memory (LSTM) neural network classifier. Subsequently, combining all extracted features significantly improves the performance of the LSTM network to precisely discriminate between COVID-19, pneumonia and healthy cases. The maximum achieved accuracy for classifying the collected dataset comprising 321 patients is 99.68%.