Perspectives of Medical Students on E-Learning in the Surgery Module

Objectives: The goal of this study was to explore medical students' perceptions, assess their experiences, and identify obstacles to e-learning during the COVID-19 pandemic, as well as to understand the factors behind its adoption and application as a learning means in the surgery module. Materials and Methods: Data were gathered from undergraduate medical students, at the University of Hail, KSA, in their surgery module using an electronic questionnaire. Subsequently, SPSS version 25.0 (IBM, Armonk, NY, USA) has been used for analysis. Results: The study included 72 students, with a participation rate of 85.7%. Students positively perceived technology (M = 4.024 +/- 0.94 and P < 0.001). Most respondents (79.5%) claimed that e-learning required less time for studying than the conventional learning technique (M = 4.14 +/- 1.052 and P < 0.001). They had positive perceptions of the implications of e-learning (M = 3.92 +/- 0.89 and P < 0.001). Regarding the perception toward instructors, 53.9% admitted that when instructors use computer technologies, it adequately met their needs. The mean agreeability to online quizzes was high (M = 3.8264 +/- 0.910). More than half preferred the blended style of learning. A-70.9% interested in using e-learning (M = 3.83 +/- 1.278 and P < 0.001). There was no statistical difference among genders (P > 0.05). Conclusion: This study addresses the determinants behind the adoption and use of e-learning within the surgery module. Therefore, it will support the event of a rational approach to an effective application of e-learning and look at it as a positive initiative toward development and alteration.

Effective Diagnosing of Covid-19 from CXR Images Using Deep Learning Approaches and Optimized XG Boost Model

Since the first confirmed incidence of the novel coronavirus Covid-19 in China, it has spread fast around the world, reaching a population of 442,602,593(at the start of 2022), according to World Health Organization figures.Therefore, the diagnosis of the virus is crucial to prevent its separation. However, the tools available for Covid-19 diagnosis are limited compared to the pressure at the increasing number of infected people. Therefore, to prevent the virus thread, it is necessary to find a quick automated system that can handle a bulk amount of data with high accuracy and a lower amount of false positive or false negative. This research presents a hybrid machine learning-based system that uses a pre-trained MobilNet model for feature extraction from chest X-ray images, followed by a dimensionality reduction technique to speed up the classification process and an XGBoost classifier to complete the task. Furthermore, the Bayesian algorithm is used to choose the optimum hyperparameters for the XGBoost classifier. The suggested approach was evaluated on two datasets of X-ray images and produced both high and near results.The results for the first dataset were 97.65% accuracy, 97.63% F1-score, 97.65% recall, and 97.69% precision,and for the second dataset were 96.35% accuracy, 95.82% F1-score, 98.35% recall, and 96.38 precision.