Educational Data Mining (EDM) is used to extract and discover interesting patterns from educational institution datasets using Machine Learning (ML) algorithms. There is much academic information related to students available. Therefore, it is helpful to apply data mining to extract factors affecting students’ academic performance. In this paper, a web-based system for predicting academic performance and identifying students at risk of failure through academic and demographic factors is developed. The ML model is developed to predict the total score of a course at the early stages. Several ML algorithms are applied, namely: Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Linear Regression (LR). This model applies to the data of female students of the Computer Science Department at Imam Abdulrahman bin Faisal University (IAU). The dataset contains 842 instances for 168 students. Moreover, the results showed that the prediction’s Mean Absolute Percentage Error (MAPE) reached 6.34%, and the academic factors had a higher impact on students’ academic performance than the demographic factors, the midterm exam score in the top. The developed web-based prediction system is available on an online server and can be used by tutors.
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Background Acute eosinophilic pneumonia (AEP) is well-known as one of the primary eosinophilic pulmonary diseases of unknown etiology. It’s defined as a febrile illness along with acute onset respiratory failure that is commonly misdiagnosed at the initial presentation as infectious pneumonia. Despite the fact that AEP sometimes classified as idiopathic as no exact cause can be identified in most cases, it has been suggested recently to be linked with electronic cigarette or vaping products and associated with electronic cigarette or vaping associated lung injury (EVALI). Therefore, history of recent tobacco smoking or vaping exposure along with peripheral eosinophilia are crucial clinical findings suggestive of AEP. Case presentation A previously healthy 17-year-old female presented to the Emergency Room with one day history of progressively worsening shortness of breath accompanied by left sided pleuritic chest pain and fever. She wasn’t taking any medications, denied traditional cigarette smoking, exposure to pulmonary irritants, recent travel and had no history of close contact with sick patient. She recently started vaping 20 days prior to the presentation. Initially, she was admitted with a presumptive diagnosis of atypical pneumonia but was found to have AEP due to a recent vaping exposure. Conclusion Vaping is a well-known health hazard that has become a growing trend among adolescents and have been promoted as a safe and effective alternative to traditional cigarettes. The etiology of AEP remains unclear, but many studies suggest a possible link with recent tobacco smoking or vaping. A key challenge for this clinical entity is to reach the diagnosis after excluding all other pulmonary eosinophilia causes, and it has an excellent prognosis if diagnosed early and treated appropriately.
Students’ attendance is one of the methods used to ensure students success by making sure that a student does not miss many classes. The most dominant method used nowadays in the Saudi universities such as, Imam Abdulrahman Bin Faisal University (IAU), is the traditional method where the instructors are the ones responsible for taking student’s attendance. Developing new and effective methods is critical to improve the traditional way and minimise their errors. A proposed facial recognition based attendance system is one efficient method that can substitute the traditional methods. Installing cameras in classrooms where it detects faces and recognise each face for attendance registration is more competent. The system can also track students’ attendance and warn them if they exceeded the permitted absences rate. The proposed system facilitates the attendance registration process and reduces the time and effort for both students and instructors.
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