Abstract. Data mining is the process of extracting useful information from a huge amount of data. One of the most common applications of data mining is the use of different algorithms and tools to estimate future events based on previous experiences. In this context, many researchers have been using data mining techniques to support and solve challenges in higher education. There are many challenges facing this level of education, one of which is helping students to choose the right course to improve their success rate. An early prediction of students' grades may help to solve this problem and improve students' performance, selection of courses, success rate and retention. In this paper we use different classification techniques in order to build a performance prediction model, which is based on previous students' academic records. The model can be easily integrated into a recommender system that can help students in their course selection, based on their and other graduated students' grades. Our model uses two of the most recognised decision tree classification algorithms: ID3 and J48. The advantages of such a system have been presented along with a comparison in performance between the two algorithms.
Atrial fibrillation (AF) is one of the most common cardiac arrhythmias, and it is an indication of high-risk factors for stroke, myocardial ischemia, and other malignant cardiovascular diseases. Most of the existing AF detection methods typically convert one-dimensional time-series electrocardiogram (ECG) signals into two-dimensional representations to train a deep and complex AF detection system, which results in heavy training computation and high implementation costs. In this paper, a multiscale signal encoding scheme is proposed to improve feature representation and detection performance without the need for using any transformation or handcrafted feature engineering techniques. The proposed scheme uses different kernel sizes to produce the encoded signal by using multiple streams that are passed into a one-dimensional sequence of blocks of a residual convolutional neural network (ResNet) to extract representative features from the input ECG signal. This also allows networks to grow in breadth rather than in depth, thus reducing the computing time by using the parallel processing capability of deep learning networks. We investigated the effects of the use of a different number of streams with different kernel sizes on the performance. Experiments were carried out for a performance evaluation using the publicly available PhysioNet CinC Challenge 2017 dataset. The proposed multiscale encoding scheme outperformed existing deep learning-based methods with an average F1 score of 98.54%, but with a lower network complexity.
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