Deep learning approaches can be applied to a large amount of data for the purpose of simplifying and improving the engineering practice of automated decision-making activities rather than relying on human encoded heuristics. The need for generating faster and effective decisions about systems, processes, and applications gave rise to many artificial intelligences motivated approaches such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), fuzzy analytics, etc. Deep learning deploys diverse multiple layers of cascaded processing elements to enable features extraction and transformations. These deep learning approaches conduct multiple levels of depiction corresponding to distinct abstraction levels. There are several applications of deep learning algorithms including weather forecasting, object recognition, stock market performance forecasts, medical diagnosis, and emergency warning systems. This paper investigates the performance of the deep learning approach on the basis of processing components, data representation, and data types. To achieve this, a deep learning algorithm based on a long short-term memory-recurrent neural network (LSTM-RNN) was utilized to learn hidden patterns and features in the textual and image datasets respectively. The outcomes reveal that the performance of the image-based deep learning model was better in terms of speed due to well-defined patterns of data representation against the data with sentiments-based deep learning by 3.49 mins. to 18.25 mins. While the LSTM-RNN with images offered better classification accuracy by 96.50% to 85.69% due to complex network architecture, processing elements, and features of the underlying datasets. Povzetek: .

Academic institutions always try to use a solid platform for supporting their short-to-long term decisions related to academic performance. These platforms utilize historical data and turn them into strategic decisions. The hidden patterns in the data need tools and approaches to be discovered. This paper aims to present a short roadmap for implementing educational data mart based on a data set from Alexandria Private Elementary School, located in the Basrah province of Iraq in the 2017-2018 academic year. The educational data mart is implemented, then the cube is constructed to perform OLAP operations and present OLAP reports. Next, OLAP mining is performed on the educational cube using nine algorithms, namely: decision tree with score method (entropy) and split method (complete)), decision tree with score method (entropy) and split method (complete)), decision tree with score method (entropy) and split method (both)), Logistic, Naïve Bayes, Neural Network, clustering with expectation maximization, clustering with K-means clustering, and association rules mining. According to a comparison of all algorithms, clustering with expectation-maximization proved the highest accuracy with 96.76% for predicting the students' performance and 96.12% for predicting students' grades amongst all other algorithms.

The educational sector faced many types of research in predicting student performance based on supervised and unsupervised machine learning algorithms. Most students' performance data are imbalanced, where the final classes are not equally represented. Besides the size of the dataset, this problem affects the model's prediction accuracy. In this paper, the Synthetic Minority Oversampling Technique (SMOTE) filter is applied to the dataset to find its effect on the model's accuracy. Four feature selection approaches are applied to find the most correlated attributes that affect the students' performance. The SMOTE filter is examined before and after applying feature selection approaches to measure the model's accuracy with supervised and unsupervised algorithms. Three supervised/unsupervised algorithms are examined based on feature selection approaches to predict the students' performance. The findings show that supervised algorithms (LMT, Simple Logistic, and Random Forest) got high accuracy after applying SMOTE without feature selection. The prediction accuracies of unsupervised algorithms (Canopy, EM, and Farthest First) are enhanced after applying feature selection approaches and SMOTE filter.