Clustering Arabic text documents is of high importance for many Natural Language Technologies. This study uses a combined method to cluster Arabic text documents. Mainly, we use generative models and clustering techniques. The study uses Latent Dirichlet Allocation (LDA) and kmeans clustering algorithm and applies them to a news dataset used in previous similar studies. The aim of this research is twofold: it first shows that normalizing the weights in the vector space, for the documentterm matrix of the text documents, dramatically improves the quality of clusters and hence the accuracy of clustering when using k-means algorithm. The results are compared to a recent study on clustering Arabic text documents. Second, it shows that the combined method is superior in terms of clustering quality for Arabic text documents according to external measures like purity, F-measure, entropy, accuracy, and other measures. It is shown in this study that the purity of the combined method is 0.933 compared to 0.82 for K-means algorithm, these figures are higher in comparison to a recent similar study. This is also confirmed by the other used validation measures. The correctness of the combined method is then confirmed using different Arabic datasets.

Currently, social media plays an important role in daily life and routine. Millions of people use social media for different purposes. Large amounts of data flow through online networks every second, and these data contain valuable information that can be extracted if the data are properly processed and analyzed. However, most of the processing results are affected by preprocessing difficulties. This paper presents an approach to extract information from social media Arabic text. It provides an integrated solution for the challenges in preprocessing Arabic text on social media in four stages: data collection, cleaning, enrichment, and availability. The preprocessed Arabic text is stored in structured database tables to provide a useful corpus to which, information extraction and data analysis algorithms can be applied. The experiment in this study reveals that the implementation of the proposed approach yields a useful and full-featured dataset and valuable information. The resultant dataset presented the Arabic text in three structured levels with more than 20 features. Additionally, the experiment provides valuable information and processed results such as topic classification and sentiment analysis.

Evaluating students’ academic performance is crucial for assessing the quality of education and educational strategies. However, it can be challenging to predict and evaluate academic performance under uncertain and imprecise conditions. To address this issue, many research works have employed fuzzy concepts to analyze, predict, and make decisions about students’ academic performance. This paper investigates the use of fuzzy concepts in research related to evaluating, analyzing, predicting, or making decisions about student academic performance. The paper proposes a fuzzy model, called FPM (Fuzzy Propositional Model), for reasoning and predicting students’ academic performance. FPM aims to address the limitations of previous studies by incorporating propositional logic with fuzzy sets concept, which allows for the representation of uncertainty and imprecision in the data. FPM integrates and transforms if-then rules into weighted fuzzy production rules to predict and evaluate academic performance. This paper tests and evaluates the FPM in two scenarios. In the first scenario, the model predicts and examines the impact of absenteeism on academic performance where there is no clear relation between the two parts of the dataset. In the second scenario, the model predicts the final exam results using the lab exam results, where the data are more related. The FPM provides good results in both scenarios, demonstrating its effectiveness in predicting and evaluating students’ academic performance. A comparison study of the FPM’s results with a linear regression model and previous work showed that the FPM performs better in predicting academic performance and provides more insights into the underlying factors affecting it. Therefore, the FPM could be useful in educational institutions to predict and evaluate students’ academic performance, identify underlying factors affecting it, and improve educational strategies.

Teaching practical courses has always constituted burdens on colleges offering non-technical degrees. One of these courses is computer programming, especially in degrees like computer science, information systems and software engineering. In such programs, students are expected to take between 2-5 computer programming courses. Success ratio in the first course is usually low. Why is it low and how this ratio can be increased is discussed in this paper. This is carried out in the context of an introductory computer programming course at the college of computer engineering and sciences (CCES) at Prince Sattam Bin Abdulaziz University in Kingdom of Saudi Arabia. This study also relies on the teaching experience of the authors in different countries including-for around ten years: UK, USA, Sudan, Jordan and Kingdom of Saudi Arabia. To figure out problems and solutions for teaching introductory computer programming course, then interviews with selected instructors who teach such course at the college are conducted. Also, a questionnaire has been designed and distributed to students. The results of the analysis for both the interviews and the questionnaire have been used along with the results of similar studies to recommend solutions of problems occur in such course. As questionnaire shows, most students think that giving the whole course in the lab will be much better and also they prefer not to work alone. These and other recommendations presented in this study are especially appropriate for similar institutions in the Middle-East and Gulf area.

This paper presents a novel approach for simulating and optimizing production costing systems using linear programming. The proposed method employs a linear programming algorithm to simulate the behavior of production costs and to derive optimal solutions, including cost minimization, resource maximization, and economic returns. This approach has been implemented as a standard platform computer application, which automatically processes the costing system to reflect real-world behavior accurately. The developed application was tested on a university college costing system. The results demonstrated that the approach effectively simulates production costing systems and provides comprehensive reports and results, such as optimal costs, efficient resource utilization, and profit calculations. All processes are stored automatically in a database, supported by a full user interface with forms for ease of use. This approach is valuable for controlling costs and aiding decision-making processes. The versatility and efficiency of the linear programming model, particularly in handling large datasets and linear relationships, make it a powerful tool in various sectors.