Mohamed Osman Hegazi

Coronary artery disease (CAD) classification requires integration of clinical and diagnostic variables whose interpretations are often gradual rather than strictly binary. This study evaluates a fixed 45-rule Mamdani fuzzy classifier on the 13 variables of the processed Cleveland Heart Disease dataset (297 complete cases) as an auditable post-diagnostic evidence-integration framework rather than a replacement for optimized machine learning classifiers. Three repeats of five-fold nested cross-validation on the 237-record development cohort yielded mean accuracies of 83.7% for random forest, 82.3% for support vector machine (SVM), and 78.2% for multilayer perceptron, while the fixed fuzzy classifier achieved 79.7% selective accuracy with 99.6% coverage (bootstrap 95% CI for selective accuracy: 74.2–84.7%). On the descriptive 60-record internal holdout, fuzzy accuracy was 81.7% (95% CI: 70.1–89.4%), compared with 83.3% for SVM and random forest; exact paired McNemar tests did not show evidence of an accuracy difference (all p = 1.00). A conflict audit based on simultaneous Low and High rule supports was evaluated as an exploratory review flag. Applying the prespecified exploratory threshold C(x) ≥ 0.5 to the holdout retained 95.0% of cases and produced 82.5% selective accuracy, a small change from 81.7%. Additional analyses examine feature dependence, rule activation, membership-boundary perturbation, alternative t-norms and defuzzification procedures, and competing ambiguity measures. The results support computational auditability but not clinical validity: the author-specified rules, reliance on ca and thal, small historical dataset, and lack of external cardiologist validation remain important limitations.

Accurate prediction of student academic performance distributions is essential for institutional planning and decision support. This paper introduces a novel Monte Carlo (MC) framework for forecasting cohort-level grade distributions using historical data. The MC model extracts empirical probability distributions and generates predictions via stochastic sampling, explicitly capturing uncertainty and variability in student outcomes. Evaluated across engineering, medical, and aggregated student cohorts, the MC model outperformed a linear regression benchmark, particularly in capturing nonlinear grade transitions and stochastic variability. The MC approach preserves distributional structure and offers superior fidelity in modeling cohort behavior, serving as a reliable, uncertainty-aware complement to traditional machine learning. This work presents a validated probabilistic methodology and evaluation framework for predicting academic performance.

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.

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.

Heterogeneous databases now facing an emerging challenge of moving towards big data. These databases are adhoc polyglot systems, complex, and NoSQL tools which are semantically annotated. Integration of these heterogeneous databases becoming very challenging because of big data analytics is integrating human and machines contexts. In this paper, an attempt is made to study heterogeneous databases and their interoperability issues and integration issues, their impact on analysis of data. The data science has grown exponentially and a new paradigm has emerged which is of integration of heterogeneous data to big data. Information, knowledge and decision making become easier but the size of databases has grown and it became big data.

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.