Forests are the lungs of our planet. Conserving the plants may require the development of an automated system that will identify plants using leaf features such as shape, color, and texture. In this paper, a leaf shape descriptor based on sinuosity coefficients is proposed. The sinuosity coefficients are defined using the sinuosity measure, which is a measure expressing the degree of meandering of a curve. The initial empirical experiments performed on the LeafSnap dataset on the usage of four sinuosity coefficients to characterize the leaf images using the Radial Basis Function Neural Network (RBF) and Multilayer Perceptron (MLP) classifiers achieved accurate classification rates of 88% and 65%, respectively. The proposed feature extraction technique is further enhanced through the addition of leaf geometrical features, and the accurate classification rates of 93% and 82% were achieved using RBF and MLP, respectively. The overall results achieved showed that the proposed feature extraction technique based on the sinuosity coefficients of leaves, complemented with geometrical features improve the accuracy rate of plant classification using leaf recognition.

This study examines the application of Educational Data Mining (EDM) to predict the academic per-formance of first-year students at the Catholic University of Bukavu and the Higher Institute of Edu-cation (ISP) in the Democratic Republic of Congo. The primary objective is to develop a model that can identify at-risk students early, providing the university with a tool to enhance student support and academic guidance. To address the challenges posed by data imbalance (where successful cases outnumber failures), the study adopts a hybrid methodological approach. First, the SMOTE algorithm was applied to balance the dataset. Then, a stacking classification model was developed to combine the predictive power of multiple algorithms. The variables used for prediction include the National Exam score (PEx), the secondary school track (Humanities), and the type of prior institution (public, private, or religious-affiliated schools), as well as age and sex. The results demonstrate that this approach is highly effective. The model is not only capable of predicting success or failure but also of forecasting students' performance levels (e.g., honors or distinctions). Moreover, the use of the Apriori association rule mining algorithm allowed the identification of faculty-specific success profiles, transforming prediction into an interpretable decision-support tool. This research makes several significant contributions. Practically, it provides the University of Bukavu with a tool for student orientation and early risk detection. Methodologically, it illustrates the effectiveness of a combined approach to EDM in an African context. However, the study acknowledges certain limitations, including the non-public nature of the data and the geographical specificity of the sample. It therefore proposes avenues for future research, such as the integration of Explainable AI (XAI) techniques for more refined and transparent analysis of the results.