 |
Volume 41/45 - Special issue JRI 2023 - 2026
This special issue is associated with the "Journée de Recherche en Informatique" (JRI 2023), the main annual Computer Science Research Days. This special issue focuses on applications of computer science research in relevant domains in Africa as (1) Cyber-security (Internet of Things, Blockchain, image processing) to fight against terrorism ; (2) precision agriculture (Internet of Things) to improve productivity ; (3) Water and sanitation with Internet of things and big data analytics ; (4) Health to establish algorithms in medical imaging ; (5) Education (e-learning and m-learning).
1. Formalizing PI Planning in Scaled Agile with Model-Driven Engineering: A Metamodel and Framework for Strategic Alignment
Program Increment (PI) Planning is a critical synchronization event in scaled Agile methodologies, yet remains largely informal, with limited analytical rigor or strategic alignment. This paper introduces a formal metamodel and a model-driven framework to support structured PI Planning in large-scale software development. Our metamodel captures essential planning artifacts, including features, teams, iterations, risks, and dependencies, and is enriched with OCL constraints for validation and traceability. We extend the model to align planning with business value streams and introduce stakeholder-specific viewpoints for tailored analysis. Compared to tools like Jira or Mural, our approach provides formal semantics, automated reasoning, and extensibility. This work lays the foundation for a Domain-Specific Language (DSL) for PI Planning, bridging the gap between Agile practice and formal modeling.
2. Comparative Analysis between Generalized Hough Transform and Neural Networks in Fingerprint Recognition
This study evaluates and compares the performance of two prominent fingerprint recognition methods: the Generalized Hough Transform (GHT) and Siamese neural networks. Using the diverse and well suited CASIA database, we conducted an exhaustive comparison on a representative sample of 628 fingerprints per model.Siamese neural networks demonstrated exceptional accuracy, surpassing 99.2\%. Their efficiency in processing large datasets highlights their suitability for biometric applications requiring detailed analysis. In contrast, the Generalized Hough Transform achieved a strong accuracy of 97.7\%, proving its reliability even with more constrained data.Our analysis underscores the distinct strengths of each method, particularly the adaptability of Siamese networks to complex fingerprint variations. Their robust generalization capabilities make them ideal for large-scale biometric systems.Performance was assessed using key metrics, including the confusion matrix (visualizing predicted vs. actual matches) and the F1-score (balancing precision and recall).These findings advance our understanding of biometric security challenges and suggest promising directions for future research in this rapidly evolving field.
3. Prediction of Water Consumption Using Machine Learning Algorithms
The objective of this study is to propose solutions for predicting drinking water consumption over aperiod of 5 days. To achieve this, machine learning methods such as neural network algorithms areused to confront different results in order to obtain a model that optimizes daily water consumptionthe most. The algorithms used include Multi-Layer Perceptron (MLP), Simple Recurrent NeuralNetwork, Long Short-Term Memory (LSTM) recurrent neural network, and Gated Recurrent Unit(GRU) recurrent neural network. Since the data comes from multiple sources, the algorithms areused to train models on each dataset. At the end of this study, the model that optimizes the best isselected for each dataset after comparing the results of different algorithms. Based on the variousresults obtained, the performance of the algorithms varies depending on the periods and the data.For example, the best performance in predicting consumption in the KUA area on the J+5 dayperiod is attributed to the MLP algorithm based on mean absolute errors (MAE).