Faculté des mathématiques et de l'informatique
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Item Distributions Course & Exercises(University of Mohamed El Bachir El Ibrahimi - Bordj Bou Arréridj, 2026) Debbiche HaneneItem OPTIMISATION(Faculté des Mathématiques et Informatique, 2025) Smail AddouneItem Etude d’un problème d’optimisation à deux niveaux multicritères(University of Mohamed El Bachir El Ibrahimi - Bordj Bou Arréridj, 2026) Wafa BouguernThis thesis focuses on developing new methods for solving bilevel programming problems and multicriteria optimization problems, through algorithms based on Difference of Convex Functions (DC) programming with regularization, as well as a metaheuristic approach combining particle swarm optimization and grey wolf optimization, using dense curves to simplify the formulation. Numerical results demonstrate high effectiveness in terms of accuracy and computational time, and the approach is extended to multicriteria problemsItem Differential Systems for Biology(University of Mohamed El Bachir El Ibrahimi - Bordj Bou Arréridj, 2026-05-14) Salhi TayebThis lesson is presented for first-year Master’s students in Dynamical Systems. It introduces the basic principles of modeling via differential equations and develops three major biological applications: infectious disease models, predator–prey systems, and competition between species. The emphasis is on assumptions, model construction, equilibrium analysis, qualitative interpretation, and mathematical reasoningItem Optimisation multi-objectifs : Étude théorique et algorithmique(university of bordj bou arreridj, 2026) Ramdani ZoubirCette thèse traite de l’optimisation multi-objectif sous contraintes linéaires (LC-MOO), dont l’objectif est d’approximer efficacement le front de Pareto. Elle propose une amélioration d’une méthode de directions réalisables reposant sur deux contributions complémentaires : d’une part, la reformulation du sous-problème de recherche de direction au moyen d’un cône de directions réalisables réduit ; d’autre part, l’introduction d’une procédure d’échantillonnage Hit-and-Run permettant de générer une population initiale mieux répartie dans le polyèdre réalisable. La convergence théorique de l’approche est établie, et une validation numérique réalisée sous MATLAB avec la boîte à outils MPT3, sur 25 problèmes LC-MOO, compare la MDR proposée à une autre MDR ainsi qu’à NSGA-II. Les résultats mettent en évidence une amélioration significative tant en qualité d’approximation du front de Pareto qu’en rapidité de convergence.Item Cours notes- Stochastic Processes(university of bordj bou arreridj, 2025) ZEGHDANE REBIHAItem PROBABILITY AND STATISTICS(university of bordj bou arreridj, 2026) ZEGHDANE REBIHAItem The Use of big data and data analytics in the prevention, Diagnosis and prediction of long term diseases.(university of bordj bou arreridj, 2026) MECILI OUALIDThe increasing prevalence of long-term diseases, particularly diabetes, presents significant chal lenges to global healthcare systems. Early prediction, accurate diagnosis, and continuous mon itoring are crucial for improving patient outcomes and reducing healthcare costs. This thesis explores the use of Big Data and data analytics in the prevention, diagnosis, and monitoring of long-term diseases, focusing specifically on diabetes. The core objective is the development of an integrated system that supports individuals throughout the disease lifecycle. The proposed system is structured into three main phases: first, the creation of predictive algorithms capa ble of estimating an individual’s risk of developing diabetes within a ten-year period; second, the application of explainable neural networks to diagnose diabetes based on retinal imaging, ensuring transparency and trust in AI-driven decisions; and third, the development of a digital platform to continuously monitor patients, facilitating proactive management and personalized care. By leveraging machine learning, Big Data technologies, and explainable AI, this work aims to contribute to a more predictive, preventive, and participatory healthcare model for chronic disease managementItem Modélisation de la diffusion de l’innovation dans les réseaux sociaux(University of Mohamed El Bachir El Ibrahimi - Bordj Bou Arréridj, 2026) Rima BenfredjRecently, the diffusion of innovation has seen a paradigm shift and emerged as a renewed and interesting field due to advances in artificial intelligence, behavioral modeling, and empirical simulation. Understanding adoption behavior is increasingly complex, as individuals’ decisions are influenced not only by innovation attributes and social pressure but also by psychological characteristics, especially personality traits. This thesis offers a personality-driven diffusion model that integrates Big Five personality framework (OCEAN) into the Rogers' Diffusion of Innovation theory. Using agent-based modeling (ABM), individuals are presented as agents having distinct profiles, which shapes their perception of innovation features (relative_advantage, compatibility, complexity, trialability, observability). The framework consists of four major phases: perception, communication, persuasion, and decision. The special feature of the present research is the two-phase modeling strategy: (1) an exploratory simulation with randomly generated values in order to understand the fundamental diffusion dynamics. (2) the model is applied empirically, by applying a hybrid BERT-Random Forest model to predict Twitter users' personality traits based on ChatGPT-related data, these extracted qualities are subsequently used to regenerate the values of the remaining model' attributes and simulate adoption processes. The findings demonstrate that actually adoption behavior is profoundly influenced by personality driven dimensions, resulting in more realistic adoption curves than traditional models. This approach opens up new perspectives for the behavioral study of innovation diffusionItem Conception et développement d’une plateforme intelligente de soins médicaux à domicile.(Université Mohamed El Bachir El Ibrahimi B.B.A., 2025) - Bengrine Abderrahmane; Hammache Riadh; Djelloul Raid; Benmessaoud Wassim; Slimane Hadjrioua; Firas BensalemDigital services play a crucial role in modernizing the healthcare sector, where efficiency, traceability, and coordination are key. In Algeria, home nursing care still faces a lack of advanced technological tools. The MediCall project aims to bridge this gap by developing an intelligent and secure digital platform dedicated to managing and booking home nursing services. The platform acts as a reliable intermediary between patients and nurses, automating the care process from the initial request to payment. It consists of three interconnected applica- tions : a web application for administration and supervision, a mobile application (Flutter) for patients and nurses, and a desktop application for medical coordinators to manage and monitor operations. The system also integrates an intelligent server powered by artificial intelligence, featuring a medical chatbot capable of providing accurate, contextualized responses — enhancing user experience and service efficiency.