Faculté des mathématiques et de l'informatique
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Item 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.Item Khososi(Université Mohamed El Bachir El Ibrahimi B.B.A., 2025) M. Khaldi Mouhib eddine; Mechta Souhil; Nassah AymenThis research presents the design and implementation of an online educational platform called “Khososi”, which was developed to facilitate distance learning and collaborative education. The platform aims to provide a dynamic and interactive environment for teachers and students to share educational resources, deliver virtual lessons, and assess learners. The Khososi platform was developed using HTML, CSS, PHP, and MySQL technologies to ensure simplicity, ease of use, and adaptability to different learning contexts. This research highlights the pedagogical and technological aspects of e-learning, with a focus on the importance of usability and interactivity to enhance the user experience in the digital learning environment. The project also explores future prospects, including the integration of artificial intelligence technologies, strengthening security, and providing multilingual access, thereby ensuring the achievement of sustainable development in modern education.Item Stochastic viability under the flow of a stochastic equation (Itô - Stratonovich) and application "the kubo oscillator equation"(Université Mohamed El Bachir El Ibrahimi B.B.A., 2025) Yazid AyaIn thiswork,we study the invariance property of the solution of the Kubo oscillator equation under a stochastic model, following a review of stochastic calculus and the concepts of invariance and viability in both deterministic and stochastic settings.Item A Hybrid Genetic Algorithm to Solve the Container Loading Problem in Condor Logistics(Université Mohamed El Bachir El Ibrahimi B.B.A., 2025) Benabida Sif EddineItem Similarité globale pour la prédiction de liens dans les réseaux complexes(Université Mohamed El Bachir El Ibrahimi B.B.A., 2025) Ouali Aya; Zitouni RayaneAbstract This thesis addresses the problem of link prediction in complex networks, a critical task for anticipating the emergence of connections between entities. We specifically focus on global similarity methods, which leverage the entire network structure to estimate the likelihood of a link between two nodes. Five methods are studied and compared : Shortest Path, SimRank, Newton’s Gravitational Law Index (NGLI), Katz Index, and Common Neighbor Distance (CND). After presenting the theoretical foundations of graph theory and complex networks, we implemented these methods using Python and applied them to several real-world networks from different domains (biology, transportation, social networks, etc.). The performance of each method was evaluated using standard metrics such as precision, recall, F-measure, and accuracy. The results show that each method has its strengths depending on the network structure, and no single method consistently outperforms the others. This study thus provides valuable insights to guide the choice of link prediction techniques based on specific application contexts.Item Etude du problème d’optimisation de la distribution d’eau Cas de la ville de Bordj Bou Arréridj(Université Mohamed El Bachir El Ibrahimi B.B.A., 2025) Anfel BEN ABBES; Houda TAIAROptimizing water distribution is a crucial challenge in urban planning and infrastructure management. This work is part this strategic axis, which deals with the problem of optimizing water distribution in the city of Bordj Bou Arreridj, by reducing the energy consumption of pumps while ensuring an efficient distribution plan. This problem was modeled as a linear program composed of two objective functions and several constraints that guarantee pumping optimization while satisfying the needs of all areas of the city of Bordj. For the resolution, we used the Python programming language and the package "Pulp". The results obtained show the optimization made on the current energy bill.Item Allocation efficace des ressources hospitalières à l’aide de techniques d’optimisation Étude de cas : Hôpital Bouzidi Lakhdar(Université Mohamed El Bachir El Ibrahimi B.B.A., 2025) Silya Ouidir; Fatiha KraifaThis research addresses one of the most important operational issues, namely the question of resource allocation in emergency departments. This challenge consists in allocating resources in accordance with needs, taking into account patient severity. The main objective of this work is to improve the management of human and material resources in emergency departments. A resource allocation system has been adopted as the main mechanism in the proposed mathematical model. This model is based on integer linear programming to improve the distribution of resources while integrating the degree of severity of the patient’s condition. The resolution of the model was carried out using an intlinprog function under MATLAB software. The results obtained show a significant reduction in resource consumption, as well as a significant improvement in the organization of patient flow. To evaluate the effectiveness of the proposed methodology, real data were used, collected from the Bouzidi Lakhdar Hospital, located in the wilaya of Bordj Bou Arreridj.Item Agrinity - Smart Agriculture System(Université Mohamed El Bachir El Ibrahimi B.B.A., 2025) SAID HADDAD abdel hakim; NOUIOUA djamel eddine islemAbstract This project presents the design and implementation of a Smart Agriculture System aimed at improving agricultural efficiency through the integration of hydroponic cultivation, IoT technologies, and Operations Research (OR) methods. Using a mobile application connected to an ESP32 microcontroller, the system enables real-time monitoring and control of key environmental parameters such as pH, temperature, water level, and irrigation cycles. The goal is to maximize crop yield while minimizing resource consumption (water, energy, fertilizers).This smart agriculture solution offers a replicable, scalable, and eco-friendly alternative to conventional farming, paving the way for future developments in precision agriculture in Algeria and similar environments.Item Intelligent algorithms for feature selection in supervised and unsupervised classification(University of Mohamed El Bachir El Ibrahimi - Bordj Bou Arréridj, 2026) Khaoula Zineb LegouiThe growing availability of high-dimensional datasets across various domains has made feature selection a critical step in machine learning pipelines, as reducing irrelevant or redundant features enhances model interpretability and generalization. Due to the combinatorial nature of feature selection, traditional meth ods often lack the scalability and adaptability required for real-world problems. In response, this thesis investigates the application of intelligent metaheuristic algorithms to feature selection in both supervised and unsupervised learning settings. First, a comparative analysis of the Equilibrium Optimizer (EO) and Henry Gas Solubility Optimization (HGSO) algorithms is conducted for supervised classification tasks. Both algorithms are adapted to a binary feature space and evaluated on benchmark datasets using classification accuracy and feature reduction as performance criteria, highlighting their respective strengths and motivat ing a hybrid approach. Consequently, this thesis proposes HGSOEO, a hybrid algorithm that integrates the complementary exploration and exploitation capabilities of HGSO and EO. The proposed HGSOEO algo rithm is evaluated on the Twitter Spam Detection dataset and demonstrates superior performance in terms of classification accuracy and the number of selected features when compared to conventional metaheuristic and classical feature selection methods. Furthermore, the application of EO is extended to feature selection for clustering tasks, where labeled data are unavailable, by employing clustering validity criteria such as the Adjusted Rand Index (ARI) to guide the selection process. Experimental results across multiple datasets confirm the effectiveness and robustness of the proposed approaches. Overall, the findings of this thesis demonstrate that intelligent metaheuristic algorithms provide efficient and scalable solutions to the feature selection problem in both supervised and unsupervised learning contexts.