Faculté des mathématiques et de l'informatique
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Item Sur quelques problèmes d’évolution paraboliques(university of bordj bou arreridj, 2024) ILEAM HEDDADJIThe aim of this thesis is to study the theory of sums of linear operators and the theory of semigroups in Banach space and how to apply them to differential equations and partial differential equationsItem Optimisation par simulation de la gestion de stocks en avenir aléatoire(Université Mohamed El Bachir El Ibrahimi B.B.A., 2021) Larrasse Imane ; Houamed Kenza; Houamed KenzaFor several decades, companies have generally seen their fund inflows decrease. Some companies are required to maintain fairly high inventory levels in order to provide excellent customer service. In such a context, importance of properly managing inventory is crucial. Another interesting fact is that inventory management is no longer perceived as a narrow discipline and simply associated with specific issues such as determining the quantities to order and their delivery times when these are fixed, the resolution is simple if not. This is not the case therefore the structural analysis and the optimal policy calculation, the resolution is by Monte Carlo simulation methods are generally applicable.Item Mod´elisation math´ematique et optimisation du probl`eme de planification de l’emploi du temps.(Université Mohamed El Bachir El Ibrahimi B.B.A., 2024) Assas Ichraq; Sakhraoui HibaThis thesis explores the challenges of timetable planning, focusing on time management in university course timetables. Tests were carried out on different datasets using a programming solver (FICO Xpress), showing encouraging results for the problems. However, it was necessary to simplify the formulation in order to improve the computation time and the quality of the solution for difficulties of medium to large size. In summary, this paper highlights the continuing importance of seeking new approaches to the crucial challenge of timetabling in universities.Item IMAGE SYNTHESIS(2024) SAIFI ABDELHAMIDItem Analysis IV(university of bordj bou arreridj, 2024) FARES BENSAIDItem Analysis III(university of bordj bou arreridj, 2024) FARES BENSAIDItem Development of an online retail platform with multiple sellers and integrated recommendation systems.(university of bordj bou arreridj, 2024) MOHAMADI EL HADJ; HADROUG ABD EL MOUNAIM; NAIT SEGHIR NOUR EL HOUDA; RADJAI SABAHE-commerce in Algeria offers immense growth potential but faces several challenges, including the lack of direct links between sellers and buyers, insufficient features to promote small businesses, and payment limitations. To address these issues, our project aims to develop an innovative e-commerce platform. Our platform will act as an intermediary between manufacturers and buyers, facilitating transactions and ensuring a secure experience for all parties involved. We will implement advanced technologies such as artificial intelligence and recommendation systems to personalize the user experience, boost online sales, and stimulate the local economy by fostering entrepreneurship and small business growth. Our thesis presents a strategic vision to address the gaps in the Algerian e-commerce market, emphasizing technological innovation, customer satisfaction, and local economic developmentItem Fonctionnalités approfondies pour les systèmes de vérification Palmaire(university of bordj bou arreridj, 2024) - KACIMI lina; - TABET chaimaBiometrics is the automated identification of individuals based on their physical and behavioral characteristics. It helps provide certainty when interacting with familiar or unfamiliar people, authorizing the granting of specific rights or the denial of certain privileges. The underlying principle of biometrics is the assumption that each individual has unique physical and behavioral characteristics that distinguish them from others. Improving human identification techniques currently focuses on exploring new and emerging methods. This development is driven by growing security concerns and the emergence of tampering techniques. The goal is to leverage distinct parts of the human body that can be used for accurate identification, such as fingerprints, palm prints, iris and lips. However, many existing systems and methods suffer from slow processing or require expensive technical equipment. Palmprints have proven to be a promising biometric modality for personal identification due to their uniqueness and stability. This master's dissertation presents an in-depth study on the use of deep features for palm print identity verification systems. We have experimented with CNN models for pre-processing of TANTRIGGS, DOG methods and for feature extraction such as BSIF, GABOR. For classification, we used K-Nearest Neighbors (KNN), Support Vector Machines (SVM), ALMO.Item Deep Learning-based Anomaly Detection in Network Traffic Patterns(university of bordj bou arreridj, 2024) HEDJAM Lidia; BELOUAHRI AyaThe anomaly in network traffic is a crucial issue that can cause significant losses in network security and performance. This prompted us to undertake this work to detect these anomalies accurately and promptly using deep learning techniques. This thesis investigates the use of long short-term memory (LSTM) neural networks, one of the deep learning methods, to detect anomalies in network data flows. LSTMs are well suited to this task thanks to their ability to capture long-term temporal dependencies. Our approach is distinguished by its ability to detect complex and varied anomalies, thus improving the security and efficiency of computer networks. The results show a significant improvement over traditional methodsItem Fouille d’épisodes à partir de séquences d’événements : Application à l’analyse de l’historique des visites de pages web(university of bordj bou arreridj, 2024) HEBBOUL AHLEM; MEKHOUKH LAMISIn a constantly evolving digital environment, the analysis of user navigation on websites represents a major challenge for companies wishing to optimize their digital strategy. This dissertation explores the application of the EMDO (Episode Mining under Distinct Occurrence) algorithm to predict user behavior on the Web by analyzing their web page visit sequences. The algorithm offers an innovative approach to extracting episode-based rules based on distinct occurrences, thus improving the accuracy of predictions