Faculté des mathématiques et de l'informatique
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Item Classification des Signaux EEG basée sur l’Apprentissage automatique et l'apprentissage profond(university of bordj bou arreridj, 2025) Temhachet Rayane; Laichaoui YasminaApproximately 50 million people worldwide suffer from epilepsy, a chronic neurological disorder. The automatic detection of epileptic seizures from EEG (electroencephalogram) signals remains a major challenge for researchers. This study proposes a methodology for classifying EEG signals using machine learning techniques, including Support Vector Machines (SVM), k-Nearest Neighbors (KNN), the Naive Bayes (NB) algorithm, and a Convolutional Neural Network (CNN). To evaluate model performance, metrics such as Accuracy (AC), Sensitivity (SE), Specificity (SP), and Receiver Operating Characteristic (ROC) curves were used. The results demonstrate the effectiveness of the applied models, particularly the CNN, in the automated detection of epilepsy from EEG signals.Item Brain Tumor Detection Using U-Net and SVM(university of bordj bou arreridj, 2025) BENGUEZZOUMohammed; BENYAHIAOUI Mohamed AssilBrain tumors, particularly gliomas, pose a significant clinical challenge, requiring both precise localization and accurate grading to guide treatment. Accurate segmentation of tumor regions is a critical first step, enabling meaningful analysis and interpretation of the affected areas. In this project, we present a hybrid framework that first segments tumor regions in brain Magnetic Resonance Imaging (MRI) scans using a U-Net model trained on the Brain Tumor Segmentation dataset, and then classifies these regions as Low-Grade or High-Grade Gliomas with a Support Vector Machine (SVM) model based on features extracted from the segmented masks. On the held-out test set, our U-Net achieved an accuracy of 99.3%, while the SVM classifier delivered an overall accuracy of 93%.Item Classification thématique des textes multilingue Etude de cas dans le domaine de sport(university of bordj bou arreridj, 2025) - ABERKANEAyoub; ATTIA AsmWith the rise of digital development and the growing volume of textual content published daily, particularly in the sports domain, the need to organize such content has become increasingly important. This study aims to process multilingual sports texts using natural language processing and machine learning techniques, in order to classify them according to the topics they address. To standardize the linguistic processing of multilingual texts, the automatic translation model NLLB was used to translate the content into English, which contributed to improving the thematic segmentation of the texts. Several supervised algorithms were applied, including Naive Bayes, Support Vec tor Machine SVM,andMultilayer Perceptron MLP, on a sports dataset collected from the Kaggle platform. After data cleaning and converting the texts into numerical rep resentations using the TF-IDF algorithm, the models were trained and compared. Re sults showed that SVM and MLP achieved the best performance in terms of accuracy, while the Naive Bayes model stood out for its execution speed. This study demon strates the effectiveness of multilingual thematic classification in the sports domain and paves the way for future improvements using more advanced language models.Item 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.