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 La Segmentation et La Détection des images IRM pour identifier les tumeurs cérébrales par l’apprentissage profond(university of bordj bou arreridj, 2025) Derradj Zoubir; Derradj ZoubirLe traitement des images médicales est devenu un domaine central dans l’aide au diagnostic. Dans ce mémoire, nous proposons une approche automatique basée sur l’ap prentissage profond pour détecter et segmenter les tumeurs cérébrales à partir d’images IRM. Deux architectures de réseaux neuronaux convolutifs (CNN) ont été utilisées : In ceptionV3 pour la classification binaire et ResUNet pour la segmentation. L’ensemble de données utilisé provient de Kaggle. L’évaluation a montré une précision de 92.7% pour la classification et un Dice de 0.85 pour la segmentation. Ces résultats prouvent l’efficacité de notre méthode et son potentiel pour des applications médicales réelles.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.