Master Informatique
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Item Machine Learning for Misbehavior Detection in Next-Generation Vehicular Networks(university of bordj bou arreridj, 2025) MADI Ahmed Salah Eddine; MEKHFI BayaConnected vehicles have great potential to enhance road safety, reduce traffic congestion, and play a vital role in green engineering by reducing pollution and fuel consumption. By enabling more efficient traffic flow, eco-routing, and optimized driving behaviors, connected vehicles contribute to a cleaner environment. However, when a vehicle is compromised, it can pose a serious threat to the entire network due to the potential harm it can cause. One of the major challenges in vehicular networks is the detection of misbehaving vehicles, which should then be blacklisted or their certificates revoked. In this work, we propose a novel scheme that leverages machine learning to accurately detect and classify vehicle behavior, enabling effective identification and management of misbehaving vehicles. To assess the effectiveness of our approach, a comprehensive comparative analysis was performed. The results demonstrate that our model outperforms existing methods in accurately classifying vehicle behaviors, highlighting its potential for real-world deployment in securing vehicular networks.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 Impact des techniques de prétraitement sur la performance des modèles de classification du diabète.(university of bordj bou arreridj, 2025) Dif marwa Mahmoud; Zineb GhezlaneDiabetes is a chronic disease for which early diagnosis is crucial to prevent serious com plications. In this work, we study the impact of preprocessing techniques on the performance of classification models applied to diabetes data. To this end, we use two medical datasets : the Pima Indians dataset and a local dataset from Iraq. We evaluate three classification algo rithms : logistic regression, support vector machines (SVM), and decision trees. We apply two normalization techniques (MinMaxScaler and StandardScaler) and three feature selection me thods (SelectKBest, GenericUnivariateSelect, SelectFromModel). The results, evaluated using cross-validation, show that a well-chosen preprocessing strategy significantly improves model accuracy, with varying performance depending on the nature of the data and the algorithm used.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.