Faculté des mathématiques et de l'informatique
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Item At the heart of (Io)RT-E ecosystems(university of bordj bou arreridj, 2025) MAZA AbdelouahabOne of the major research areas in artificial intelligence focuses on designing and improving a robot’s cognitive capabilities. This involves enabling robots to accurately interpret human behavior and intentions based on their perception of the environment. To achieve this, it is essential not only to understand human intentions, but also to anticipate the causal effects of elementary and complex actions and their consequences within a given context. Modes of action preparation and emotions play an important theoretical role in this pro cess. Frijda explains that the way individuals perceive and appraise events triggers different modes of action preparation. Similarly, psychologist James J. Gibson describes interaction with the environment through the concept of affordances, which guide action. Consequently, the contribution of artificial intelligence to modeling contextual understanding in (Io)RT-E ecosystems is undeniable. Action recognition remains a critical research challenge, particularly in the field of hu man–robot interaction. Many questions remain unresolved, especially those related to un derstanding human behavior and anticipating future actions. This requires making spatio temporal projections, predicting multiple possible futures, and inferring the effects of actions based on the current or inferred context. When a robot lacks information, it must adapt by enriching its knowledge through the properties of observed actions. This process involves endowing robots with social cognitive capabilities that enable them to engage in joint actions with humans. In this context, we refer to joint human–robot agency. This thesis proposes a hybrid framework that combines semantic annotation, spatio temporal ontological modeling, narrative reasoning using NKRL, and reinforcement learning for adaptive human activity recognition. The proposed approach enables contextual, tempo ral, and causal interpretation of human actions. Experimental results conducted in Internet of Everything (IoE) environments demonstrate improved robustness, adaptability, and ac curacy compared to classical activity recognition approaches.Item Lecture notes on Text and Web Mining(University of Mohamed El Bachir El Ibrahimi - Bordj Bou Arréridj, 2025) SABRI LyazidItem Lecture notes on Ontology and Semantic Web(University of Mohamed El Bachir El Ibrahimi - Bordj Bou Arréridj, 2025) SABRI LyazidItem Lecture notes on Internet of Things course(University of Mohamed El Bachir El Ibrahimi - Bordj Bou Arréridj, 2025) SABRI LyazidItem Profilage hybride et mise à jour adaptative des profils utilisateur pour la recommandation Personnalisée dans les plateformes e-Learning(university of bordj bou arreridj, 2025) Bouafia Amani; Charifi KarimaLes systèmes de recommandation ont énormément amélioré l’expérience utilisateur sur internet. En particulier, les systèmes de recommandation dans l’e-Learning ont joué un rôle clé en aidant les étudiants à découvrir de nouveaux cours pertinents, basés sur des facteurs spécifiques et leur comportement sur la plateforme. Notre objectif principal est de créer un système de recommandation hybride en combinant deux modèles : l’approche basée sur le contenu et l’approche de filtrage collaboratif. Le principal problème pour les étudiants lorsqu’ils étudient en ligne est qu’ils sont exposés à une grande quantité de données qui peut nuire à leur réussite académique notre algorithme améliore l’apprentissage automatique, et les résultats démontrent son efficacité en termes de qualité et de pertinence .L’apprentissage automatique aidera notre système à comprendre le comportement des étudiants grâce a de nombreuses méthodes ce qui permettra d’obtenir des informations sur le contenu le plus susceptible de leur être pertinent, cette recherche constitue une modeste contribution au domaine des systèmes de recommandation et met en lumière leur potentiel à améliorer l’expérience et la productivité des étudiants.Item Eye tracking in simple visual search tasks(university of bordj bou arreridj, 2025) BOUHADDA KENZA; BOUDIAF FELLAEye tracking has become an essential technique for understanding human visual attention and behavior across a wide range of fields. One of the core challenges in this domain is ac curately predicting gaze during visual search tasks. As traditional models using handcrafted features often lack generalizability,Recent advances in deep learning offers a powerful alterna tives by enabling data-driven learning of complex spatial patterns in visual attention. This research introduces a deep learning-based eye-tracking system aimed at predicting vi sual attention through saliency maps, using the uEyes dataset which features a variety of image categories including desktop, mobile, web, and posters, along with corresponding human eye tracking data . the system employs a U-Net convolutional neural network optimized for pixel level saliency prediction. The model is trained and evaluated using a robust set of performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Kullback-Leibler Divergence (KLD), Correlation Coefficient (CC), Histogram Similarity (SIM), and Accuracy. This system showcases the effectiveness of deep learning in modeling human visual behav ior in visual search tasks.Item Détection automatique des faux comptes sur les réseaux sociaux à l’aide de l’apprentissage profond(university of bordj bou arreridj, 2025) Boumaiza Amdjad; Mohammedi ChahinezIn the digital age, social media platforms have become essential tools for communication, information dissemination, and brand visibility. However, this widespread use has given rise to a growing concern: the proliferation of fake accounts, particularly on Instagram. These inauthentic profiles, often automated or maliciously crafted, pose serious threats to user security, distort engagement metrics, and serve as vehicles for disinformation and fraudulent activities. To address this challenge, this thesis presents a deep learning-based approach using Long Short-Term Memory (LSTM) neural networks, which are well-suited to modeling the sequential and behavioral data of social media users. A synthetic dataset representing Instagram accounts was used to train and evaluate the model. The results highlight the method’s ability to accurately classify accounts as genuine or fake, offering strong performance metrics and promising generalization capabilities. This research contributes to the broader field of cybersecurity and illustrates the potential of artificial intelligence in detecting online threats and enhancing digital platform integrityItem 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 Autism Spectrum Disorder Detection Using Deeplearning Techniques(university of bordj bou arreridj, 2025) Benyzid Slimane; Fareh SamirThis project aims to develop an intelligent system for detecting autism spectrum disorder (ASD) using deep learning techniques. Autism is a complex condition that affects communica tion and behavior, and early diagnosis is critical for effective, appropriate, and prompt interven tion. The system uses convolutional neural networks (CNNs), such as MobileNet and VGG19, to classify individuals as having or not having autism based on face images and eye-tracking data. Apublicly available Kaggle dataset containing images representing typical visual behavior of individuals with ASD was used. The data was preprocessed through resizing, normalization, and augmentation to improve model performance. The model was evaluated using precision, accuracy, recall, F1 score, and ROC-AUC. Theresults demonstrated high performance and outperformed traditional methods, demons trating the model’s effectiveness in detecting autism. This project highlights the role of artificial intelligence in advancing healthcare by enabling faster and more accurate diagnosis of complex conditionsItem Catégorisation multi-étiquette des textes arabes(university of bordj bou arreridj, 2025) Bendib Yassamine; Bouaziz MeriemCe mémoire porte sur la classification multi-étiquettes des textes arabes à l’aide de techniques d’apprentissage profond. La langue arabe présente plusieurs défis, notamment une complexité morphologique élevée, une richesse lexicale importante et une grande variabilité dialectale. Ce travail se concentre sur l’utilisation de réseaux de neurones profonds, en particulier le modèle convolutionnel (CNN), BiLSTM et le modèle pré-entraîné AraBERT, afin d’améliorer la précision du classement. Le mémoire aborde les étapes clés du traitement automatique de la langue arabe, les méthodes de représentation des textes, ainsi que les métriques d’évaluation adaptées à la classification multi-étiquettes. Une attention particulière est portée à l’apprentissage par transfert, qui permet de tirer parti de la puissance des modèles linguistiques pré-entraînés pour la langue arabe