University of Bordj Bou Arreridj
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- كلية الحقوق والعلوم السياسية
- كلية الآداب واللغات
- كلية الرياضيات و الأعلام الآلي
- كلية علوم الطبيعة و الحياة و علوم الأرض و الكون
- كلية العلوم الاقتصادية والتجارية وعلوم التسيير
- Faculté des sciences et de la technologie
- كلية العلوم الاجتماعية والإنسانية
Recent Submissions
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 Karima
Les 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.
Eye tracking in simple visual search tasks
(university of bordj bou arreridj, 2025) BOUHADDA KENZA; BOUDIAF FELLA
Eye 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.
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 Chahinez
In 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 integrity
Machine Learning for Misbehavior Detection in Next-Generation Vehicular Networks
(university of bordj bou arreridj, 2025) MADI Ahmed Salah Eddine; MEKHFI Baya
Connected 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.
التحكيم كآلية لتسوية منازعات الاستثمار
(مكتبة كلية الحقوق و العلوم السياسية جامعة محمد البشير الابراهيمي برج بوعريريج, 2025-11-13) مناصرية سارة; خبابة ريان
التحكيم كألية يُعد من أبرز الوسائل البديلة لتسوية منازعات الإستثمار،لإكتسابه مكانة متميزة في مجال منازعات الإستثمار بفضل مرونته وفعاليته في معالجة وفض النزاعات المعقدة التي تنشأ بين المستثمر الأجنبي والدولة المضيفة، وما يقدمه من مزايا وضمانات لكلا الأطراف المتعاقدة، برزت أهميته خاصة مع تطور العلاقات الإقتصادية الدولية والحاجة إلى وسيلة قانونية تضمن للمستثمر الحماية الكافية لحقوقه، وفي ذات الوقت تضمن للدول الحفاظ على سيادتها ومصالحها، وما زاد من إبراز أهميته هو إدراجه من خلال العديد من الإتفاقيات والمراكز الدوليةوبهذا أصبح التحكيم اليوم إحدى الركائز الأساسية في نظام الحماية القانونية للاستثمارات كما أصبح محور ثقة بين المستثمر والدولة، مما يساهم في دعم التنمية الإقتصادية والحدّ من نشوب النزاعات الناشئة عن الإستثمار.
الكلمات المفتاحية :المستثمر الأجنبي، الدولة المضيفة، الاتفاقيات الدولية، حماية القانونية.
Summary :
Arbitration, as a mechanism, is considered one of the most prominent alternative means for settling investment disputes. It has gainrd a distinguished position in this field due to its flexibility and effectiveness in resolving complex disputes that arise between foreign investors and host states, offering advantages and guarantees to both contracting parties. Its importance has particularly emerged with the development of international economic relations and the growing need for a legal instrument that ensures sufficient protection of investors’ rights, while at the same time safeguarding the sovereignty and interests of states. Its significance has further been reinforced through its incorporation in numerous international agreements and arbitration centers.
Thue, arbitration has become one of the fundamental pillars of the legal protection system for investments. It has also became a contributing to the support of economic development and the reducyion of investment-related disputes.
Keywords:foreinginvestor,host state, international agreements, legal protection.