Bienvenue sur la plateforme DSpace de l’ESTIN
Nous sommes ravis de vous accueillir sur DSpace, la bibliothèque numérique officielle de l’École Supérieure en Technologies de l’Information et de la Communication (ESTIN).
Notre plateforme est dédiée à la gestion, la préservation et le partage des ressources académiques et scientifiques produites par notre communauté. Vous y trouverez :
- Des mémoires et projets de fin d’études.
- Des publications scientifiques et rapports de recherche.
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- المؤتمرات والندوات واللقاءات وأيام الدراسات
- الأمن السيبراني
- الذكاء الاصطناعي و علوم البيانات
Recent Submissions
Review of Techniques for Optimizing Intelligent Video Recording Using Activity Detection in Surveillance Systems
(Tassadit, 2025-01-25) MELIZOU Ouassila; TOUATI Hayet
This master thesis presents a comprehensive review of various activity detection methods that can be used in surveillance systems to capture relevant footage. The review encompasses twenty articles, covering a wide range of intelligent video system approaches. The key methodologies examined include background subtraction, optical flow, machine learning techniques such as Support Vector Machines, and deep learning techniques including You Only Look Once, Convolutional Neural Networks, and Faster Region-based Convolutional Neural Networks. Each method is thoroughly explained, with an emphasis on their respective strengths and weaknesses. The analysis provides insights into the current state of the art and identifies potential areas for future research and development in surveillance systems.
Literature Review on energy Efficiency Wireless Sensor Net Works
(Tassadit, 2025-01-25) BOUTAOUI Nada
Energy efficiency in WSNs is a problem of resource optimization among sensor nodes that needs to meet the operational constraints for maximum network lifetime and performance.
In essence, achieving optimal energy efficiency in WSNs means bing capable of balancing power consumptions, quality of data, and reliability of networks within various deployment scenarios. This issue has become increasingly complicated due to the rapid growth of IoT applications, heterogeneous sensor technologies, and dynamic operation environments. Because of this, it has become a topic of top priority for researchers who are working on the development of proficient methods so that energy management can be done easily and effectively by WSNs operators and designers. The present research work includes a bibliographic study on different approaches followed in WSN energy optimization, focusing on the most recent advances that have incorporated ML and Graph-based techniques. In the following, we will talk about some classic energy-saving approaches, modern ML techniques including Reinforcement Learning (RL), and the potential of Graph Convolutional Networks (GCNs) and Federated Learning (FL) in addressing energy efficiency challenges in WSNs.
State-of-the-Art Analysis : Exploring the Integration of Visual Data for Enhancing the Accuracy and Reliability of Recommender Systems
(Tassadit, 2025-01-25) TOUATI Yanis
This dissertation presents a comprehensive analysis of recommender systems, focusing on the integration of visual data to enhance recommendation accuracy and reliability. Chapter One provides a General introduction to recommender systems, emphasizing their importance in our nowadays technologies. Chapter Two explores the state-of-the-art in recommender systems, discussing various methodologies and advancements, with a particular focus on the incorporation of visual data. The chapter highlights the transformative potential of visual data integration in shaping personalized recommendation experiences across different platforms and domains and then we conclude the study by synthesizing the the summarizing of the State-of-the-Art and the challenges associated with visual data integration.
Recommender systems for multimodal transportation systems in smart cities
(Tassadit, 2025-01-25) Madadi Mounia
Traffic Management Optimization in the Presence of Automated Vehicles
(Tassadit, 2025-01-25) Cheima MEZDOUR
Traffic congestion on freeways is a significant issue today due to the high volume of vehicles. This congestion impacts traffic flow and can lead to severe delays. Effective traffic management methods and solutions are essential to mitigate this problem and enhance safety for all road users. One specific issue in freeway traffic congestion is the problem of ramp metering. This occurs when vehicles merge from an entrance ramp onto the main freeway. Any malfunction or inefficiency in the traffic light system at these ramps can cause significant congestion and even lead to accidents. By implementing a sophisticated decision-making system that follows a realworld network, we can develop better policies to address this issue. This approach can optimize traffic flow, reduce congestion, and improve overall road safety. We proposed a deep q-learning algorithm to address this problem, which guarantees better learning policies and adapt to changing traffic conditions in real-time, learning optimal actions to take at different states of traffic flow in complex environments. Deep Q-Networks(DQNs) can be used across multiple ramps, so this can improve the scalability of the model. So during this study, we will present the performance of this algorithm compared to traditional systems in improving the efficiency of traffic control.