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Yasmine Amor

Informations générales

Yasmine Amor
Grade

Post Doc

Biographie courte

Dr. Yasmine Amor obtained her PhD within the framework of an international thesis co-supervision between the University of Tunis and Normandy University. Her research focuses on tackling the challenge of road traffic congestion, with a particular emphasis on real-time estimation and prediction of traffic conditions. She earned her Master’s degree in Science and Technology of Decision Making from the Higher Institute of Management of Tunis (2020).

Équipes

Axes de recherche

Artificial Intelligence
Deep Learning
Multi Agent System
intelligent transportation
Graph Theory
Machine Learning

Publications

  • 2024
    Yasmine Amor, Lilia Rejeb, Nabil Sahli, Wassim Trojet, Lamjed Ben Said, Ghaleb Hoblos

    Real-Time Traffic Prediction Through Stochastic Gradient Descent

    10th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2024), 2024

    Résumé

    The escalating challenges of urban traffic congestion pose a critical issue that calls for efficient traffic management system solutions. Traffic forecasting stands out as a paramount area of exploration in the field of Intelligent Transportation Systems. Various traditional machine learning techniques have been employed for predicting traffic congestion, often requiring a significant amount of data to train the model. For that reason, historical data are usually used. In this paper, our first concern is to use real-time traffic data. We adopted Stochastic Gradient Descent, an online learning method characterized by its ability to continually adapt to incoming data, facilitating real-time updates and rapid predictions. We studied a network of streets in the city
    of Muscat, Oman. Our model showed its accuracy through comparisons with actual traffic data.

  • Yasmine Amor, Lilia Rejeb, Nabil Sahli, Wassim Trojet, Ghaleb Hoblos, Lamjed Ben Said

    Rule-based Recommendation System for Traffic Congestion Measures

    KES STS 2023, 2023

    Résumé

    Traffic congestion has become a serious concern in both developed and developing countries. Increasing demand for urban transport has led to plenty of issues including longer travel times, higher fuel consumption and greater vehicular crash rates and therefore to a deterioration in the quality of life. On grounds of the wide range of problems that traffic congestion can cause, the study of traffic congestion measures and their implementation is a crucial step that should be considered in analyzing traffic. However, these measures might vary per country. They are context-sensitive. Therefore, the purpose of this study is to develop a recommendation system able to generate the congestion measures in accordance with the context under study. The goal of this research is to assist researchers and traffic operators to choose the most suitable congestion measures to the studied area.

  • Yasmine Amor, Lilia Rejeb, Rahma Ferjani, Lamjed Ben Said, Mohamed Ridha Ben Cheikh

    Hierarchical Multi-agent System for Sleep Stages Classification

    International Journal on Artificial Intelligence Tools, 2022

    Résumé

    Sleep is a fundamental restorative process for human mental and physical health. Considering the risks that sleep disorders can present, sleep analysis is considered as a primordial task to identify the different abnormalities. Sleep scoring is the gold standard for human sleep analysis. The manual sleep scoring task is considered exhausting, subjective, time-consuming and error prone. Moreover, sleep scoring is based on fixed epoch lengths usually of 30 seconds, which leads to an information loss problem. In this paper, we propose an automatic unsupervised sleep scoring model. The aim of our work is to consider different epoch’s durations to classify sleep stages. Therefore, we developed a model based on Hierarchical Multi-Agent Systems (HMASs) that presents different layers where each layer contains a number of adaptive agents working with a specific time epoch. The effectiveness of our approach was investigated using real electroencephalography (EEG) data. Good results were reached according to a comparative study realized with the often used machine learning techniques for sleep stages classification problems.

    Mariem Sebai, Lilia Rejeb, Mohamed Ali Denden, Yasmine Amor, Lassaad Baati, Lamjed Ben Said

    Optimal Electric Vehicles Route Planning with Traffic Flow Prediction and Real-Time Traffic Incidents

    International Journal of Electrical and Computer Engineering Research, 2022

    Résumé

    Electric Vehicles (EVs) are regarded to be among the most environmentally and economically efficient transportation solutions. However, barriers and range limitations hinder this technology’s progress and deployment. In this paper, we examine EV route planning to derive optimal routes considering energy consumption by analyzing historical trajectory data. More specifically, we propose a novel approach for EV route planning that considers real-time traffic incidents, road topology, charging station locations during battery failure, and finally, traffic flow prediction extracted from historical trajectory data to generate energy maps. Our approach consists of four phases: the off-line phase which aims to build the energy graph, the application of the A* algorithm to deliver the optimal EV path, the NEAT trajectory clustering which aims to produce dense trajectory clusters for a given period of the day, and finally, the on-line phase based on our algorithm to plan an optimal EV path based on real traffic incidents, dense trajectory clusters, road topology information, vehicle characteristics, and charging station locations. We set up experiments on real cases to establish the optimal route for electric cars, demonstrating the effectiveness and efficiency of our proposed algorithm.

Projets