Lilia Rejeb

Follower

Following

Public Views

Interests

Uploads

Papers by Lilia Rejeb

Route planning for electric vehicles

IET Intelligent Transport Systems

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

International Journal of Electrical and Computer Engineering Research, 2022

Electric Vehicles (EVs) are regarded to be among the most environmentally and economically effici... more 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 ba...

Download

Unsupervised Sleep Stages Classification Based on Physiological Signals

Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection, 2020

Automatic sleep scoring has, recently, captured the attention of authors due to its importance in... more Automatic sleep scoring has, recently, captured the attention of authors due to its importance in sleep abnormalities detection and treatments. The majority of the proposed works are based on supervised learning and considered mostly a single physiological signal as input. To avoid the exhausting pre-labeling task and to enhance the precision of the sleep staging process, we propose an unsupervised classification model for sleep stages identification based on a flexible architecture to handle different physiological signals. The efficiency of our approach was investigated using real data. Promising results were reached according to a comparative study carried out with the often used classification models.

Download

Belief eXtended Classifier System: A New Approach for Dealing with Uncertainty in Sleep Stages Classification

Hybrid Intelligent Systems, 2021

Simulation multi-agents des systèmes économiques : Vers des systèmes multi-agents adaptatifs

Deux principales approches ont ete identifiees parmi les theories economiques issues des travaux ... more Deux principales approches ont ete identifiees parmi les theories economiques issues des travaux de Simon : l'ecologie organisationnelle et le management strategique. Elles etudient separement les deux principaux problemes de selection (materialisee par l'integration des formes organisationnelles) et d'adaptation. De recentes reflexions s'orientent vers l'unification des deux approches. Cependant, aucun modele, a notre connaissance, n'a encore ete propose pour etudier simultanement les deux problemes jusque-la abordes independamment. Ceci est notamment du a la complexite des systemes economiques. Le but de la these est de palier cette lacune en suggerant un modele qui integre les deux niveaux firmes et formes organisationnelles. Notre solution est basee sur l'utilisation des systemes multi-agents adaptatifs. Nous montrons donc que les systemes multi-agents adaptatifs permettent d'etudier les deux problemes (adaptation et selection) simultanement et qu...

Architecture multi-agents pyramidale et adaptative pour la segmentation des images volumiques (présentation courte)

Simulation multi-agents des firmes et formes organisationnelles

Une approche multi-agents pour la segmentation d'images volumiques [présentation courte]

Evidential learning classifier system

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2017

The main objective of this work is to study the applicability of ensemble methods in the context ... more The main objective of this work is to study the applicability of ensemble methods in the context of deep learning with limited amounts of labeled data. We exploit an ensemble of neural networks derived using Monte Carlo dropout, along with an ensemble of SVM classifiers which owes its effectiveness to the hand-crafted features used as inputs and to an active learning procedure. In order to leverage each classifier's respective strengths, we combine them in an evidential framework, which models specifically their imprecision and uncertainty. The application we consider in order to illustrate the interest of our Multiple Classifier System is pedestrian detection in high-density crowds, which is ideally suited for its difficulty, cost of labeling and intrinsic imprecision of annotation data. We show that the fusion resulting from the effective modeling of uncertainty allows for performance improvement, and at the same time, for a deeper interpretation of the result in terms of commitment of the decision.

Download

A Hybrid Approach for Sleep Stages Classification

Proceedings of the Genetic and Evolutionary Computation Conference 2016, 2016

Healthy sleep is essential for human well-being. Sleep analysis is a necessary process for the ma... more Healthy sleep is essential for human well-being. Sleep analysis is a necessary process for the majority of sleep disorders diagnosis. In this work we propose to analyze brain activity through Electroencephalogram analysis in order to identify sleep stages variation. We focus on the classification phase. Most works in sleep stages classification are based on prior experts signal scoring which is a hard task. So many available unlabeled data remain unused. To explore more these data and enrich the study of sleep classification, we propose a hybrid approach based on learning classifier systems and artificial neural networks. The effectiveness of the proposed approach was investigated using real electroencephalography data. Good results were reached comparing to supervised learning methods usually used. The proposed approach provides also, an explicit model that could be analyzed a posteriori by experts.

An Adaptive Approach for the Exploration-Exploitation Dilemma for Learning Agents

Lecture Notes in Computer Science, 2005

Learning agents have to deal with the exploration-exploitation dilemma. The choice between explor... more Learning agents have to deal with the exploration-exploitation dilemma. The choice between exploration and exploitation is very difficult in dynamic systems; in particular in large scale ones such as economic systems. Recent research shows that there is neither an optimal nor a unique solution for this problem. In this paper, we propose an adaptive approach based on meta-rules to adapt

Firms Adaptation in Dynamic Economic Systems

Lecture Notes in Economics and Mathematical Systems, 2006

Using adaptive multi-agent systems to simulate economic models

Economic markets are complex systems. They are characterized by a large and dynamic population of... more Economic markets are complex systems. They are characterized by a large and dynamic population of firms. To deal with this complexity, we propose an adaptive multiagent system which models a set of firms in competition with each other within a shared market. The firms are represented by agents; each firm is represented by an adaptive agent. We show the advantages of adaptive agents to represent firms. Moreover, we underline the limits of the economic models which account for the firms only and ignore the organizational forms. We propose a new adaptive multiagent model that includes the organizational forms into the economic models. We simulate this model and discuss its advantages.

Download

Lilia Rejeb

Uploads

Papers by Lilia Rejeb

Log In