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Connexion

Rahma Ferjani

Informations générales

Rahma Ferjani
Grade

Maître Assistant

Biographie courte

Dr. Rahma Ferjani is a researcher in artificial intelligence specializing in explainable AI, belief diagrams, and machine learning applied to biomedical data. Her work focuses on developing interpretable and reliable models for applications such as sleep stage classification and smart systems.

Axes de recherche

Artificial Intelligence
Machine Learning
Health care

Publications

  • 2024
    Rahma Ferjani, Lamjed Ben Said, Lilia Rejeb, Chedi Abdelkarim

    Evidential Supervised Classifier System: A New Learning Classifier System Dealing with Imperfect Information

    International Journal of Information Technology & Decision Making, 23(02), 917-938., 2024

    Résumé

    Learning Classifier Systems (LCSs) are a kind of evolutionary machine learning algorithms that provide highly adaptive components to deal with real world problems. They have been widely used in resolving complex problems such as decision making and classification. LCSs are flexible algorithms that are able to construct, incrementally, a set of rules and evolve them through the Evolutionary Algorithm (EA). Despite their efficiency, LCSs are not capable of handling imperfect information, which may lead to reduced performance in terms of classification accuracy. We propose a new accuracy-based Michigan-style LCS that integrates the belief function theory in the supervised classifier system. The belief function or evidence theory represents an efficient framework for treating imperfect information. The new approach shows promising results in real world classification problems.

  • 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.

  • Rahma Ferjani, Lilia Rejeb, Lamjed Ben Said

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

    In International Conference on Hybrid Intelligent Systems (pp. 454-463). Cham: Springer International Publishing., 2021

    Résumé

    Sleep is an essential element that affects directly our daily life thus sleep analysis is a very interesting field. Sleep stages classification represents the base of all sleep analysis activities. However, the classification of sleep stages suffers from high uncertainty between its stages which could lead to degrade the performance of classification methods. To cope partially with this issue, we propose a new approach that deals with uncertainty especially with imprecision. Our method integrates the belief function theory in eXtended Classifier System (XCS). The proposed approach shows a good performance ability comparing to classical methods.

  • Rahma Ferjani, Lilia Rejeb, Lamjed Ben Said

    Cooperative Reinforcement Multi-Agent Learning System for Sleep Stages Classification

    2020 International Multi-Conference on Organization of Knowledge and Advanced Technologies (OCTA), 2020

    Résumé

    Sleep analysis is considered as an important process in sleep disorders identification and highly dependent of sleep scoring. Sleep scoring is a complex, time consuming and exhausting task for experts. In this paper, we propose an automatic sleep scoring model based on unsupervised learning to avoid the pre-labeling task. Taking advantage of the distributed nature of Multi-agent Systems (MAS), we propose a classification model based on various physiological signals coming from heterogeneous sources. The proposed model offers a totally cooperative learning to automatically score sleep into several stages based on unlabeled data. The existing heterogeneous adaptive agents are dealing with a dynamic environment of various 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. The generic proposed model could be used in fields where data are coming from heterogeneous sources and classification rules are not predefined.

    Rahma Ferjani, Lilia Rejeb, Lamjed Ben Said

    Unsupervised Sleep Stages Classification Based on Physiological Signals

    In International Conference on Practical Applications of Agents and Multi-agent Systems (pp. 134-145). Cham: Springer International Publishing., 2020

    Résumé

    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.