smart.lab.tunisia@gmail.com +216 71 588 443
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Wiem Ben Ghozzi

Informations générales

Wiem Ben Ghozzi
Grade

Doctorant

Biographie courte

Wiem Ben Ghozzi is a PhD student in Business Computing, jointly enrolled at the Higher Institute of Management of Tunis (ISG) and Paris 8 University. She earned a Bachelor’s degree in Computer Science and Multimedia from the Higher Institute of Multimedia and Computer Science of Sfax (ISIMS) in 2020, followed by a Research Master’s degree in Decision-making Computer Science from ISG in 2022. Her academic and research journey is deeply rooted in artificial intelligence, with a particular focus on the application of machine learning and deep learning techniques to address complex real-world challenges, especially in the medical field.

Équipes

Axes de recherche

Machine Learning
Artificial Intelligence
Deep Learning
Computer Science

Publications

  • 2024
    Wiem Ben Ghozzi, Zahra Kodia, Nadia Ben Azzouna

    Fatigue Detection for the Elderly Using Machine Learning Techniques

    10th International Conference on Control, Decision and Information Technologies (CoDIT), Vallette, Malta, 2024, pp. 2055-2060, doi: 10.1109/CoDIT62066.2024.10708516., 2024

    Résumé

    Elderly fatigue, a critical issue affecting the health and well-being of the aging population worldwide, presents as a substantial decline in physical and mental activity levels. This widespread condition reduces the quality of life and introduces significant hazards, such as increased accidents and cognitive deterioration. Therefore, this study proposed a model to detect fatigue in the elderly with satisfactory accuracy. In our contribution, we use video and image processing through a video in order to detect the elderly’s face recognition in each frame. The model identifies facial landmarks on the detected face and calculates the Eye Aspect Ratio (EAR), Eye Fixation, Eye Gaze Direction, Mouth Aspect Ratio (MAR), and 3D head pose. Among the various methods evaluated in our study, the Extra Trees algorithm outperformed all others machine learning methods, achieving the highest results with a sensitivity of 98.24%, specificity of 98.35%, and an accuracy of 98.29%.

  • Wiem Ben Ghozzi, Abir Chaabani, Zahra Kodia, Lamjed Ben Said

    DeepCNN-DTI: A Deep Learning Model for Detecting Drug-Target Interactions

    International Conference on Control Decision and Information Technology Codit’9, Rome, 2023

    Résumé

    Drug target interaction is an important area of drug discovery, development, and repositioning. Knowing that in vitro experiments are time-consuming and computationally expensive, the development of an efficient predictive model is a promising challenge for Drug-Target Interactions (DTIs) prediction. Motivated by this problem, we propose in this paper a new prediction model called DeepCNN-DTI to efficiently solve such complex real-world activities. The main motivation behind this work is to explore the advantages of a deep learning strategy with feature extraction techniques, resulting in an advanced model that effectively captures the complex relationships between drug molecules and target proteins for accurate DTIs prediction. Experimental results generated based on a set of data in terms of accuracy, precision, sensitivity, specificity, and F1-score demonstrate the superiority of the model compared to other competing learning strategies.