Sehla Loussaief

Tunis El Manar University, University of Carthage

Papers

Total Citations

124

H-Index

About

Sehla Loussaief is a researcher whose work sits at the intersection of machine learning, computer vision, and intelligent transportation systems. Her primary contributions focus on advancing image classification and recognition methodologies, with a particular emphasis on evaluating feature extraction techniques and classifier algorithms. Her most influential work, a 2016 paper on a machine learning framework for image classification, has garnered 74 citations, establishing a foundation for subsequent studies in the field. She has continued to refine these approaches in follow-up papers from 2017 and 2018, collectively accumulating over 120 citations. More recently, Loussaief has applied deep learning techniques to a critical real-world problem: traffic sign recognition for controlling intelligent vehicles. Her 2022 study leverages convolutional neural networks and the AlexNet pre-trained model to improve the accuracy and robustness of traffic sign classification—a vital component for autonomous driving systems. Through her systematic exploration of training models on benchmark datasets like Caltech 101, Loussaief has contributed valuable insights into optimizing machine learning pipelines for visual recognition tasks, bridging foundational research with practical applications in vehicular automation.