Nadia Jmour

Papers

Total Citations

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About

Nadia Jmour is a researcher in intelligent transportation systems, with a primary focus on applying deep learning to autonomous vehicle technologies. Her most cited work, "Traffic sign recognition for controlling intelligent vehicle" (2022), demonstrates her expertise in computer vision and convolutional neural networks. In this study, Jmour leverages the AlexNet pre-trained model to develop a robust framework for traffic sign image classification, a critical component for safe and reliable autonomous driving. While her citation count is currently modest, her research addresses a fundamental challenge in real-world AI deployment: enabling vehicles to accurately interpret their environment. Jmour’s work sits at the intersection of artificial intelligence and automotive safety, contributing to the broader goal of creating smarter, more responsive intelligent vehicles. Her approach—using transfer learning to adapt existing deep learning architectures for specialized traffic sign recognition—reflects a practical, efficiency-driven methodology that is essential for real-time systems. As the field of autonomous driving continues to expand, Jmour’s foundational contributions to traffic sign classification provide a stepping stone for further innovations in vehicle perception and control systems.