This paper presents a multi-sensor fusion architecture with camera data segmentation. Robust perception is a fundamental prerequisite for the safety of autonomous vehicles, particularly in dynamic environments and varying weather conditions. Multi-sensor fusion approaches, integrating camera and LiDAR data, have emerged as the reference solution for 3D object detection, thanks to the complementary information provided by each modality. However, most existing work validates their architectures on static benchmarks such as the KITTI dataset, which do not allow for the evaluation of the system's robustness under controlled and reproducible variations in environmental conditions. In this work, we propose to deploy and evaluate a multi-sensor fusion pipeline in the CARLA nearrealistic simulator, which offers a dynamic, configurable, and physically realistic environment that faithfully reproduces realworld driving conditions. The adopted architecture is based on a fusion at the intermediate representation level, combining features from the camera, after segmentation of the raw data, and from LiDAR. The results obtained show that the simulation in CARLA constitutes a complementary and rigorous evaluation framework, bridging the gap between laboratory validation and deployment in real-world conditions.
Rihem Sebai, A. Sahbani, T. Bejaoui· International Conference on...· 0 citations
Coronary Heart Disease (CHD) has remained one of the foremost causes of death in the world, and thus, there is a need to ensure that there are dependable early diagnosis mechanisms that would aid clinicians in making decisions at the right time. The rapid development of electronic health records and sensor-based medical data has presented more opportunities in predictive analytics in healthcare than ever before. However, the sensitivity, complexity, and scale of health data require robust analytical models and a safe and reliable data processing system. In this respect, machine learning (ML) methods have become effective instruments in deriving significant patterns of heterogeneous healthcare data. This study hypothesizes an ensemble learning framework that is used in the early identification of CHD. The proposed ensemble model is more accurate and stronger in predictions than any of the individual models by incorporating several ML classifiers. The study provides a scalable method to prevent cardiovascular diseases, and the model may help healthcare professionals to identify high-risk patients at an early stage and, thus, implement interventions in time and enhance patient outcomes. Experimental results demonstrate that the ensemble model outperforms conventional ML models, highlighting its effectiveness as a supportive diagnostic tool for CHD prediction.
Sania Batool, Muhammad Hassan Jamal, Warisha Siddiqui et al.· International Conference on...· 0 citations