Nouf Abdullah Almujally

Princess Nourah bint Abdulrahman University

Papers

Total Citations

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About

Nouf Abdullah Almujally is a leading researcher at the intersection of artificial intelligence, computer vision, and intelligent transportation systems. Her work focuses on developing advanced machine learning architectures for real-time object detection, segmentation, and traffic surveillance in dynamic environments. Dr. Almujally’s most impactful contribution is her pioneering use of recurrent neural networks combined with Swin transformers for UAV-based traffic monitoring, a paper that has already garnered 19 citations since its 2025 publication. This work addresses critical challenges in urban congestion and road safety by enabling drones to make adaptive, real-time decisions. She has also advanced the field through her research on multi-method fusion for enhanced object detection (11 citations) and UNet-based convolutional neural networks for multi-object segmentation (6 citations). Her contributions are particularly notable for tackling persistent computer vision challenges such as dynamic backgrounds, occlusion, and limited labeled data. Dr. Almujally’s work has direct implications for autonomous driving, robotic navigation, and augmented reality systems, positioning her as an emerging authority in intelligent aerial surveillance and machine vision.