We are thrilled to announce a groundbreaking achievement for RIOTU Lab and Prince Sultan University (PSU)! Our patent, "Detection of the Red Palm Weevil Infestation in Palm Farms," has been officially accepted by the Saudi Authority for Intellectual Property (SAIP). This milestone is particularly significant as it represents the first patent from Prince Sultan University to be recognized by SAIP. It reflects our commitment to pioneering cutting-edge innovations that address real-world challenges. The patent focuses on a critical issue in sustainable agriculture—the early detection of Red Palm Weevil infestations, which pose a severe threat to palm farms globally. By leveraging advanced technologies, this innovation enhances pest detection, helping to protect harvests, support farmers, and promote long-term agricultural resilience. At RIOTU Lab, we remain dedicated to driving impactful research and technological advancements that contribute to sustainable solutions in agriculture and beyond. Stay tuned for more updates as we continue to push the boundaries of innovation!

Dr. Wadii Boulila awarded for his recognition of outstanding achievements and contributions as one of the Top 3 Faculty Members for the Most Impactful Research Output of Pure Lab Papers at Prince Sultan University in 2023.

We are thrilled to release our UAV trajectories datasets which we generated using our FlightGen tool. This tool automates the generation of arbitrary random synthetic UAV trajectories using ROS 2 and the Gazebo simulator. We also used the PX4 autopilot to mimic the flight controls on actual UAVs. This dataset can be used to train UAV trajectory prediction models.

• Dataset: https://huggingface.co/datasets/riotu-lab/Synthetic-UAV-Flight-Trajectories
• FlighGen code: https://github.com/mzahana/uav_dataset_generation_ros
• Dataset post processing code: https://github.com/mzahana/drone_trajectories

SMART-TRACK is a novel multi-modal sensor fusion framework for robust real-time object detection and tracking in 3D.

• Paper: https://doi.org/10.1109/JSEN.2024.3505939
• Code: https://github.com/mzahana/smart_track

This research presents a novel heuristic approach to route planning in VANET-oriented grid networks, contributing to advancements in intelligent transportation systems.

• Full paper: https://ieeexplore.ieee.org/abstract/document/10505020

Congratulations to Dr. Zahid Khan along with his co-authors Dr. Nauman Khan, Prof. Anis Koubaa, Dr. Adel Ammar, and Dr. Wadii Boulila on the publications of their paper entitled "RADAR: A Rapid Discovery Algorithm for Routes in SDN-based IoV" in the esteemed journal Digital Communications and Networks, which is ranked 10th in the Telecommunications category with an impact factor of 7.5.

• Full paper: https://www.sciencedirect.com/science/article/pii/S2352864824000932

This study explores the use of big data analytics and ensemble few-shot learning techniques for early Alzheimer’s Disease (AD) detection in IoMT ecosystems. The approach aims to improve classification accuracy in medical imaging and clinical data, even with limited labeled samples. By integrating deep learning architectures, the proposed model enhances diagnostic efficiency, especially in situations where obtaining large training datasets is difficult. The results show improved diagnostic performance, making the system a practical solution for real-world medical applications.

Dr. Maha Driss has been awarded the Women in STEM Honorary Award for the third consecutive year during the International Day of Women and Girls in Science 2025. This award recognizes her outstanding contributions to STEM research and commitment to advancing innovation in computer science.