Tareq Babaqi
Papers
1
Total Citations
5
H-Index
1
About
Dr. Tareq Babaqi is a rising researcher at the forefront of integrating autonomous systems with machine learning for critical infrastructure monitoring. His work centers on the intersection of unmanned aerial vehicle (UAV) technology and deep learning, with a primary focus on enhancing the reliability and efficiency of power grid maintenance. In his most cited work, "Integration of drone and machine learning technology for predicting power infrastructure faults efficiently" (2024, 5 citations), Dr. Babaqi introduces a fully autonomous UAV-based inspection system that achieves an impressive 89.76% precision in fault prediction. A key contribution is his development of a novel mathematical model that significantly improves system robustness, reducing dependency on specific, limited datasets. By leveraging YOLO V8 deep learning for insulator fault detection, his research offers a practical, scalable solution for preemptive maintenance of power infrastructure. This work not only demonstrates a high-impact application of computer vision but also paves the way for more resilient and automated energy systems, marking Dr. Babaqi as an innovator in smart grid technology and autonomous inspection.
Research Focus
Key Achievements
Top Papers
- 1