Moufida Ksouri
Papers
1
Total Citations
13
H-Index
1
About
Moufida Ksouri is a researcher whose work lies at the intersection of intelligent robotics and deep learning control systems. Her most cited paper, "A Deep Learning Approach for the Mobile-Robot Motion Control System" (2021, 13 citations), addresses a fundamental challenge in autonomous robotics: enabling line follower robots to adapt accurately, quickly, and cost-effectively to changing environments. By proposing a deep learning-based controller, Ksouri moves beyond traditional control methods, offering a more robust and intelligent solution for mobile robot navigation. This contribution is particularly relevant for applications in industrial automation, logistics, and educational robotics, where efficient and adaptive motion control is critical. While her citation count is still growing, Ksouri's work demonstrates a clear focus on practical, real-world robotics problems, leveraging modern AI techniques to enhance system performance. Her research is a valuable resource for students and engineers interested in the practical deployment of deep learning in autonomous systems, showcasing how neural networks can replace or augment conventional control algorithms for greater flexibility and reliability.
Research Focus
Key Achievements
Top Papers
- 1A Deep Learning Approach for the Mobile-Robot Motion Control System13 citations · 2021