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About

Afef Abdelkrim is a researcher whose work bridges the fields of computer vision, intelligent control, and human–machine interaction. Her most impactful contributions center on machine learning for image classification, where she has systematically evaluated feature extraction methods and classifier algorithms using benchmark datasets like Caltech 101. Her foundational paper on this topic has garnered 74 citations, demonstrating its influence in the field. Beyond computer vision, Abdelkrim has made notable advances in robotics and control systems. She has developed adaptive sliding mode control laws for robot manipulators, ensuring robust trajectory tracking under nonlinear uncertainties. In a particularly innovative line of work, she has modeled the human handwriting process using electromyography (EMG) signals, treating the hand as a two-link robot to inform the design of more intuitive hand prostheses. More recently, she has applied deep learning—specifically convolutional neural networks and transfer learning with AlexNet—to traffic sign recognition for intelligent vehicle control. Her research portfolio reflects a consistent focus on applying adaptive and learning-based methods to real-world systems, from image recognition to robotic control and biomedical signal processing.