Karim Dabbabi | Unmanned Aerial Vehicle Tracking | Best Academic Researcher Award Published on 04/10/202504/10/2025 by Computer Vision Awards Dr. Karim Dabbabi | Unmanned Aerial Vehicle Tracking | Best Academic Researcher Award Assistant Professor | Faculty of Sciences of Tunis | Tunisia Dr. Karim Dabbabi is an accomplished researcher and assistant professor specializing in artificial intelligence, computer vision, natural language processing, and biomedical signal processing. He holds a Ph.D. in Electronics from the Faculty of Sciences of Tunis and has completed advanced studies in automation, signal processing, and embedded electronics. His professional experience includes teaching courses in image and signal processing, machine and deep learning, AI, coding languages, embedded systems, and IoT across multiple institutions, as well as mentoring and supervising numerous student theses. He has actively contributed to national and international research projects, including UAV real-time tracking, smart grids, COVID-19 patient monitoring, intelligent wheelchairs, and disaster management robotics. His research interests focus on multimodal speech and image analysis, intelligent systems, emotion-aware speech recognition, and healthcare applications such as early detection of Parkinson’s and Alzheimer’s diseases. He has published extensively in reputed journals and conferences, including IEEE, Scopus, and Springer, and holds certifications in AI, machine learning, embedded systems, and data science. His work reflects strong leadership in research supervision, active involvement in academic communities, and commitment to advancing technology for societal benefit. His research skills encompass programming (Python, MATLAB, C, C++, Java, VHDL), machine learning frameworks, embedded systems, and IoT development. Dr. Dabbabi’s contributions are evidenced by 39 citations, 17 documents, and an h-index of 4. Profiles: Google Scholar | Scopus | ResearchGate Featured Publications Dabbabi, K., Hajji, S., & Cherif, A. (2020). Real-time implementation of speaker diarization system on Raspberry PI3 using TLBO clustering algorithm. Circuits, Systems, and Signal Processing, 39(8), 4094–4109. Walid, M., Bousselmi, S., Dabbabi, K., & Cherif, A. (2019). Real-time implementation of isolated-word speech recognition system on Raspberry Pi 3 using WAT-MFCC. International Journal of Computer Science and Network Security, 19(3), 42. Dabbabi, K., Kehili, A., & Cherif, A. (2023). Parkinson detection using VOT-MFCC combination and fully-connected deep neural network (FC-DNN) classifier. In Proceedings of the 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET 2023). Dabbabi, K., Delleji, T. (2025). Graph neural network-tracker: A graph neural network-based multi-sensor fusion framework for robust unmanned aerial vehicle tracking. Visual Computing for Industry, Biomedicine, and Art, 8(1), 18. Dabbabi, K., Mars, A. (2024). Self-supervised learning for speech emotion recognition task using audio-visual features and Distil Hubert model on BAVED and RAVDESS databases. Journal of Systems Science and Systems Engineering, 33(5), 576–606.
Dr. Karim Dabbabi is an accomplished researcher and assistant professor specializing in artificial intelligence, computer vision, natural language processing, and biomedical signal processing. He holds a Ph.D. in Electronics from the Faculty of Sciences of Tunis and has completed advanced studies in automation, signal processing, and embedded electronics. His professional experience includes teaching courses in image and signal processing, machine and deep learning, AI, coding languages, embedded systems, and IoT across multiple institutions, as well as mentoring and supervising numerous student theses. He has actively contributed to national and international research projects, including UAV real-time tracking, smart grids, COVID-19 patient monitoring, intelligent wheelchairs, and disaster management robotics. His research interests focus on multimodal speech and image analysis, intelligent systems, emotion-aware speech recognition, and healthcare applications such as early detection of Parkinson’s and Alzheimer’s diseases. He has published extensively in reputed journals and conferences, including IEEE, Scopus, and Springer, and holds certifications in AI, machine learning, embedded systems, and data science. His work reflects strong leadership in research supervision, active involvement in academic communities, and commitment to advancing technology for societal benefit. His research skills encompass programming (Python, MATLAB, C, C++, Java, VHDL), machine learning frameworks, embedded systems, and IoT development. Dr. Dabbabi’s contributions are evidenced by 39 citations, 17 documents, and an h-index of 4. Profiles: Google Scholar | Scopus | ResearchGate Featured Publications
Dabbabi, K., Hajji, S., & Cherif, A. (2020). Real-time implementation of speaker diarization system on Raspberry PI3 using TLBO clustering algorithm. Circuits, Systems, and Signal Processing, 39(8), 4094–4109. Walid, M., Bousselmi, S., Dabbabi, K., & Cherif, A. (2019). Real-time implementation of isolated-word speech recognition system on Raspberry Pi 3 using WAT-MFCC. International Journal of Computer Science and Network Security, 19(3), 42. Dabbabi, K., Kehili, A., & Cherif, A. (2023). Parkinson detection using VOT-MFCC combination and fully-connected deep neural network (FC-DNN) classifier. In Proceedings of the 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET 2023). Dabbabi, K., Delleji, T. (2025). Graph neural network-tracker: A graph neural network-based multi-sensor fusion framework for robust unmanned aerial vehicle tracking. Visual Computing for Industry, Biomedicine, and Art, 8(1), 18. Dabbabi, K., Mars, A. (2024). Self-supervised learning for speech emotion recognition task using audio-visual features and Distil Hubert model on BAVED and RAVDESS databases. Journal of Systems Science and Systems Engineering, 33(5), 576–606.