Oumaima Majdoubi is visiting AIRi@UTCN for 3 months with a Eugen Ionescu Research Grant, under the supervision of Professor Mircea Giurgiu. The research is related to Detection of neurological diseases based on artificial intelligence and signal processing.
Oumaima is a PhD researcher in Electrical Engineering affiliated with ENSAM Rabat - École Nationale Supérieure d'Arts et Métiers/ National Higher School of Arts and Crafts and ENSIAS -École Nationale Supérieure d'Informatique et d'Analyse des Systèmes/ National Higher School of Computer Science and Systems Analysis, both part of Mohammed V University in Rabat.
She is a member of the Electronic Systems, Sensors and Nanobiotechnology - E2SN research team at ENSAM Rabat, specializing in artificial intelligence and biomedical signal processing for the early detection and diagnosis of neurological disorders, where she is supervised by Prof. Ahmed Hammouch.
I combine a strong engineering foundation with advanced research in machine learning, deep learning, and voice signal analysis applied to healthcare. My work focuses on developing non-invasive, AI-driven approaches for the detection and severity assessment of neurodegenerative diseases, particularly Parkinson's disease, through speech and biomedical signal processing. My research interests lie at the intersection of artificial intelligence, biomedical signal processing, and digital health, with a growing focus on trustworthy AI, explainable models for medical decision support, and next-generation diagnostic systems.
Thesis topic:
Detection of neurological diseases based on artificial intelligence and signal processing.
Keywords:
Artificial Intelligence, Biomedical Signal Processing, Machine Learning, Deep Learning, Voice Analysis, Parkinson's Disease Detection, Neurological Disorders, Digital Health, Trustworthy AI
Publications:
- Majdoubi, O., et al. (2026). "Non-invasive detection of Parkinson's disease using voice analysis and machine learning techniques." International Journal of Speech Technology, 29(1), 3.
- Majdoubi, O., Benba, A., & Hammouch, A. (2025). "Optimized Deep Learning Model for Early Parkinson's Disease Detection Using Speech Spectrograms." 2025 5th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), IEEE.
- Majdoubi, O., Benba, A., & Hammouch, A. (2023). "Comprehensive machine learning and deep learning approaches for Parkinson's disease classification and severity assessment." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 13(4), 15-20.
- Majdoubi, O., Benba, A., & Hammouch, A. (2023). "Classification of Parkinson's disease and other neurological disorders using voice features extraction and reduction techniques." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 13(3), 16-22.