Professor | Tunis University | Tunisia
Prof. Jaouher Ben Ali, from the École Nationale Supérieure d’Ingénieurs de Tunis, Tunisia, is a prominent researcher in machine learning, signal processing, and intelligent fault diagnosis, with impactful work spanning biomedical and industrial applications. He has authored 41 publications, accumulated 2,656 citations, and achieved an h-index of 18, reflecting the strong influence of his research. Prof. Ben Ali’s work focuses on developing advanced algorithms for fault detection, condition monitoring, and health prediction using cutting-edge computational methods such as Empirical Mode Decomposition (EMD), Higher-Order Statistics (HOS), and deep learning models like LSTM-XGBoost fusion. His recent study, “Optimization of blood glucose prediction with LSTM-XGBoost fusion and integration of statistical features for enhanced accuracy” (2025, Biomedical Signal Processing and Control), showcases his efforts to integrate artificial intelligence with biomedical signal analysis for more precise and reliable health monitoring. He has also contributed significantly to mechanical fault diagnosis, as seen in works such as “Fault Diagnosis in Rolling Element Bearings Using Bi-Spectrum-Based EMD and Simplified Fuzzy ARTMAP” and “Advanced Feature Extraction Techniques for Bearing Fault Diagnosis Using Higher-Order Statistics and Machine Learning.” By combining data-driven techniques, nonlinear modeling, and adaptive learning, Prof. Ben Ali advances both theoretical understanding and practical applications of intelligent diagnostics. His interdisciplinary research strengthens links between academia and industry, promoting innovations that enhance system reliability, healthcare accuracy, and sustainable industrial performance worldwide.
Profiles: Scopus | Google Scholar
Featured Publications
1. Ali, J. B., Fnaiech, N., Saidi, L., Chebel-Morello, B., & Fnaiech, F. (2015). Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Applied Acoustics, 89, 16–27.
Cited by: 900
2. Ali, J. B., Chebel-Morello, B., Saidi, L., Malinowski, S., & Fnaiech, F. (2015). Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network. Mechanical Systems and Signal Processing, 56, 150–172.
Cited by: 625
3. Saidi, L., Ali, J. B., Bechhoefer, E., & Benbouzid, M. (2017). Wind turbine high-speed shaft bearings health prognosis through a spectral Kurtosis-derived indices and SVR. Applied Acoustics, 120, 1–8.
Cited by: 279
4. Saidi, L., Ali, J. B., & Fnaiech, F. (2015). Application of higher order spectral features and support vector machines for bearing faults classification. ISA Transactions, 54, 193–206.
Cited by: 237
5. Saidi, L., Ali, J. B., & Fnaiech, F. (2014). Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis. ISA Transactions, 53(5), 1650–1660.
Cited by: 192
Dr. Ben Ali’s research advances intelligent diagnostic technologies that enhance system reliability, healthcare precision, and industrial safety—driving progress toward a smarter, data-driven, and sustainable future.
