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Al-Sakkari, E. G., Ragab, A., Ali, M., Dagdougui, H., Boffito, D. C., & Amazouz, M. (2024, July). Learn-To-Design: Reinforcement Learning-Assisted Chemical Process Optimization [Paper]. Foundations of Computer Aided Process Design (FOCAPD 2024), Breckenridge, Colorado, USA. Published in Systems and Control Transactions, 3. External link
Amer, M., Abuelnasr, A., Ali, M., Hassan, A., Trigui, A., Ragab, A., Sawan, M., & Savaria, Y. (2024). Enhanced dynamic regulation in Buck Converters: integrating input-voltage feedforward with voltage-mode feedback. IEEE Access, 12, 7310-7328. Available
Al-Sakkari, E. G., Ragab, A., Dagdougui, H., Boffito, D. C., & Amazouz, M. (2024). Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities. Science of the Total Environment, 917, 170085 (32 pages). External link
Abuelnasr, A., Ragab, A., Amer, M., Gosselin, B., & Savaria, Y. (2024). Incremental reinforcement learning for multi-objective analog circuit design acceleration. Engineering Applications of Artificial Intelligence, 129, 107426 (18 pages). External link
Amer, M., Abuelnasr, A., Hassan, A., Ragab, A., Sawan, M., & Savaria, Y. (2023). A Half-bridge Gate Driver with Self-adjusting and Tunable Dead-time Modes for Efficient Switched-mode Power Systems. IEEE Transactions on Power Electronics, 15 pages. External link
Al-Sakkari, E. G., Ragab, A., So, T. M. Y., Shokrollahi, M., Dagdougui, H., Navarri, P., Elkamel, A., & Amazouz, M. (2023). Machine learning-assisted selection of adsorption-based carbon dioxide capture materials. Journal of Environmental Chemical Engineering, 11(5), 110732 (25 pages). External link
Abdeldayem, O. M., Dabbish, A. M., Habashy, M. M., Mostafa, M. K., Elhefnawy, M., Amin, L., Al-Sakkari, E. G., Ragab, A., & Rene, E. R. (2022). Viral outbreaks detection and surveillance using wastewater-based epidemiology, viral air sampling, and machine learning techniques: A comprehensive review and outlook. Science of The Total Environment, 803, 24 pages. External link
Abuelnasr, A., Amer, M., Ragab, A., Gosselin, B., & Savaria, Y. (2021, May). Causal information prediction for analog circuit design using variable selection methods based on machine learning [Paper]. 53rd IEEE International Symposium on Circuits and Systems (ISCAS 2021), Daegu, Korea (5 pages). External link
Amer, M., Abuelnasr, A., Ragab, A., Hassan, A., Ali, M., Gosselin, B., Sawan, M., & Savaria, Y. (2021, May). Design and analysis of combined input-voltage feedforward and PI controllers for the buck converter [Paper]. 53rd IEEE International Symposium on Circuits and Systems (ISCAS 2021), Daegu, Korea (5 pages). External link
Alizadeh, E., Koujok, M. E., Ragab, A., & Amazouz, M. (2018, August). A Data-Driven Causality Analysis Tool for Fault Diagnosis in Industrial Processes [Paper]. 10th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS 2018), Warsaw, Poland. Published in IFAC-PapersOnLine, 51(24). External link
Amer, M., Hassan, A., Ragab, A., Yacout, S., Savaria, Y., & Sawan, M. (2018, May). High-Temperature Empirical Modeling for the I-V Characteristics of GaN150-Based HEMT [Paper]. IEEE International Symposium on Circuits and Systems (ISCAS 2018), Florence, Italy. External link
Abubakr, A., Hassan, A., Ragab, A., Yacout, S., Savaria, Y., & Sawan, M. (2018, May). High-temperature modeling of the I-V characteristics of GaN150 HEMT using machine learning techniques [Paper]. IEEE International Symposium on Circuits and Systems (ISCAS 2018), Florence, Italie (5 pages). External link
Elhefnawy, M., Ouali, M.-S., Ragab, A., & Amazouz, M. (2023). Fusion of heterogeneous industrial data using polygon generation & deep learning. Results in Engineering, 19, 11 pages. Available
Elhefnawy, M., Ragab, A., & Ouali, M.-S. (2022). Fault classification in the process industry using polygon generation and deep learning. Journal of Intelligent Manufacturing, 33(5), 1531-1544. External link
Elhefnawy, M., Ragab, A., & Ouali, M.-S. (2022). Polygon generation and video-to-video translation for time-series prediction. Journal of Intelligent Manufacturing, 34(1), 261-279. External link
Lejeune, M., Lozin, V., Lozina, I., Ragab, A., & Yacout, S. (2019). Recent advances in the theory and practice of Logical Analysis of Data. European Journal of Operational Research, 275(1), 1-15. External link
Murray, B. A., Coops, N. C., Winiwarter, L., White, J. C., Dick, A., Barbeito, I., & Ragab, A. (2024). Estimating tree species composition from airborne laser scanning data using point-based deep learning models. ISPRS Journal of Photogrammetry and Remote Sensing, 207, 282-297. External link
Nadim, K., Ouali, M.-S., Ghezzaz, H., & Ragab, A. (2023). Learn-to-supervise: Causal reinforcement learning for high-level control in industrial processes. Engineering Applications of Artificial Intelligence, 126, 106853 (29 pages). External link
Nadim, K., Ragab, A., & Ouali, M.-S. (2022). Data-driven dynamic causality analysis of industrial systems using interpretable machine learning and process mining. Journal of Intelligent Manufacturing, 34(1), 57-83. External link
Ragab, A., Elhefnawy, M., & Ouali, M.-S. (2022, January). Artificial Intelligence-Based Survival Analysis For Industrial Equipment Performance Management [Paper]. 68th Annual Reliability and Maintainability Symposium (RAMS 2022), Tucson, AZ, USA (7 pages). External link
Ragab, A., Ghezzaz, H., & Amazouz, M. (2022). Decision fusion for reliable fault classification in energy-intensive process industries. Computers in Industry, 138, 13 pages. External link
Ragab, A., El Koujok, M., Ghezzaz, H., Amazouz, M., Ouali, M.-S., & Yacout, S. (2019). Deep understanding in industrial processes by complementing human expertise with interpretable patterns of machine learning. Expert Systems With Applications, 122, 388-405. External link
Ragab, A., Yacout, S., Ouali, M.-S., & Osman, H. (2019). Prognostics of multiple failure modes in rotating machinery using a pattern-based classifier and cumulative incidence functions. Journal of Intelligent Manufacturing, 30(1), 255-274. External link
Ragab, A., El-Koujok, M., Poulin, B., Amazouz, M., & Yacout, S. (2018). Fault diagnosis in industrial chemical processes using interpretable patterns based on logical analysis of data. Expert Systems With Applications, 95, 368-383. External link
Ragab, A., de Carné de Carnavalet, X., Yacout, S., & Ouali, M.-S. (2017). Face recognition using multi-class Logical Analysis of Data. Pattern Recognition and Image Analysis, 27(2), 276-288. External link
Ragab, A., El-Koujok, M., Amazouz, M., & Yacout, S. (2017, January). Fault detection and diagnosis in the Tennessee Eastman Process using interpretable knowledge discovery [Paper]. 63rd Annual Reliability and Maintainability Symposium (RAMS 2017), Orlando, FL, United states. External link
Ragab, A., Yacout, S., Ouali, M.-S., & Osman, H. (2017). Pattern-based prognostic methodology for condition-based maintenance using selected and weighted survival curves. Quality and Reliability Engineering International, 33(8), 1753-1772. External link
Ragab, A., Yacout, S., & Ouali, M.-S. (2016, January). Remaining useful life prognostics using pattern-based machine learning [Paper]. Annual Reliability and Maintainability Symposium (RAMS 2016), Tucson, AZ (7 pages). External link
Ragab, A., Yacout, S., & Ouali, M.-S. (2015, January). Interpretable pattern-based machine learning for condition-based maintenance [Paper]. 61st Annual Reliability and Maintainability Symposium (RAMS 2015), Palm Harbor, Florida (6 pages). Unavailable
Ragab, A., Yacout, S., Ouali, M.-S., & Osman, H. (2015, January). Multiple failure modes prognostics using logical analysis of data [Paper]. Annual Reliability and Maintainability Symposium (RAMS 2015), Palm Harbor, FL, USA (7 pages). External link
Ragab, A., Ouali, M.-S., Yacout, S., & Osman, H. (2014, May). Condition-based maintenance prognostics using logical analysis of data [Paper]. IIE Annual Conference and Expo 2014, Montréal, Québec. Unavailable
Ragab, A., Ouali, M.-S., Yacout, S., & Osman, H. (2014). Remaining useful life prediction using prognostic methodology based on logical analysis of data and Kaplan-Meier estimation. Journal of Intelligent Manufacturing, 27(5), 943-958. External link
Seely, H., Coops, N. C., White, J. C., Montwé, D., Winiwarter, L., & Ragab, A. (2023). Modelling tree biomass using direct and additive methods with point cloud deep learning in a temperate mixed forest. Science of Remote Sensing, 8, 100110 (17 pages). Available
Soualhi, M., El Koujok, M., Nguyen, K. T. P., Medjaher, K., Ragab, A., Ghezzaz, H., Amazouz, M., & Ouali, M.-S. (2021). Adaptive prognostics in a controlled energy conversion process based on long- and short-term predictors. Applied Energy, 283, 116049 (14 pages). External link
Zhang, M., Carné de Carnavalet, X., Wang, L., & Ragab, A. (2019). Large-Scale Empirical Study of Important Features Indicative of Discovered Vulnerabilities to Assess Application Security. IEEE Transactions on Information Forensics and Security, 14(9), 2315-2330. External link
