Documents dont l'auteur est "Ragab, Ahmed"

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Nombre de documents: 35

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Al-Sakkari, E. G., Ragab, A., Ali, M., Dagdougui, H., Boffito, D. C., & Amazouz, M. (juillet 2024). Learn-To-Design: Reinforcement Learning-Assisted Chemical Process Optimization [Communication écrite]. Foundations of Computer Aided Process Design (FOCAPD 2024), Breckenridge, Colorado, USA. Publié dans Systems and Control Transactions, 3. Lien externe

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. Disponible

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). Lien externe

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). Lien externe

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. Lien externe

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). Lien externe

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. Lien externe

Abuelnasr, A., Amer, M., Ragab, A., Gosselin, B., & Savaria, Y. (mai 2021). Causal information prediction for analog circuit design using variable selection methods based on machine learning [Communication écrite]. 53rd IEEE International Symposium on Circuits and Systems (ISCAS 2021), Daegu, Korea (5 pages). Lien externe

Amer, M., Abuelnasr, A., Ragab, A., Hassan, A., Ali, M., Gosselin, B., Sawan, M., & Savaria, Y. (mai 2021). Design and analysis of combined input-voltage feedforward and PI controllers for the buck converter [Communication écrite]. 53rd IEEE International Symposium on Circuits and Systems (ISCAS 2021), Daegu, Korea (5 pages). Lien externe

Alizadeh, E., Koujok, M. E., Ragab, A., & Amazouz, M. (août 2018). A Data-Driven Causality Analysis Tool for Fault Diagnosis in Industrial Processes [Communication écrite]. 10th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS 2018), Warsaw, Poland. Publié dans IFAC-PapersOnLine, 51(24). Lien externe

Amer, M., Hassan, A., Ragab, A., Yacout, S., Savaria, Y., & Sawan, M. (mai 2018). High-Temperature Empirical Modeling for the I-V Characteristics of GaN150-Based HEMT [Communication écrite]. IEEE International Symposium on Circuits and Systems (ISCAS 2018), Florence, Italy. Lien externe

Abubakr, A., Hassan, A., Ragab, A., Yacout, S., Savaria, Y., & Sawan, M. (mai 2018). High-temperature modeling of the I-V characteristics of GaN150 HEMT using machine learning techniques [Communication écrite]. IEEE International Symposium on Circuits and Systems (ISCAS 2018), Florence, Italie (5 pages). Lien externe

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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. Disponible

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. Lien externe

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. Lien externe

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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. Lien externe

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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. Lien externe

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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). Lien externe

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. Lien externe

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Ragab, A., Elhefnawy, M., & Ouali, M.-S. (janvier 2022). Artificial Intelligence-Based Survival Analysis For Industrial Equipment Performance Management [Communication écrite]. 68th Annual Reliability and Maintainability Symposium (RAMS 2022), Tucson, AZ, USA (7 pages). Lien externe

Ragab, A., Ghezzaz, H., & Amazouz, M. (2022). Decision fusion for reliable fault classification in energy-intensive process industries. Computers in Industry, 138, 13 pages. Lien externe

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. Lien externe

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. Lien externe

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. Lien externe

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. Lien externe

Ragab, A., El-Koujok, M., Amazouz, M., & Yacout, S. (janvier 2017). Fault detection and diagnosis in the Tennessee Eastman Process using interpretable knowledge discovery [Communication écrite]. 63rd Annual Reliability and Maintainability Symposium (RAMS 2017), Orlando, FL, United states. Lien externe

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. Lien externe

Ragab, A., Yacout, S., & Ouali, M.-S. (janvier 2016). Remaining useful life prognostics using pattern-based machine learning [Communication écrite]. Annual Reliability and Maintainability Symposium (RAMS 2016), Tucson, AZ (7 pages). Lien externe

Ragab, A., Yacout, S., & Ouali, M.-S. (janvier 2015). Interpretable pattern-based machine learning for condition-based maintenance [Communication écrite]. 61st Annual Reliability and Maintainability Symposium (RAMS 2015), Palm Harbor, Florida (6 pages). Non disponible

Ragab, A., Yacout, S., Ouali, M.-S., & Osman, H. (janvier 2015). Multiple failure modes prognostics using logical analysis of data [Communication écrite]. Annual Reliability and Maintainability Symposium (RAMS 2015), Palm Harbor, FL, USA (7 pages). Lien externe

Ragab, A., Ouali, M.-S., Yacout, S., & Osman, H. (mai 2014). Condition-based maintenance prognostics using logical analysis of data [Communication écrite]. IIE Annual Conference and Expo 2014, Montréal, Québec. Non disponible

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. Lien externe

S

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). Disponible

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). Lien externe

Z

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. Lien externe

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