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A word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Al-Sakkari, E. G., Ragab, A., Amer, M., Ajao, O., Benali, M., Boffito, D. C., Dagdougui, H., & Amazouz, M. (2024). Ensemble machine learning to accelerate industrial decarbonization: Prediction of Hansen solubility parameters for streamlined chemical solvent selection. Digital Chemical Engineering, 14, 100207 (26 pages). External link
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
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
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]. 10th International Conference on Foundations of Computer Aid Process Design (FOCAPD 2024), Breckenridge, Colorado, USA. Published in Systems & Control Transactions, 3. 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
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
Amazouz, M. (1995). Analyse du transfert de chaleur et de la dissipation visqueuse dans un composite unidirectionnel [Ph.D. thesis, École Polytechnique de Montréal]. Unavailable
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
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., 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., 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
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