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Word cloudA 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.
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Ali, A. M., Yacout, S., Rabeih, E.-A., & Shaban, Y. (2022). A cyber process control system based on pattern recognition and cloud computing. Gestão & Produção, 29. External link
Ali, A. M., Mohamed, E.-A., Yacout, S., & Shaban, Y. (2020). Cloud computing based unsupervised fault diagnosis system in the context of Industry 4.0. Gestão & Produção, 27(3), 19 pages. Available
Aly, M., Yacout, S., & Shaban, Y. (2017, January). Analysis of massive industrial data using MapReduce framework for parallel processing [Paper]. 63rd Annual Reliability and Maintainability Symposium (RAMS 2017), Orlando, FL (6 pages). External link
Aramesh, M., Shaban, Y., Balazinski, M., Attia, H., Kishawy, H. A., & Yacout, S. (2014, June). Survival life analysis of the cutting tools during turning titanium metal matrix composites (Ti-MMCs) [Paper]. 6th CIRP Conference on Performance Cutting (HPC 2014), Berkeley, Cal.. Published in Procedia CIRP, 14. External link
Bassetto, S., Yacout, S., Bassetto, S., & Shaban, Y. (2023). Experimental vibration data collected for a belt drive system under different operating conditions. Data in Brief, 48, 6 pages. Available
Elsheikh, A., Yacout, S., Ouali, M.-S., & Shaban, Y. (2020). Failure time prediction using adaptive logical analysis of survival curves and multiple machining signals. Journal of Intelligent Manufacturing, 31(2), 403-415. External link
Gonzalez Rubio, J. L., Shaban, Y., & Yacout, S. (2016, March). Visual data mining of faults in machining process based on machine learning [Paper]. International Conference on Industrial Engineering and Operations Management (IEOM 2016), Kuala Lumpur, Malaysia. External link
Khalifa, R. M., Yacout, S., Bassetto, S., & Shaban, Y. (2024). Condition monitoring and warning of a belt drive system based on a logical analysis of data regression-based residual control chart. Structural Health Monitoring. External link
Shaban, Y., Yacout, S., & Aly, M. (2017, January). Condition-based reliability prediction based on logical analysis of survival data [Paper]. 63rd Annual Reliability and Maintainability Symposium (RAMS 2017), Orlando, FL (6 pages). External link
Shaban, Y., Yacout, S., Balazinski, M., & Jemielniak, K. (2017). Cutting tool wear detection using multiclass logical analysis of data. Machining Science and Technology, 21(4), 526-541. External link
Shaban, Y., & Yacout, S. (2016, January). Cutting tool remaining useful life during turning of metal matrix composites [Paper]. Annual Reliability and Maintainability Symposium (RAMS 2016), Tucson, AZ (6 pages). External link
Shaban, Y., & Yacout, S. (2016, March). Identifying optimal intervene hazard for cutting tools considering cost-availability optimization [Paper]. International Conference on Industrial Engineering and Operations Management (IEOM 2016), Kuala Lumpur, Malaysia. External link
Shaban, Y., & Yacout, S. (2016). Predicting the remaining useful life of a cutting tool during turning titanium metal matrix composites. Proceedings of the Institution of Mechanical Engineers. Part B, Journal of Engineering Manufacture, 232(4), 681-689. External link
Shaban, Y., Yacout, S., Balazinski, M., Meshreki, M., & Attia, H. (2015, March). Diagnosis of machining outcomes based on machine learning with logical analysis of data [Paper]. International Conference on Industrial Engineering and Operations Management (IEOM 2015), Dubai, United Arab Emirates (8 pages). External link
Shaban, Y., Aramesh, M., Yacout, S., Balazinski, M., Attia, H., & Kishawy, H. (2015). Optimal replacement times for machining tool during turning titanium metal matrix composites under variable machining conditions. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 231(6), 924-932. External link
Shaban, Y., Yacout, S., & Balazinski, M. (2015, January). Tool replacement based on pattern recognition with LAD [Paper]. Annual Reliability and Maintainability Symposium (RAMS 2015), Palm Harbor, FL (6 pages). External link
Shaban, Y. (2014). Diagnosis of Machining Conditions Based on Logical Analysis of Data [Ph.D. thesis, École Polytechnique de Montréal]. Available
Shaban, Y., Aramesh, M., Yacout, S., Balazinski, M., Attia, H., & Kishawy, H. (2014, May). Optimal replacement of tool during turning titanium metal matrix composites [Paper]. IIE Annual Conference and Expo 2014, Montréal, Québec. Unavailable
Shaban, Y., Meshreki, M., Yacout, S., Balazinski, M., & Attia, H. (2014). Process control based on pattern recognition for routing carbon fiber reinforced polymer. Journal of Intelligent Manufacturing, 28(1), 165-179. External link
Taha, H. A., Yacout, S., & Shaban, Y. (2023). Online failure analysis and autonomous risk control scheme for electric buses. Engineering Failure Analysis, 154, 15 pages. External link
Taha, H. A., Yacout, S., & Shaban, Y. (2022). Autonomous self-healing mechanism for a CNC milling machine based on pattern recognition. Journal of Intelligent Manufacturing, 34(5), 2185-2205. External link
Taha, H. A., Yacout, S., & Shaban, Y. (2022). Deep reinforcement learning for autonomous pre-failure tool life improvement. International Journal of Advanced Manufacturing Technology, 121(9-10), 6169-6192. External link
