Ambre Dupuis, Camélia Dadouchi
, Bruno Agard and Robert Pellerin
Paper (2022)
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| Department: | Department of Mathematics and Industrial Engineering |
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| Research Center: | LID - Laboratoire en intelligence des données |
| PolyPublie URL: | https://publications.polymtl.ca/54099/ |
| Conference Title: | International Conference on ENTERprise Information Systems (CENTERIS 2022) |
| Conference Location: | Lisbon, Portugal |
| Conference Date(s): | 2022-11-09 - 2022-11-11 |
| Journal Title: | Procedia Computer Science (vol. 219) |
| Publisher: | Elsevier |
| DOI: | 10.1016/j.procs.2023.01.300 |
| Official URL: | https://doi.org/10.1016/j.procs.2023.01.300 |
| Date Deposited: | 10 Jul 2023 16:30 |
| Last Modified: | 25 Sep 2024 16:45 |
| Cite in APA 7: | Dupuis, A., Dadouchi, C., Agard, B., & Pellerin, R. (2022, November). Forecasting future product sequences to be processed in tire production using deep learning technique [Paper]. International Conference on ENTERprise Information Systems (CENTERIS 2022), Lisbon, Portugal. Published in Procedia Computer Science, 219. https://doi.org/10.1016/j.procs.2023.01.300 |
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