Open Access
| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01031 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/epjconf/202533701031 | |
| Published online | 07 October 2025 | |
- Mobarak Abumohsen et al. “Hybrid machine learning model combining of CNN-LSTM-RF for time series forecasting of Solar Power Generation”. In: e-Prime - Advances in Electrical Engineering, Electronics and Energy 9 (2024), p. 100636. issn: 2772-6711. [Google Scholar]
- Christian Ariza-Porras, Valentin Kuznetsov, and Federica Legger. “The CMS monitoring infrastructure and applications”. In: Computing and Software for Big Science 5.1 (Jan. 2021), p. 5. issn: 2510-2044. [Google Scholar]
- Kyunghyun Cho. “Learning phrase representations using RNN encoder-decoder for statistical machine translation”. In: arXiv preprint arXiv:1406.1078 (2014). [Google Scholar]
- Cms Collaboration et al. “The CMS experiment at the CERN LHC”. In: Journal of instrumentation 3.August 2008 (2008), pp. 1–334. [Google Scholar]
- Faming Huang et al. “A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction”. In: Landslides 17 (2020), pp. 217–229. [CrossRef] [Google Scholar]
- M.S. Ibrahim, S.M. Gharghory, and H.A. Kamal. “A hybrid model of CNN and LSTM autoencoder-based short-term PV power generation forecasting”. In: Electrical Engineering 106 (2024), pp. 4239–4255. [Google Scholar]
- Muhammad Imran et al. “Migration of CMSWEB cluster at CERN to Kubernetes: a comprehensive study”. In: Cluster Computing 24.4 (2021), pp. 3085–3099. [Google Scholar]
- Pankaj Malhotra et al. “Long short term memory networks for anomaly detection in time series.” In: Esann. Vol. 2015. 2015, p. 89. [Google Scholar]
- Bendong Zhao et al. “Convolutional neural networks for time series classification”. In: Journal of systems engineering and electronics 28.1 (2017), pp. 162–169. [Google Scholar]
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