Open Access
| Issue |
E3S Web Conf.
Volume 723, 2026
2026 International Conference on Artificial Intelligence in Energy and Infrastructure (AIEI 2026)
|
|
|---|---|---|
| Article Number | 01005 | |
| Number of page(s) | 6 | |
| Section | Artificial Intelligence, Machine Learning & Data Analytics | |
| DOI | https://doi.org/10.1051/e3sconf/202672301005 | |
| Published online | 08 July 2026 | |
- J. E. Lawn et al., Stillbirths: rates, risk factors, and acceleration towards 2030. The Lancet 387, 587–603 (2016) [Google Scholar]
- D. Ayres-de-Campos, C. Y. Spong, E. Chandraharan, FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. Int. J. Gynecol. Obstet. 131, 13–24 (2015) [Google Scholar]
- Z. Alfirevic, D. Devane, G. M. L. Gyte, A. Cuthbert, Continuous cardiotocography as electronic fetal monitoring for fetal assessment during labour. Cochrane Database Syst. Rev. 2 (2017) [Google Scholar]
- American College of Obstetricians and Gynecologists, ACOG Practice Bulletin No. 106: Intrapartum fetal heart rate monitoring. Obstet. Gynecol. 114, 192–202 (2009) [Google Scholar]
- A. Petrozziello, A. T. Papageorghiou, C. W. Redman, L. Georgieva, Multimodal convolutional neural networks to detect fetal compromise during labor and delivery. IEEE Access 7, 112026–112036 (2019) [Google Scholar]
- J. Ogasawara et al., Deep neural network-based classification of cardiotocograms outperformed conventional algorithms. Sci. Rep. 11, 13367 (2021) [Google Scholar]
- Y. Nie, N. H. Nguyen, P. Sinthong, J. Kalagnanam, A time series is worth 64 words: Long-term forecasting with Transformers, in Proc. ICLR (2023) [Google Scholar]
- Y. Zhang, J. Yan, Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting, in Proc. ICLR (2023) [Google Scholar]
- H. I. Fawaz et al., InceptionTime: Finding AlexNet for time series classification. Data Min. Knowl. Discov. 34, 1936–1962 (2020) [Google Scholar]
- A. Vaswani et al., Attention is all you need, in Proc. NeurIPS, 5998–6008 (2017) [Google Scholar]
- J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks, in Proc. CVPR, 7132–7141 (2018) [Google Scholar]
- R. Xiong et al., On layer normalization in the Transformer architecture, in Proc. ICML, 10524–10533 (2020) [Google Scholar]
- T. Y. Lin, P. Goyal, R. Girshick, K. He, P. Dollár, Focal loss for dense object detection, in Proc. ICCV, 2980–2988 (2017) [Google Scholar]
- L. N. Smith, N. Topin, Super-convergence: very fast training of neural networks using large learning rates, in Proc. SPIE 11006, 369–386 (2019) [Google Scholar]
- V. Chudácˇek et al., Open access intrapartum CTG database. BMC Pregnancy Childbirth 14, 16 (2014) [Google Scholar]
- E. R. DeLong, D. M. DeLong, D. L. Clarke-Pearson, Comparing the areas under two or more correlated ROC curves: A nonparametric approach. Biometrics 44, 837–845 (1988) [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

