Weiwei Yin
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Correction: Fetal health state detection method based on parameters efficient ensembling of deep learning
A Correction on Fetal health state detection method based on parameters efficient ensembling of deep learning By Yin, W., Shen, Z., Cheng, Z., Feng, C., and Sun, G. (2026). Front. Public Health 14:1808284. doi: 10.3389/fpubh.2026.1808284 The Data availability statement was erroneously given as “The datasets analyzed for this study can be found in the UC Irvin Machine Learning Repository.
Fetal health state detection method based on parameters efficient ensembling of deep learning
Abstract Background: The classification of cardiotocography (CTG) can assist obstetricians in assessing the health status of the fetus. However, traditional fetal heart rate monitoring data has the problem of strong subjectivity in manual interpretation, and deep learning models have poor representation ability on tabular data. Methods: This study proposed PLE-TabM, a tabular deep learning model combined piecewise linear encoding (PLE) and efficient weight integration.
Effect of Al2O3 Content on the Microstructure and Performance of Fe–Cr–Ni‐Based Coatings Fabricated Underwater
Conflict of Interest The authors declare no conflict of interest. Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. References 1, , , , , Int. J. Hydrogen Energy 2023, 48, 17983. 2, , , , , Int. J. Hydrogen Energy 2015, 40, 17102. 3, , , , , , , , Mater. Charact. 2025, 221, 114732. 4, , , , , , , , J. Mater. Res. Technol. 2023, 25, 13. 5, , , J. Mater. Eng. Perform. 2004, 13, 575. 6, , , , , , J. Mater. Sci. Technol.
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