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Cited by in F6Publishing
For: Abed AK, Qahwaji R, Abed A. The automated prediction of solar flares from SDO images using deep learning. Advances in Space Research 2021;67:2544-57. [DOI: 10.1016/j.asr.2021.01.042] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 6.0] [Reference Citation Analysis]
Number Citing Articles
1 Chola C, Benifa JVB. Detection and classification of sunspots via deep convolutional neural network. Global Transitions Proceedings 2022;3:177-82. [DOI: 10.1016/j.gltp.2022.03.006] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
2 Deshmukh V, Flyer N, van der Sande K, Berger T. Decreasing False-alarm Rates in CNN-based Solar Flare Prediction Using SDO/HMI Data. ApJS 2022;260:9. [DOI: 10.3847/1538-4365/ac5b0c] [Reference Citation Analysis]
3 Zheng M, Luo J, Dang Z. Feedforward neural network based time-varying state-transition-matrix of Tschauner-Hempel equations. Advances in Space Research 2022;69:1000-11. [DOI: 10.1016/j.asr.2021.10.023] [Reference Citation Analysis]
4 Quan L, Xu L, Li L, Wang H, Huang X. Solar Active Region Detection Using Deep Learning. Electronics 2021;10:2284. [DOI: 10.3390/electronics10182284] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]