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Cited by in F6Publishing
For: Yi K, Moon Y, Lim D, Park E, Lee H. Visual Explanation of a Deep Learning Solar Flare Forecast Model and Its Relationship to Physical Parameters. ApJ 2021;910:8. [DOI: 10.3847/1538-4357/abdebe] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
1 Ma Q, Du QF, Feng SW, Hou YC, Ji WZ, Han CS. Solar Radio-Burst Forecast Based on a Convolutional Neural Network. Sol Phys 2022;297:130. [DOI: 10.1007/s11207-022-02069-3] [Reference Citation Analysis]
2 Ran H, Liu YD, Guo Y, Wang R. Relationship between Successive Flares in the Same Active Region and SHARP Parameters. ApJ 2022;937:43. [DOI: 10.3847/1538-4357/ac80fa] [Reference Citation Analysis]
3 Wrench D, Parashar TN, Singh RK, Frean M, Rayudu R. Exploring the Potential of Neural Networks to Predict Statistics of Solar Wind Turbulence. Space Weather 2022;20. [DOI: 10.1029/2022sw003200] [Reference Citation Analysis]
4 Sun Z, Bobra MG, Wang X, Wang Y, Sun H, Gombosi T, Chen Y, Hero A. Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data. ApJ 2022;931:163. [DOI: 10.3847/1538-4357/ac64a6] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Kawai T, Imada S. Factors That Determine the Power-law Index of an Energy Distribution of Solar Flares. ApJ 2022;931:113. [DOI: 10.3847/1538-4357/ac6aca] [Reference Citation Analysis]
6 Guastavino S, Marchetti F, Benvenuto F, Campi C, Piana M. Implementation paradigm for supervised flare forecasting studies: A deep learning application with video data. A&A 2022;662:A105. [DOI: 10.1051/0004-6361/202243617] [Reference Citation Analysis]