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For: Sadykov VM, Kosovichev AG. Relationships between Characteristics of the Line-of-sight Magnetic Field and Solar Flare Forecasts. ApJ 2017;849:148. [DOI: 10.3847/1538-4357/aa9119] [Cited by in Crossref: 20] [Cited by in F6Publishing: 17] [Article Influence: 4.0] [Reference Citation Analysis]
Number Citing Articles
1 Sinha S, Gupta O, Singh V, Lekshmi B, Nandy D, Mitra D, Chatterjee S, Bhattacharya S, Chatterjee S, Srivastava N, Brandenburg A, Pal S. A Comparative Analysis of Machine-learning Models for Solar Flare Forecasting: Identifying High-performing Active Region Flare Indicators. ApJ 2022;935:45. [DOI: 10.3847/1538-4357/ac7955] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Nechaeva AB, Sharykin IN, Zimovets IV, Chen F. Relationship between the Horizontal Gradient of the Vertical Magnetic Field and the Horizontal Electric Current on the Photosphere in a Model Active Region of the Sun. Geomagn Aeron 2021;61:956-63. [DOI: 10.1134/s0016793221070148] [Reference Citation Analysis]
3 Deng Z, Wang F, Deng H, Tan L, Deng L, Feng S. Fine-grained Solar Flare Forecasting Based on the Hybrid Convolutional Neural Networks*. ApJ 2021;922:232. [DOI: 10.3847/1538-4357/ac2b2b] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Tang R, Liao W, Chen Z, Zeng X, Wang J, Luo B, Chen Y, Cui Y, Zhou M, Deng X, Li H, Yuan K, Hong S, Wu Z. Solar Flare Prediction Based on the Fusion of Multiple Deep-learning Models. ApJS 2021;257:50. [DOI: 10.3847/1538-4365/ac249e] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
5 Zheng Y, Li X, Si Y, Qin W, Tian H. Hybrid deep convolutional neural network with one-versus-one approach for solar flare prediction. Monthly Notices of the Royal Astronomical Society 2021;507:3519-39. [DOI: 10.1093/mnras/stab2132] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 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: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
7 Benvenuto F, Campi C, Massone AM, Piana M. Machine Learning as a Flaring Storm Warning Machine: Was a Warning Machine for the 2017 September Solar Flaring Storm Possible? ApJ 2020;904:L7. [DOI: 10.3847/2041-8213/abc5b7] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 2.5] [Reference Citation Analysis]
8 Nishizuka N, Kubo Y, Sugiura K, Den M, Ishii M. Reliable Probability Forecast of Solar Flares: Deep Flare Net-Reliable (DeFN-R). ApJ 2020;899:150. [DOI: 10.3847/1538-4357/aba2f2] [Cited by in Crossref: 8] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
9 Cinto T, Gradvohl ALS, Coelho GP, da Silva AEA. A framework for designing and evaluating solar flare forecasting systems. Monthly Notices of the Royal Astronomical Society 2020;495:3332-49. [DOI: 10.1093/mnras/staa1257] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
10 Sharykin IN, Zimovets IV, Myshyakov II. Flare Energy Release at the Magnetic Field Polarity Inversion Line during the M1.2 Solar Flare of 2015 March 15. II. Investigation of Photospheric Electric Current and Magnetic Field Variations Using HMI 135 s Vector Magnetograms. ApJ 2020;893:159. [DOI: 10.3847/1538-4357/ab84ef] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 4.0] [Reference Citation Analysis]
11 Isaeva ES, Tomozov VM, Yazev SA. X-Ray Flares and Activity Complexes on the Sun in Solar Cycle 24. Astron Rep 2020;64:58-65. [DOI: 10.1134/s1063772920010035] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
12 Li X, Zheng Y, Wang X, Wang L. Predicting Solar Flares Using a Novel Deep Convolutional Neural Network. ApJ 2020;891:10. [DOI: 10.3847/1538-4357/ab6d04] [Cited by in Crossref: 20] [Cited by in F6Publishing: 15] [Article Influence: 10.0] [Reference Citation Analysis]
13 Zheng Y, Li X, Wang X. Solar Flare Prediction with the Hybrid Deep Convolutional Neural Network. ApJ 2019;885:73. [DOI: 10.3847/1538-4357/ab46bd] [Cited by in Crossref: 15] [Cited by in F6Publishing: 12] [Article Influence: 5.0] [Reference Citation Analysis]
14 Campi C, Benvenuto F, Massone AM, Bloomfield DS, Georgoulis MK, Piana M. Feature Ranking of Active Region Source Properties in Solar Flare Forecasting and the Uncompromised Stochasticity of Flare Occurrence. ApJ 2019;883:150. [DOI: 10.3847/1538-4357/ab3c26] [Cited by in Crossref: 24] [Cited by in F6Publishing: 20] [Article Influence: 8.0] [Reference Citation Analysis]
15 Lee E, Park S, Moon Y. Flare Productivity of Major Flaring Solar Active Regions: A Time-Series Study of Photospheric Magnetic Properties. Sol Phys 2018;293. [DOI: 10.1007/s11207-018-1381-7] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
16 Vasantharaju N, Vemareddy P, Ravindra B, Doddamani VH. Statistical Study of Magnetic Nonpotential Measures in Confined and Eruptive Flares. ApJ 2018;860:58. [DOI: 10.3847/1538-4357/aac272] [Cited by in Crossref: 21] [Cited by in F6Publishing: 18] [Article Influence: 5.3] [Reference Citation Analysis]
17 Nishizuka N, Sugiura K, Kubo Y, Den M, Ishii M. Deep Flare Net (DeFN) Model for Solar Flare Prediction. ApJ 2018;858:113. [DOI: 10.3847/1538-4357/aab9a7] [Cited by in Crossref: 58] [Cited by in F6Publishing: 43] [Article Influence: 14.5] [Reference Citation Analysis]