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For: Nishizuka N, Kubo Y, Sugiura K, Den M, Ishii M. Operational solar flare prediction model using Deep Flare Net. Earth Planets Space 2021;73. [DOI: 10.1186/s40623-021-01381-9] [Cited by in Crossref: 10] [Cited by in F6Publishing: 12] [Article Influence: 10.0] [Reference Citation Analysis]
Number Citing Articles
1 Wang J, Luo B, Liu S. Precursor identification for strong flares based on anomaly detection algorithm. Front Astron Space Sci 2022;9:1037863. [DOI: 10.3389/fspas.2022.1037863] [Reference Citation Analysis]
2 Kontogiannis I. The characteristics of flare- and CME-productive solar active regions. Advances in Space Research 2022. [DOI: 10.1016/j.asr.2022.10.008] [Reference Citation Analysis]
3 Pandey C, Ji A, Angryk RA, Georgoulis MK, Aydin B. Towards coupling full-disk and active region-based flare prediction for operational space weather forecasting. Front Astron Space Sci 2022;9:897301. [DOI: 10.3389/fspas.2022.897301] [Reference Citation Analysis]
4 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]
5 Moulshree, Anjum M, Goel A. Solar Flare Prediction using Machine Learning Algorithms on RHESSI Dataset. 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) 2022. [DOI: 10.1109/icscds53736.2022.9760755] [Reference Citation Analysis]
6 Sun H, Manchester W, Chen Y. Improved and Interpretable Solar Flare Predictions With Spatial and Topological Features of the Polarity Inversion Line Masked Magnetograms. Space Weather 2021;19. [DOI: 10.1029/2021sw002837] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
7 Krista LD, Chih M. A DEFT Way to Forecast Solar Flares. ApJ 2021;922:218. [DOI: 10.3847/1538-4357/ac2840] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Kusano K, Ichimoto K, Ishii M, Miyoshi Y, Yoden S, Akiyoshi H, Asai A, Ebihara Y, Fujiwara H, Goto T, Hanaoka Y, Hayakawa H, Hosokawa K, Hotta H, Hozumi K, Imada S, Iwai K, Iyemori T, Jin H, Kataoka R, Katoh Y, Kikuchi T, Kubo Y, Kurita S, Matsumoto H, Mitani T, Miyahara H, Miyoshi Y, Nagatsuma T, Nakamizo A, Nakamura S, Nakata H, Nishizuka N, Otsuka Y, Saito S, Saito S, Sakurai T, Sato T, Shimizu T, Shinagawa H, Shiokawa K, Shiota D, Takashima T, Tao C, Toriumi S, Ueno S, Watanabe K, Watari S, Yashiro S, Yoshida K, Yoshikawa A. PSTEP: project for solar–terrestrial environment prediction. Earth Planets Space 2021;73. [DOI: 10.1186/s40623-021-01486-1] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
9 Kusano K, Ishii M, Berger T, Miyoshi Y, Yoden S, Liu H, Onsager T, Ichimoto K. Special issue “Solar–terrestrial environment prediction: toward the synergy of science and forecasting operation of space weather and space climate”. Earth Planets Space 2021;73. [DOI: 10.1186/s40623-021-01530-0] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 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]
11 Fidor T, Sitarek J. Assessing the capability of random forest to predict the evolution of enhanced gamma-ray states of active galactic nuclei. Astroparticle Physics 2021;132:102625. [DOI: 10.1016/j.astropartphys.2021.102625] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]