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For: Ahmadzadeh A, Aydin B, Georgoulis MK, Kempton DJ, Mahajan SS, Angryk RA. How to Train Your Flare Prediction Model: Revisiting Robust Sampling of Rare Events. ApJS 2021;254:23. [DOI: 10.3847/1538-4365/abec88] [Cited by in Crossref: 14] [Cited by in F6Publishing: 15] [Article Influence: 14.0] [Reference Citation Analysis]
Number Citing Articles
1 Huwyler C, Melchior M. Using multiple instance learning for explainable solar flare prediction. Astronomy and Computing 2022. [DOI: 10.1016/j.ascom.2022.100668] [Reference Citation Analysis]
2 Ji A, Wen J, Angryk R, Aydin B. Solar Flare Forecasting with Deep Learning-based Time Series Classifiers. 2022 26th International Conference on Pattern Recognition (ICPR) 2022. [DOI: 10.1109/icpr56361.2022.9956097] [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 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] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Whitman K, Egeland R, Richardson IG, Allison C, Quinn P, Barzilla J, Kitiashvili I, Sadykov V, Bain HM, Dierckxsens M, Leila Mays M, Tadesse T, Lee KT, Semones E, Luhmann JG, Núñez M, White SM, Kahler SW, Ling AG, Smart DF, Shea MA, Tenishev V, Boubrahimi SF, Aydin B, Martens P, Angryk R, Marsh MS, Dalla S, Crosby N, Schwadron NA, Kozarev K, Gorby M, Young MA, Laurenza M, Cliver EW, Alberti T, Stumpo M, Benella S, Papaioannou A, Anastasiadis A, Sandberg I, Georgoulis MK, Ji A, Kempton D, Pandey C, Li G, Hu J, Zank GP, Lavasa E, Giannopoulos G, Falconer D, Kadadi Y, Fernandes I, Dayeh MA, Muñoz-jaramillo A, Chatterjee S, Moreland KD, Sokolov IV, Roussev II, Taktakishvili A, Effenberger F, Gombosi T, Huang Z, Zhao L, Wijsen N, Aran A, Poedts S, Kouloumvakos A, Paassilita M, Vainio R, Belov A, Eroshenko EA, Abunina MA, Abunin AA, Balch CC, Malandraki O, Karavolos M, Heber B, Labrenz J, Kühl P, Kosovichev AG, Oria V, Nita GM, Illarionov E, O’keefe PM, Jiang Y, Fereira SH, Ali A, Paouris E, Aminalragia-giamini S, Jiggens P, Jin M, Lee CO, Palmerio E, Bruno A, Kasapis S, Wang X, Chen Y, Sanahuja B, Lario D, Jacobs C, Strauss DT, Steyn R, den Berg J, Swalwell B, Waterfall C, Nedal M, Miteva R, Dechev M, Zucca P, Engell A, Maze B, Farmer H, Kerber T, Barnett B, Loomis J, Grey N, Thompson BJ, Linker JA, Caplan RM, Downs C, Török T, Lionello R, Titov V, Zhang M, Hosseinzadeh P. Review of Solar Energetic Particle Models. Advances in Space Research 2022. [DOI: 10.1016/j.asr.2022.08.006] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 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]
7 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]
8 Chen Y, Kempton DJ, Ahmadzadeh A, Wen J, Ji A, Angryk RA. CGAN-based synthetic multivariate time-series generation: a solution to data scarcity in solar flare forecasting. Neural Comput & Applic. [DOI: 10.1007/s00521-022-07361-8] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Palladino L, Ntagiou E, Klug J, Palacios J, Keil R. Sunspot Groups Detection and Classification on SDO/HMI Images using Deep Learning Techniques. 2022 IEEE Aerospace Conference (AERO) 2022. [DOI: 10.1109/aero53065.2022.9843222] [Reference Citation Analysis]
10 Soós S, Korsós MB, Morgan H, Erdélyi R. On the Differences in the Periodic Behavior of Magnetic Helicity Flux in Flaring Active Regions with and without X-class Events. ApJ 2022;925:129. [DOI: 10.3847/1538-4357/ac4094] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
11 Pandey C, Angryk RA, Aydin B. Deep Neural Networks Based Solar Flare Prediction Using Compressed Full-disk Line-of-sight Magnetograms. Information Management and Big Data 2022. [DOI: 10.1007/978-3-031-04447-2_26] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Pandey C, Angryk RA, Aydin B. Solar Flare Forecasting with Deep Neural Networks using Compressed Full-disk HMI Magnetograms. 2021 IEEE International Conference on Big Data (Big Data) 2021. [DOI: 10.1109/bigdata52589.2021.9671322] [Reference Citation Analysis]
13 Yeolekar A, Patel S, Talla S, Puthucode KR, Ahmadzadeh A, Sadykov VM, Angryk RA. Feature Selection on a Flare Forecasting Testbed: A Comparative Study of 24 Methods. 2021 International Conference on Data Mining Workshops (ICDMW) 2021. [DOI: 10.1109/icdmw53433.2021.00138] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Joshi C, Sobha B, Erdélyi R. Periodicities in X-ray solar flare occurrences and coherency with daily mean magnetic field. Monthly Notices of the Royal Astronomical Society 2021;508:3604-3610. [DOI: 10.1093/mnras/stab2877] [Reference Citation Analysis]
15 Georgoulis MK, Bloomfield DS, Piana M, Massone AM, Soldati M, Gallagher PT, Pariat E, Vilmer N, Buchlin E, Baudin F, Csillaghy A, Sathiapal H, Jackson DR, Alingery P, Benvenuto F, Campi C, Florios K, Gontikakis C, Guennou C, Guerra JA, Kontogiannis I, Latorre V, Murray SA, Park S, von Stachelski S, Torbica A, Vischi D, Worsfold M. The flare likelihood and region eruption forecasting (FLARECAST) project: flare forecasting in the big data & machine learning era. J Space Weather Space Clim 2021;11:39. [DOI: 10.1051/swsc/2021023] [Cited by in Crossref: 10] [Cited by in F6Publishing: 11] [Article Influence: 10.0] [Reference Citation Analysis]
16 Chen Y, Kempton DJ, Ahmadzadeh A, Angryk RA. Towards Synthetic Multivariate Time Series Generation for Flare Forecasting. Artificial Intelligence and Soft Computing 2021. [DOI: 10.1007/978-3-030-87986-0_26] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]