21 citations as recorded by crossref.

1. Forecasting Ionospheric foF2 Based on Deep Learning Method X. Li et al. https://doi.org/10.3390/rs13193849
2. Discussing Total Electron Content over the Solar Wind Parameters E. Eroglu & T. Muhammad https://doi.org/10.1155/2022/9592008
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4. Machine Learning-Based Short-Term GPS TEC Forecasting During High Solar Activity and Magnetic Storm Periods Y. Han et al. https://doi.org/10.1109/JSTARS.2021.3132049
5. Data fusion of ground-based GNSS, radio occultation and empirical model to predict the ionospheric peak electron density via artificial neural networks Y. Hou et al. https://doi.org/10.1007/s10291-025-01889-7
6. An attempt to validate HF propagation prediction conditions over Sub‐Saharan Africa M. Tshisaphungo et al. https://doi.org/10.1029/2010SW000643
7. Ionospheric anomalies related to the Mw 6.5 Samar, Philippines earthquake E. Eroglu https://doi.org/10.1007/s11600-022-00980-8
8. Ionosphere time series modeling using adaptive neuro-fuzzy inference system and principal component analysis M. Ghaffari Razin & B. Voosoghi https://doi.org/10.1007/s10291-020-0964-6
9. A deep learning approach for the prediction of ionospheric total electron content (TEC) based on combined prediction and two-step loss fine-tuning M. Deng et al. https://doi.org/10.1016/j.measurement.2025.116904
10. Investigating the performance of neural network backpropagation algorithms for TEC estimations using South African GPS data J. Habarulema & L. McKinnell https://doi.org/10.5194/angeo-30-857-2012
11. The Bézier curve and neural network model of the time-domain transient signals E. Eroglu & O. Tretyakov https://doi.org/10.1016/j.cpc.2024.109211
12. Bézier Cubics and Neural Network Agreement along a Moderate Geomagnetic Storm E. Eroglu et al. https://doi.org/10.1155/2024/3559969
13. Bézier cubics’ agreement with the neural network of the TEC map E. Eroglu https://doi.org/10.1007/s00500-024-09863-0
14. Regional application of generalized regression neural network in ionosphere spatio-temporal modeling and forecasting S. Ghaffari-Razin et al. https://doi.org/10.1007/s10291-022-01389-y
15. Forecast of global ionospheric TEC using an improved transformer model X. Wu et al. https://doi.org/10.1016/j.asr.2024.02.003
16. Single Parameter-Based Neural Networks Prediction of TEC V. Jayapal & M. Ahmad Faizal https://doi.org/10.4028/www.scientific.net/AMM.225.481
17. Ionosphere tomography using wavelet neural network and particle swarm optimization training algorithm in Iranian case study M. Ghaffari Razin & B. Voosoghi https://doi.org/10.1007/s10291-017-0614-9
18. Bi-LSTM based vertical total electron content prediction at low-latitude equatorial ionization anomaly region of South India V. Maheswaran et al. https://doi.org/10.1016/j.asr.2023.08.054
19. Machine learning approach for detection of plasma depletions from TEC C. Kapil & G. Seemala https://doi.org/10.1016/j.asr.2023.04.042
20. Implementation of Hybrid Ionospheric TEC Forecasting Algorithm Using PCA-NN Method I. Mallika et al. https://doi.org/10.1109/JSTARS.2018.2877445
21. Simultaneous evaluation of solar activity proxies during geomagnetic storms using principal component analysis: Case study of the African low and mid-latitude regions J. Uwamahoro et al. https://doi.org/10.1016/j.jastp.2025.106477