“…Long Short-Term Memory (LSTM) neural network was used in combination with other models for the prediction of SC-25 (Pala and Atici, 2019;Benson et al, 2020;Lee, 2020;Prasad et al, 2022). Several machine learning methods were used by Dani and Sulistiani (2019) to compare the predicted peak amplitude of SSN for SC-25, and the obtained results were different among these methods, namely: 159.4 ± 22.3, 95.5 ± 21.9, 110.2 ± 12.8, and 93.7 ± 23.2 respectively for Linear Regression (LR), Radial Basis Function (RBF), Random Forest (RF) and Support Vector Machine (SVM), and peak occurring times of SC-25 would be September 2023, December 2024, December 2024 and July 2024. Other methods based on a non-linear model (Kitiashvili, 2020;Sarp et al, 2018), statistical methods used feature parameters of the solar cycle to predict the behavior of SC-25 (Li, Feng, and Li, 2015;Singh and Bhargawa, 2017;Kakad, Kumar, and Kakad, 2020), and spectral methods (Rigozo et al, 2011) also obtained different prediction results of the maximum SSN or the peak amplitude of SC-25 with the occurring time.…”