| The Interpretability of LSTM Models for Predicting Oil Company Stocks: Impact of Correlated Features PK Javad T Firouzjaee arXiv preprint arXiv:2201.00350 , International Journal of Energy Research 2024, 2022 | 8* | 2022 |
| Jewelry rock discrimination as interpretable data using laser-induced breakdown spectroscopy and a convolutional LSTM deep learning algorithm P Khalilian, F Rezaei, N Darkhal, P Karimi, A Safi, V Palleschi, ... Scientific Reports 14 (1), 5169, 2024 | 6 | 2024 |
| Modeling the Central Supermassive Black Hole Mass of Quasars via the LSTM Approach SS Tabasi, RV Salmani, P Khaliliyan, JT Firouzjaee The Astrophysical Journal 954 (2), 164, 2023 | 4 | 2023 |
| Recurrent Neural Networks and classical machine learning methods for concentrations prediction of aluminum alloy in laser Induced breakdown spectroscopy F Rezaei, P Khalilian, M Rezaei, P Karimi, B Ashrafkhani Optik 309, 171838, 2024 | 3 | 2024 |
| The interpretability of LSTM models for predicting oil company stocks: impact of correlated features JT Firouzjaee, P Khalilian International Journal of Energy Research 2024 (1), 5526692, 2024 | 3 | 2024 |
| Design interpretable experience of dynamical feed forward machine learning model for forecasting NASDAQ P Khalilian, S Azizi, MH Amiri, JT Firouzjaee arXiv preprint arXiv:2212.12044, 2022 | 3 | 2022 |
| Machine learning model to project the impact of Ukraine crisis JT Firouzjaee, P Khaliliyan Austin Journal of Accounting, Audit and Finance Management 2 (1), 2022 | 2 | 2022 |
| A comparison between Recurrent Neural Networks and classical machine learning approaches In Laser induced breakdown spectroscopy F Rezaei, P Khaliliyan, M Rezaei, P Karimi, B Ashrafkhani Optik, arXiv preprint arXiv:2304.08500 309, 171838, 2023 | 1 | 2023 |
Javad Taghizadeh FirouzjaeeK. N. Toosi University of Technology確認したメール アドレス: ipm.ir