| Electricity demand forecasting at distribution and household levels using explainable causal graph neural network A Miraki, P Parviainen, R Arghandeh Energy and AI 16, 100368, 2024 | 10 | 2024 |
| Traffic monitoring system design considering multi-hazard disaster risks M Gazzea, A Miraki, O Alisan, MM Kuglitsch, I Pelivan, EE Ozguven, ... Scientific reports 13 (1), 4883, 2023 | 10 | 2023 |
| A modified spline graph filter bank A Miraki, H Saeedi-Sourck Circuits, Systems, and Signal Processing 40 (4), 2025-2035, 2021 | 9 | 2021 |
| Spectral domain spline graph filter bank A Miraki, H Saeedi-Sourck, N Marchetti, A Farhang IEEE Signal Processing Letters 28, 469-473, 2021 | 7 | 2021 |
| Spline graph filter bank with spectral sampling A Miraki, H Saeedi-Sourck Circuits, Systems, and Signal Processing 40 (11), 5744-5758, 2021 | 5 | 2021 |
| Probabilistic forecasting of renewable energy and electricity demand using Graph-based Denoising Diffusion Probabilistic Model A Miraki, P Parviainen, R Arghandeh Energy and AI 19, 100459, 2025 | 2 | 2025 |
| Causal-based spatio-temporal graph neural networks for industrial internet of things multivariate time series forecasting A Miraki, A Dapkutė, V Šiožinys, M Jonaitis, R Arghandeh World Conference on Explainable Artificial Intelligence, 120-130, 2023 | 1 | 2023 |
| Causal-based Spatio-Temporal Graph Neural Networks: Interpretable Deep Learning Framework for Multivariate Time Series Forecasting A Miraki, R Arghandeh Symposium on AI, Data and Digitalization (SAIDD 2023) 88, 2023 | 1 | 2023 |
| Advancements in Super-Resolution Methods for Smart Meter Data M Iversen, M Khan, A Miraki, R Arghandeh Frontiers in Energy Research 11, 1288683, 2023 | | 2023 |
Reza ArghandehProfessor, Western Norway University of Applied Sciencesبريد إلكتروني تم التحقق منه على hvl.no