Amirkabir University of Technology | Iran
Seyed Matin Malakouti holds an MS in Electrical Engineering (Control Systems) from the University of Tabriz, Iran (2019-2022) following a BS in Electrical Engineering from the Isfahan University of Technology, Iran. He has been active since 2022 in machine learning research, applying algorithms to biomedical engineering, renewable energy and optimization problems. His output includes over 10 peer-reviewed journal articles in Q1 and Q2 journals published between 2023-2025; one example is his work on wind-power generation prediction that has accrued 81 citations. While a formal comprehensive bibliometric profile is not publicly documented, his documented work suggests approximately 21documents, 612 citations, and an estimated h-index of 14. Malakouti’s research interests encompass applied machine learning, biomedical engineering, renewable energy systems, optimization and fuzzy logic. He has also contributed as a peer-reviewer for multiple journals (e.g., IEEE Access, Scientific Reports, Environmental Challenges) and is actively seeking a PhD position to expand his research portfolio. In summary, Malakouti is an emerging researcher committed to advancing machine-learning methodologies for engineering and environmental applications, bridging academic theory with real-world data-driven solutions.
Featured Publications
Malakouti, S. M. (2025). Enhanced epilepsy detection using discrete wavelet transform and bandpass filtering on EEG data: Integration of ART-based and LVQ models. Clinical eHealth. Advance online publication.
Malakouti, S. M. (2025). From accurate to actionable: Interpretable PM₂.₅ forecasting with feature engineering and SHAP for the Liverpool–Wirral region. Environmental Challenges. Advance online publication.
Malakouti, S. M., Menhaj, M. B., & Suratgar, A. A. (2025). Efficiency and accuracy comparison of machine learning algorithms for predicting U.S. energy consumption across sectors. South African Journal of Chemical Engineering.
Ansari, I., Hassani, K., Malakouti, S. M., & Suratgar, A. A. (2025). Predicting electrical energy consumption for Finland using a CNN–LSTM hybrid model. Next Research.
Malakouti, S., Menhaj, M. B., & Suratgar, A. A. (2024). Machine learning and transfer learning techniques for accurate brain tumor classification. Clinical eHealth, 8, Article 08.001.