A. Landenberger, S. Szénási: Financial Forecasting using Quantum-Inspired Deep Learning. In IEEE 29th International Conference on Intelligent Engineering Systems (INES 2025) Proceedings. pp. 325–328, 2025. ISBN 979-8-3315-9771-9 link Abstract: Forecasting stock market prices has always been a difficult field, particularly with the incorporation of multiple scientific fields, adding to its complexity. This study presents a [...]

S. Szénási, G. Légrádi, G. Kovács: Deep Learning-Based Step Size Determination for Hill Climbing Metaheuristics. ALGORITHMS Vol. 18, No. 5, Article No. 298, pp. 1–15, 2025. ISSN 1999-4893 link Abstract: Machine Learning-assisted metaheuristics is a new and promising research topic, combining the advantages of both method families. Metaheuristics are widely used general problem solvers that [...]

S. Tyagi, S. Szénási: Advancements and Challenges in Emotion Extraction from Speech: A PRISMA-Guided Systematic Review of Machine and Deep Learning Technique. In Advances in Service and Industrial Robotics (Mechanisms and Machine Science). Cham, CH : Springer, pp. 439–447, 2025. ISSN 2211-0984, ISBN 978-3-032-02105-2 link Abstract: This PRISMA-compliant systematic review synthesizes 127 studies (2018–2023) on [...]

H. K. Fatlawi, A. Kiss: Dynamic Fusion of LSTM Predictions Using Reinforcement Learning-Based GOWLA for Human Activity Recognition. IEEE ACCESS Vol. 13, pp. 104779–104790, 2025. ISSN 2169-3536 link Abstract: Human Activity Recognition (HAR) contributes significantly to vital areas in healthcare, IoT, and smart monitoring applications. Generally, the current models rely on deep learning and traditional [...]

W. Pengyu, A. Mosavi, I. Felde, M. Azodinia, A. Delavar, A. Azamat, S. Wei: Machine Learning for Modeling Stress Evolution. ACTA POLYTECHNICA HUNGARICA Vol. 22, No. 12, 2025. pp. 95–114. ISSN 1785-8860 link Abstract: We present a detailed review and evaluation of machine learning (ML) methods for modeling and predicting stress evolution in various materials [...]

E. Akbari, T. N. Chakherlou, H. Tabrizchi, A. Mosavi: Physics-Informed Neural Networks for Multiaxial Fatigue Life Prediction of Aluminum Alloy. COMPUTER MODELING IN ENGINEERING & SCIENCES Vol. 145, No. 1, 2025. pp. 305–325. ISSN 1526-1492 link Abstract: The ability to predict multiaxial fatigue life of Al-Alloy 7075-T6 under complex loading conditions is critical to assessing [...]

M. Mudabbir, A. Mosavi, H. Perez: Detecting Building Defects with Deep Learning. EURASIAN JOURNAL OF MATHEMATICAL AND COMPUTER APPLICATIONS Vol. 13, No. 3, pp. 50–67, 2025. ISSN 2306–6172 link Abstract: Building defects on external walls can include cracks, mould, dampness from waterproofing failures, fungus growth due to high humidity, and paint peeling. These building defects [...]

M. Azodinia, A. Shayakhmetov, A. Mosavi: Machine Learning for Pavement Performance and Service Life Prediction. EURASIAN JOURNAL OF MATHEMATICAL AND COMPUTER APPLICATIONS Vol. 13, No. 4, pp. 41–52, 2025. ISSN 2306–6172 link Abstract: The way we predict service life and model pavement performance gradually evolves due to the popularity of machine learning (ML). We now [...]

M. Azodinia, M. Mudabbir, S. Ardabili, A. R. Várkonyi-Kóczy, K. Iskakov, A. Mosavi: Service Life Modeling of Pavement with Ensemble Learning. In IEEE 12th International Conference on Computational Cybernetics and Cyber-Medical Systems (ICCC 2025) Proceedings. pp. 167–174, 2025. ISBN 979-8-3315-0246-1 link Abstract: Random Forest (RF) is an ensemble learning which creates multiple decision trees and [...]

B. Tusor, A. R. Várkonyi-Kóczy, S. Gubo: Hyperparameter Tuning for Sequential Fuzzy Indexed Search Trees Classifiers for Biosignal Processing. In IEEE International Symposium on Medical Measurements and Applications (MeMeA 2025) Proceedings. pp. 1–6, 2025. ISBN 979-8-3315-2347-3 link Abstract: Classification is an integral part of machine learning, that is widely used in numerous applications in many [...]