Informatikai Kar

1.

Nagy-Lakatos, M.: A composite-loss graph neural network for the multivariate post-processing of ensemble weather forecasts.
Q. J. R. Meteorol. Soc. [Epub ahead of print], (cikkazonosító: 70119), 2026.

doi DEA

Folyóirat-mutatók:

Q1 Atmospheric Science (2025)

2.

Papp, I., Bodroginé Zichar, M.: Applicability of AI in code-based 3D modelling: Who will solve the modelling tasks: the students or the AI?.
In: Book of abstracts from the Innovation in Engineering Education Conference 2026. Ed.: Szilvia Árvai-Homolya; ed. Sándor Lajos; ed. László Rónai, Faculty of Mechanical Engineering, University of Miskolc, Miskolc, 15-15, 2026. ISBN: ISBN9789633584255

DEA

3.

Kádek, T., Biró, P.: Assessment System for IT Configuration Tasks.
Acta Polytech. Hung. 23 (5), 87-100, 2026.

doi DEA

Folyóirat-mutatók:

Q2 Engineering (miscellaneous) (2025)

Q2 Multidisciplinary (2025)

4.

Mohammed, A. A., Al, S. Z. A., Abd, Z. M. M., Abduljabbar, Z. A., Aldarbandee, M., Jaber, H., Alshuwaili, D. G. H., Szathmáry, L., Nyangaresi, V. O., Ali, A. H., Neamah, H. A.: Enhanced Arrhythmia Diagnosis Using a Hybrid Deep Learning Model: A CNN-LSTM-GRU Approach on ECG Data.
Biosens Bioelectron: X. [Epub ahead of print], 1-19, (cikkazonosító: 100767), 2026.

doi DEA

Folyóirat-mutatók:

Q2 Biomedical Engineering (2025)

Q2 Biophysics (2025)

Q2 Biotechnology (2025)

Q2 Electrochemistry (2025)

5.

Pándy, Á., Lakatos, R., Hajdu, A.: Error-Driven Prompt Optimization for Arithmetic Reasoning: A Code Generation Approach Using On-Premises Small Language Models on Tabular Data.
IEEE Access. [Epub ahead of print], 1- 16, 2026.

doi DEA

Folyóirat-mutatók:

Q1 Computer Science (miscellaneous) (2025)

Q1 Engineering (miscellaneous) (2025)

Q1 Materials Science (miscellaneous) (2025)

6.

Martos, R., Szokol, Á., Veres, M., Gáll, J., Csik, A., Hegedűs, C., Keczánné Üveges, A., Tóth, E. R., Szalóki, M.: Evaluation of Two-Body Wear of Nanocomposites With Different Filler Morphology and Composition Using White-Light Interferometry.
Int J Dent. 2026, 1-14, (cikkazonosító: 8929009), 2026.

doi DEA

Folyóirat-mutatók:

Q1 Dentistry (miscellaneous) (2025)

7.

Alfayyadh, F. K. J., Gilányi, A.: Exploring student engagement and reuse intention in a VR-based art exhibition.
Comput. Hum. Behav. Rep. 21, 1-11, (cikkazonosító: 100925), 2026.

doi DEA WoS

Folyóirat-mutatók:

Q1 Applied Psychology (2025)

Q1 Artificial Intelligence (2025)

Q1 Cognitive Neuroscience (2025)

Q1 Computer Science Applications (2025)

Q1 Human-Computer Interaction (2025)

Q1 Neuroscience (miscellaneous) (2025)

8.

Terdik, G.: On Fisher information for multivariate elliptically contoured distributions.
Stat. Probab. Lett. 234, 1-5, (cikkazonosító: 110690), 2026.

doi DEA

Folyóirat-mutatók:

Q3 Statistics and Probability (2025)

Q3 Statistics, Probability and Uncertainty (2025)

9.

Battyányi, P.: On rule synchronization in membrane computing.
J. Membr. Comput. [Epub ahead of print], 1-18, 2026.

doi DEA WoS

Folyóirat-mutatók:

Q3 Computational Theory and Mathematics (2025)

Q3 Applied Mathematics (2025)

10.

Vágner, A.: Perspective Chapter: The Role of Data Quality Dimensions in Data-Driven Decision-Making.
In: Data Quality Matters - Best Practices for Integrity and Assurance [Working Title]. Eds.: Sebastian Ventura, José M. Luna and Antonio R. Moya Martín-Castaño

doi DEA

11.

Mayer, M. J., Baran, Á., Lerch, S., Horat, N., Yang, D., Baran, S.: Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods.
Sol. Energy. 307, 1-36, (cikkazonosító: 114361), 2026.

doi DEA

Folyóirat-mutatók:

Q1 Materials Science (miscellaneous) (2025)

Q1 Renewable Energy, Sustainability and the Environment (2025)

12.

Mayer, M. J., Baran, S.: Probabilistic PV forecasting.
In: AI-Based Forecasting of Solar Photovoltaics Power Generation / Elham Shirazi; Wilfried van Sark (eds), The Institution of Engineering and Technology (The IET), London, 149-176, 2026. ISBN: 9781837240197

doi DEA

13.

Dawod, A. B. A., Terdik, G.: Railway Track Quality Inspection via GLasso with Zerocrossing Segmentation.
In: , Springer, New York, 244-250, 2026, (Lecture Notes in Networks and Systems, ISSN 2367-3389 ; 1768.) ISBN: 9783032138972

doi DEA

14.

Tardi, T., Bogacsovics, G., Szabó, R., Sarneczky, K., Hajdu, A.: Reinforcement Learning for Optimal Astronomical Observatory Placement: Geometrical Constraints and Temporal Variations.
Publ. Astron. Soc. Pac. 138 (4), 1-15, (cikkazonosító: 044506), 2026.

doi DEA

Folyóirat-mutatók:

D1 Space and Planetary Science (2025)

Q1 Astronomy and Astrophysics (2025)

15.

Gao, S., Do, T. V., Hajdu, A., Bérczes, T., Wang, J.: Reliability evaluation and optimization for a redundant repairable cluster computing system.
Reliab. Eng. Syst. Saf. 272 (3), (cikkazonosító: 112628), 2026.

doi DEA

Folyóirat-mutatók:

D1 Applied Mathematics (2025)

D1 Industrial and Manufacturing Engineering (2025)

D1 Safety, Risk, Reliability and Quality (2025)

16.

Baran, S., Nagy-Lakatos, M.: Statistical post-processing of operational dual-resolution wind-speed ensemble forecasts.
Q. J. R. Meteorol. Soc. [Epub ahead of print], 2026.

doi DEA

Folyóirat-mutatók:

Q1 Atmospheric Science (2025)

17.

Masasila, N. H., Fazekas, I.: Strong law of large numbers for [phi]-sub-Gaussian random variables under sub-linear expectation spaces.
Probab. Uncertain. Quant. Risk. [Submitted], 1-10, 2026.

DEA

18.

Masasila, N. H., Fazekas, I.: Strong Laws of Large Numbers for General Random Variables Under Conditional Sub-Additive Expectation and Capacity.
Mathematics. 14 (5), 1-19, (cikkazonosító: 775), 2026.

doi DEA

Folyóirat-mutatók:

Q2 Computer Science (miscellaneous) (2025)

Q2 Engineering (miscellaneous) (2025)

Q2 Mathematics (miscellaneous) (2025)

19.

Dömösi, P., Horváth, G.: Szkrembler berendezés és eljárás különösen kriptográfiai alkalmazásokhoz, valamint deszkrembler berendezés és eljárás azokhoz.

DEA

20.

Al-Hamad, A., Gilányi, A.: TAP express: A simplified think-aloud protocol for evaluating traditional and virtual training and education.
Soc. Sci. Human. Open. 13, 1-13, (cikkazonosító: 102641), 2026.

doi DEA

Folyóirat-mutatók:

Q2 Decision Sciences (miscellaneous) (2025)

Q2 Psychology (miscellaneous) (2025)

Q1 Social Sciences (miscellaneous) (2025)

21.

Al-Bazooni, A. M. R., Gilányi, A.: Towards Automatic VR Scene Generation for Safety Training Scenarios Using Manually Annotated 3D Object and Spatial Relation Data.
In: 13th International Conference on Applied Informatics (ICAI 2026): Abstracts, //icai.uni-eszterhazy.hu/2026/abstracts/ : Eszterházy Károly Catholic University, https, 1-3, 2026.

DEA

22.

Wei, S., Yu, S., Qian, C., Lan, Y., Huang, X.: Unveiling the relationship between stress-hyperglycemia ratio and cardiometabolic multimorbidity risk using interpretable machine learning.
Eur. J. Med. Res. 31 (1), 1-13, (cikkazonosító: 284), 2026.

doi DEA

23.

Csámer, L., Kovács, Á. É., Méhes, N., Horváth, H., Manó, S.: 3D nyomtatással készülő cranioplastica öntőforma továbbfejlesztése.
Biomech Hung. 17 (2), 40-46, 2025.

doi DEA

24.

Gao, T., Sütő, J.: Acceleration of Image Classification and Object Tracking by the Intel Neural Compute Stick 2 with Power Efficiency Evaluation on Raspberry Pi 4B.
Sensors. 25 (6), 1-16, (cikkazonosító: 1794), 2025.

doi DEA WoS Scopus

Folyóirat-mutatók:

Q1 Analytical Chemistry

Q2 Atomic and Molecular Physics, and Optics

Q2 Biochemistry

Q1 Electrical and Electronic Engineering

Q2 Information Systems

Q1 Instrumentation

Q2 Medicine (miscellaneous)

25.

Al-Hamad, A., Gilányi, A.: A data-analytics framework for optimizing user-centered virtual reality training.
Decis. Anal. J. 16, 1-16, (cikkazonosító: 100610), 2025.

doi DEA

Folyóirat-mutatók:

D1 Analysis

D1 Applied Mathematics

Q1 Decision Sciences (miscellaneous)

D1 Modeling and Simulation

Informatikai Kar

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