Baran Ágnes

1.

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, (article identifier: 114361), 2026.

doi DEA

Journal metrics:

Q1 Materials Science (miscellaneous) (2024)

Q1 Renewable Energy, Sustainability and the Environment (2024)

2.

Baran, Á., Baran, S.: A two-step machine learning approach to statistical post-processing of weather forecasts for power generation.
Q. J. R. Meteorol. Soc. 150 (759), 1029-1047, 2024.

doi DEA WoS Scopus

Journal metrics:

Q1 Atmospheric Science

3.

Stoyan, G., Baran, Á.: Elementary Numerical Mathematics for Programmers and Engineers.
Springer International Publishing Ag, Cham, 301 p., 2024. ISBN: 9783031660887

DEA

4.

Baran, Á., Baran, S.: Parametric model for post-processing visibility ensemble forecasts.
Adv. Stat. Clim. Meteorol. Oceanogr. 10 (2), 105-122, 2024.

doi DEA Scopus

Journal metrics:

Q2 Applied Mathematics

Q3 Atmospheric Science

Q2 Oceanography

Q2 Statistics and Probability

5.

Harangi, B., Baran, Á., Beregi-Kovács, M., Hajdu, A.: Composing Diverse Ensembles of Convolutional Neural Networks by Penalization.
Mathematics. 11 (23), 1-19, (article identifier: 4730), 2023.

doi DEA Scopus

Journal metrics:

Q2 Computer Science (miscellaneous)

Q2 Engineering (miscellaneous)

Q2 Mathematics (miscellaneous)

6.

Szépszó, G., Baran, Á., Baran, S., Jávorné, R. K., Kornyik, M., Tajti, D.: Sugárzásra és magassági szélre vonatkozó rövidtávú előrejelzések operatív statisztikai utófeldolgozása.
Légkör. 68 (3), 118-125, 2023.

doi DEA

7.

Baran, S., Baran, Á.: Calibration of wind speed ensemble forecasts for power generation.
Idojaras. 125 (4), 609-624, 2021.

doi DEA WoS Scopus

Journal metrics:

Q4 Atmospheric Science

8.

Baran, Á., Lerch, S., El Ayari, M., Baran, S.: Machine learning for total cloud cover prediction.
Neural Comput. Appl. 33, 2605-2620, 2021.

doi DEA WoS Scopus

Journal metrics:

Q2 Artificial Intelligence

Q1 Software

9.

Harangi, B., Baran, Á., Hajdu, A.: Assisted deep learning framework for multi-class skin lesion classification considering a binary classification support.
Biomed. Signal Process. Control. 62, 1-7, (article identifier: 102041), 2020.

doi DEA WoS Scopus

Journal metrics:

Q2 Health Informatics

Q2 Signal Processing

10.

Beregi-Kovács, M., Baran, Á., Hajdu, A.: Efficient Learning of Model Weights via Changing Features During Training.
In: 2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES), Institute of Electrical and Electronics Engineers (IEEE), Piscataway (NJ), 43-48, 2020. ISBN: 9781728110592

doi DEA WoS Scopus

11.

Porwal, P., Pachade, S., Kokare, M., Deshmukh, G., Son, J., Bae, W., Liu, L., Wang, J., Liu, X., Gao, L., Wu, T., Xiao, J., Wang, F., Yin, B., Wang, Y., Danala, G., He, L., Choi, Y. H., Lee, Y. C., Jung, S. H., Li, Z., Sui, X., Wu, J., Li, X., Zhou, T., Tóth, J., Baran, Á., Kori, A., Chennamsetty, S. S., Mohammed, S., Alex, V., Lyu, X., Cheng, L., Chu, Q., Li, P., Ji, X., Zhang, S., Shen, Y., Dai, L., Saha, O., Sathish, R., Melo, T., Araújo, T., Harangi, B., Sheng, B., Fang, R., Sheet, D., Hajdu, A., Zheng, Y., Mendonça, A. M., Zhang, S., Campilho, A., Zheng, B., Shen, D., Giancardo, L., Quellec, G., Mériaudeau, F.: IDRiD: Diabetic Retinopathy: segmentation and grading challenge.
Med. Image Anal. 59, 1-26, (article identifier: 101561), 2020.

doi DEA WoS Scopus

Journal metrics:

D1 Computer Graphics and Computer-Aided Design

D1 Computer Vision and Pattern Recognition

D1 Health Informatics

D1 Radiological and Ultrasound Technology

D1 Radiology, Nuclear Medicine and Imaging

12.

Baran, S., Baran, Á., Pappenberger, F., Ben Bouallègue, Z.: Statistical post-processing of heat index ensemble forecasts: Is there a royal road?.
Q. J. R. Meteorol. Soc. 146 (732), 3416-3434, 2020.

doi DEA WoS Scopus

Journal metrics:

Q1 Atmospheric Science

13.

Harangi, B., Tóth, J., Baran, Á., Hajdu, A.: Automatic screening of fundus images using a combination of convolutional neural network and hand-crafted features.
In: 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Ed.: Riccardo Barbieri, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, USA, 2699-2702, 2019. ISBN: 9781538613122

doi DEA

14.

Baran, Á., Vertse, T.: Damping the tail of the Woods-Saxon potential.
Int. J. Mod. Phys. E-Nucl. Phys. 28 (10), 1-11, (article identifier: 1950085), 2019.

doi DEA WoS Scopus

Journal metrics:

Q3 Nuclear and High Energy Physics

Q2 Physics and Astronomy (miscellaneous)

15.

Harangi, B., Baran, Á., Hajdu, A.: Classification of skin lesions using an ensemble of deep neural networks.
In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) / Gregg Suaning, Olaf Dossel, IEEE, Hawaii, USA, 2575-2578, 2018. ISBN: 9781538636466

doi DEA

16.

Hajdu, A., Harangi, B., Tóth, J., Pap, M., Baran, Á.: Combining Convolutional Neural Networks and Hand-Crafted Features in Medical Image Classification Tasks.
In: 20th European Conference on Mathematics for Industry : Book of Abstracts. Ed.: Bodó Á., Fekete I., Izsák F., Maros G., Simon L. P, Bolyai János Matematikai Társulat, Budapest, 299, 2018.

DEA

17.

Baran, Á., Noszály, C., Vertse, T.: JOZSO, a computer code for calculating broad neutron resonances in phenomenological nuclear potentials.
Comput. Phys. Commun. 228, 185-191, 2018.

doi DEA WoS Scopus

Journal metrics:

D1 Hardware and Architecture

Q1 Physics and Astronomy (miscellaneous)

18.

Lévai, G., Baran, Á., Salamon, P., Vertse, T.: Analytical solutions for the radial Scarf II potential.
Phys. Lett. A. 381 (23), 1936-1942, 2017.

doi DEA WoS Scopus

Journal metrics:

Q2 Physics and Astronomy (miscellaneous)

19.

Baran, Á., Vertse, T.: Matching polynomial tails to the cut-off Woods-Saxon potential.
Int. J. Mod. Phys. E-Nucl. Phys. 26 (11), 1-15, (article identifier: 1750078), 2017.

doi DEA WoS Scopus

Journal metrics:

Q2 Nuclear and High Energy Physics

Q2 Physics and Astronomy (miscellaneous)

20.

Salamon, P., Baran, Á., Vertse, T.: Distributions of the S-matrix poles in Woods-Saxon and cut-off Woods-Saxon potentials.
Nucl. Phys. A. 952, 1-17, 2016.

doi DEA WoS Scopus

Journal metrics:

Q2 Nuclear and High Energy Physics

21.

Baran, Á., Noszály, C., Salamon, P., Vertse, T.: Calculating broad neutron resonances in a cut-off Woods-Saxon potential.
Eur. Phys. J. A. 51 (7), 76, 2015.

doi DEA

Journal metrics:

Q3 Nuclear and High Energy Physics

22.

Baran, Á., Terdik, G.: Power spectrum estimation of spherical random fields based on covariances.
Ann. Math. Inform. 44, 15-22, 2015.

DEA

Journal metrics:

Q4 Computer Science (miscellaneous)

Q4 Mathematics (miscellaneous)

23.

Baran, Á.: Egy magas rendű nemkonform végeselem család a kétdimenziós Stokes-feladat megoldására.
Alkalm. mat. l. 26 (2), 193-206, 2009.

DEA

24.

Baran, Á., Stoyan, G.: Gauss-Legendre elements: a stable, higher order non-conforming finite element family.
Computing. 79 (1), 1-21, 2007.

doi DEA

Journal metrics:

Q2 Computational Mathematics

Q2 Computational Theory and Mathematics

Q1 Computer Science Applications

Q2 Numerical Analysis

Q1 Software

Q2 Theoretical Computer Science

25.

Stoyan, G., Baran, Á.: Crouzeix-Velte decompositions for higher-order finite elements.
Comput. Math. Appl. 51 (6-7), 967-986, 2006.

doi

Journal metrics:

Q2 Computational Mathematics

Q2 Computational Theory and Mathematics

Q2 Modeling and Simulation

Baran Ágnes

Publication list