BIOINTELLIGENCE Research Group

With biointelligence we mean the intersection of machine learning with biology and medicine.

The Biointelligence Group focuses on developing and adapting machine learning approaches for biomedical and biometric data. This includes classification of time series (ECG, EEG, other biometric signals), recognition tasks related to imaging data (x-ray, FMRI) and the analysis of biomedical networks (e.g. interaction prediction, community detection).

As a research group of the Department of Mathematics-Informatics, the group has its roots in computer science while we are collaborating and initiating new collaborations with medical doctors, pharmacists and biologists.

Members

• As. Prof. Margit Antal PhD.
• Krisztián Búza

Publications

1. Antal Margit, Előd Egyed-Zsigmond, Intrusion detection using mouse dynamics, IET Biometrics, Vol. 8, No 5, 2019, pp. 285–294.
2. Margit Antal, Krisztian Buza, Norbert Fejer (2021), SapiAgent: A Bot Based on Deep Learning to Generate Human-Like Mouse Trajectories, IEEE Access, 9, September 2021, pp. 124396-124408.
3. Buza K., Antal M. (2021) Convolutional Neural Networks with Dynamic Convolution for Time Series Classification, pp. 304-312. In: Wojtkiewicz K., Treur J., Pimenidis E., Maleszka M. (eds) Advances in Computational Collective Intelligence. ICCCI 2021. Communications in Computer and Information Science, vol 1463. Springer, Cham.
4. Margit Antal, Norbert Fejer, Krisztian Buza (2021), SapiMouse: Mouse Dynamics-based User Authentication Using Deep Feature Learning, 2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI), 2021.