doc. Ing. Michal Čerňanský, PhD.

Čerňanský, M., Luptáková, I.D. (2022). Multi-reservoir Echo State Networks with Encoders. In: Silhavy, R. (eds) Artificial Intelligence Trends in Systems. CSOC 2022. Lecture Notes in Networks and Systems, vol 502. Springer, Cham. https://doi.org/10.1007/978-3-031-09076-9_43

Čerňanský, M., Huraj, L. Šimon, M.: Controlled DDoS attack on IPv4/IPv6 network using distributed computing infrastructure, Journal of Information and Organizational Sciences, vol. 44, no. 2 (2020): 297-316.

Tino, P., Cernansky, M., & Benuskova, L. (2004). Markovian architectural bias of recurrent neural networks. IEEE Transactions on Neural Networks, 15(1), 6-15.

Cernansky, M., & Benuskova, L. (2003). Simple recurrent network trained by RTRL and extended Kalman filter algorithms. Neural Network World, 13(3), 223-234.

Čerňanský, M., & Tiňo, P. (2008, September). Predictive modeling with echo state networks. In International Conference on Artificial Neural Networks (pp. 778-787). Springer, Berlin, Heidelberg.

Erasmus+ Cooperation Partnerships (KA220-HED): 2022-1-PL01-KA220-HED-000088359, FAAI: The Future is in Applied Artificial Intelligence. 2022 - 2024.

KEGA 017UCM-4/2022 - KEGA - Vývoj interaktívneho e-kurzu s využitím „SMART“ technológií na rozvoj algoritmického myslenia a programátorských zručností. 2022 - 2023.

2016 – 2019, OPVaI-VA/DP/2018/1.1.3-09 313011W258 Sieťová bezpečnosť s podporou výpočtovej inteligencie a IoT/2016 – 2019, OPVaI-VA/DP/2018/1.1.3-09 313011W258 Network security supported by computational intelligence and IoT.