1. Abdel-Nasser, M., Mahmoud, K.: Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Comput. Appl.31(7), 2727–2740 (2017).https://doi.org/10.1007/s00521-017-3225-z

   Article  Google Scholar 

2. Ciresan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3642–3649. IEEE, New York (2012). wOS:000309166203102

   Google Scholar 

3. Dobrovolny, M., Mls, K., Krejcar, O., Mambou, S., Selamat, A.: Medical image data upscaling with generative adversarial networks. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L.J., Ortuño, F. (eds.) IWBBIO 2020. LNCS, vol. 12108, pp. 739–749. Springer, Cham (2020).https://doi.org/10.1007/978-3-030-45385-5_66

   Chapter  Google Scholar 

4. Dobrovolny, M., Selamat, A., Krejcar, O.: Session based recommendations using recurrent neural networks - long short-term memory. In: Nguyen, N.T., Chittayasothorn, S., Niyato, D., Trawiński, B. (eds.) ACIIDS 2021. LNCS (LNAI), vol. 12672, pp. 53–65. Springer, Cham (2021).https://doi.org/10.1007/978-3-030-73280-6_5

   Chapter  Google Scholar 

5. Dobrovolny, M., Soukal, I., Lim, K.C., Selamat, A., Krejcar, O.: Forecasting of FOREX price trend using recurrent neural network - long short-term memory, pp. 95–103 April 2020. 10.36689/uhk/hed/2020-01-011,http://hdl.handle.net/20.500.12603/212

6. Gunawardana, A., Shani, G.: Evaluating recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 265–308. Springer, Boston (2015).https://doi.org/10.1007/978-1-4899-7637-6_8

   Chapter  Google Scholar 

7. Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst.5(4), 1–19 (2016).https://doi.org/10.1145/2827872

   Article  Google Scholar 

8. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science313(5786), 504–507 (2006).https://doi.org/10.1126/science.1127647, wOS:000239308600057

9. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput.9(8), 1735–1780 (1997).https://doi.org/10.1162/neco.1997.9.8.1735

   Article  Google Scholar 

10. Kuchaiev, O., Ginsburg, B.: Training deep autoencoders for collaborative filtering.arXiv:1708.01715 (2017)

11. Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), pp. 105–114. IEEE, New York (2017). wOS:000418371400012

    Google Scholar 

12. Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., Alsaadi, F.E.: A survey of deep neural network architectures and their applications. Neurocomputing234, 11–26 (2017).https://doi.org/10.1016/j.neucom.2016.12.038, wOS:000395221800002

13. Mambou, S., Krejcar, O., Selamat, A., Dobrovolny, M., Maresova, P., Kuca, K.: Novel thermal image classification based on techniques derived from mathematical morphology: case of breast cancer. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L.J., Ortuño, F. (eds.) IWBBIO 2020. LNCS, vol. 12108, pp. 683–694. Springer, Cham (2020).https://doi.org/10.1007/978-3-030-45385-5_61

    Chapter  Google Scholar 

14. Pena-Barragan, J.M., Ngugi, M.K., Plant, R.E., Six, J.: Object-based crop identification using multiple vegetation indices, textural features and crop phenology. Remote Sens. Envir.115(6), 1301–1316 (2011).https://doi.org/10.1016/j.rse.2011.01.009

    Article  Google Scholar 

15. Sedhain, S., Menon, A.K., Sanner, S., Xie, L.: AutoRec: autoencoders meet collaborative filtering. In: Proceedings of the 24th International Conference on World Wide Web, WWW 2015 Companion, pp. 111–112. Association for Computing Machinery, Florence, May 2015.https://doi.org/10.1145/2740908.2742726

16. Sun, Y., Chen, J., Liu, Q., Liu, G.: Learning image compressed sensing with sub-pixel convolutional generative adversarial network. Pattern Recogn.98, 107051 (2020).https://doi.org/10.1016/j.patcog.2019.107051,http://www.sciencedirect.com/science/article/pii/S003132031930353X

17. Vaiyapuri, T., Binbusayyis, A.: Application of deep autoencoder as an one-class classifier for unsupervised network intrusion detection: a comparative evaluation. PeerJ Comput. Sci.6, e327 (2020).https://doi.org/10.7717/peerj-cs.327 wOS:000599181100001

18. Varsamopoulos, S., Bertels, K., Almudever, C.G.: Designing neural network based decoders for surface codes, p. 13 (2018)

    Google Scholar 

19. Wolterink, J.M., Leiner, T., Viergever, M.A., Isgum, I.: Generative adversarial networks for noise reduction in low-dose CT. IEEE Trans. Med. Imaging36(12), 2536–2545 (2017).https://doi.org/10.1109/TMI.2017.2708987, wOS:000417913600013

20. Wu, Y., DuBois, C., Zheng, A.X., Ester, M.: Collaborative denoising auto-encoders for top-n recommender systems. In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining WSDM 2016, pp. 153–162. Association for Computing Machinery, San Francisco, California February 2016.https://doi.org/10.1145/2835776.2835837

21. Yang, Q., et al.: Low-dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss. IEEE Trans. Med. Imaging37(6), 1348–1357 (2018).https://doi.org/10.1109/TMI.2018.2827462, wOS:000434302700006

Download references