@inproceedings{08068a18fdf448349207b137c78afa2f,
title = "BiLSTM Deep Learning Model for Heart Problems Detection",
abstract = "Deep learning architectures find applications where analysis of complex data inputs is demanding and regular neural networks may have problems. There are many types of deep learning models, however the most important to fit architecture and training model to the input data. In this article we propose a model of deep learning based on architecture in which we use BiLSTM neural network. Proposed model is trained by using Adam algorithm. For the research experiment we have examined also other latest algorithms to select the best configuration of proposed model. Results show that our proposed BiLSTM deep learning neural network archived over 99\% of accuracy.",
keywords = "Adam, BiLSTM, Deep learning, Heart signal",
author = "Jakub Si{\l}ka and Micha{\l} Wieczorek and Martyna Kobielnik and Marcin Wo{\'z}niak",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 21st International Conference on Artificial Intelligence and Soft Computing, ICAISC 2022 ; Conference date: 19-06-2022 Through 23-06-2022",
year = "2023",
doi = "10.1007/978-3-031-23492-7\_9",
language = "English",
isbn = "9783031234910",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "93--104",
editor = "Leszek Rutkowski and Leszek Rutkowski and Rafal Scherer and Marcin Korytkowski and Witold Pedrycz and Ryszard Tadeusiewicz and Zurada, \{Jacek M.\}",
booktitle = "Artificial Intelligence and Soft Computing - 21st International Conference, ICAISC 2022, Proceedings",
address = "Germany",
}