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. 2019 Jul 16;9:10267. doi: 10.1038/s41598-019-45748-1

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Comparison of different network architectures on a test data set. Accuracy (a) and mean absolute error (b) for the proposed architecture of this study (CRED), a network containing both convolutional and fully connected layers (CNN + NN), a network similar to the CRED with bidirectional GRU units instead of LSTM (GRU), a traditional fully connected neural network (NN), a network containing both convolutional and fully connected layers in residual structure(ResCNN + NN), and a network similar to the CRED with unidirectional LSTM (UniLSTM).