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. 2023 Jun 23;13:10221. doi: 10.1038/s41598-023-34795-4

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© The Author(s) 2023

Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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Comparison of GRU and LSTM architectures’ performance for HS3D dataset. These networks are performing suboptimally compared to their bidirectional counterparts, and they are also unable to learn any distinguishing features in some of the experiments. Columns from left to right are (a) Accuracy per epoch, (b) F1 Score per epoch, and (c) AUR-PR per epoch.