BAT-Net: An enhanced RNA Secondary Structure prediction via bidirectional GRU-based network with attention mechanism

Shen, Cong; Chen, Yu; Xiao, Feng; Yang, Tian; Wang, Xinyue; Chen, Shengyong; Tang, Jijun; Liao, Zhijun

doi:10.1016/j.compbiolchem.2022.107765

Cited by 5 publications

(1 citation statement)

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“…BiGRU is a branch of RNN. It can solve the gradient vanishing or gradient explosion problem in backpropagation and makes learning the contextual information of the provided sequence easier [21,22]. Sequence data is the input for BiGRU and, as it can create global dependencies between sequence data, it is frequently used for tasks like language production and speech recognition.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network–Bidirectional Gated Recurrent Unit Facial Expression Recognition Method Fused with Attention Mechanism

Tang,

Zhang,

Tian

2023

Applied Sciences

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The relationships among different subregions in facial images and their varying contributions to facial expression recognition indicate that using a fixed subregion weighting scheme would result in a substantial loss of valuable information. To address this issue, we propose a facial expression recognition network called BGA-Net, which combines bidirectional gated recurrent units (BiGRUs) with an attention mechanism. Firstly, a convolutional neural network (CNN) is employed to extract feature maps from facial images. Then, a sliding window cropping strategy is applied to divide the feature maps into multiple subregions. The BiGRUs are utilized to capture the dependencies among these subregions. Finally, an attention mechanism is employed to adaptively focus on the most discriminative regions. When evaluated on CK+, FER2013, and JAFFE datasets, our proposed method achieves promising results.

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Section: Introductionmentioning

confidence: 99%