This work developed a CNN-BiLSTM-AM model for convective weather forecasting using deep learning algorithms based on reanalysis and forecast data from the NCEP GFS, the performance of the model was evaluated. The results show that: (1) Compared to traditional machine learning al...
This study introduces a novel hybrid model named STL-CNN-BILSTM-AM. It combines the seasonal-trend decomposition method with LOESS (STL) to simplify learning tasks and increase prediction accuracy for complex, nonlinear time-series data. Convolutional neural networks (CNNs) extract features from ...
The structure of proposed model, which is called as the M-C&M-BL model, is shown in Fig.4. M-C&M-BL is built using two separate deep learning architectures. First, single-layer CNN and BiLSTM and then triple CNN and triple BiLSTM were developed separately. By increasing the number o...
BiLSTM + CNN + CRF (our)NER-2003 shared task (English)90.59 STag_BLCC,Eger et. al., 2017AM Persuasive Essays, Paragraph Level64.74 +/- 1.97 BiLSTM + CNN + CRF (our)AM Persuasive Essays, Paragraph Level64.54 In order to ensure the consistency of the experiments, for evaluation purposes...
A novel framework using 3D-CNN and BiLSTM model with dynamic learning rate scheduler for visual speech recognition. SIViP 18, 5433–5448 (2024). https://doi.org/10.1007/s11760-024-03245-7 Download citation Received07 March 2024 Revised22 April 2024 Accepted26 April 2024 Published18 May 2024 ...
基于这些影响,本文在 Faster R-CNN 的共享网络 ResNet-50 中引入了深度残差收缩网络,此网络利用注意力机制实现与当前任务目标有关的重要特征提取,抑制无关因素干扰。 [179] 任燕, 张瑞, 汤何胜等. 基于多模态深度残差收缩网络的液压防水阀故障诊断方法[P]. 浙江省:CN114358189A, 2022-04-15. ...
introduced the attention mechanism, combined it with CNN and LSTM respectively, and achieved good results. Zhou et al.[16] combined bidirectional LSTM(BiLSTM) and multiple attention mechanisms for relation classification. Experimental results on the SemEval-2010 Task8 datasets show that this method ...
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et al. Landslide susceptibility prediction and mapping using the LD-BiLSTM model in seismically active mountainous regions. Landslides, 2024, 21(1): 17-34. DOI:10.1007/s10346-023-02141-4 111. Manaouch, M., Sadiki, M., Aghad, M. et al. Assessment of landslide susceptibility using ...
Mental illnesses, such as depression, are highly prevalent and have been shown to impact an individual’s physical health. Recently, artificial intelligence (AI) methods have been introduced to assist mental health providers, including psychiatrists and