… In this work, two hybrid models were proposed (CNN-LSTM and … In this paper two hybrid
models CNN-LSTM and ConvLSTM … Both approaches incorporate an LSTM layer which is an …

… two different networks came into mind. In this paper, a hybrid architecture of CNN-BLSTM
is … of hidden units, and ideal pooling strategy for CNN to achieve a high recognition rate. …

… convolutional neural network, and long short-term memory (1D CNN-LSTM) … network setups
and high scalability. Although the current model only addresses the fully connected hybrid …

… namely ResNet-50 and Inception-v3 in the hybrid network to encounter the limited size of …
In this article, we used CNN and LSTM hybrid neural networks to learn the spatial and temporal …

… hybrid model ICEEMDAN-LSTM-CNN-CBAM to improve the prediction accuracy of gold price.
Secondly, the combined model LSTM-CNN-… -LSTM-CNN-CBAM (ILCC) hybrid algorithm to …

… ) based long and short term memory (LSTM) is used to classify … complexity of the hybrid
CNN-LSTM architecture by optimizing … the hybrid network has possessed a better accuracy rate. …

… Cohen, “Data augmentation for recognition of handwritten words and lines using a CNN-LSTM
network,” in ICDAR, 2017. [19] J. Puigcerver, “Are multidimensional recurrent layers really …

… performed between the CNN-based, LSTM-based, and hybrid CNN + LSTM methods proposed
… These results prove that the hybrid CNN + LSTM model leads to an improvement in the …

… hybrid CNN-LSTM model is proposed to classify HRM curves. In the proposed model, LSTM
… In this study, a hybrid CNN-LSTM model is proposed to classify HRM curves efficiently. The …

… , we have found that the hybrid CNN-LSTM model outperforms CNN and LSTM models in
all … that the hybrid model is faster in classification than the separate CNN and LSTM models, …