… 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 hybridnetwork to encounter the limited size of … In this article, we used CNN and LSTMhybrid neural networks to learn the spatial and temporal …
Y Liang, Y Lin, Q Lu - Expert Systems with Applications, 2022 - Elsevier
… 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 hybridnetwork 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 …
HC Altunay, Z Albayrak - … Science and Technology, an International Journal, 2023 - Elsevier
… performed between the CNN-based, LSTM-based, and hybridCNN + LSTM methods proposed … These results prove that the hybridCNN + LSTM model leads to an improvement in the …
FO Ozkok, M Celik - Biomedical Signal Processing and Control, 2022 - Elsevier
… hybridCNN-LSTM model is proposed to classify HRM curves. In the proposed model, LSTM … In this study, a hybridCNN-LSTM model is proposed to classify HRM curves efficiently. The …
… , we have found that the hybridCNN-LSTM model outperforms CNN and LSTM models in all … that the hybrid model is faster in classification than the separate CNN and LSTM models, …