… of convolutional neural network (CNN) and long short-term memory (LSTM) methods are …
Bajgain, ‘‘Improved deep hybrid networks for urban traffic flow prediction using trajectory …

… In general, deep learning can work as a single network or as a hybrid network. A study in
reference [11] examined several research works on load forecasting using different techniques, …

… CNN and LSTM networks to automatically detect COVID-19 from X-ray images. In the proposed
system, CNN is used for feature extraction and LSTM is … the proposed hybrid network for …

… CNN-LSTM scheme combining CNN and LSTM was proposed for the prediction of NO x
concentration observed during FCC process. Dropout were introduced to accelerate network …

… CNN-LSTM HYBRID MODEL In this section, a CNN-LSTM hybrid model after the fusion of
CNN and LSTM is … For LSTM, when the input sample has a long time interval, some important …

… by a hybrid CNN-LSTM (convolutional neural network-long short-term memory network) …
the transfer learning mechanism, and the hybrid CNN-LSTM model is designed to improve the …

… In this paper, we proposed a novel CNN-LSTM hybrid neural network model … CNN and
the time series analysis capability of LSTM by fusing CNN and LSTM models, so that CNN-LSTM …

… 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 …