… to compare the powerful CNN-LSTM hybrid model with some … ML in SVM and in reversible
LSTM, was chosen for deep … (ARIMA, SVM, LSTM, ANFIS, VIC and CNN-LSTM) is studied …

… In order to capture complementarity of CNN, LSTM and DNN/TDNN networks and to … , we
propose a hybrid deep learning architecture that employs CNN, LSTM and TDNN networks for …

… a hybrid CNN-LSTM model that seeks to take full advantages of the superiority of CNN for
… Then, the GHI is predicted 1 h in advance through the dense layer of the hybrid network …

… novel attentionbased hybrid LSTM-CNN (ABH-LSTM-CNN) model… hybrid network structure
by combining a stacked bidirectional LSTM (SB-LSTM) and a two-dimensional CNN (TD-CNN…

… , the CNN-Bi-LSTM hybrid model is proposed. CNN, it can effectively extract the inherent
characteristics of historical water consumption and meteorological data. And Bi-LSTM can fully …

… using only the CNN part of the network sensitivity and specificity … LSTM part of the network
the above values were 96.30% and 98.77%, respectively. This means that the hybrid network …

… networks (3DCNN) and 2D convolutional neural networks (… We also use LSTM to examine
the temporal sequence of … three datasets show that our hybrid CNN-LSTM model is superior to …

… network of CNN and LSTM, use CNN to abstract space characteristics of data, and LSTM to
… The traffic data on flows is one-dimensional, so a one-dimensional CNN is used. Specifically…

… A new hybrid DL model, which process the input data with a … ) for feature selection, CNN,
LSTM network, CNN and a final … DL approaches (CNN-LSTM and Deep Neural Network (DNN…

… network. Although the test time is slightly longer, it is still within the acceptable range, and the
hybrid network … the time and efficiency, this paper chooses the CNN–LSTM hybrid network. …