… we develop 1D-CNN-LSTM hybrid model to improve seizure … In the LSTM model, each of the
time step output characteristics … for epileptic EEG signals that use CNN and LSTM networks. …

… CNN-LSTM hybrid neural network considering the real-time electricity price is proposed in
this paper, referred to as multi-scale CNN-LSTM … The CNN is used to cascade the shallower …

… Both of the approaches (hybrid CNN LSTM and 3D CNN) proposed in this study are briefly
explained in section VII. … In this hybrid network, CNN handles the spatial dependencies while …

… network models to predict time series, achieving excellent results. In this paper, a five layer
CNN-LSTM … In the proposed hybrid model, the convolutional layer acts like a filter, extracting …

… hybrid network has five layers that are alternating convolutional layer and pooling layers.
The network structure, ie, the depth of the network … effectiveness of hybrid CNN and LSTM for …

… The LSTM model is capable to capture long-term dependencies between word sequences.
… a hybrid model using LSTM and very deep CNN model named as Hybrid CNN-LSTM Model …

… networks (CNN) and long short-term memory networks (LSTM) models are combined. Then
a … In the hybrid CNN-LSTM model, the CNN model is used to learn spatial features, while the …

… a Hybrid model, which combines the ConvLSTM model processing spatiotemporal information
at the same time sequentially and the CNN … including LSTM model or CNN-LSTM model …

… algorithm based on hybrid CNN-LSTM hybrid model is … vector, using CNN to extract local
features of text, LSTM saves … vector output by CNN as the input of LSTM, using Softmax …

… In this paper, six CNN and hybrid CNN-LSTM models are used to learn the object manipulation
by applying teleoperation method of LfD on the humanoid robot named as Robotis-Op3 …