… In this study, we adopted a malicious traffic analysis method based on CNN and LSTM to
extract and analyze network traffic information of network raw dataset from both spatial and …

… Using three layers of hybrid CNN and LSTM, the 605 structure of the … CNN and LSTM
layers in our model and took advantage of 646 CNN’s ability to extract spatial features and LSTM…

… the method of manual feature extraction, a neural network fault prediction method based …
CNN-LSTM neural network model is proposed in this paper, CNN is used to extract log features, …

… propose a CNN-LSTM hybrid network that can extract spatio-… see if the performance of
CNN-LSTM improves. In particular, … of extracting spatial features from time series data using CNN…

… the energy production utilizing the CNN-LSTM hybrid topology. The data used belongs to …
CNN-LSTM model is proposed for forecasting PV plant energy output by extracting features …

… a nucleotide-level hybrid deep learning method based on a CNN and LSTM network together.
… For concatenation of all extracted features, we employed a flatten layer for passing to the …

… In this section, we concatenate the feature vectors obtained from the CNN and BLSTM
components to form \(q=[{E}_{s}, {h}_{t}]\), where \({q}_{i}\in q\) represents the i-th word of the input …

… In this work, we have incorporated a dropout layer between the CNN feature extraction
block and the LSTM sequence learning to prevent overfitting. The output of the sequence …

… -driven Deep LSTM) for the intrusion detection process. A CNN feature extraction process is
necessary for … Here, the Deep LSTM is applied for detecting network intrusion, and the Deep …

… or feature extraction. As a result presents a one-dimensional convolutional neural network-long
short-term memory (1D-CNN-LSTM… In contrast with recent studies, our hybrid automated …