… The proposed system in this study is a hybrid recommender … , we develop an LSTM deep
neural network considering the … , a deep learning network based on CNN is used to extract …

… a nucleotide-level hybrid deep learning method based on a CNN and LSTM network together.
… The results indicate that the hybrid CNN and LSTM networks can be employed to achieve …

… of network parameters, we find that increasing the number of filters as well as LSTM units
(width) is the most effective way to improve the performance of the 1D-CNN-LSTM model. But it …

… In this paper, we propose a CNN-LSTM hybrid network that can … CNN-LSTM hybrid networks,
which linearly combine convolutional neural network (CNN), long short-term memory (LSTM…

… In particular, the Long Short-Term Memory network model (LSTM) … , CNN and LSTM are
combined to form a CNN-LSTM hybrid … CNN is used to extract features from data matrix, and then …

… , namely the LSTM, CNN and hybrid CNN–LSTM models, … compare the performances of the
LSTM and CNN methods for short-… In summary, the CNN–LSTM hybrid model was the best at …

… developed network intrusion detection process using a ChCSO-based Deep LSTM model. …
Later, the CNN feature [16] is extracted for further detection in the feature extraction process. …

… on spatiotemporal hybrid convolution neural network long–short term memory (CNN–LSTM)
is presented to predict the heating load more reasonably for the SDHS. This hybrid model …

… In this article, we proposed a hybrid model by integrating the CNN with LSTM algorithms in
order to improve the SDN capability to detect the malicious network activities. One of the main …

… In this paper, CNN, LSTM, and a hybrid of these two models are utilized as three deep
learning applications to categorize the type and exact location of transmission line faults. …