S Deep, X Zheng - 2019 20th international conference on …, 2019 - ieeexplore.ieee.org
… architecture for HAR using images and videos, we also decide to use hybrid … hybridCNNLSTM model on UCI HAR dataset. In this work, we apply LSTM and hybridCNN-LSTMnetworks …
X Zhang, X Xie, S Tang, H Zhao, X Shi, L Wang… - Journal of Civil …, 2024 - Springer
… CNN-LSTMnetworkhybrid model response prediction approach based on the CNN and the LSTMnetwork … It combines CNN and LSTMnetwork features, and employs quasi-distributed …
… To address above issues, we propose a hybrid prediction approach using 1D CNN and LSTM. First, we use LSTM to learn the complex nonlinearities within the data. This enables the …
… However, the CNN–LSTMnetwork proposed in this paper increases the convergence speed and recognition accuracy as the input sequence size increases. This indicates that the …
… HYBRIDCNN-LSTMNETWORKLSTM and CNN are both outlined to deliver a high … CNN-LSTMhybridnetwork basically comprises of a convolution neural network module, a long short-…
… deep learning models CNN and CNN-LSTM to predict the MI … we have proposed: CNN, hybridCNN-LSTM, and ensemble … , data balancing, CNN, and LSTM models. Section 3 explains …
R Banerjee, A Ghose… - … on Neural Networks …, 2020 - ieeexplore.ieee.org
… our proposed hybridnetwork structure. Hence, it yields the optimum performance for CAD classification. 3) Comparison with existing approaches: The proposed CNN-LSTMnetwork is …
Y Yang, Q Xiong, C Wu, Q Zou, Y Yu, H Yi… - … Science and Pollution …, 2021 - Springer
… In this paper, we propose a water quality prediction model named CNN-LSTM with Attention … The hybrid model CNN-LSTM is highly capable of resolving nonlinear time series prediction …
J Zheng, L Ma, Y Wu, L Ye, F Shen - ACS omega, 2022 - ACS Publications
… To address this issue, a supervised hybridnetwork based on … , the LSTMnetwork is concatenated to the CNN layer. … of the hybridCNN-LSTMnetwork, a deep DCNN-LSTM structure …