Attention meets long short-term memory: A deep learning network for traffic flow forecasting

2021 ◽  
pp. 126485
Author(s):  
Weiwei Fang ◽  
Wenhao Zhuo ◽  
Jingwen Yan ◽  
Youyi Song ◽  
Dazhi Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Learning Network ◽  
Traffic Flow Forecasting ◽  
Long Short Term Memory ◽  
Deep Learning Network

Ionospheric TEC prediction using Long Short-Term Memory deep learning network

2021 ◽  
Vol 366 (1) ◽  
Author(s):  
Zhichao Wen ◽  
Shuhui Li ◽  
Lihua Li ◽  
Bowen Wu ◽  
Jianqiang Fu
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Learning Network ◽  
Long Short Term Memory ◽  
Deep Learning Network

A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals

2018 ◽  
Vol 99 ◽  
pp. 24-37 ◽  
Author(s):  
Κostas Μ. Tsiouris ◽  
Vasileios C. Pezoulas ◽  
Michalis Zervakis ◽  
Spiros Konitsiotis ◽  
Dimitrios D. Koutsouris ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Epileptic Seizures ◽  
Eeg Signals ◽  
Short Term ◽  
Term Memory ◽  
Learning Network ◽  
Long Short Term Memory ◽  
Deep Learning Network

scholarly journals Long Short-Term Memory Networks for Traffic Flow Forecasting: Exploring Input Variables, Time Frames and Multi-Step Approaches

Informatica ◽  
2020 ◽  
pp. 1-27
Author(s):  
Bruno Fernandes ◽  
Fabio Silva ◽  
Hector Alaiz-Moreton ◽  
Paulo Novais ◽  
Jose Neves ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Traffic Flow Forecasting ◽  
Long Short Term Memory ◽  
Input Variables ◽  
Time Frames

scholarly journals Analyzing protein dynamics from fluorescence intensity traces using unsupervised deep learning network

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Jinghe Yuan ◽  
Rong Zhao ◽  
Jiachao Xu ◽  
Ming Cheng ◽  
Zidi Qin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fluorescence Intensity ◽  
Short Term Memory ◽  
Short Term ◽  
Learning Network ◽  
The Past ◽  
Unsupervised Deep Learning ◽  
Long Short Term Memory ◽  
Deep Learning Network ◽  
Membrane Protein Interaction

AbstractWe propose an unsupervised deep learning network to analyze the dynamics of membrane proteins from the fluorescence intensity traces. This system was trained in an unsupervised manner with the raw experimental time traces and synthesized ones, so neither predefined state number nor pre-labelling were required. With the bidirectional Long Short-Term Memory (biLSTM) networks as the hidden layers, both the past and future context can be used fully to improve the prediction results and can even extract information from the noise distribution. The method was validated with the synthetic dataset and the experimental dataset of monomeric fluorophore Cy5, and then applied to extract the membrane protein interaction dynamics from experimental data successfully.

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