Automated Detection and Classification of Arrhythmia From ECG Signals Using Feature-Induced Long Short-Term Memory Network

2020 ◽  
Vol 4 (8) ◽  
pp. 1-4
Author(s):  
Biswarup Ganguly ◽  
Avishek Ghosal ◽  
Anirbed Das ◽  
Debanjan Das ◽  
Debanjan Chatterjee ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Automated Detection ◽  
Short Term ◽  
Term Memory ◽  
Ecg Signals ◽  
Memory Network ◽  
Long Short Term Memory

scholarly journals Automated detection of atrial fibrillation using long short-term memory network with RR interval signals

2018 ◽  
Vol 102 ◽  
pp. 327-335 ◽  
Author(s):  
Oliver Faust ◽  
Alex Shenfield ◽  
Murtadha Kareem ◽  
Tan Ru San ◽  
Hamido Fujita ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Short Term Memory ◽  
Automated Detection ◽  
Short Term ◽  
Term Memory ◽  
Rr Interval ◽  
Memory Network ◽  
Long Short Term Memory

scholarly journals Classification of Microblog Users’ Sentiments Based on BERT-BiLSTM-CBAM

CONVERTER ◽  
2021 ◽  
pp. 281-287
Author(s):  
Juan Chen, Ruyun Chen, Di Yu
Keyword(s):  
Hybrid Model ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Feature Vectors ◽  
Text Information ◽  
Memory Network ◽  
Long Short Term Memory ◽  
New Feature

This study proposes a method for more accurate classification of microblog users' sentiments based on the BERT-BiLSTM-CBAM hybrid model. First, the text information is pre-trained by the bidirectional encoder representation from transformers (BERT) model to get feature vectors. Then, the feature vectors are spliced and recombined using bidirectional long-short-term memory network (BiLSTM) and CBAM mechanism to obtain new feature vectors. Finally, these new feature vectors are input to the full connection layer and then processed by the softmax function to obtain the sentiment category of the text. The experiment conducted on the sample dataset demonstrates that the model proposed in this study yielded accurate and dependable result in classifying microblog texts in the sample dataset. The model based on BERT-BiLSTM-CBAM algorithm is more efficient than the traditional depth model in processing microblog contents

Classification of Power Quality Disturbances Using Convolutional Network and Long Short-Term Memory Network

2019 ◽  
Author(s):  
Wilson Leal Rodrigues Junior ◽  
Fabbio Anderson Silva Borges ◽  
Ricardo de A. Lira Rabelo ◽  
Bruno Vicente Alves de Lima ◽  
Jose Eduardo Almeida de Alencar
Keyword(s):  
Power Quality ◽  
Short Term Memory ◽  
Short Term ◽  
Convolutional Network ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Power Quality Disturbances
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