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Borui Hou
Researcher at Southeast University
Publications - 10
Citations - 354
Borui Hou is an academic researcher from Southeast University. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 4, co-authored 6 publications receiving 128 citations.
Papers
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Journal ArticleDOI
LSTM-Based Auto-Encoder Model for ECG Arrhythmias Classification
TL;DR: A novel deep learning-based algorithm that integrates a long short-term memory (LSTM)-based auto-encoder (AE) network with support vector machine (SVM) for electrocardiogram (ECG) arrhythmias classification that can learn better features than the traditional method without any prior knowledge is introduced.
Journal ArticleDOI
Convolutional Autoencoder Model for Finger-Vein Verification
Borui Hou,Ruqiang Yan +1 more
TL;DR: Experiments prove that the proposed deep learning-based approach has superior performance in learning features than traditional method without any prior knowledge, presenting a good potential in the verification of finger vein.
Journal ArticleDOI
ECG Arrhythmias Detection Using Auxiliary Classifier Generative Adversarial Network and Residual Network
TL;DR: The proposed abnormality detection framework for electrocardiogram (ECG) signals, which owns unbalance distribution among different classes and gaining high accuracy in rhythm/morphology abnormalities classification, can achieve high performance in robustness and accuracy for class-imbalanced dataset.
Journal ArticleDOI
ArcVein-Arccosine Center Loss for Finger Vein Verification
Borui Hou,Ruqiang Yan +1 more
TL;DR: In this article, the authors proposed a new loss function termed arccosine center loss, which can learn interclass and intraclass information simultaneously, to improve the discriminative ability of convolutional neural networks for finger vein verification.
Proceedings ArticleDOI
Convolutional Auto-Encoder Based Deep Feature Learning for Finger-Vein Verification
Borui Hou,Ruqiang Yan +1 more
TL;DR: Experimental study proves that the proposed deep learning based method has superior performance in learning features than traditional method without any prior knowledge, presenting a good potential in the verification of finger vein.