[A study on heart sound classification algorithm based on improved Mel-frequency cepstrum coefficient feature extraction and deep Transformer]. [PDF]
Meng X, Zhang S.
europepmc +1 more source
[Effect of AI-assisted compressed sensing acceleration on MRI radiomic feature extraction and staging model performance for nasopharyngeal carcinoma]. [PDF]
Li X, Xu G, Liu J, Feng Y.
europepmc +1 more source
[Sampling intervals dependent feature extraction for state transfer networks of epileptic signals]. [PDF]
Zhang L, Yan S, Gu C.
europepmc +1 more source
[Fatigue feature extraction and classification algorithm of forehead single-channel electroencephalography signals]. [PDF]
Yang H, Liu Y, Xia L.
europepmc +1 more source
[Heart sound classification algorithm based on bispectral feature extraction and convolutional neural networks]. [PDF]
Peng L, Quan H.
europepmc +1 more source
[Multi-modal physiological time-frequency feature extraction network for accurate sleep stage classification]. [PDF]
Hu K, Chen J, Zhang P, Xue W, Xie J.
europepmc +1 more source
[Fully Automatic Glioma Segmentation Algorithm of Magnetic Resonance Imaging Based on 3D-UNet With More Global Contextual Feature Extraction: An Improvement on Insufficient Extraction of Global Features]. [PDF]
Tian H, Wang Y, Ji Y, Rahman MM.
europepmc +1 more source
为了提高特高压换流站设备在地震中的损伤识别精度和实时性,文中提出了一种基于长短期记忆网络(LSTM)结合小波散射特征提取的设备损伤识别方法。通过有限元仿真模拟换流站设备在地震中的加速度响应,生成包含不同损伤工况的时程数据。采用小波散射技术对加速度信号进行特征提取,以减少信号噪声并保留损伤特征,再将提取后的特征输入LSTM模型进行损伤识别。结果表明,基于小波散射特征的LSTM网络相比直接使用原始加速度数据,显著提升了识别速度与精度,模型的最终识别准确率达到95 ...
李强, 宋云海, 杨洋, 张世洪
doaj
[Liver lesion classification in multi-modal magnetic resonance imaging via medical foundation segmentation models and radiomics integration]. [PDF]
Liu Y +5 more
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[Multi-scale motor imagery electroencephalogram classification framework with dynamic weighted multi-source domain alignment]. [PDF]
Kong X, Zhang P, Chen J.
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