动词
发表于 2025-3-21 19:38:28
书目名称Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Com影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0620563<br><br> <br><br>书目名称Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Com影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0620563<br><br> <br><br>书目名称Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Com网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0620563<br><br> <br><br>书目名称Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Com网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0620563<br><br> <br><br>书目名称Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Com被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0620563<br><br> <br><br>书目名称Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Com被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0620563<br><br> <br><br>书目名称Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Com年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0620563<br><br> <br><br>书目名称Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Com年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0620563<br><br> <br><br>书目名称Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Com读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0620563<br><br> <br><br>书目名称Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Com读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0620563<br><br> <br><br>
罗盘
发表于 2025-3-21 21:31:14
een ontwijkende persoonlijkheidsstoornis valt in de eerste plaats de sociale fobie op; zo iemand is buitensporig verlegen. Maar het gaat verder. We hebben te maken met iemand die zich eigenlijk nergens en nooit echt op zijn gemak voelt. De gevolgen op de lange duur zijn verstrekkend: er bestaat een
颠簸下上
发表于 2025-3-22 02:14:59
Chengbin Huang,Renjie Zhao,Weiting Chen,Huazheng Likende persoonlijkheidsstoornis valt in de eerste plaats de sociale fobie op; zo iemand is buitensporig verlegen. Maar het gaat verder. We hebben te maken met iemand die zich eigenlijk nergens en nooit echt op zijn gemak voelt. De gevolgen op de lange duur zijn verstrekkend: er bestaat een groot risi
非秘密
发表于 2025-3-22 05:08:30
Arrhythmia Classification with Attention-Based Res-BiLSTM-Netmanual diagnosis for cardiac arrhythmias is tedious and error-prone through ECG signals. In this work, we propose an end-to-end deep neural network called attention-based Res-BiLSTM-Net for automatic diagnosis of cardiac arrhythmias. Our model is capable of classifying ECG signals with different len
somnambulism
发表于 2025-3-22 09:54:48
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Asseverate
发表于 2025-3-22 16:55:48
An Ensemble Neural Network for Multi-label Classification of Electrocardiogram in clinical scenarios. In this paper, we propose an ensemble neural network to address the multi-label classification of 12-lead ECG. The proposed network contains two modules, which treat the multi-label task from two different perspectives. The first module deals with the task in a sequence-gener
性上瘾
发表于 2025-3-22 19:42:29
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孤僻
发表于 2025-3-22 23:32:03
Diagnosing Cardiac Abnormalities from 12-Lead Electrocardiograms Using Enhanced Deep Convolutional NECGs) using the dataset of 14000 ECGs. Instead of straightforwardly applying an end-to-end deep learning approach, we find that deep convolutional neural networks enhanced with sophisticated hand crafted features show advantages in reducing generalization errors. Additionally, data preprocessing and
不理会
发表于 2025-3-23 05:09:39
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致命
发表于 2025-3-23 05:55:04
Multi-label Classification of Abnormalities in 12-Lead ECG Using 1D CNN and LSTMories (1 normal, 8 abnormal). The only preprocessing techniques we used are the baseline drift removal based on median filtering and signal segmentation. Then an 18-layer deep 1D CNN consisting of residual blocks and skip architectures that is followed by a bi-directional LSTM layer was developed. D