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| 1 | 改进Bi-LSTM的文本相似度计算方法显示文摘为提高自然语言处理任务中文本相似度检测的准确率,提出一种改进双向长短期记忆网络(Bi-LSTM)的文本相似度计算方法。将输入的句子转换成多个单词向量,通过Bi-LSTM提取出每个单词向量的最佳词特征,引入注意力机制,减小非关键因素的影响;采用多层相似加权对两个句子分别从词与词、句子与句子、词与句子3个层面进行多层比较,加权得到其最终的相似度;基于SMTeuroparl、MSRvid和MSRpar这3个数据集对所提方法的性能进行评估。实验结果表明,相比于其它方法,所提方法的文本相似度计算更佳,适用于处理复杂的长文本。 | 冯月春 陈惠娟 | 2022 | 计算机工程与设计2022，43，5： | 2 |
| 2 | ECG Classification Using Deep CNN Improved by Wavelet Transform显示文摘Atrial fibrillation is the most common persistent form of arrhythmia.A method based on wavelet transform combined with deep convolutional neural network is applied for automatic classification of electrocardiograms.Since the ECG signal is easily inferred,the ECG signal is decomposed into 9 kinds of subsignals with different frequency scales by wavelet function,and then wavelet reconstruction is carried out after segmented filtering to eliminate the influence of noise.A 24-layer convolution neural network is used to extract the hierarchical features by convolution kernels of different sizes,and finally the softmax classifier is used to classify them.This paper applies this method of the ECG data set provided by the 2017 PhysioNet/CINC challenge.After cross validation,this method can obtain 87.1%accuracy and the F1 score is 86.46%.Compared with the existing classification method,our proposed algorithm has higher accuracy and generalization ability for ECG signal data classification. | Yunxiang Zhao Jinyong Cheng Ping Zhang Xueping Peng | 2020 | Computers, Materials & Continua2020，，9： | 0 |
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