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Yingxiang Li

Bio: Yingxiang Li is an academic researcher from Chengdu University of Information Technology. The author has contributed to research in topics: Convolutional neural network. The author has an hindex of 1, co-authored 2 publications receiving 20 citations.

Papers
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Proceedings ArticleDOI
01 Oct 2018
TL;DR: A hybrid model of LSTM and CNN is proposed that can effectively improve the accuracy of text classification and the performance of the hybrid model is compared with that of other models in the experiment.
Abstract: Text classification is a classic task in the field of natural language processing. however, the existing methods of text classification tasks still need to be improved because of the complex abstraction of text semantic information and the strong relecvance of context. In this paper, we combine the advantages of two traditional neural network model, Long Short-Term Memory(LSTM) and Convolutional Neural Network(CNN). LSTM can effectively preserve the characteristics of historical information in long text sequences, and extract local features of text by using the structure of CNN. We proposes a hybrid model of LSTM and CNN, construct CNN model on the top of LSTM, the text feature vector output from LSTM is further extracted by CNN structure. The performance of the hybrid model is compared with that of other models in the experiment. The experimental results show that the hybrid model can effectively improve the accuracy of text classification.

67 citations

Proceedings ArticleDOI
12 Mar 2021
TL;DR: Wang et al. as discussed by the authors developed a set of human exercise posture analysis and guidance software to analyze the user's exercise posture through the video captured by the camera and define two correct forms of common movements: squats and push-ups.
Abstract: Exercise is an indispensable activity in people's spare time. Proper and correct exercise can not only help people to get a healthy body, but also help to reduce pressure and relax their mood. However, the wrong way of exercising not only makes the exercise fall short of expectations, but also causes muscle damage. Therefore, we have developed a set of human exercise posture analysis and guidance software to analyze the user's exercise posture through the video captured by the camera. This software chooses OpenPose as the basic pose estimation network, and two methods are proposed to improve the model effect. In addition, referring to the professional movement specifications, we define two correct forms of common movements: squats and push-ups. The software firstly conducts the human body posture detection, obtains the coordinate information of key points, then carries on the exercise posture analysis, and gives relevant suggestions according to the definition of the correct exercise form.

6 citations


Cited by
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Journal ArticleDOI
TL;DR: This study addresses the early detection of suicide ideation through deep learning and machine learning-based classification approaches applied to Reddit social media by employing an LSTM-CNN combined model to evaluate and compare to other classification models.
Abstract: Suicide ideation expressed in social media has an impact on language usage. Many at-risk individuals use social forum platforms to discuss their problems or get access to information on similar tasks. The key objective of our study is to present ongoing work on automatic recognition of suicidal posts. We address the early detection of suicide ideation through deep learning and machine learning-based classification approaches applied to Reddit social media. For such purpose, we employ an LSTM-CNN combined model to evaluate and compare to other classification models. Our experiment shows the combined neural network architecture with word embedding techniques can achieve the best relevance classification results. Additionally, our results support the strength and ability of deep learning architectures to build an effective model for a suicide risk assessment in various text classification tasks.

109 citations

Journal ArticleDOI
TL;DR: In this paper, a hybrid deep learning-LSTM-CNN model was proposed to forecast layer-wise melt pool temperature using a hybrid CNN-LstM technique. And the model results showed that combining CNN and LSTM networks can extract the spatial and temporal information.
Abstract: Melt pool temperature contains abundant information on metallurgical and mechanical aspects of products produced by additive manufacturing. Forecasting melt pool temperature profile during a process can help in reducing microstructural porosity and residual stresses. Although analytical and numerical models were reported, the performance of these are questionable when applied in real-time. Hence, we developed data-driven models to address this challenge, for continuous forecasting layer-wise melt pool temperature using a hybrid deep learning technique. The melt pool temperature forecasting by the proposed CNN-LSTM model is found to be better than other benchmark models in terms of accuracy and efficiency. The model results have shown that combining CNN and LSTM networks can extract the spatial and temporal information from the melt pool temperature data. Further, the proposed model results are compared with existing statistical and machine learning models. The performance measures of the proposed CNN-LSTM model indicate a greater potential for in-situ monitoring of additive manufacturing process.

14 citations

Journal ArticleDOI
TL;DR: In this article, a bidirectional gated temporal convolutional attention (BG-TCA) model is proposed for text classification, which uses the attention mechanism to distinguish the importance of different features while retaining the text features.

13 citations

Proceedings ArticleDOI
17 Jun 2019
TL;DR: Experiments show that the model proposed in this paper has great advantages in Chinese news text classification, and using a C-LSTM with word embedding model to deal with this problem.
Abstract: Traditional text classification methods are based on statistics and feature selection. It does not perform well in processing large - scale corpus. In recent years, with the rapid development of deep learning and artificial neural networks, many scholars use them to solve text classification problems and achieve good results. Common text classification neural network models include textCNN, LSTM, and C-LSTM. Using a specific model can obtain more accurate features but ignore the context information. This paper proposes a C-LSTM with word embedding model to deal with this problem. Experiments show that the model proposed in this paper has great advantages in Chinese news text classification.

11 citations