Juan Tian

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

LSTM-CNN Hybrid Model for Text Classification

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.

Lightweight mesh detection and analysis in automated breast ultrasound using deep convolutional neural networks and level set method

Abstract: In abdominal hernia surgery, accurate detection of the Lightweight (LW) mesh has critical clinical significance for the diagnosis and treatment of mesh-related complications. Reviewing the large number of slices produced by Automated Breast Ultrasound (ABUS), however, is not only time-consuming and laborious but also easy to miss or misdiagnose the micro-structured LW mesh near the fascia tissue. Therefore, in this paper, an automatic and accurate computer-aided detection system based on deep convolutional neural networks and the level set method is proposed to improve this review. Firstly, the ABUS image is pre-processed using an intelligent speckle reducing anisotropic diffusion (ISRAD) to enhance the edge details of the LW mesh while reducing speckle noise. Then, combine the ROI prior information output by the deep convolutional neural networks and the level set method to outline the contour of the LW mesh. Finally, 3D reconstruction, and analysis of the LW mesh changes over time. The LW mesh with different imaging time, sizes, degrees of aggregation (DOA), and imaging depths are used to test the proposed method, experimental results show that the proposed method has a satisfactory application for detecting and analyzing the LW mesh in ABUS images.

Detection of Suicide Ideation in Social Media Forums Using Deep Learning

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.

A Hybrid Deep Learning Model for Layer-Wise Melt Pool Temperature Forecasting in Wire-Arc Additive Manufacturing Process

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.

A C-LSTM with Word Embedding Model for News Text Classification

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.