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.

https://www.mdpi.com/1999-4893/13/1/7/pdf

Detection of Suicide Ideation in Social Media Forums Using Deep Learning

Machine Learning Techniques for Sentiment Analysis: A Review

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.

/pdf/a-hybrid-deep-learning-model-for-layer-wise-melt-pool-37kqklrrm7.pdf

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

Bidirectional Gated Temporal Convolution with Attention for text classification

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.

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

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.

LSTM-CNN Hybrid Model for Text Classification

Diabetic retinopathy (DR) is one of the major complications caused by diabetes and can lead to severe vision loss or even complete blindness if not diagnosed and treated in a timely manner. In this paper, a new feature map global channel attention mechanism (GCA) is proposed to solve the problem of the early detection of DR. In the GCA module, an adaptive one-dimensional convolution kernel size algorithm based on the dimension of the feature map is proposed and a deep convolutional neural network model for DR color medical image severity diagnosis named GCA-EfficientNet (GENet) is designed. The training process uses transfer learning techniques with a cosine annealing learning rate adjustment strategy. The image regions of interest of GENet are visualized using a heat map. The final accuracy, precision, sensitivity and specificity of the DR dataset of the Kaggle competition reached 0.956, 0.956, 0.956, and 0.989, respectively. A large number of experiment results show that GENet based on the GCA attention mechanism can more effectively extract lesion features and classify the severity of DR.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09664550.pdf

Classification of Diabetic Retinopathy Severity Based on GCA Attention Mechanism

The main task of football video analysis is to detect and track players. In this work, we propose a deep convolutional neural network-based football video analysis algorithm. This algorithm aims to detect the football player in real time. First, five convolution blocks were used to extract a feature map of football players with different spatial resolution. Then, features from different levels are combined together with weighted parameters to improve detection accuracy and adapt the model to input images with various resolutions and qualities. Moreover, this algorithm can be extended to a framework for detecting players in any other sports. The experimental results assure the effectiveness of our algorithm.

/pdf/deep-learning-based-football-player-detection-in-videos-2o2vq1cj.pdf

Deep Learning-Based Football Player Detection in Videos

Semantic matching plays a crucial technical supporting role in question answering systems. For semantic matching, it is mainly based on neural networks to solve the sentence representation and interaction in semantic matching. The Siamease network structure is a commonly used structure in semantic matching. In view of the problem of information exchange and semantic extraction not saving points in the independent training of the network structure using shared parameters for the input two sequences. Therefore, in this paper, a multi-head attention mechanism is added to the Siamease model based on bidirectional LSTM to solve the problem of insufficient semantic extraction, and on this basis, it is proposed to add fine-grained matching information in the form of an interactive multi-head attention mechanism to solve the interaction problem. Experimental results show that the performance of the model is further improved compared to previous deep learning models.

Tongyan Li

Papers

LSTM-CNN Hybrid Model for Text Classification

Classification of Diabetic Retinopathy Severity Based on GCA Attention Mechanism

Deep Learning-Based Football Player Detection in Videos

Semantic Matching Research based on Interactive Multi-head Attention Mechanism