Xiangyang She

Bio: Xiangyang She is an academic researcher from Xi'an University of Science and Technology. The author has contributed to research in topics: Identification (biology) & Artificial intelligence. The author has an hindex of 1, co-authored 1 publications receiving 9 citations.

Text Classification Based on Hybrid CNN-LSTM Hybrid Model

Abstract: Aiming at the traditional methods of text classification, the dimensions need to be reduced, the features are extracted manually, and the classification accuracy is poor, furthermore, convolutional neural network CNN can only extract local information, cannot better express context information, long short-term memory network LSTM can extract context dependencies, and the classification effect is good, but the training time is long, a text classification algorithm based on hybrid CNN-LSTM hybrid model is proposed. The algorithm uses the Skip-Gram (continuous skip-gram) model and the CBOW (continuous bag-of-words) model in word2vec to represent words as vector, using CNN to extract local features of text, LSTM saves historical information, extracts contextual dependencies of text, and uses the feature vector output by CNN as the input of LSTM, using Softmax classifier for classification. Tests on the Chinese news corpus of Sogou.com show that the algorithm can effectively improve the precision of text classification.

Video-based Person Re-identification Method based on Attention and Feature Fusion

Abstract: Aiming at the problems of low accuracy caused by low quality frames such as occlusion and noise, and the inability to effectively utilize local features of cross-channel in video surveillance, a video-based person re-identification method based on attention and feature fusion was proposed. First, channel mutual attention is added to ResNet50 residual network to mine the local features of each channel and its adjacent channels to enhance the expression of local features. Then, temporal attention was used to assign different weights to different quality frames to reduce the weights of occluded frames and noisy frames, and fuse multiple frames according to weight distribution to improve the discrimination ability of the model. Finally, the cross-entropy loss function of label smoothing and the triple loss function of hard mining are combined for supervised training to improve the generalization performance of the model. Experiments on MARS and DukeMTMC-VideoReID datasets show that the method can improve the accuracy of video-based person re-identification.

Bi-LSTM Model to Increase Accuracy in Text Classification: Combining Word2vec CNN and Attention Mechanism

Abstract: There is a need to extract meaningful information from big data, classify it into different categories, and predict end-user behavior or emotions. Large amounts of data are generated from various sources such as social media and websites. Text classification is a representative research topic in the field of natural-language processing that categorizes unstructured text data into meaningful categorical classes. The long short-term memory (LSTM) model and the convolutional neural network for sentence classification produce accurate results and have been recently used in various natural-language processing (NLP) tasks. Convolutional neural network (CNN) models use convolutional layers and maximum pooling or max-overtime pooling layers to extract higher-level features, while LSTM models can capture long-term dependencies between word sequences hence are better used for text classification. However, even with the hybrid approach that leverages the powers of these two deep-learning models, the number of features to remember for classification remains huge, hence hindering the training process. In this study, we propose an attention-based Bi-LSTM+CNN hybrid model that capitalize on the advantages of LSTM and CNN with an additional attention mechanism. We trained the model using the Internet Movie Database (IMDB) movie review data to evaluate the performance of the proposed model, and the test results showed that the proposed hybrid attention Bi-LSTM+CNN model produces more accurate classification results, as well as higher recall and F1 scores, than individual multi-layer perceptron (MLP), CNN or LSTM models as well as the hybrid models.

Sentiment Analysis using Word2vec-CNN-BiLSTM Classification

Abstract: Traditional neural network based short text classification algorithms for sentiment classification is easy to find the errors In order to solve this problem, the Word Vector Model (Word2vec), Bidirectional Long-term and Short-term Memory networks (BiLSTM) and convolutional neural network (CNN) are combined The experiment shows that the accuracy of CNN-BiLSTM model associated with Word2vec word embedding achieved 9148% This proves that the hybrid network model performs better than the single structure neural network in short text

Malicious Text Identification: Deep Learning from Public Comments and Emails

Abstract: Identifying internet spam has been a challenging problem for decades. Several solutions have succeeded to detect spam comments in social media or fraudulent emails. However, an adequate strategy for filtering messages is difficult to achieve, as these messages resemble real communications. From the Natural Language Processing (NLP) perspective, Deep Learning models are a good alternative for classifying text after being preprocessed. In particular, Long Short-Term Memory (LSTM) networks are one of the models that perform well for the binary and multi-label text classification problems. In this paper, an approach merging two different data sources, one intended for Spam in social media posts and the other for Fraud classification in emails, is presented. We designed a multi-label LSTM model and trained it on the joint datasets including text with common bigrams, extracted from each independent dataset. The experiment results show that our proposed model is capable of identifying malicious text regardless of the source. The LSTM model trained with the merged dataset outperforms the models trained independently on each dataset.

A Hybrid CNN-LSTM Model for Psychopathic Class Detection from Tweeter Users

Abstract: In today’s digital era, the use of online social media networks, such as Google, YouTube, Facebook, and Twitter, permits people to generate a massive amount of textual content. The textual content that is produced by people reveals essential information regarding their personality, with psychopathy being among these distinct personality types. This work was aimed at classifying input texts according to the traits of psychopaths and non-psychopaths. Several studies based on traditional techniques, such as the SRPIII technique, using small-sized datasets have been conducted for the detection of psychopathic behavior. However, the purpose of the current study was to build an effective computational model for the detection of psychopaths in the domain of text analytics and computational intelligence. This study was aimed at developing a technique based on a convolutional neural network + long short-term memory (CNN-LSTM) model by using a deep learning approach to detect psychopaths. A convolutional neural network was used to extract local information from a text, while the long short-term memory was used to extract the contextual dependencies of the text. By combining the advantages of convolutional neural network and long short-term memory, the proposed hybrid CNN-LSTM was able to yield a good classification accuracy of 91.67%. Additionally, a large-sized benchmark dataset was acquired for the effective classification of the given input text into psychopath vs. non-psychopath classes, thereby enabling persons with such personality traits to be identified.