Bidirectional Long Term Memory Network Research Articles

A Rumor Detection Method Based on Adaptive Fusion of Statistical Features and Textual Features

Many rumors spread quickly and widely on social media, affecting social stability. The rumors of most current detection methods only use textual information or introduce external auxiliary information (such as user information and propagation information) to enhance the detection effect, and the inherent statistical features of the corpus have not been fully used and compared with the external auxiliary features; in addition, statistical features are more certain and can only be obtained from textual information. Therefore, we adopted a method based on the adaptive fusion of word frequency distribution features and textual features to detect rumors. Statistical features were extracted by encoding statistical information through a variational autoencoder. We extracted semantic features and sequence features as textual features through a parallel network comprising a convolutional neural network and a bidirectional long-term memory network. In addition, we also designed an adaptive valve to only fuse useful statistical features with textual features according to the credibility of textual features, which can solve the over-fitting problem caused by the excessive use of statistical features. The accuracy of the model in three public datasets (Twitter15, Twitter16, and Weibo) reached 87.5%, 88.6%, and 95.8%, respectively, and the F1 value reached 87.4%, 88.5%, and 95.8%, respectively, showing that the model can effectively improve the performance of rumor detection.

Short-Term Traffic Flow Prediction for Urban Road Sections Based on Time Series Analysis and LSTM_BILSTM Method

The real-time performance and accuracy of traffic flow prediction directly affect the efficiency of traffic flow guidance systems, and traffic flow prediction is a hotspot in the field of intelligent transportation. To further improve the accuracy of short-term traffic flow prediction, a short-term traffic flow prediction model based on traffic flow time series analysis, and an improved long short-term memory network (LSTM) is proposed. First, perform time series analysis on traffic flow data and perform smoothing and standardization processing to obtain a stable time series as model input data, which can improve the accuracy of model training and eliminate the impact of a wide range of feature values. Then, an improved LSTM model based on LSTM and bidirectional LSTM networks are established. Combining the advantages of sequential data and the long-term dependence of forwarding LSTM and reverse LSTM, the bidirectional long-term memory network (BILSTM) is integrated into the prediction model. The first layer of the LSTM network learns and predicts the input time series and further learns and trains through the bidirectional LSTM network to effectively overcome the large prediction errors. Finally, the performance of the proposed method is evaluated by comparing the predicted results with actual traffic data. The model that is proposed in this paper is compared with the long short-term memory network (LSTM) model and the bidirectional long-term memory network (BILSTM) model. The results demonstrate that the proposed method outperforms both compared methods in terms of accuracy and stability.

Short-Term Subway Passenger Flow Prediction Based on GCN-BiLSTM

Aiming at the problem that the accuracy of passenger flow prediction is not high, this paper presents a short-term passenger flow forecasting model based on Graph Convolutional Neural Network (GCN) and Bidirectional Long-term Memory Network (BiLSTM). Firstly, the historical traffic time series is divided into three time modes: recent period, daily period and weekly period; Secondly, we construct three models based on GCN and BiLSTM to capture the spatial and temporal dependence of the three patterns; Finally, the parameter matrix is used to fuse the output of the three time modes to obtain the final prediction result. By testing the data set of subway passenger flow in a city in January 2019, the experimental results show that the root mean square error of the model is reduced by 8.515% and the average absolute error is reduced by 4.239% compared with the single BiLSTM model, it has a high fitting degree with the real passenger flow value and has certain application value for the reasonable allocation of subway capacity.

Early Drought Plant Stress Detection with Bi-Directional Long-Term Memory Networks

Early Drought Plant Stress Detection with Bi-Directional Long-Term Memory Networks