Text Sentiment Analysis Based on Multi-Layer Bi-Directional LSTM with a Trapezoidal Structure

Sentiment analysis,commonly called opinion mining or emotion artificial intelligence(AI),employs biometrics,computational linguistics,nat-ural language processing,and text analysis to systematically identify,extract,measure,and investigate affective states and subjective data.Sentiment analy-sis algorithms include emotion lexicon,traditional machine learning,and deep learning.In the text sentiment analysis algorithm based on a neural network,multi-layer Bi-directional long short-term memory(LSTM)is widely used,but the parameter amount of this model is too huge.Hence,this paper proposes a Bi-directional LSTM with a trapezoidal structure model.The design of the trapezoidal structure is derived from classic neural networks,such as LeNet-5 and AlexNet.These classic models have trapezoidal-like structures,and these structures have achieved success in the field of deep learning.There are two benefits to using the Bi-directional LSTM with a trapezoidal structure.One is that compared with the single-layer configuration,using the of the multi-layer structure can better extract the high-dimensional features of the text.Another is that using the trapezoidal structure can reduce the model’s parameters.This paper introduces the Bi-directional LSTM with a trapezoidal structure model in detail and uses Stanford sentiment treebank 2(STS-2)for experiments.It can be seen from the experimental results that the trapezoidal structure model and the normal structure model have similar performances.However,the trapezoidal structure model parameters are 35.75%less than the normal structure model.

基于BERT与Bi-LSTM融合注意力机制的中医病历文本的提取与自动分类

中医逐渐成为热点,中医病历文本中包含着巨大而宝贵的医疗信息。而在中医病历文本挖掘和利用方面,一直面临中医病历文本利用率低、抽取有效信息并对信息文本进行分类的难度大的问题。针对这一问题,研究一种对中医病历文本的提取与自动分类的方法具有很大的临床价值。文中尝试提出一种基于BERT+Bi-LSTM+Attention融合的病历短文本分类模型。使用BERT预处理获取短文本向量作为模型输入,对比BERT与word2vec模型的预训练效果,对比Bi-LSTM+Attention和LSTM模型的效果。实验结果表明,BERT+Bi-LSTM+Attention融合模型在中医病历文本的提取和分类方面达到了最高的AverageF1值(即89.52%)。通过对比发现,BERT较word2vec模型的预训练效果有显著的提升,且Bi-LSTM+Attention模型较LSTM模型的效果有显著的提升,因此提出的BERT+Bi-LSTM+Attention融合模型在病历文本抽取与分类上有一定的医学价值。

基于多元因素的Bi-LSTM高速公路交通流预测

针对影响高速公路交通流量因素多样而复杂的问题,提出了一种基于多元因素的Bi-LSTM (双向长短期记忆网络)高速公路交通流预测模型.首先对原始数据进行清理和相关性分析,提高研究准确性,降低数据维度;其次,基于时间滑动窗口,构建多元因素交通流时序矩阵,并以MAE与RMSE为评估指标,训练优化Bi-LSTM交通流预测模型.本模型同时考虑了天气状况、节假日、收费情况等高相关度影响因素,及交通流前序、后序变化的影响.以陕西省高速公路收费数据为实验对象,结果表明:与GRU和LSTM两种神经网络相比较,本模型在高速公路短期交通流预测中的适用性更强、精确度更高.

基于Bi-LSTM的结构变形预测

伴随着社会经济的快速发展,地铁、隧道、桥梁等建筑在人们的生活中占据的地位越来越高,预测分析建筑的结构变形数据,及时发现存在的安全隐患,至关重要.结合长短时记忆网络(Long Short Time Memory, LSTM)的优点,本文提出了一种基于双向长短时记忆网络(Bidirectional Long Short Time Memory, Bi-LSTM)的结构变形预测模型.该模型通过记忆时间节点前后的规律,预测当前节点变形数据,充分挖掘变形数据内部的关联信息.与WNN、LSTM、GRU模型进行对比,结果表明,该模型RMSE、MAPE、MAE分别下降了66.0%、61.2%、66.2%,是一种有效预测结构形变的方法.

Device-Free Through-the-Wall Activity Recognition Using Bi-Directional Long Short-Term Memory and WiFi Channel State Information

Activity recognition plays a key role in health management and security.Traditional approaches are based on vision or wearables,which only work under the line of sight(LOS)or require the targets to carry dedicated devices.As human bodies and their movements have influences on WiFi propagation,this paper proposes the recognition of human activities by analyzing the channel state information(CSI)from the WiFi physical layer.The method requires only the commodity:WiFi transmitters and receivers that can operate through a wall,under LOS and non-line of sight(NLOS),while the targets are not required to carry dedicated devices.After collecting CSI,the discrete wavelet transform is applied to reduce the noise,followed by outlier detection based on the local outlier factor to extract the activity segment.Activity recognition is fulfilled by using the bi-directional long short-term memory that takes the sequential features into consideration.Experiments in through-the-wall environments achieve recognition accuracy>95%for six common activities,such as standing up,squatting down,walking,running,jumping,and falling,outperforming existing work in this field.

基于双向长短期记忆网络的煤矿瓦斯浓度预测

煤矿瓦斯是造成煤矿安全事故主要原因之一,为有效降低煤矿瓦斯安全事故发生率,整合煤矿瓦斯浓度、温度和风速数据,使用KNN算法补全缺失值,最大最小归一化处理数据集并划分训练集和测试集,以MAE和RMSE为评价指标,利用经验公式和多次实验确定模型隐藏层神经元数量,建立了基于双向长短期记忆网络的瓦斯浓度预测模型。结果表明,相比于LSTM模型、FC模型和RNN模型,双向长短期记忆网络模型虽耗时较长,但预测误差MAE为0.00061,RMSE为0.00847,模型预测效果较好,可为煤矿瓦斯事故防治提供决策依据。

基于局部注意力Seq2Seq的中医文本多标签分类研究

针对传统多标签分类模型未充分考虑文本中临近标签之间存在的复杂关联性问题,提出一种基于局部注意力Seq2Seq的中医文本多标签分类模型。首先利用ALBERT模型提取文本的动态语义向量;然后多层Bi-LSTM构成的编码层用于提取文本间的语义关系;最后解码层中使用多层LSTM的局部注意力,突出文本序列中临近标签之间的相互影响力,以预测多标签序列。在中医数据集上验证方法的有效性,实验结果表明,所提出的算法能够有效捕获标签之间的相关性,适用于中医文本的分类预测。

Detection of Abnormal Network Traffic Using Bidirectional Long Short-Term Memory

Nowadays,web systems and servers are constantly at great risk from cyberattacks.This paper proposes a novel approach to detecting abnormal network traffic using a bidirectional long short-term memory(LSTM)network in combination with the ensemble learning technique.First,the binary classification module was used to detect the current abnormal flow.Then,the abnormal flows were fed into the multilayer classification module to identify the specific type of flow.In this research,a deep learning bidirectional LSTM model,in combination with the convolutional neural network and attention technique,was deployed to identify a specific attack.To solve the real-time intrusion-detecting problem,a stacking ensemble-learning model was deployed to detect abnormal intrusion before being transferred to the attack classification module.The class-weight technique was applied to overcome the data imbalance between the attack layers.The results showed that our approach gained good performance and the F1 accuracy on the CICIDS2017 data set reached 99.97%,which is higher than the results obtained in other research.

Enhanced Deep Learning for Detecting Suspicious Fall Event in Video Data

Suspicious fall events are particularly significant hazards for the safety of patients and elders.Recently,suspicious fall event detection has become a robust research case in real-time monitoring.This paper aims to detect suspicious fall events during video monitoring of multiple people in different moving back-grounds in an indoor environment;it is further proposed to use a deep learning method known as Long Short Term Memory(LSTM)by introducing visual atten-tion-guided mechanism along with a bi-directional LSTM model.This method contributes essential information on the temporal and spatial locations of‘suspi-cious fall’events in learning the video frame in both forward and backward direc-tions.The effective“You only look once V4”(YOLO V4)–a real-time people detection system illustrates the detection of people in videos,followed by a track-ing module to get their trajectories.Convolutional Neural Network(CNN)fea-tures are extracted for each person tracked through bounding boxes.Subsequently,a visual attention-guided Bi-directional LSTM model is proposed for the final suspicious fall event detection.The proposed method is demonstrated using two different datasets to illustrate the efficiency.The proposed method is evaluated by comparing it with other state-of-the-art methods,showing that it achieves 96.9%accuracy,good performance,and robustness.Hence,it is accep-table to monitor and detect suspicious fall events.

基于GPS轨迹数据的货车交通流量需求预测循环神经网络模型

动力性差、尺寸大是货车影响道路交通流运行效率的重要原因,为提高货车运行效率,对快速路货车流量预测问题进行研究.基于货车GPS轨迹数据,构建长短时循环神经网络(Long Short Term Memory,LSTM),门控神经单元(Gated Recurrent Unit,GRU),双向长短时记忆网络(Bidirectional Long Short Term Memory,Bi-LSTM)和双向门控神经单元(Bidirectional Gated Recurrent Unit,Bi-GRU)四种货车交通流量需求预测循环神经网络模型.研究结果表明:货车交通流量需求预测循环神经网络模型对货车交通流量具有很好的预测能力,平均预测精度为91.55%,较ARIMA高出10.45%;GRU模型对整体货车流量序列预测精度最高;低峰时段平均预测精度高于高峰时段,LSTM在波动较强的高峰时段预测精度最高,为96.83%;Bi-GRU在低峰时段的预测精度最高,为97.66%.研究成果将为政策制定者选用合适的循环神经网络模型,精准预测货车流量,提高货车交通运行效率提供理论和技术支持.

基于深度神经网络的不常用备件需求预测研究

为了解决采购计划制定时无科学依据而造成资源浪费的问题,采用LSTM和Bi-LSTM神经网络实现对不常用备件的需求预测,建立了不常用备件需求预测的深度神经网络,通过实例分析和预测结果的对比,证明两种神经网络能够实现对不常用备件需求的有效预测。

TKCA:a timely keystroke-based continuous user authentication with short keystroke sequence in uncontrolled settings

Keystroke-based behavioral biometrics have been proven effective for continuous user authentication.Current state-of-the-art algorithms have achieved outstanding results in long text or short text collected by doing some tasks.It remains a considerable challenge to authenticate users continuously and accurately with short keystroke inputs collected in uncontrolled settings.In this work,we propose a Timely Keystroke-based method for Continuous user Authentication,named TKCA.It integrates the key name and two kinds of timing features through an embedding mechanism.And it captures the relationship between context keystrokes by the Bidirectional Long Short-Term Memory(Bi-LSTM)network.We conduct a series of experiments to validate it on a public dataset-the Clarkson II dataset collected in a completely uncontrolled and natural setting.Experiment results show that the proposed TKCA achieves state-of-the-art performance with 8.28%of EER when using only 30 keystrokes and 2.78%of EER when using 190 keystrokes.