scholarly journals Automatic Detection of Grammatical Errors in English Verbs Based on RNN Algorithm: Auxiliary Objectives for Neural Error Detection Models

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yizhou He
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Error Detection ◽  
Rapid Development ◽  
Automatic Detection ◽  
Detection Accuracy ◽  
Original Text ◽  
Detection Technology ◽  
Grammatical Errors ◽  
English Verb

With the rapid development of neural network technology, we have widely used this technology in various fields. In the field of language translation, the research on automatic detection technology of English verb grammatical errors is in a hot stage. The traditional manual detection cannot be applied to the current environment. Therefore, this paper proposes an automatic detection technology of English verb grammatical errors based on recurrent neural network (RNN) algorithm to solve this problem. Firstly, the accuracy and feedback speed of traditional manual detection and recurrent neural network RNN algorithm are compared. Secondly, a detection model which can be calculated according to grammatical order combined with context is designed. Finally, when the output verb result is inconsistent with the original text, it can automatically mark the error detection effect. The experimental results show that the algorithm model studied in this paper can effectively improve the detection accuracy and feedback efficiency and is more applicable and effective than the traditional manual detection method.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

scholarly journals System-independent ASR error detection and classification using Recurrent Neural Network

2019 ◽  
Vol 55 ◽  
pp. 187-199 ◽  
Author(s):  
Rahhal Errattahi ◽  
Asmaa EL Hannani ◽  
Thomas Hain ◽  
Hassan Ouahmane
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Error Detection

scholarly journals On Using XMC R-CNN Model for Contraband Detection within X-Ray Baggage Security Images

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yong Zhang ◽  
Weiwu Kong ◽  
Dong Li ◽  
Xudong Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Detection Rate ◽  
Automatic Detection ◽  
User Needs ◽  
Labor Intensity ◽  
Detection Rates ◽  
X Ray ◽  
Detection Technology ◽  
Security Inspection

We present an X-ray material classifier region-based convolutional neural network (XMC R-CNN) model for detecting the typical guns and the typical knives in X-ray baggage images. The XMC R-CNN model is used to solve the problem of contraband detection in overlapped X-ray baggage images by the X-ray material classifier algorithm and the organic stripping and inorganic stripping algorithm, and better detection rate and the miss rate are achieved. The detection rates of guns and knives are 96.5% and 95.8%, and the miss rates of guns and knives are 2.2% and 4.2%. The contraband detection technology based on the XMC R-CNN model is applied to X-ray baggage images of security inspection. According to user needs, the safe X-ray baggage images can be automatically filtered in some specific fields, which reduces the number of X-ray baggage images that security inspectors need to screen. The efficiency of security inspection is improved, and the labor intensity of security inspection is reduced. In addition, the security inspector can screen X-ray baggage images according to the boxes of automatic detection, which can improve the effect of security inspection.

scholarly journals A Method for Detecting Surface Defects in Railhead by Magnetic Flux Leakage

2021 ◽  
Vol 11 (20) ◽  
pp. 9489
Author(s):  
Yinliang Jia ◽  
Shicheng Zhang ◽  
Ping Wang ◽  
Kailun Ji
Keyword(s):  
Magnetic Flux ◽  
Surface Defects ◽  
Rapid Development ◽  
Magnetic Flux Leakage ◽  
Detection Accuracy ◽  
Physical Experiments ◽  
Detection Technology ◽  
Element Simulation ◽  
Lift Off ◽  
Flux Leakage

With the rapid development of the world’s railways, rail is vital to ensure the safety of rail transit. This article focuses on the magnetic flux leakage (MFL) non-destructive detection technology of the surface defects in railhead. A Multi-sensors method is proposed. The main sensor and four auxiliary sensors are arranged in the detection direction. Firstly, the root mean square (RMS) of the x-component of the main sensor signal is calculated. In the data more significant than the threshold, the defects are determined by the relative values of the sensors signal. The optimal distances among these sensors are calculated to the size of a defect and the lift-off. From the finite element simulation and physical experiments, it is shown that this method can effectively suppress vibration interference and improve the detection accuracy of defects.

scholarly journals A Review of Intelligent Driving Pedestrian Detection Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Di Tian ◽  
Yi Han ◽  
Biyao Wang ◽  
Tian Guan ◽  
Wei Wei
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Rapid Development ◽  
Pedestrian Detection ◽  
Evaluation Criteria ◽  
Human Perception ◽  
Detection Accuracy ◽  
Learning Stage ◽  
Detection Technology ◽  
General Object

Pedestrian detection is a specific application of object detection. Compared with general object detection, it shows similarities and unique characteristics. In addition, it has important application value in the fields of intelligent driving and security monitoring. In recent years, with the rapid development of deep learning, pedestrian detection technology has also made great progress. However, there still exists a huge gap between it and human perception. Meanwhile, there are still a lot of problems, and there remains a lot of room for research. Regarding the application of pedestrian detection in intelligent driving technology, it is of necessity to ensure its real-time performance. Additionally, it is necessary to lighten the model while ensuring detection accuracy. This paper first briefly describes the development process of pedestrian detection and then concentrates on summarizing the research results of pedestrian detection technology in the deep learning stage. Subsequently, by summarizing the pedestrian detection dataset and evaluation criteria, the core issues of the current development of pedestrian detection are analyzed. Finally, the next possible development direction of pedestrian detection technology is explained at the end of the paper.

Logic Error Detection Algorithm Based on RNN with Threshold Selection

2020 ◽  
Author(s):  
Taku Matsumoto ◽  
Yutaka Watanobe ◽  
Keita Nakamura ◽  
Yunosuke Teshima
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Error Detection ◽  
Language Model ◽  
Source Code ◽  
Detection Algorithm ◽  
Detection Performance ◽  
Educational Systems ◽  
Determination Method ◽  
Logical Errors

Logical errors in source code can be detected by probabilities obtained from a language model trained by the recurrent neural network (RNN). Using the probabilities and determining thresholds, places that are likely to be logic errors can be enumerated. However, when the threshold is set inappropriately, user may miss true logical errors because of passive extraction or unnecessary elements obtained from excessive extraction. Moreover, the probabilities of output from the language model are different for each task, so the threshold should be selected properly. In this paper, we propose a logic error detection algorithm using an RNN and an automatic threshold determination method. The proposed method selects thresholds using incorrect codes and can enhance the detection performance of the trained language model. For evaluating the proposed method, experiments with data from an online judge system, which is one of the educational systems that provide the automated judge for many programming tasks, are conducted. The experimental results show that the selected thresholds can be used to improve the logic error detection performance of the trained language model.

scholarly journals Calibrating Network Traffic with One-Dimensional Convolutional Neural Network with Autoencoder and Independent Recurrent Neural Network for Mobile Malware Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Songjie Wei ◽  
Zedong Zhang ◽  
Shasha Li ◽  
Pengfei Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recurrent Neural Network ◽  
Network Traffic ◽  
Malware Detection ◽  
Smart Devices ◽  
Detection Accuracy ◽  
One Dimensional ◽  
Mobile Malware ◽  
Mobile Malware Detection

In response to the surging challenge in the number and types of mobile malware targeting smart devices and their sophistication in malicious behavior camouflage, we propose to compose a traffic behavior modeling method based on one-dimensional convolutional neural network with autoencoder and independent recurrent neural network (1DCAE-IndRNN) for mobile malware detection. The design solves the problem that most existing approaches for mobile malware traffic detection struggle with capturing the network traffic dynamics and the sequential characteristics of anomalies in the traffic. We reconstruct and apply the one-dimensional convolutional neural network to extract local features from multiple network flows. The autoencoder is applied to digest the principal traffic features from the neural network and is integrated into the independent recurrent neural network construction to highlight the sequential relationship between the highly significant features. In addition, the Softmax function with the LReLU activation function is adjusted and embedded to the neurons of the independent recurrent neural network to effectively alleviate the problem of unstable training. We conduct a series of experiments to evaluate the effectiveness of the proposed method and its performance for the 1DCAE-IndRNN-integrated detection procedure. The detection results of the public Android malware dataset CICAndMal2017 show that the proposed method achieves up to 98% detection accuracy and recall rates with clear advantages over other benchmark methods.

scholarly journals Metal Surface Defect Detection Using Modified YOLO

Algorithms ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 257
Author(s):  
Yiming Xu ◽  
Kai Zhang ◽  
Li Wang
Keyword(s):  
Neural Network ◽  
Metal Surface ◽  
Defect Detection ◽  
Surface Defect ◽  
Detection Methods ◽  
Cluster Center ◽  
Detection Accuracy ◽  
Detection Technology ◽  
Speed Advantage ◽  
Surface Defect Detection

Aiming at the problems of inefficient detection caused by traditional manual inspection and unclear features in metal surface defect detection, an improved metal surface defect detection technology based on the You Only Look Once (YOLO) model is presented. The shallow features of the 11th layer in the Darknet-53 are combined with the deep features of the neural network to generate a new scale feature layer using the basis of the network structure of YOLOv3. Its goal is to extract more features of small defects. Furthermore, then, K-Means++ is used to reduce the sensitivity to the initial cluster center when analyzing the size information of the anchor box. The optimal anchor box is selected to make the positioning more accurate. The performance of the modified metal surface defect detection technology is compared with other detection methods on the Tianchi dataset. The results show that the average detection accuracy of the modified YOLO model is 75.1%, which ia higher than that of YOLOv3. Furthermore, it also has a great detection speed advantage, compared with faster region-based convolutional neural network (Faster R-CNN) and other detection algorithms. The improved YOLO model can make the highly accurate location information of the small defect target and has strong real-time performance.

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