scholarly journals Intelligent Detection Model Based on a Fully Convolutional Neural Network for Pavement Cracks

2020 ◽  
Vol 123 (3) ◽  
pp. 1267-1291
Author(s):  
Duo Ma ◽  
Hongyuan Fang ◽  
Binghan Xue ◽  
Fuming Wang ◽  
Mohammed A. Msekh ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Detection Model ◽  
Model Based ◽  
Intelligent Detection

scholarly journals Adoption of Convolutional Neural Network Algorithm Combined with Augmented Reality in Building Data Visualization and Intelligent Detection

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Minghui Wei ◽  
Jingjing Tang ◽  
Haotian Tang ◽  
Rui Zhao ◽  
Xiaohui Gai ◽  
...  
Keyword(s):  
Neural Network ◽  
Augmented Reality ◽  
Data Processing ◽  
Convolutional Neural Network ◽  
Detection Model ◽  
Model Based ◽  
Urban Buildings ◽  
Intelligent Detection ◽  
Neural Network Algorithm ◽  
Public Datasets

It aims to improve the degree of visualization of building data, ensure the ability of intelligent detection, and effectively solve the problems encountered in building data processing. Convolutional neural network and augmented reality technology are adopted, and a building visualization model based on convolutional neural network and augmented reality is proposed. The performance of the proposed algorithm is further confirmed by performance verification on public datasets. It is found that the building target detection model based on convolutional neural network and augmented reality has obvious advantages in algorithm complexity and recognition accuracy. It is 25 percent more accurate than the latest model. The model can make full use of mobile computing resources, avoid network delay and dependence, and guarantee the real-time requirement of data processing. Moreover, the model can also well realize the augmented reality navigation and interaction effect of buildings in outdoor scenes. To sum up, this study provides a research idea for the identification, data processing, and intelligent detection of urban buildings.

scholarly journals A Malicious URL Detection Model Based on Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhiqiang Wang ◽  
Xiaorui Ren ◽  
Shuhao Li ◽  
Bingyan Wang ◽  
Jianyi Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Middle Layer ◽  
Word Embedding ◽  
Internet Technology ◽  
Detection Methods ◽  
Embedding Method ◽  
Detection Model ◽  
Model Based ◽  
Convolution Algorithm

With the development of Internet technology, network security is under diverse threats. In particular, attackers can spread malicious uniform resource locators (URL) to carry out attacks such as phishing and spam. The research on malicious URL detection is significant for defending against these attacks. However, there are still some problems in the current research. For instance, malicious features cannot be extracted efficiently. Some existing detection methods are easy to evade by attackers. We design a malicious URL detection model based on a dynamic convolutional neural network (DCNN) to solve these problems. A new folding layer is added to the original multilayer convolution network. It replaces the pooling layer with the k-max-pooling layer. In the dynamic convolution algorithm, the width of feature mapping in the middle layer depends on the vector input dimension. Moreover, the pooling layer parameters are dynamically adjusted according to the length of the URL input and the depth of the current convolution layer, which is beneficial to extracting more in-depth features in a wider range. In this paper, we propose a new embedding method in which word embedding based on character embedding is leveraged to learn the vector representation of a URL. Meanwhile, we conduct two groups of comparative experiments. First, we conduct three contrast experiments, which adopt the same network structure and different embedding methods. The results prove that word embedding based on character embedding can achieve higher accuracy. We then conduct the other three experiences, which use the same embedding method proposed in this paper and use different network structures to determine which network is most suitable for our model. We verify that the model designed in this paper has the highest accuracy (98%) in detecting malicious URL through these experiences.

Lateral distance detection model based on convolutional neural network

2019 ◽  
Vol 13 (1) ◽  
pp. 31-39 ◽  
Author(s):  
Xiang Zhang ◽  
Wei Yang ◽  
Xiaolin Tang ◽  
Yun Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Detection Model ◽  
Model Based ◽  
Lateral Distance

scholarly journals A Model Based on Convolutional Neural Network for Online Transaction Fraud Detection

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Zhaohui Zhang ◽  
Xinxin Zhou ◽  
Xiaobo Zhang ◽  
Lizhi Wang ◽  
Pengwei Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Fraud Detection ◽  
Transaction Data ◽  
Detection Model ◽  
Model Based ◽  
Input Feature ◽  
Low Dimensional ◽  
Fully Connected ◽  
Wireless Mobile

Using wireless mobile terminals has become the mainstream of Internet transactions, which can verify the identity of users by passwords, fingerprints, sounds, and images. However, once these identity data are stolen, traditional information security methods will not avoid online transaction fraud. The existing convolutional neural network model for fraud detection needs to generate many derivative features. This paper proposes a fraud detection model based on the convolutional neural network in the field of online transactions, which constructs an input feature sequencing layer that implements the reorganization of raw transaction features to form different convolutional patterns. Its significance is that different feature combinations entering the convolution kernel will produce different derivative features. The advantage of this model lies in taking low dimensional and nonderivative online transaction data as the input. The whole network consists of a feature sequencing layer, four convolutional layers and pooling layers, and a fully connected layer. Verifying with online transaction data from a commercial bank, the experimental results show that the model achieves excellent fraud detection performance without derivative features. And its precision can be stabilized at around 91% and recall can be stabilized at around 94%, which increased by 26% and 2%, respectively, comparing with the existing CNN for fraud detection.

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