scholarly journals Neural Network Topology Construction and Classroom Interaction Benchmark Graph Based on Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Congcong Luan ◽  
Peng Shang
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Network Model ◽  
Neural Network Model ◽  
Rapid Development ◽  
Video Recording ◽  
Classroom Interaction ◽  
Recognition Method ◽  
Convolutional Network ◽  
Status Analysis

With the rapid development of artificial intelligence and deep learning in recent years, many universities have put forward the goal of achieving digitalization, intelligent, and education informatization on campus. Throughout the lecture and learning process, the classroom status is an important reference factor to assess students’ acceptance of the course and the quality of lectures. However, at present, classroom status analysis is mainly conducted manually, which can distract teachers’ attention, so it is of great research significance to find a method that can improve the efficiency of classroom status analysis. In this paper, we choose an offline method to analyze the status of a classroom video recording in terms of students’ behavior and attendance in terms of frames, in which student behavior is identified by an improved target detection algorithm and attendance is analyzed by face recognition. By analyzing the structure of the neural network model, an improved neural network model is proposed for its characteristics of a large number of parameters and poor detection of small targets in the basic network. The backbone network is replaced by the improved neural network, and the depth-separable convolutional network is used to reduce the network parameters and increase the computation speed. The information in the deeper feature map is fused upward into the shallow layer to improve the accuracy of small target recognition. Finally, the optimization algorithm is incorporated into the network to optimize the network model and accelerate the model convergence speed. In addition, this paper incorporates the improved behavior recognition method and face recognition method into the system to realize the analysis of the offline classroom status. The system is divided into a teacher side and a management side, where the teacher side is responsible for uploading course recordings and the management side is responsible for randomly analyzing students’ status and attendance at any time, and the combination of the two forms a convenient and comprehensive classroom status analysis system platform. Users can upload classroom videos through the instructor interface and can view the classroom status analysis results of a course at any time by searching randomly in the administration. In this paper, the classroom status is mainly judged by the recognition of students’ behaviors.

scholarly journals Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Ying Yu ◽  
Yirui Wang ◽  
Shangce Gao ◽  
Zheng Tang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Rapid Development ◽  
Seasonal Trend ◽  
Moving Averages ◽  
Forecasting Methods ◽  
Tourism Economy ◽  
The Impact ◽  
D Model

With the impact of global internationalization, tourism economy has also been a rapid development. The increasing interest aroused by more advanced forecasting methods leads us to innovate forecasting methods. In this paper, the seasonal trend autoregressive integrated moving averages with dendritic neural network model (SA-D model) is proposed to perform the tourism demand forecasting. First, we use the seasonal trend autoregressive integrated moving averages model (SARIMA model) to exclude the long-term linear trend and then train the residual data by the dendritic neural network model and make a short-term prediction. As the result showed in this paper, the SA-D model can achieve considerably better predictive performances. In order to demonstrate the effectiveness of the SA-D model, we also use the data that other authors used in the other models and compare the results. It also proved that the SA-D model achieved good predictive performances in terms of the normalized mean square error, absolute percentage of error, and correlation coefficient.

scholarly journals Research on recognition method of cloud precipitation particle shape based on BP neural network

2021 ◽  
Vol 336 ◽  
pp. 06011
Author(s):  
Haonan Dong ◽  
Ruili Jiao ◽  
Minsong Huang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Particle Shape ◽  
Northern China ◽  
Recognition Method ◽  
Cloud Particle ◽  
Particle Images ◽  
Bp Neural Network Model

In order to solve the problem that the shape of cloud particle images measured by airborne cloud imaging probe (CIP) cannot be automatically recognized, this paper proposes an automatic recognition method of cloud and precipitation particle shape based on BP neural network. This method mainly uses a set of geometric parameters which can better describe the shape characteristics of cloud precipitation particles. Based on the cloud precipitation particle images measured by CIP in the precipitation stratiform clouds in northern China, a particle shape data training set and a testing set were constructed to train and verify the effect of the selected BP neural network model. The selected BP neural network model can classify the cloud particle image into tiny, column, needle, dendrite, aggregate, graupel, sphere, hexagonal and irregular. Utilizing the field campaign data measured by CIP, the habit identified results by the improved Holroyd method and by the selected BP neural network model were compared, which shows that the accuracy of BP neural network method is better than that of improved Holroyd method.

scholarly journals Neural Network Model for Face Recognition from Dynamic Vision Sensor

2020 ◽  
pp. short17-1-short17-8
Author(s):  
Fedor Shvetsov ◽  
Anton Konushin ◽  
Anna Sokolova
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Network Model ◽  
Neural Network Model ◽  
Synthetic Data ◽  
Simulated Data ◽  
Fine Tuning ◽  
Vision Sensor ◽  
Dynamic Vision ◽  
The Face

In this work, we consider the applicability of the face recognition algorithms to the data obtained from a dynamic vision sensor. A basic method using a neural network model comprised of reconstruction, detection, and recognition is proposed that solves this problem. Various modifications of this algorithm and their influence on the quality of the model are considered. A small test dataset recorded on a DVS sensor is collected. The relevance of using simulated data and different approaches for its creation for training a model was investigated. The portability of the algorithm trained on synthetic data to the data obtained from the sensor with the help of fine-tuning was considered. All mentioned variations are compared to one another and also compared with conventional face recognition from RGB images on different datasets. The results showed that it is possible to use DVS data to perform face recognition with quality similar to that of RGB data.

A mask recognition algorithm based on improved YOLO-V4 neural network (Preprint)

2021 ◽  
Author(s):  
Song Zhang ◽  
Shaoqiang Wang ◽  
Shaoqiang Wang
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Target Detection ◽  
Network Model ◽  
Neural Network Model ◽  
Prevention And Control ◽  
Preventive Measures ◽  
Recognition Algorithm ◽  
Facial Features ◽  
And Control

BACKGROUND With the spread of the new crown virus, the wearing of masks as one of the effective preventive measures is getting more and more attention, and the behavior of not wearing a mask is likely to cause the spread of the virus, which is not conducive to the prevention and control of the epidemic. OBJECTIVE In this paper, a new neural network model is used to better recognize the facial features of people with exit masks. METHODS This paper proposes a mask recognition algorithm based on improved YOLO-V4 neural network that can solve this problem well. This paper integrates SE-Net and DenseNet network as the reference neural network of YOLO-V4 and introduces deformable convolution. RESULTS Compared with other target detection networks, the improved YOLO-V4 neural network used in this paper improves the accuracy of mask detection to a certain extent. CONCLUSIONS The improved YOLO-V4 network proposed in this article has verified its feasibility and accuracy through experiments and has great value in use. Improving the YOLO-V4 network can help better respond to face recognition with masks in the epidemic. However, the model studied in this article focuses on accuracy and is slightly lacking in speed. The next step is to increase its speed based on ensuring accuracy and consider actual deployment and use.

scholarly journals Application of Convolution Neural Network (CNN) Model Combined with Pyramid Algorithm in Aerobics Action Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qi Liang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Action Recognition ◽  
Network Model ◽  
Neural Network Model ◽  
Convolution Neural Network ◽  
Recognition Method ◽  
Recognition Model ◽  
Recognition Function ◽  
The Common

In order to realize high-accuracy recognition of aerobics actions, a highly applicable deep learning model and faster data processing methods are required. Therefore, it is a major difficulty in the field of research on aerobics action recognition. Based on this, this paper studies the application of the convolution neural network (CNN) model combined with the pyramid algorithm in aerobics action recognition. Firstly, the basic architecture of the convolution neural network model based on the pyramid algorithm is proposed. Combined with the application strategy of the common recognition model in aerobics action recognition, the traditional aerobics action capture information is processed. Through the characteristics of different aerobics actions, different accurate recognition is realized, and then, the error of the recognition model is evaluated. Secondly, the composite recognition function of the convolution neural network model in this application is constructed, and the common data layer effect recognition method is used in the optimization recognition. Aiming at the shortcomings of the composite recognition function, the pyramid algorithm is used to improve the convolution neural network recognition model by deep learning optimization. Finally, through the effectiveness comparison experiment, the results show that the convolution neural network model based on the pyramid algorithm is more efficient than the conventional recognition method in aerobics action recognition.

scholarly journals CAPTCHA Recognition Method Based on CNN with Focal Loss

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhong Wang ◽  
Peibei Shi
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Loss Function ◽  
Neural Network Model ◽  
Recognition Rate ◽  
Recognition Method ◽  
High Adhesion ◽  
Recognition Ability ◽  
Different Types ◽  
Simple Neural Network

In order to distinguish between computers and humans, CAPTCHA is widely used in links such as website login and registration. The traditional CAPTCHA recognition method has poor recognition ability and robustness to different types of verification codes. For this reason, the paper proposes a CAPTCHA recognition method based on convolutional neural network with focal loss function. This method improves the traditional VGG network structure and introduces the focal loss function to generate a new CAPTCHA recognition model. First, we perform preprocessing such as grayscale, binarization, denoising, segmentation, and annotation and then use the Keras library to build a simple neural network model. In addition, we build a terminal end-to-end neural network model for recognition for complex CAPTCHA with high adhesion and more interference pixel. By testing the CNKI CAPTCHA, Zhengfang CAPTCHA, and randomly generated CAPTCHA, the experimental results show that the proposed method has a better recognition effect and robustness for three different datasets, and it has certain advantages compared with traditional deep learning methods. The recognition rate is 99%, 98.5%, and 97.84%, respectively.

scholarly journals A Deep Convolution Neural Network Model for Vehicle Recognition and Face Recognition

2017 ◽  
Vol 107 ◽  
pp. 715-720 ◽  
Author(s):  
Xingcheng Luo ◽  
Ruihan Shen ◽  
Jian Hu ◽  
Jianhua Deng ◽  
Linji Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Network Model ◽  
Neural Network Model ◽  
Convolution Neural Network ◽  
Vehicle Recognition ◽  
Deep Convolution Neural Network

The Applications of Neural Networks in the Engineering Cost of Transmission Line

2013 ◽  
Vol 319 ◽  
pp. 485-490
Author(s):  
Hong Fei Sun ◽  
Qing Song Tang ◽  
Yu Ling Li
Keyword(s):  
Neural Network ◽  
Transmission Line ◽  
Network Model ◽  
Neural Network Model ◽  
Cost Estimation ◽  
Rapid Development ◽  
Electric Power Industry ◽  
Linear Mapping ◽  
Main Body ◽  
Estimation Model

With the rapid development of the electric power industry in recent years, the strengthening of the power construction market and the diversification of the main body of power investment, there appears a prominent question in front of the project owners——How to control and reduce construction costs? There are many methods to estimate the cost quickly and accurately. Among the common methods and some new ways which have appeared in recent years, people can find about seven types out of them, in which, neural network model is known for its versatility and adaptability. It does not exclude new sample. On the contrary, it improves its ability to generalize and forecast with the increasing number of samples. Therefore this paper establish a cost estimation model by introducing neural network which is based on the optimization of genetic algorithm, and expresses the relationship implied in the interior of data by using the network topology and parameters by studying a large number of samples so as to fit the conventional non-linear mapping relationship between the amount and cost of a transmission line project. The results show that the artificial neural network model has a significant effect on the project cost estimation. The introduction of neural network model will certainly promote the development of informatization of power project costs management.

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