[Retracted] Feature Extraction of Ancient Chinese Characters Based on Deep Convolution Neural Network and Big Data Analysis

Zhang, Cheng; Liu, Xingjun

doi:10.1155/2021/2491116

Cited by 8 publications

(5 citation statements)

References 28 publications

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“…e empirical results show that the prediction effect is good. Zhang [4] put forward different neural network models for different research topics in the research process of enterprise credit risk. Liu [5] mainly focused on the prediction of corporate failure.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Business Intelligence Based on the Deep Neural Network in Credit Scoring

Feng

Chen

2022

Security and Communication Networks

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In order to solve the economic globalization and the intensification of financial market volatility, this study puts forward the judgment of credit risk of loan enterprises, so as to effectively improve the ability of commercial banks to control credit risk. At the same time, neural network technology is applied to solve nonlinear data modeling. Experiments and evaluation are carried out by using the artificial neural network (ANN) evaluation model. The experimental evaluation model data obtained from the experimental evaluation model show that the prediction effect is good, which proves the effectiveness of this method. According to the data of China Banking Regulatory Commission, at the end of the third quarter of 2016, the balance of nonperforming loans of Chinese commercial banks was 1493.9 billion yuan. By the end of the third quarter of 2021, the balance of nonperforming loans of Chinese commercial banks had reached 28153.5 trillion yuan, a month on month increase of about 42.7 billion yuan. At the end of the third quarter of 2021, the domestic and foreign currency assets of Chinese banking financial institutions were 339.4 trillion yuan, a year-on-year increase of 7.7%. This shows that the credit risk remains high, but it is still at a controllable level. Commercial banks are the center of the modern economic system. Their credit risk is very important to the stability and safe development of national economy. It is also the key factor for its own business sustainability and the protection of the interests of depositors. Therefore, effectively preventing and reducing credit risk is a key field in the financial risk management of commercial banks.

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Section: Literature Reviewmentioning

confidence: 99%

Application of Business Intelligence Based on the Deep Neural Network in Credit Scoring

Feng

Chen

2022

Security and Communication Networks

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“…In static scenes, the background model can be preimaged without moving objects or noise. Different processing is performed on the current image frame and the background reference model, and the region of the moving object is determined by counting the change information in the histogram [12]. Therefore, the size, position, shape, and other related information of the moving object can be known.…”

Section: Image Object Extraction Algorithmmentioning

confidence: 99%

Object Extraction of Tennis Video Based on Deep Learning

Huang

2022

Wireless Communications and Mobile Computing

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Moving target detection and tracking technology is the core technology in the field of computer vision. It integrates image processing, pattern recognition and intelligence, and artificial intelligence and automatic control are the keys to an intelligent video surveillance system. The method acquires video image signals through visible light or infrared sensors, performs digital image processing on the video images, detects moving targets, and then extracts moving targets for target recognition. Then, the moving target is predicted and tracked according to the image features and spatiotemporal features, and the contour shape, position, and motion trajectory of the target are obtained, which provides data support for subsequent tasks. This paper uses the convolutional neural network model HyperNet as the technical support to study the deep learning (DL) tennis video target extraction. The final experimental results show that the loss value of the loss function in the training process of this method is stably maintained between 1.5% and 2.5%, and the speed performance is also greatly improved. The number of boxes for extracting candidate regions is significantly reduced, the calculation time for each frame will not exceed 1.8 s, the orientation accuracy of target extraction is 96.32%, and the size accuracy is 91.05%.

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“…Gu and Zhang ( 2020 ) pointed out that the intelligent composition review system can help students develop the habit of repeated revision, but it should not be It is still necessary to have teacher guidance. Zhang and Liu ( 2021 ) study found that course assessment mechanisms, students' vocabulary levels, their perceptions of the feedback from the intelligent writing system, and the quality of the intelligent system itself affect students' use, and that students' motivation to learn English affects their learning outcomes in the automatic evaluation feedback system. Zhao et al ( 2017 ) concluded that at the individual level, students' familiarity with computers, online learning experience, existing language ability and writing level, and learning autonomy bring about different usage effects in the collaborative artificial intelligence system, but there are no related research topics.…”

Section: Introductionmentioning

confidence: 99%

A Study of English Learning Vocabulary Detection Based on Image Semantic Segmentation Fusion Network

Pan¹

2022

Front. Comput. Neurosci.

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College students learn words always under both teachers' and school administrators' control. Based on multi-modal discourse analysis theory, the analysis of English words under the synergy of different modalities, students improve the motivation and effectiveness of word learning, but there are still some problems, such as the lack of visual modal memory of pictures, incomplete word meanings, little interaction between users, and lack of resource expansion function. To this end, this paper proposes a stepped image semantic segmentation network structure based on multi-scale feature fusion and boundary optimization. The network aims at improving the accuracy of the network model, optimizing the spatial pooling pyramid module in Deeplab V3+ network, using a new activation function Funnel ReLU (FReLU) for vision tasks to replace the original non-linear activation function to obtain accuracy compensation, improving the overall image segmentation accuracy through accurate prediction of the boundaries of each class, reducing the intra-class error in the prediction results. The accuracy compensation is obtained by replacing the original linear activation function with FReLU. Experimental results on the Englishhnd dataset demonstrate that the improved network can achieve 96.35% accuracy for English characters with the same network parameters, training data and test data.

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[Retracted] Feature Extraction of Ancient Chinese Characters Based on Deep Convolution Neural Network and Big Data Analysis

Cited by 8 publications

References 28 publications

Application of Business Intelligence Based on the Deep Neural Network in Credit Scoring

Application of Business Intelligence Based on the Deep Neural Network in Credit Scoring

Object Extraction of Tennis Video Based on Deep Learning

A Study of English Learning Vocabulary Detection Based on Image Semantic Segmentation Fusion Network

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