A novel sparse representation model for pedestrian abnormal trajectory understanding

Chen, Zhijun; Cai, Hao; Zhang, Yishi; Wu, Chaozhong; Mu, Mengchao; Li, Zhixiong; Sotelo, Miguel Ángel

doi:10.1016/j.eswa.2019.06.041

Cited by 71 publications

(27 citation statements)

References 21 publications

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“…According to the above discussion, the models established in this paper are as follows: According to the collected panel data of listed commercial banks, this paper firstly carries out the fixed effect regression and then carries out the Hausman test according to the obtained parameters. The test results show that the individual effect has nothing to do with the regression variables, so the random effect model is used to regression the sample panel data, and the equation results are as follows [21,22]:…”

Section: Block Chain Financial Risk Assessmentmentioning

confidence: 99%

Research on the risk of block chain technology in Internet finance supported by wireless network

Chen

Zhang

Zhou

2020

J Wireless Com Network

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In this paper, a cascaded depth learning framework is constructed. A cascaded depth model is successfully implemented by studying and analyzing the specific feature transformation, feature selection, and classifier algorithm used in the framework. A feature combination method based on enhanced feature selection and classification is proposed according to the different features learned by each layer of the model. Combining block chain cryptography technology, distributed technology, consensus accounting mechanism of technology innovation, transaction data encapsulation into specific format data unit, encapsulated into a linear list in chronological order, using encryption algorithm trading transparency, traceability of data, security, credibility, and uniqueness in financial data analysis. The experimental results show that with the increase of the number of model layers, our method can significantly improve the classification accuracy. This result also verifies that the proposed model can learn more effective data representation features and also verifies the effectiveness of the proposed feature combination method.

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Section: Block Chain Financial Risk Assessmentmentioning

confidence: 99%

Research on the risk of block chain technology in Internet finance supported by wireless network

Chen

Zhang

Zhou

2020

J Wireless Com Network

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show abstract

“…In the practice of solving problems, the ultimate goal of genetic algorithm is to determine the global optimal solution. The essence of the whole process is random search, which has great uncertainty [22]. The operation process of genetic algorithm is continuously optimized under the expected value of the optimal solution, and it is regarded as the initial sequence.…”

Section: Convergence Theory Of Genetic Algorithmmentioning

confidence: 99%

Research on Investment Portfolio Model Based on Neural Network and Genetic Algorithm

Wei

Zhao

Chen

et al. 2020

Preprint

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With the maturity of neural network theory, it provides new ideas and methods for the prediction and analysis of stock market investment. The purpose of this paper is to improve the accuracy of stock market investment prediction, we build neural network model and genetic algorithm model, study the law of stock market operation, and improve the effectiveness of neural network and genetic algorithm. Through the empirical research, it is found that the neural network model can make up for the shortcomings of the traditional algorithm through the optimization of genetic algorithm.

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“…Based on the analysis of the computer network attack principle under the virus propagation model, the computer network attack interface is built, and the computer network attacks under the virus propagation model include password attack, denial of service attack, buffer overflow attack, data-driven attack, forged information attack, and ARP attack, to implement the construction of the computer network attack analysis model [24].…”

Section: Analysis Of Computer Network Attackmentioning

confidence: 99%

Analysis of computer network attack based on the virus propagation model

Wang

Xie

et al. 2020

J Wireless Com Network

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The conventional method can make a reasonable analysis of common network attacks, but the reliability of the analysis is low under the virus propagation model. This paper proposes a new research method of computer network attack analysis based on the virus propagation model. Based on the relationship between the framework and daemon, the framework of the model for computer network attack analysis is set up, the attack analysis technology of computer network is determined, and the construction of the model for computer network attack analysis is completed. Computer attack objects and computer attack process are analyzed, and computer network attack analysis is carried out. Using the coverage test and the uncertainty test, the parameters of the reliability calculation variables are measured and the reliability calculation formula is replaced. It is concluded that the designed method of computer network attack analysis is 47.15% more reliable than the conventional analysis method, and is suitable for the network attack analysis under the virus propagation model.

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A novel sparse representation model for pedestrian abnormal trajectory understanding

Cited by 71 publications

References 21 publications

Research on the risk of block chain technology in Internet finance supported by wireless network

Research on the risk of block chain technology in Internet finance supported by wireless network

Research on Investment Portfolio Model Based on Neural Network and Genetic Algorithm

Analysis of computer network attack based on the virus propagation model

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