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Construction of Economic Security Early Warning System Based on Cloud Computing and Data Mining.

Guanghui Yuan1, Fei Xie2,3, Huiling Tan4

  • 1School of Economics and Management, Shanghai University of Political Science and Law, Shanghai 201701, China.

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Summary
This summary is machine-generated.

This study introduces an economic security early warning system using cloud computing, data mining, and a CNN-SVM algorithm. The model achieves high prediction accuracy, aiding economic stability and national development.

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Area of Science:

  • Economics
  • Computer Science

Background:

  • Economic security is crucial for financial market stability and national economic development.
  • Economic instability causes significant losses and hinders progress.
  • Monitoring and forecasting economic security are vital for regulation and decision-making.

Purpose of the Study:

  • To construct an advanced economic security early warning system.
  • To develop an adaptive model for evaluating and warning about economic security states.

Main Methods:

  • Integration of cloud computing and data mining technologies.
  • Development of a hybrid CNN-SVM algorithm for economic security forecasting.
  • Utilization of ReLU activation function in CNN and RBF function in SVM.

Main Results:

  • The proposed model achieved a prediction accuracy of 0.98 with optimal function choices.
  • The system accurately predicted economic security indices for Q province in 2018 (0.893) and 2019 (0.829).

Conclusions:

  • The developed economic security early warning system demonstrates high accuracy and effectiveness.
  • The system provides a reliable tool for monitoring and forecasting economic security, supporting economic stability.