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Challenges and Countermeasures of Arab Immigrants and International Trade in the Era of Big Data.

Authors :
Huang, Yi
Shao, Miao
Source :
Mathematical Problems in Engineering. 7/16/2022, p1-11. 11p.
Publication Year :
2022

Abstract

In recent years, the development of intelligent iteration technology and the use of big data processing technology have set off an upsurge, and the analysis and application of artificial intelligence algorithms have been paid more and more attention. In order to face the challenges of Arab migration and international trade, this paper constructs the basic structure of the Arab migration action imitation model. In this paper, a simulated servo clustering algorithm based on big data and intelligent iteration is used. Then, through the analysis of pseudo servo clustering algorithm, an optimization model is established, and a big data analysis system is formed. This paper focuses on the wide application of big data statistics to solve the construction of Arab immigration and entrepreneurship data system. This paper studies and applies the big data statistics and intelligent iterative algorithm of Arab immigration behavior, focuses on the annual ladder degree of Arab immigrants, and constructs a pseudo servo cluster system based on Intelligent iterative algorithm. Finally, the simulation experiment verifies whether the clustering model can accurately retrieve the behavior of Arab immigrants in China. The era of big data provides good development opportunities for Arab immigrants and international trade, but it also faces severe challenges. The study provides a reference for strengthening the analysis of international trade, and puts forward perfect countermeasures in combination with the actual situation, so as to improve the efficiency of international trade management and promote the better implementation of international trade. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1024123X
Database :
Academic Search Index
Journal :
Mathematical Problems in Engineering
Publication Type :
Academic Journal
Accession number :
158019890
Full Text :
https://doi.org/10.1155/2022/1025453