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ARTICLE

Employment Quality Evaluation Model Based on Hybrid Intelligent Algorithm

Xianhui Gu1,*, Xiaokan Wang1, Shuang Liang2

1 Henan Mechanical Electrical Vocational College, Xinzheng Hennan, 451191, China
2 University of Florence, Firenze, 50041, Italy

* Corresponding Author: Xianhui Gu. Email: email

Computers, Materials & Continua 2023, 74(1), 131-139. https://doi.org/10.32604/cmc.2023.028756

Abstract

In order to solve the defect of large error in current employment quality evaluation, an employment quality evaluation model based on grey correlation degree method and fuzzy C-means (FCM) is proposed. Firstly, it analyzes the related research work of employment quality evaluation, establishes the employment quality evaluation index system, collects the index data, and normalizes the index data; Then, the weight value of employment quality evaluation index is determined by Grey relational analysis method, and some unimportant indexes are removed; Finally, the employment quality evaluation model is established by using fuzzy cluster analysis algorithm, and compared with other employment quality evaluation models. The test results show that the employment quality evaluation accuracy of the design model exceeds 93%, the employment quality evaluation error can meet the requirements of practical application, and the employment quality evaluation effect is much better than the comparison model. The comparison test verifies the superiority of the model.

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Cite This Article

APA Style
Gu, X., Wang, X., Liang, S. (2023). Employment quality evaluation model based on hybrid intelligent algorithm. Computers, Materials & Continua, 74(1), 131-139. https://doi.org/10.32604/cmc.2023.028756
Vancouver Style
Gu X, Wang X, Liang S. Employment quality evaluation model based on hybrid intelligent algorithm. Comput Mater Contin. 2023;74(1):131-139 https://doi.org/10.32604/cmc.2023.028756
IEEE Style
X. Gu, X. Wang, and S. Liang "Employment Quality Evaluation Model Based on Hybrid Intelligent Algorithm," Comput. Mater. Contin., vol. 74, no. 1, pp. 131-139. 2023. https://doi.org/10.32604/cmc.2023.028756



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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