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Vespa mandarina diffusion model based on AdaBoost and CNN

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DOI: 10.23977/jeis.2021.61010 | Downloads: 3 | Views: 241

Author(s)

Zerong Wang 1, Yiran Wang 1

Affiliation(s)

1 Leicester international Institute, Dalian University of Technology, Dalian 116000, China

Corresponding Author

Zerong Wang

ABSTRACT

In this paper, we build two different models at the same time. The first model is a classification model based on Adaboost, and the second model is an image recognition model based on CNN, which takes the pictures in 2021mcm _ problem _ files as input and classifies them. According to the classification results of the two models, we can find the accuracy of the Adaboost model is 0.57 and the accuracy of the CNN model is 0.971, which is found on the test data.

KEYWORDS

AdaBoost, CNN, Bagging, SVM

CITE THIS PAPER

Zerong Wang, Yiran Wang. Vespa mandarina diffusion model based on AdaBoost and CNN. Journal of Electronics and Information Science (2021) 6: 62-66. DOI: http://dx.doi.org/10.23977/jeis.2021.61010

REFERENCES

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[2] Xiaoming Xu. SVM parameter optimization and its application in classification [D]. Dalian Maritime University, 2014
[3] Federico Magliani,Andrea Prati. LSH kNN graph for diffusion on image retrieval [J]. Information Retrieval Journal, 2021 (prepublish).
[4] Feiyan Zhou, Linpeng Jin, Jun Dong. a review of convolutional neural networks [J]. Acta Sinica Sinica, 2017, 40 (06): 1229-1251
[5] Li Yuan, Geng zewei. Fault detection based on K-means clustering and local outlier algorithm [J]. Chemical automation and instrumentation, 2019, 46 (10): 816-821

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