The Prediction and Classification of Vespa Mandarinia Based On LSTM and Decision Tree
DOI: 10.23977/erej.2021.050106 | Downloads: 14 | Views: 1161
Author(s)
Cheng Yi 1
Affiliation(s)
1 Tongji University, Shanghai, 200082
Corresponding Author
Cheng YiABSTRACT
As it know that Vespa mandarinia hunt bees and other natural creatures in large quantities, and its venom is very harmful to the human body. Once discovered in the United States, they attracted widespread attention from relevant departments and the public.In view of this situation, this article aims to solve these five problems: The first problem is to predict and analyze the spread of the Vespa mandarinia. The second problem is to establish a model based on the providing information to analyze whether the witnesses misclassified or not. The third problem is to carry out a quantitative analysis of the priority processing order of the report on the basis of the second model. The fourth problem is to use statistics to analyze the update time of the model. The last question is to judge whether the Vespa mandarinia is eradicated or not according to the model and providing data.
KEYWORDS
Vespa mandarinia, the Asian giant hornet, LSTM, CNN, rating, level, Decision Tree, classificationCITE THIS PAPER
Cheng Yi, The Prediction and Classification of Vespa Mandarinia Based On LSTM and Decision Tree. Environment, Resource and Ecology Journal (2021) 5: 37-45. DOI: http://dx.doi.org/10.23977/erej.2021.050106
REFERENCES
[1] Jeff Donahue, Lisa Anne Hendricks, Marcus Rohrbach, Subhashini Venugopalan, Sergio Guadarrama, Kate Saenko, Trevor Darrell.Long-Term Recurrent Convolutional Networks for Visual Recognition and Description, 2016
[2] QIN Chuan. Image recognition based on convolutional neural network [J]. Electronic Technology and Software Engineering, 2020 (01): 98-99.
[3] AlexNet Recognition of Dog and Cat Data Set with TensorFlow (Cats vs. Dogs) https://blog.csdn.net/xiamencomingsoon/article/details/112263353?utm_medium=distribute.pc_relevant.n one-task-blog-baidujs_utm_term-14&spm=1001.2101.3001.4242
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