Machine Learning in identification and control of biological disasters

Chen Ling, Xingjian Lyu, Tian Zhenyu, Gabriela Mogos*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Biological invasion is a very common phenomenon. All alien species must have sites to grow and reproduce, which allows them to be called “colonizer” in a general sense. They would have adverse effects on the agricultural ecosystem by reducing the growth and output of the desired species.
In this case, the invasion of wasps caused serious potential adverse effects on bee populations in Europe, as well as on the other local biological populations. To reduce this adverse effect, avoiding invalid identify and reducing the possibility of the biological invasion, we construct model to identify wasps.
In this paper, we use Grey Forecast Model and Convolution Neural Network Model to improve the accuracy of identification and better control the disaster
Original languageEnglish
JournalInternational Journal of Computer Science and Information Security
Issue number4
Publication statusPublished - 2021


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