  The Asian giant hornet (AGH) appeared in Washington State appears to have a
potential danger of bioinvasion. Washington State has collected public photos
and videos of detected insects for verification and further investigation. In
this paper, we analyze AGH using data analysis,statistics, discrete
mathematics, and deep learning techniques to process the data to controlAGH
spreading.First, we visualize the geographical distribution of insects in
Washington State. Then we investigate insect populations to varying months of
the year and different days of a month.Third, we employ wavelet analysis to
examine the periodic spread of AGH. Fourth, we apply ordinary differential
equations to examine AGH numbers at the different natural growthrate and
reaction speed and output the potential propagation coefficient. Next, we
leverage cellular automaton combined with the potential propagation coefficient
to simulate the geographical spread under changing potential propagation. To
update the model, we use delayed differential equations to simulate human
intervention. We use the time difference between detection time and submission
time to determine the unit of time to delay time. After that, we construct a
lightweight CNN called SqueezeNet and assess its classification performance. We
then relate several non-reference image quality metrics, including NIQE, image
gradient, entropy, contrast, and TOPSIS to judge the cause of
misclassification. Furthermore, we build a Random Forest classifier to identify
positive and negative samples based on image qualities only. We also display
the feature importance and conduct an error analysis. Besides, we present
sensitivity analysis to verify the robustness of our models. Finally, we show
the strengths and weaknesses of our model and derives the conclusions.

Artificial Intelligence;Quantitative Methods