No abstract

The BP neural network model is a hot issue in recent academic research, and it has been successfully applied to many other fields, but few researchers apply the BP neural network model to the field of automobile insurance. The main method that has been used in the prediction of the total claim amount in automobile insurance is the generalized linear model, where the BP neural network model could provide a different approach to estimate the total claim loss. This paper uses a genetic algorithm to optimize the structure of the BP neural network at first, and the calculation speed is significantly improved. At the same time, by considering the overfitting problem, an early stop method is introduced to avoid the overfitting problem. In the model, a three-layer BP neural network model, which includes the input layer, hidden layer, and output layer, is trained. With consideration of various factors, a total claim amount prediction model is established, and the trained BP neural network model is used to predict the total claim amount of automobile insurance based on the data of the training set. The results show that the accuracy of the prediction by using the BP neural network model to both the data of Shandong Province and to the data of six cities is over 95%. Then, the predicted total claim amount is used to calculate premiums for five cities in Shandong Province according to credibility theory. The results show that the average premium of the five cities is slightly higher than the actual claim amount of the city. The combination of BP neural network and credibility theory can perform accurate claim amount estimation and pricing for automobile insurance, which can effectively improve the current situation of the automobile insurance business and promote the development of insurance industry.

No abstract

This paper investigates secure transmission in unmanned aerial vehicle (UAV) relay-assisted millimeter wave (mmWave) networks, where the selected UAV relay performs secure transmission in both the on-off and non-on-off schemes. Meanwhile, there are multiple eavesdroppers randomly distributed on the ground and attempting to wiretap the transmission. Leveraging the air-to-ground channel model and the tools of stochastic geometry, the novel expressions of transmit probability (TP) and secrecy outage probability (SOP) are derived in both the on-off and non-on-off transmission schemes with perfect beam alignment. The secrecy performance improvement is demonstrated in the on-off transmission scheme, and we find that there exists an optimal altitude of UAV relays to achieve the best TP. In addition, due to the limitations of UAV carriers, such as its low computational capacity and high mobility, the perfect beam alignment is difficult to achieve in the mmWave networks aided by UAV relays, and the effect of beam alignment error on the secrecy performance is investigated in the considered networks. Analyzing the numerical and simulation results, we find that the SOP will not have obvious deterioration when the beam alignment error is relatively small, and the SOP can be improved by using the antennas with a large number of elements. However, in high beam alignment error regime, the antenna arrays with a smaller number of elements will provide the better SOP.