Search

Artificial neural network (ANN) theory is a rapidly developing information science around the world. The Back Propagation (BP) network is one of many artificial neural network types. It is a multilayer feedforward network with sophisticated nonlinear mapping capabilities. It is actually a relatively complex and nonlinear comprehensive decision-making problem when taking into account the comprehensive evaluation of test scores based on evaluation indicators that reflect the quality of the test papers. The five major factors of education service quality are tentatively proposed in this paper, which uses a neural network to model and analyze it. This paper examines the five factors of the education service quality evaluation model one by one and analyzes the issues that exist in the education service quality of international students in China. According to empirical research, overall satisfaction with the quality of education service in Guangxi among foreign students in China is low, and subindicators of education service quality are unbalanced. This paper analyzes the reasons for this situation from the three levels of service consciousness, emotional investment, and professional quality and proposes that the main task of improving education service quality is to optimize the entire service process, in order to narrow the gap between foreign students' expectations and actual perceptions of educational services. [ABSTRACT FROM AUTHOR]

The study of forest fire prediction is of great environmental and scientific significance. China's Guangxi Autonomous Region has a high incidence rate of forest fires. At present, there is little research on forest fires in this area. The application of the artificial neural network and support vector machines (SVM) in forest fire prediction in this area can provide data for forest fire prevention and control in Guangxi. In this paper, based on Guangxi's 2010–2018 satellite monitoring hotspot data, meteorology, terrain, vegetation, infrastructure, and socioeconomic data, the researchers determined the main forest fire driving factors in Guangxi. They used feature selection and backpropagation neural networks and radial basis SVM to build forest fire prediction models. Finally, the researchers use the accuracy, precision, and area under the characteristic curve (ROC-AUC) and other indicators to evaluate the predictive performance of the two models. The results showed that the prediction accuracy of the BP neural network and SVM is 92.16% and 89.89%, respectively. As both results are over 85%, the requirements of prediction accuracy is met. These results can be used for forest fire prediction in the Guangxi Autonomous Region. Specifically, the accuracy of the BP neural network was 0.93, which was higher than that of the SVM model (0.89); the recall of the SVM model was 0.84, which was lower than the BANN model (0.92), and the AUC value of the SVM model was 0.95, which was lower than the BP neural network model. The obtained results confirm that the BP neural network model can provide more prediction accuracy than support vector machines and is therefore more suitable for forest fire prediction in Guangxi, China. This research provides the necessary theoretical basis and data support for application in the field of forestry of the Guangxi Autonomous Region, China. [ABSTRACT FROM AUTHOR]