| Title | Optimization and Prediction of Intelligent Tourism Data |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Zhong, Luoyifan |
| Conference Name | 2022 IEEE 8th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS) |
| Keywords | ARIMA model, composability, compositionality, Computational modeling, Conferences, Data visualization, intelligent data, network model, Neural networks, optimize, Predictive models, pubcrawl, resilience, Resiliency, Scalability, security, Technological innovation, Time series analysis, visualize |
| Abstract | Tourism is one of the main sources of income in Australia. The number of tourists will affect airlines, hotels and other stakeholders. Predicting the arrival of tourists can make full preparations for welcoming tourists. This paper selects Queensland Tourism data as intelligent data. Carry out data visualization around the intelligent data, establish seasonal ARIMA model, find out the characteristics and predict. In order to improve the accuracy of prediction. Based on the tourism data around Queensland, build a 10 layer Back Propagation neural network model. It is proved that the network shows good performance for the data prediction of this paper. |
| DOI | 10.1109/BigDataSecurityHPSCIDS54978.2022.00043 |
| Citation Key | zhong_optimization_2022 |
Optimization and Prediction of Intelligent Tourism Data