Development of cultural tourism platform based on FPGA and convolutional neural network

Data mining can be described as a typical analysis of large datasets to investigate early unknown types, styles, and interpersonal relationships to generate the right decision information. It improves their markets and today to maintain control over whether these companies are forced into the data mining tools and technologies they use to develop and manage tourism products and services in the market. It is falling out of the favorable situation of the travel and tourism industry. Objective work is to provide and display its application in data mining and tourism. Advances in mobile technology provide an opportunity to obtain real-time information of travelers, such as time and space behavior, at the destination they visit. This study analyzed a large-scale mobile phone data set to capture the mobile phone traces of international tourists who visited South Korea. We adopt the trajectory data mining method to understand tourism activities' spatial structure in three different destinations. The research reveals tourist destinations and multiple "hot spots" (or popular areas) that interact spatially in these places through spatial cluster analysis and sequential pattern mining. Therefore, this article provides the planning of spatial model destinations to integrate important tourism influences, which is based on tourism design. The proposed system is modelled in Field Programmable Gate Array (FPGA) using Xilinx software.