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

机译：基于FPGA和卷积神经网络的文化旅游平台发展

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摘要

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.
机译：数据挖掘可以被描述为大型数据集的典型分析，以调查早期未知类型，样式和人际关系以生成正确的决策信息。它提高了他们的市场和今天，保持控制这些公司是否被迫进入他们用于开发和管理市场旅游产品和服务的数据挖掘工具和技术。它脱离了旅游和旅游业的良好情况。客观的工作是提供并显示其在数据挖掘和旅游中的应用。移动技术的进展提供了在他们访问目的地获得旅行者的实时信息的机会，例如时间和空间行为。本研究分析了一个大型手机数据集，以捕获访问韩国的国际游客的手机痕迹。我们采用轨迹数据挖掘方法来了解三种不同目的地的旅游活动的空间结构。该研究揭示了旅游目的地和多个“热点”（或流行区域），通过空间聚类分析和顺序模式挖掘在这些地方在这些地方进行在空间上进行空间互动。因此，本文提供了空间模型目的地的规划，以整合重要的旅游影响，这是基于旅游设计。所提出的系统使用Xilinx软件在现场可编程门阵列（FPGA）中建模。

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