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- W2461738757 abstract "In recent years, big data analysis has been applied to the design and development of smart cities, which creates opportunities as well as challenges. It is necessary to retrieve a large amount of social media data and physical sensor data for this purpose. However, different cities have different infrastructures and populations, resulting in the sparsity of some types of data, such as social media data. In this paper, we propose ELM based method for smart cities and apply it to optimal retail store placement owing to its importance in the success of a business. Traditional approaches to the problem have considered demographics, revenue, and aggregated human flow statistics from nearby or remote areas; however, the acquisition of relevant data is usually expensive. The rapid growth of location-based social networks in recent years has led to the availability of fine-grained data describing the mobility of users and popularity of places. However, circumstances vary from one city to another. Furthermore, the number of sensors may not be sufficient to cover all the relevant areas of a particular city. In such cases, it would be useful to transfer knowledge to small cities. We study the predictive power of various machine-learning features with regard to the popularity of retail stores in a city by using datasets collected from open data sources in several big cities. In addition, we adopt a ELM based method to transfer knowledge to small cities. The results of experiments involving cities in China confirm the effectiveness of the proposed framework." @default.
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- W2461738757 date "2016-01-01" @default.
- W2461738757 modified "2023-09-27" @default.
- W2461738757 title "ELM Meets Urban Computing: Ensemble Urban Data for Smart City Application" @default.
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- W2461738757 doi "https://doi.org/10.1007/978-3-319-28397-5_5" @default.
- W2461738757 hasPublicationYear "2016" @default.
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