Browsing by Author "Takahashi, M."

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    A Novel Computational Method to Capture FPGA Technology Trends from Patent Information.
    (IEEE International Research Conference on Smart computing & Systems Engineering (SCSE) 2019, Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2019) Shibata, M.; Takahashi, M.
    This paper provides a novel trend analysis of FPGA development with Machine Learning. Recently, demands for the computing power are expanding due to reform of industrial structure such as the Industry 4.0 and the explosive expansion of AI. In this paper, we reveal the technical development trend of the leading FPGA companies from the patent information with Machine Learning. We focus on the classification codes in the patent and employ Link Mining method as the analytical method. Link Mining is a conventional method to analyze the structural features of things. It simplifies the objects and the relations as the nodes and the edges. With the proposed method, we succeed in revealing the companies’ focused technology fields, the transition of focusing areas, and their differences and common points from the results of extracting the graphs’ features
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    A study on classifying the store positioning from the transactional data
    (International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Takahashi, M.; Tanaka, Y.
    This paper describes a customer analysis for store positioning, using data gathered from supermarkets in Japan. Among the retail industry in Japan, there are many types of reward cards used for customer retention purposes. The rewards cards or “Point Card”, is originally aimed for customer analysis purposes, but at present the full benefits have not been extracted due to issues in data analytics. This reward card has only become a method of simply distributing “virtual money” to the customer. For the efficient use of gathering data, we propose a classification method of the customer based on the objectives of visiting stores. In this study, the customers were classified into their objectives.