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Detecting Ethnic Spatial Distribution of Business People Using Machine Learning

by Joomi Jun 1,* and Takayuki Mizuno 1,2,*
1
Department of Informatics, The Graduate University for Advanced Studies, SOKENDAI, Tokyo 101-8430, Japan
2
Information and Society Research Division, National Institute of Informatics, Tokyo 101-8430, Japan
*
Authors to whom correspondence should be addressed.
Information 2020, 11(4), 197; https://doi.org/10.3390/info11040197
Received: 28 February 2020 / Revised: 31 March 2020 / Accepted: 4 April 2020 / Published: 7 April 2020
The development of transportation and technology has spread human movements more quickly and widely. As a result, our societies are becoming more complex, composed of people of more diverse races, cultures, religions, and languages. In this study, we focus on the origins of ethnicity while analyzing the background of social members. To track the origin of the ethnicities of which a society is composed, we established a surname-nationality prediction model by learning from a Recurrent Neural Network (RNN) with data recorded by business peoples’ surnames and nationalities to predict nationality with high accuracy through surnames. This study is meaningful because it approaches the social scientific problems of ethnic composition within society through massive data and machine learning: the informatics approach. View Full-Text
Keywords: ethnicity classification; ethnic network; RNN; machine learning ethnicity classification; ethnic network; RNN; machine learning
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Jun, J.; Mizuno, T. Detecting Ethnic Spatial Distribution of Business People Using Machine Learning. Information 2020, 11, 197.

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