Name-Nationality Classification Technology under Keras Deep Learning
Author: 
Yu KangAuthors Info & Claims
Pages 70 - 74
Abstract
To improve the classification efficiency of personnel nationality information, the classification method of personnel nationality information is explored. First, a new classification method is proposed using the Keras model and deep learning theory. Two methods based on support vector machine (SVM) and convolutional neural network classification are proposed. (1) The personal name is input and a set of numbers corresponding to the positions in the alphabet are output orderly. (2) The personal name is input and the number output relies on the number of occurrences of each character of a name, regardless of order. Second, for the problem that the classification accuracy of nationality information by name is not high, the adaptive moment estimation (Adam) algorithm is used to optimize it. Finally, to prove the reliability of the proposed methods, these methods are used to verify the nationality information of Olympic personnel. The results show that comparing the two methods, the classification method that relies on the number of occurrences of characters in the name gets good grades, and the ultimate average score is 90.35. The score of the first method is only 79.34. Through this investigation, it is found that the second method proposed can effectively use the name of the person to determine the nationality information. Applying it in real life can improve the classification efficiency of personnel nationality information.
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Index Terms
- Name-Nationality Classification Technology under Keras Deep Learning
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Published In
May 2020
146 pages
ISBN:9781450377225
DOI:10.1145/3404512
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Published: 05 July 2020
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