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Proposal of a Hypothesis Test Prediction System for Educational Social Precepts using Deep Learning Models

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2020, 25(9), pp.37-44
  • DOI : 10.9708/jksci.2020.25.09.037
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : July 24, 2020
  • Accepted : August 31, 2020
  • Published : September 29, 2020

Su-Youn Choi 1 Dea-Woo Park 1

1호서대학교 벤처대학원

Accredited

ABSTRACT

AI technology has developed in the form of decision support technology in law, patent, finance and national defense and is applied to disease diagnosis and legal judgment. To search real-time information with Deep Learning, Big data Analysis and Deep Learning Algorithm are required. In this paper, we try to predict the entrance rate to high-ranking universities using a Deep Learning model, RNN(Recurrent Neural Network). First, we analyzed the current status of private academies in administrative districts and the number of students by age in administrative districts, and established a socially accepted hypothesis that students residing in areas with a high educational fever have a high rate of enrollment in high-ranking universities. This is to verify based on the data analyzed using the predicted hypothesis and the government's public data. The predictive model uses data from 2015 to 2017 to learn to predict the top enrollment rate, and the trained model predicts the top enrollment rate in 2018. A prediction experiment was performed using RNN, a Deep Learning model, for the high-ranking enrollment rate in the special education zone. In this paper, we define the correlation between the high-ranking enrollment rate by analyzing the household income and the participation rate of private education about the current status of private institutes in regions with high education fever and the effect on the number of students by age

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