• Journal of IKEEE
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• v.23 no.4
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• pp.1243-1249
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• 2019

People who gathered to enjoy a specific event or content are called audiences or spectators, and show various propensity according to the characteristics of the crowd. Although there is such a difference, in general, the number of attendance is directly related to the business aspect, which enables stable financial operation for the sale of contents through various incomes, such as the admission fee and the use of other facilities. Therefore, prediction of audience can be used as a major factor in marketing and budgeting strategies. In this study, we review several existing models for predicting the number of attendance and propose an efficient machine learning model. In addition, we studied daily attendance prediction and abnormal attendance prediction using combine DNN(Deep Neural Network) and RF(Random Forest) model.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.131-135
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• 2024

Baseball attracts the largest audience among professional sports in Korea. In particular, attendance is the primary source of income in baseball. Previous studies have limitations in reflecting the characteristics of individual stadium. For instance, the KIA Tigers exhibit the highest away game revenue among domestic teams, but they show lower home game earnings. Therefore, we aim to predict the daily attendance at the Gwangju-KIA Champions Field of the KIA Tigers using deep learning. We collected and preprocessed daily attendance, dates, weather, and team-related variables for Gwangju-KIA Champions Field from 2018 to 2023. We propose a deep learning-based linear regression model to predict the daily attendance. We expect that the proposed deep learning model will be used as basic information to increase the club's revenue.

• Korean Journal of Social Welfare
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• v.68 no.3
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• pp.105-125
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• 2016

School absenteeism is considered one of the early predictors of school drop-out and serious delinquency or criminal behavior. The primary goal of the current study was to explore the protective and risk factors related to changing school absenteeism over time based on the ecological-systemic perspective. The data was derived from the Korean Children and Youth Panel Survey (KCYPS) using the 2011 and 2012 survey waves collected from 2,378 elementary school students. Using this data, Panel Fixed Effects Analysis was conducted. Major findings indicated that daily computer usage, parental abuse, school activity attendance, and school grades had an effect on students missing school days over time. Specifically, high levels of computer usage and parental abuse were related to increased school absenteeism, while high levels of school activity attendance and school grades were associated with decreased school absenteeism. These findings emphasized the importance of predictive intervention for children and suggested the need to construct a school absenteeism monitoring system in South Korea.