• The Journal of the Korea Contents Association
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• v.20 no.8
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• pp.10-18
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• 2020

Among various contents industry, this study especially focused on music industry and tried to develop a prediction model for music box office using deep learning. The deep learning prediction model designed to predict music chart-in period based on 17 variables -singer power, singer influence, featuring singer power, featuring singer influence, number of participating singers, gender of participating singers, lyric writer power, composer power, arranger power, production agency power, distributing agency power, title track, LIKEs on streaming platform, comments on streaming platform, pre-promotion article, teaser-video view, first-week performance. Additionally we conducted a linear regression analysis to sort out factors, and tried to compare the prediction performance between the original DNN prediction model and the DNN model made of sorted out factors.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.257-269
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• 2021

The purpose of this study is to collect biosignal data in a non-invasive and non-restrictive manner using a BCG (Ballistocardiogram) sensor, and utilize artificial intelligence machine learning algorithms in ICT and high-performance computing environments. And it is to present and study a method for developing and validating a data-based blood glucose prediction model. In the blood glucose level prediction model, the input nodes in the MLP architecture are data of heart rate, respiration rate, stroke volume, heart rate variability, SDNN, RMSSD, PNN50, age, and gender, and the hidden layer 7 were used. As a result of the experiment, the average MSE, MAE, and RMSE values of the learning data tested 5 times were 0.5226, 0.6328, and 0.7692, respectively, and the average values of the validation data were 0.5408, 0.6776, and 0.7968, respectively, and the coefficient of determination (R²) was 0.9997. If research to standardize a model for predicting blood sugar levels based on data and to verify data set collection and prediction accuracy continues, it is expected that it can be used for non-invasive blood sugar level management.

• Survey Research
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• v.8 no.1
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• pp.55-79
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• 2007

This article attempts to provide an overview of the exit polls for the 2006 local elections in Korea. The sampling method, sampling error, non-response rate, and prediction error of the exit polls are reviewed. Also, we explore the fact that the propensity to vote varies according to age and gender of voters. In terms of age and gender, the representativeness of the sample is investigated by comparing to the data released by the National Election Commission. Through this empirical research, we show that the exit poll samples are unbalanced in terms of age and this unbalance may be one of the causes of bias occurred in the prediction of the 2006 local election results. The design effects of the sample design implemented for the exit polls are also examined.

• Korean Journal of Human Ecology
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• v.13 no.5
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• pp.659-672
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• 2004

This study examines different individual, family, and school environmental variables that affect victimization by peer aggression among adolescents. The sample consists of 868 seventh and eighth graders. Statistics and method for data analysis include Cronbach's alpha, percentage, means, standard deviation, Pearson correlation, multiple regression, and hierarchical regression. The major findings of this study are as follows: First, adolescents, both withdrawn and aggressive, have lower achievement in school work. Boys experience more direct victimization by peer aggression. Adolescents, especially boys, often experience indirect victimization by peer aggression, when they become withdrawn, own lower self-esteem, and have lower achievement in school work. Second, adolescents have more direct victimization by peer aggression when their parents are negligent of them. Also, adolescents seem exposed to indirect victimization by peer aggression when they receive more physical and emotional abuse and negligence from their parents. Third, adolescents experience more victimization by peer aggression-whether it's direct or indirect, when they cannot get adjusted to peer relations and get teachers' supervision. Fourth, as to direct victimization by peer aggression, withdrawal, one of the individual variables, is the most reliable prediction followed by gender, negligence, adaptability in peer relations, aggression, and teacher's supervision in sequence. For indirect victimization by peer aggression, withdrawal is the most reliable prediction followed by adaptability in peer relations, gender, physical and emotional abuse, and negligence in sequence.

• Journal of Korean Medicine for Obesity Research
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• v.17 no.2
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• pp.68-76
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• 2017

Objectives: The purpose of this study was to analyze the factors affecting weight loss effect of Gamitaeumjowee-tang and to see if weight loss could be predicted using influence factors and weight loss progress. Methods: From September 2016 to March 2017, we retrospectively reviewed the medical records of 139 patients who were visited to the Korean Oriental Clinic for 3 months. We conducted a regression analysis to determine whether age, gender, initial weight, patient health questionnaire-9, heart rate variability (HRV), sleep quality, drinking habit and the medication history of weight loss affect weight loss. We found weight loss prediction equations using multiple regression analysis applying significant factors and weight loss progress. Results: Gender and initial weight had a significant effect on weight loss in all periods (P<0.001). HRV had a significant effect on primary weight loss (P<0.01). Other factors did not have any significant effect on weight loss. Using the significant factors, weight loss of each period could be predicted from 23.9% to 44.6%, and tertiary weight loss could be predicted with 76.6% using factors, primary weight loss and secondary weight loss (P<0.001). Conclusions: This study suggests that weight loss effect of Gamitaeumjowee-tang maybe be affected by influence factors and that weight loss prediction equations using them can be used for obesity treatment.

• Journal of the Ergonomics Society of Korea
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• v.31 no.5
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• pp.695-704
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• 2012

Objective: The purpose of this study was to investigate major factors influencing adoption of smartphone to promote its use by older adults. Background: Despite increasing proportion of elderly people and elderly market, the proportion of elderly smartphone user is still relatively small compared to whole smartphone users. Thus, we need to find out major factors influencing adoption of smartphone to increase proportion of elderly smartphone users. Method: Seven major factors were extracted from 36 survey questions using factor analysis. Regression analysis was also applied to determine specific factors affecting intention of use based on user versus non-user of smartphone, age, gender, and educational background. Results: As results of factor analysis and regression analysis, major factors influencing adoption of smartphone for elderly users were significantly different according to gender, age, educational background based on smartphone users or non-users. Conclusion: The result of this study identified major factors influencing adoption of smartphone for the elderly and provided basic information related to adoption of smartphone according to elderly people's characteristics. Consequently, we can expect to reduce the information gap and to improve quality of life for the elderly. Application: The development and marketing strategy could be applied differently based on the factors influencing adoption of smartphone. It is also possible to develop a prediction model for smartphone adoption according to elderly users' characteristics.

• Korean Journal of Health Education and Promotion
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• v.14 no.1
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• pp.97-113
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• 1997

It is very important to estimate the patients satisfaction level with medical services, to classify the objectvies according to the patients characteristics and sub-satisfaction factors. The purpose of this study is to determine the factors affecting satisfaction in oriental hospital. The 549 patients' hospitalized in five oriental hospital in Taegu city and one oriental hospital in Kyungbuk province were selected for this study. The results summarized are as follows. l. The general characteristics of 549 objectvies were included gender, age, education, occupation, income level, length of stay, health status of hospitalized, and expectation for medical care. 2. Patients characteristics affecting patients total satisfaction, as for age(b=0.05), health status of patients(b=-0.052), and expectation for medical care(b=0.117) were significant, while gender, education, job, income level, and length of stay were not. As the factors according to patients satisfaction, accessibility(b=0.09l), doctor's kindness(b=0.357), staff kindness(b=0.137), nurse's skills(b=0.111), hospital facilities(b=0.211), and medical fee(b=-0.160) were significant. In total patients' satisfaction, Doctor's kindness was the most significant of prediction variables. In general the factors affecting In-patient satisfaction of oriental hospital was highly associated with doctor's kindness.

• Korean Journal of Health Education and Promotion
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• v.27 no.3
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• pp.75-84
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• 2010

Objectives: This study investigates factors influencing the perpetrations of sexual violence while dating among college students. Methods: With a correlational survey design, a self-report survey was conducted and collected 1,132 responses from college students with dating experiences. Methods including descriptive statistics, t-test, ANOVA, pearson correlation coefficients, and multiple regression were used to analyze data. Results: Compared to college students with no perpetrations of sexual violence, college students with perpetrations of sexual violence had significantly higher scores in father's violence, mother's violence, gender role stereotype, and sexual violence permissiveness. On the other hand they had significantly lower scores in sexual assault recognition than the compared group. A multiple regression model result forecasted parents' violence, sexual assault recognition, sexual violence permissiveness, and gender as prediction indicators of perpetrations of sexual violence. Conclusion: To prevent sexual violence while dating, domestic violence should be decreased through parents education and counseling from childhood. High-risk groups should be detected by surveying socio-psychological variables including experience of domestic violence, sexual assault recognition, and sexual violence permissiveness. It need to develop and implement sexual violence prevention programs to accurately inform and aware sexual violence.

• Journal of the Korean Home Economics Association
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• v.45 no.6
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• pp.11-20
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• 2007

The purpose of this study was to examine determine the relations relationship between the child's characteristics (i.e. temperament) and maternal behaviors (parenting and the content of demands) and the child's compliance. Data were gathered from 153 mothers of toddlers in Seoul, via questionnaires. The major principal results of this study were as follows: Neither gender nor age differences was found were observed in toddlers' compliance. Correlation analyses revealed significant relations relationships between both a child's emotionality and the mother's parenting, and a child's compliance. That is, in cases in which the child's emotional reactivity and the mother's authoritarian parenting were high, the child evidenced lower compliance, the child showed whereas the higher the mother's authoritative parenting were was, the higher compliance the child showed displayed. The hierarchical regressions analysis indicated that maternal demands on 'caring' was constituted the most significant variable to predict for the prediction of toddlers' compliance, and child activity level and maternal authoritative parenting behavior were also significant variables.

• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.391-404
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• 2019

Customers' fixed characteristics have often been used to predict customer behavior. It has recently become possible to track customer web logs as customer activities move from offline to online. It has become possible to collect large amounts of web log data; however, the researchers only focused on organizing the log data or describing the technical characteristics. In this study, we predict the decision-making time until each customer makes the first reservation, using Airbnb customer data provided by the Kaggle website. This data set includes basic customer information such as gender, age, and web logs. We use various methodologies to find the optimal model and compare prediction errors for cases with web log data and without it. We consider six models such as Lasso, SVM, Random Forest, and XGBoost to explore the effectiveness of the web log data. As a result, we choose Random Forest as our optimal model with a misclassification rate of about 20%. In addition, we confirm that using web log data in our study doubles the prediction accuracy in predicting customer behavior compared to not using it.