• 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 Convergence for Information Technology
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• v.10 no.9
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• pp.88-101
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• 2020

This study was conducted to identify the factors predicting binge drinking in men and women in Korea based on the results of the 7th year 3rd National Health and Nutrition Survey. The study data used the demographic and health-related characteristics and drinking characteristics of the National Health and Nutrition Survey, and were analyzed by cross-sample analysis and logistic regression analysis. As a result of the study, age (M:p=.003, F:p<.001), drinking frequency for one year (M:p<.001, F:p<.001), amount of alcohol consumed at a time (M:p<.001) 001, F:p<.001), family/doctor's recommendation for moderation (M:p<.001, F:p<.001), stress (M:p=.025, F:p<.001), Smoking (M:p<.001, F:p<.001) were predictors for binge drinking in both men and women. In addition, education level(p=.030) and economic activity status(p=.018) for men, income level(p<.001) and marital status(p=.020) for women were identified as predictors of binge drinking, and variables explained 72.4%(p < .001) and 74.5%(p < .001) of adult male and female binge drinking. This study is meaningful in that it provided basic data on the establishment of a gender-specific binge drinking prevention policy and the restructuring of drinking culture by clarifying that the risk factors of binge drinking in Korean adults differ by gender.

• The korean journal of orthodontics
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• v.27 no.3 s.62
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• pp.457-471
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• 1997

The Purpose of this study was to estimate relative importance among the causative factors o( arch length discrepancy(ALD) and Possibility of prediction of the ALD in the mixed dentition. The sample consisted of the casts of the 142 young adults who had no abnormal muscle function, no skeletal abnormalities and Class I molar relationship. We classified the sample by gender and the extent of ALD, and measured mesiodistal diameters of each tooth and the dimensions of the dental arch. The computerized statistical analyses was carried out with SPSS win program. The results were as follows ; 1. Most of the variables of spacing group and some variables of dental arch dimension of crowding group were significantly different between genders. But in normal group, there were few differences. 2. In male crowding and female spacing group, mainly measurements of tooth dimension were significantly different from those of normal group. 3. In male spacing and female crowding group, measurements of dental arch dimension were significantly different from those of normal group. 4. The measurements of dimension of dental arch were highly correlated with ALD in correlation analysis and factor analysis. 5. Prediction equations for adult's ALDs by means of what can be measured in the mixed dentition(mesiodistal dimensions of incisors and first molar, intermolar width and arch length) showed R square from $63\%$ to $80\%$.

• 한국노년학
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• v.41 no.3
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• pp.295-308
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• 2021

This study is a descriptive study conducted to find out the predictive factors according to the level of the frailty of the communitydwelling older adult in a rural area. Data were collected from 400 older adults aged 65 years or older living in rural areas of Gyeongsangnam-do from October 2019 to March 2020. Data were analyzed using logistic regression to examine the predictive factors according to the level of frailty. The results showed that 27.8% for robust older adults, 30.9% for pre-frailty older adults, and 41.3% for frailty older adults. As a result of analyzing the predictive factors according to the level of frailty, the predictors from the robust stage to the pre-frailty stage were grip strength, nutritional status, and depression. The predictive factors for entering the pre-frailty stage into the frailty stage were gender, nutritional status, physical performance ability, and depression. Also, it was found that the predictive factors for entering from the robust stage to the frailty stage were sex, occupation, nutritional status, physical performance ability, and depression. Through this study, it was possible to understand the level of the frailty of the older adults living in rural communities and the effects of multidimensional variables. These results can be used as basic data necessary to find a way to prevent and manage the progression of frailty among older adults in rural areas.

• Journal of Digital Convergence
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• v.18 no.8
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• pp.231-242
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• 2020

The thesis presents a system that continuously collects the human body's physiological vital information at rest with sensors and ICT information technology and predicts diabetes using the collected information. it shows the artificial neural network machine learning method and essential basic variable values. The study method analyzed the correlation between heart rate measurements of BCG and ECG sensors in 20 DM- and 15 DM+ subjects. Artificial Neural Network (ANN) machine learning program was used to predictability of diabetes. The input variables are time domain information of HRV, heart rate, heart rate variability, respiration rate, stroke volume, minimum blood pressure, highest blood pressure, age, and sex. ANN machine learning prediction accuracy is 99.53%. Thesis needs continuous research such as diabetic prediction model by BMI information, predicting cardiac dysfunction, and sleep disorder analysis model using ANN machine learning.

• 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.

• Phonetics and Speech Sciences
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• v.15 no.4
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• pp.1-10
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• 2023

This study investigated the relative effectiveness of letter-based versus syllable-based measures of speech rate and articulation rate in predicting the articulation score, prosody fluency, and rating sum using "English speech data of Koreans for education" from AI Hub. We extracted and analyzed 900 utterances from the training data, including three balanced age groups (13, 19, and 26 years old). The study built three models that best predicted the pronunciation assessment scores using linear mixed-effects regression and compared the predicted scores with the actual scores from the validation data (n=180). The correlation coefficients between them were also calculated. The findings revealed that syllable-based measures of speech and articulation rates were more effective than letter-based measures in all three pronunciation assessment categories. The correlation coefficients between the predicted and actual scores ranged from .65 to .68, indicating the models' good predictive power. However, it remains inconclusive whether speech rate or articulation rate is more effective.

• Journal of Digital Convergence
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• v.19 no.12
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• pp.477-486
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• 2021

This study was conducted to detect metabolic syndrome risk factors and gender difference in adults. 18,616 cases of adults are collected by Korea Health and Nutrition Examination Study from 2016 to 2019. Using 4 types of machine Learning(Logistic Regression, Decision Tree, Naïve Bayes, Random Forest) to predict Metabolic Syndrome. The results showed that the Random Forest was superior to other methods in men and women. In both of participants, BMI, diet(fat, vitamin C, vitamin A, protein, energy intake), number of underlying chronic disease and age were the upper importance. In women, education level, menarche age, menopause was additional upper importance and age, number of underlying chronic disease were more powerful importance than men. Future study have to verify various strategy to prevent metabolic syndrome.

• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.566-575
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• 2019

This study examines university students' awareness and preparedness for anticipated social problems of the Fourth Industrial Revolution and deteremines if there exist differences between gender, major fields of study and school years of students. Based on surveys with 122 university students in Seoul, students majoring in technology-engineering showed more positive outlook than students majoring in humanity-social science. Female students and humanity-social science students showed higher levels of concerns with social problems than male and technology-engineering students. More than three out of five students anticipated that they will experience the influence of the Fourth Industrial Revolution within the next five years. Two out of five students assessed their preparedness to be inadequate. Given the overarching social influence, it is necessary to develop convergent education of technology and social science that raises students' understanding and preparedness without differences in major fields of study and gender for the Fourth Industrial Revolution.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.201-208
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• 2021

This study aims to provide the relevant basis upon which prediction of dyslipidemia should be made based on body information. Using the National Health Insurance big data (3,312,971 people) canonical correlation analysis was performed between body information and lipid-profile. Body information included age, height, weight and waist circumference, while the lipid-profile included total cholesterol, triglycerides, HDL cholesterol and LDL cholesterol. As a result, when the waist circumference and the weight are large, triglycerides increase and HDL cholesterol level decreases. In terms of age, weight, waist circumference, and HDL cholesterol, the canonical variates (the degree of influence) were significantly different according to sex. In particular, the canonical variate was dramatically changed around the forties and fifties in women in terms of weight, waist circumference, and HDL cholesterol. The canonical correlation results of the health care big data presented in this study will help construct a predictive model that can evaluate an individual's health status based on body information that can be easily measured in a non-invasive manner.