• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.439 -447
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• 2023

This paper aims to develop a map for optimized class space using ZEP among the metaverse platforms. As a research method, the classroom space was organized so that the subject of learning became a learner, and the classroom space was modified and supplemented to optimize while being applied to elementary school computer classes. The contents of the study investigated learners' prior perception of metaverse, and compared and analyzed the advantages and disadvantages of the metaverse platform. In addition, the map was designed by reflecting the results of the survey, and after applying the map to the class, necessary APIs and apps were installed to supplement it. As a result, the learner became the subject of learning in the metaverse space, freely identified the space, and actively participated in the class. In particular, we found that students who were passive offline and those who had a low participation rate due to lack of skills participated more actively. In particular, students who were passive offline or whose participation was low due to lack of skills participated more actively. If API and JavaScript programs are added to collect log data of learners for learning analysis, real-time feedback is possible for learners, and learner feedback is possible for instructors with statistical data. If this is possible, the metaverse space can fully expect the role of a learning assistant for learners and a teaching assistant for instructors.

• Health Policy and Management
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• v.34 no.1
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• pp.68-77
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• 2024

Background: Depressive disorders can be categorized into daily depression and clinical depression. The experience of depressive disorder can increase health care utilization due to decreased treatment compliance and somatization. On the other hand, the clinical depression group may also experience social prejudice associated with the illness, which can limit their access to health care utilization. In terms of the significance of health care utilization as a factor in individual and social issues, this study aims to compare the health care utilization of the clinical depression group with that of the non-depressed group and the daily depression group. Methods: The analysis utilized the inverse probability of treatment weighting based on the generalized propensity score. Results: As a result of the analysis, clinical depression and daily depression were higher among women, low-income groups, individuals with low education levels, and so forth. The clinical depression group was also higher among individuals who were not economically active, did not have private health insurance, or had multiple chronic diseases. The number of outpatient department visits in the depression group was significantly higher than in the non-depressed group. In addition, the number of outpatient department visits for the clinical depression group was significantly higher than that for the daily depression group. Outpatient medical expenses were higher in the depression group than in the non-depressed group, and there was no significant difference between the clinical depression group and the daily depression group. Conclusion: Health care utilization was higher in the depression group than the non-depressed group, it was also higher in the clinical depression group than the daily depression group.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.28-34
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• 2024

Since e-learning is conducted based on the learner's autonomy, motivation to continuously participate is crucial for success in e-learning. As the number of adult learners participating in lifelong education increases, it is necessary to study learner participation and the motivating factors. Drawing upon the Expectancy-Value Theory and Self-Regulated Learning Theory, this study analyzed the influence of motivational factors (value, costs, cognitive regulation, and scheduling) on learner participation. An e-learning program was implemented on MoodleCloud, and learners completed a survey before going through the program. Regression analysis was conducted using the survey response data along with the participation score, calculated using the log data. The results of the analysis demonstrated that value and scheduling significantly influenced learner participation, with gender differences found in value. This means that as adult learners perceive higher value in the e-learning program and possess better scheduling skills, they are more likely to participate. These findings can be utilized in developing teaching and learning strategies for both learners and instructors, ultimately helping to prevent dropout in e-learning.

• Journal of Dental Rehabilitation and Applied Science
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• v.40 no.2
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• pp.64-71
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• 2024

Purpose: The purpose of this study is to evaluate Ti-base abutment's three different heights and three different cement types on the pull-off force of zirconia-based restorations. Materials and Methods: A total of 90 fixture lab analogs were embedded in auto polymerizing resin bloack. 90 Ti-base abutments heights of 3 mm, 5 mm, 7 mm were scanned and zirconia restoration were prepared from scanned files. Zirconia restoration were cemented with three different types of cements (temporary, semi-permanent, permanent) following manufacturer's instructions. All 90 specimens were placed and tested in a universal testing machine for pull-out testing. Retention was measured by recording the force at load drop. Statistical analysis was performed using Kruskal-Wallis test for detecting whether there are any statistical significance along cement types or abutment heights. After that, Mann-Whitney test was used for figuring out differences regarding abutment height and the comparison between 3 cements. Results: Temp bond showed significantly lower pull-off force compared to Fujicem regardless of any abutment height. However, there were significant differences between Cem-implant and Fujicem in abutment height of 3 mm and 7 mm, but there was no significant difference in 5 mm. Temp bond and Cem-implant had significant differences only in abutment height of 5 mm. Conclusion: Although Ti-base abutment height did not influenced zirconia restorations' retentiveness, cement types showed significant differences.

• Information Systems Review
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• v.22 no.4
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• pp.75-92
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• 2020

As population and generation structures change, more and more customers tend to avoid facing relation due to the development of information technology and spread of smart phones. This phenomenon consists with efficiency and immediacy, which are the consumption patterns of modern customers who are used to information technology, so offline network-oriented distribution companies actively try to switch their sales and services to untact patterns. Recently, untact services are boosted in various fields, but beauty products are not easy to be recommended through untact services due to many options depending on skin types and conditions. There have been many studies on recommendations and development of recommendation systems in the online beauty field, but most of them are the ones that develop recommendation algorithm using survey or social data. In other words, there were not enough studies that classify segments based on user information such as skin types and product preference. Therefore, this study classifies customer segments using machine learning technique K-prototypesalgorithm based on customer information and search log data of mobile application, which is one of untact services in the beauty field, based on which, untact marketing strategy is suggested. This study expands the scope of the previous literature by classifying customer segments using the machine learning technique. This study is practically meaningful in that it classifies customer segments by reflecting new consumption trend of untact service, and based on this, it suggests a specific plan that can be used in untact services of the beauty field.

• Information Systems Review
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• v.22 no.2
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• pp.43-52
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• 2020

The mobile game industry has become the one of the fastest growing industries with its astonishing market size. Despite its industrial importance, a few studies empirically considered actual purchasing behavior in mobile games rather than the intention to purchase. Therefore, this paper investigates the key drivers of in-app purchase by analyzing the game-log dataset provided from a mobile game company in Korea. Specifically, the effects of goal-directed, habitual and social-interacted playing behavior are analyzed on in-app purchase. Furthermore, the recursive relationship with playing and purchasing behaviorsis also considered. The result shows that all suggested factors have positive impacts on in-app purchase in the current period. In addition, the effect of previous habitual playing has a positive impact, but the effect of social-interacted playing and in-app purchase in the previous period have negative impacts on in-app purchase of the current period. These findings can improve our understanding of the impact of game playing on in-app purchase in mobile games, and provide meaningful insights for researchers and practitioners.

• Information Systems Review
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• v.25 no.1
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• pp.111-128
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• 2023

With the development of artificial intelligence technology, interest in data-based product preference estimation and personalized recommender systems is increasing. However, if the recommendation is not suitable, there is a risk that it may reduce the purchase intention of the customer and even extend to a huge financial loss due to the characteristics of the financial product. Therefore, developing a recommender system that comprehensively reflects customer characteristics and product preferences is very important for business performance creation and response to compliance issues. In the case of financial products, product preference is clearly divided according to individual investment propensity and risk aversion, so it is necessary to provide customized recommendation service by utilizing accumulated customer data. In addition to using these customer behavioral characteristics and transaction history data, we intend to solve the cold-start problem of the recommender system, including customer demographic information, asset information, and stock holding information. Therefore, this study found that the model proposed deep learning-based collaborative filtering by deriving customer latent preferences through characteristic information such as customer investment propensity, transaction history, and financial product information based on customer transaction log records was the best. Based on the customer's financial investment mechanism, this study is meaningful in developing a service that recommends a high-priority group by establishing a recommendation model that derives expected preferences for untraded financial products through financial product transaction data.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.205-212
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• 2023

Among cardiac diseases, arrhythmias can lead to serious complications such as stroke, heart attack, and heart failure if left untreated, so continuous and accurate ECG monitoring is crucial for clinical care. However, the accurate interpretation of electrocardiogram (ECG) data is entirely dependent on medical doctors, which requires additional time and cost. Therefore, this paper proposes an arrhythmia recognition module for the purpose of developing a medical platform through the analysis of abnormal pulse waveforms based on Lifelogs. The proposed method is to convert ECG data into image format instead of time series data, apply visual pattern recognition technology, and then detect arrhythmia using CNN model. In order to validate the arrhythmia classification of the CNN model by image type conversion of ECG data proposed in this paper, the MIT-BIH arrhythmia dataset was used, and the result showed an accuracy of 97%.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Journal of Korean Society of Transportation
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• v.34 no.1
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• pp.1-14
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• 2016

In the recent years, the internet has become accessible without limitation of time and location to anyone with smartphones. It resulted in more convenient travel information access both on the pre-trip and en-route phase. The main objective of this study is to conduct a stationary test for traffic information web/mobile application access indexes from TCS (Toll Collection System); and analyzing the relationship between the web/mobile application access indexes and actual traffic volume on expressways, in order to analyze searching behavior of expressway related travel information. The key findings of this study are as follows: first, the results of ADF-test and PP-test confirm that the web/mobile application access indexes by time periods satisfy stationary conditions even without log or differential transformation. Second, the Pearson correlation test showed that there is a strong and positive correlation between the web/mobile application access indexes and expressway entry and exit traffic volume. In contrast, truck entry traffic volume from TCS has no significant correlation with the web/mobile application access indexes. Third, the time gap relationship between time-series variables (i.e., concurrent, leading and lagging) was analyzed by cross-correlation tests. The results indicated that the mobile application access leads web access, and the number of mobile application execution is concurrent with all web access indexes. Lastly, there was no web/mobile application access indexes leading expressway entry traffic volumes on expressways, and the highest correlation was observed between webpage view/visitor/new visitor/repeat visitor/application execution counts and expressway entry volume with a lag of one hour. It is expected that specific individual travel behavior can be predicted such as route conversion time and ratio if the data are subdivided by time periods and areas and utilizing traffic information users' location.