• The Korean Journal of Applied Statistics
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• v.37 no.4
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• pp.525-539
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• 2024

Massive multiplayer online role playing game (MMORPG) is a common type of game these days. Predicting user churn in MMORPG is a crucial task. The retention rate of users is deeply associated with the lifespan and revenue of the service. If the churn of a specific user can be predicted in advance, targeted promotions can be used to encourage their stay. Therefore, not only the accuracy of churn prediction but also the speed at which signs of churn can be detected is important. In this paper, we propose methods to identify early signs of churn by utilizing the daily predicted user retention probabilities. We train various deep learning and machine learning models using log data and estimate user retention probabilities. By analyzing the change patterns in these probabilities, we provide empirical rules for early identification of users at high risk of churn. Performance evaluations confirm that our methodology is more effective at detecting high risk users than existing methods based on login days. Finally, we suggest novel methods for customized marketing strategies. For this purpose, we provide guidelines of the percentage of accessed users who are at risk of churn.

• The Korean Journal of Applied Statistics
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• v.37 no.4
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• pp.395-410
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• 2024

Text data is usually made up of a wide variety of unique words. Even in standard text data, it is common to find tens of thousands of different words. In text data analysis, usually, each unique word is treated as a variable. Thus, text data can be regarded as a dataset with a large number of variables. On the other hand, in text data classification, we often encounter class label imbalance problems. In the cases of substantial imbalances, the performance of conventional classification models can be severely degraded. To improve the classification performance of support vector machines (SVM) for imbalanced data, algorithms such as the Synthetic Minority Over-sampling Technique (SMOTE) can be used. The SMOTE algorithm synthetically generates new observations for the minority class based on the k-Nearest Neighbors (kNN) algorithm. However, in datasets with a large number of variables, such as text data, errors may accumulate. This can potentially impact the performance of the kNN algorithm. In this study, we propose a method for enhancing prediction performance for the minority class of imbalanced text data. Our approach involves employing variable selection to generate new synthetic observations in a reduced space, thereby improving the overall classification performance of SVM.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.163-171
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• 2024

This study applies neural network analysis techniques to examine the factors influencing the purchasing decisions of fashion eyewear among women in their 30s and 40s, comparing these findings with traditional parametric analysis methods. In the fashion area, machine learning techniques are utilized for personalized fashion recommendation systems. However, research on such applications in Korea remains insufficient. By reanalyzing a study conducted in 2017 using traditional quantitative methods with these new techniques, this study aims to confirm the utility of neural network methods. Notably, the study finds that the classification accuracy of preferred sunglasses design is highest, at 86.2%, when the L-BFGS-B neural network is activated using the hyperbolic tangent function. The most critical factors influencing purchasing decisions were consumers' occupations and their pursuit of new styles. It is interpreted that Korean sunglasses consumers prefer "safe changes." These findings are consistent for selecting both the frames and lenses of sunglasses. Traditional quantitative analysis suggests that the type of sunglasses preferred varies according to the group to which a consumer belongs. In contrast, neural network analysis predicts the preferred sunglasses for each individual, thereby facilitating the development of personalized sunglasses recommendation systems.

• Journal of Korean Society of Forest Science
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• v.113 no.3
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• pp.349-360
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• 2024

This study aimed to analyze the main forest environmental factors affecting the discernment of slow-moving landslide-prone areas in the Republic of Korea, based on data from a detailed landslide survey conducted from 2019 to 2021. Field survey data from 256 sites were collected covering 29 forest environmental factors in seven categories, including geology, soil, and topography. The analysis was conducted using the Random Forest model (AUC = 0.910) and XGBoost model (Accuracy = 0.808, Kappa = 0.594, F1 - measure = 0.494), which were evaluated as having high classification accuracy during the machine learning model development process. Consequently, factors with a high mean decrease Gini (MDG), representing classification importance, were identified as the presence of cracks (average MDG of both models: 22.1), peak elevation (14.8), and the presence of steps (7.0), indicating that these were significant factors in determining slow-moving landslide-prone areas. The presence of cracks and steps aligned well with the characteristics of slow-moving landslides, suggesting that their importance should be emphasized in future detailed landslide surveys. However, the influence of the peak elevation was considered somewhat overestimated due to the characteristics of the input data used in the analysis. These findings are expected to further improve the accuracy and efficiency of final judgments in detailed landslide surveys.

• The Transactions of the Korea Information Processing Society
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• v.13 no.9
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• pp.395-403
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• 2024

Recently, with the advancement of technology, the automotive industry has seen an increase in network connectivity. CAN (Controller Area Network) bus technology enables fast and efficient data communication between various electronic devices and systems within a vehicle, providing a platform that integrates and manages a wide range of functions, from core systems to auxiliary features. However, this increased connectivity raises concerns about network security, as external attackers could potentially gain access to the automotive network, taking control of the vehicle or stealing personal information. This paper analyzed abnormal messages occurring in CAN and confirmed that message occurrence periodicity, frequency, and data changes are important factors in the detection of abnormal messages. Through DBC decoding, the specific meanings of CAN messages were interpreted. Based on this, a model for classifying abnormalities was proposed using the GRU model to analyze the periodicity and trend of message occurrences by measuring the difference (residual) between the predicted and actual messages occurring within a certain period as an abnormality metric. Additionally, for multi-class classification of attack techniques on abnormal messages, a Random Forest model was introduced as a multi-classifier using message occurrence frequency, periodicity, and residuals, achieving improved performance. This model achieved a high accuracy of over 99% in detecting abnormal messages and demonstrated superior performance compared to other existing models.

• 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 Neurosurgical Society
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• v.30 no.10
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• pp.1210-1219
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• 2001

Introduction : The purpose of this study was to analyze the safety, pullout strength and radiographic characteristics of unicortical and bicortical screws of cervical facet within cadaveric specimens and evaluate the influence of level of training on the positioning of these screws. Methods : Twenty-one cadavers, mean 78.9 years of age, underwent bilateral placement of 3.5mm AO lateral mass screw from C3-C6(n=168) using a slight variation of the Magerl technique. Intraoperative imaging was not used. The right side(unicortical) utilized only 14mm screws(effective length of 11mm) while on the left side to determine the length of the screw after the ventral cortex had been drilled. Three spine surgeons(attending, fellow, chief resident) with varying levels of spine training performed the procedure on seven cadavers each. All spines were harvested and lateral radiographs were taken. Individual cervical vertebrae were carefully dissected and then axial radiographs were taken. The screws were evaluated clinically and radiographically for their safety. Screws were graded clinically for their safety with respect to the spinal cord, facet joint, nerve root and vertebral artery. The grades consisted of the following categories : "satisfactory", "at risk" and "direct injury". Each screw was also graded according to its zone placement. Screw position was quantified by measuring a sagittal angle from the lateral radiograph and an axial angle from the axial radiograph. Pull-out force was determined for all screws using a material testing machine. Results : Dissection revealed that fifteen screws on the left side actually had only unicortical and not bicortical purchase as intended. The majority of screws(92.8%) were satisfactory in terms of safety. There were no injuries to the spinal cord. On the right side(unicortical), 98.9% of the screws were "satisfactory" and on the left side(bicortical) 68.1% were "satisfactory". There was a 5.8% incidence of direct arterial injury and a 17.4% incidence of direct nerve root injury with the bicortical screws. There were no "direct injuries" with the unicortical screws for the nerve root or vertebral artery. The unicortical screws had a 21.4% incidence of direct injury of the facet joint, while the bicortical screws had a 21.7% incidence. The majority of "direct injury" of bicortical screws were placed by the surgeon with the least experience. The performance of the resident surgeon was significantly different from the attending or fellow(p<0.05) in terms of safety of the nerve root and vertebral artery. The attending's performance was significantly better than the resident or fellow(p<0.05) in terms of safety of the facet joint. There was no relationship between the safety of a screw and its zone placement. The axial deviation angle measured $23.5{\pm}6.6$ degrees and $19.8{\pm}7.9$ degrees for the unicortical and bicortical screws, respectively. The resident surgeon had a significantly lower angle than the attending or fellow(p<0.05). The sagittal angle measured $66.3{\pm}7.0$ degrees and $62.3{\pm}7.9$ degrees for the unicortical and bicortical screws, respectively. The attending had a significantly lower sagittal angle than the fellow or resident(p<0.05). Thirty-three screws that entered the facet joint were tested for pull-out strength but excluded from the data because they were not lateral mass screws per-se and had deviated substantially from the intended final trajectory. The mean pull-out force for all screws was $542.9{\pm}296.6N$. There was no statistically significant difference between the pull-out force for unicortical($519.9{\pm}286.9N$) and bicortical($565.2{\pm}306N$) screws. There was no significant difference in pull-out strengths with respect to zone placement. Conclusion : It is our belief that the risk associated with bicortical purchase mandates formal spine training if it is to be done safely and accurately. Unicortical screws are safer regardless of level of training. It is apparent that 14mm lateral mass screws placed in a supero-lateral trajectory in the adult cervical spine provide an equivalent strength with a much lower risk of injury than the longer bicortical screws placed in a similar orientation.

• The Journal of Korean Society for Radiation Therapy
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• v.12 no.1
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• pp.91-104
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• 2000

Recently linear accelerator in radiation therapy in asymmetric field has been easily used since the improvement and capability of asymmetrical field adjustment attached to the machine. It has been thought there have been some significant errors in dose calculation when asymmetrical radiation fields have been utilized in practice of radiation treatments if the fundamental data for dose calculation have been measured in symmetrical standard fields. This study investigated how much the measured data of dose distributions and their isodose curves are different between in asymmetrical and symmetrical standard fields, and how much there difference affect the error in dose calculation in conventional method measured in symmetrical standard field. The distributions of radiation dose were measured by photon diode detector in the water phantom (RFA-300P, Scanditronix, Sweden) as tissue equivalent material on utilization of 6 MV linear accelerator with source surface distance (SSD) 1000 mm. The photon diode detector has the velocity of 1 mm per second from water surface to 250 mm depth in the field size of $40mm{\times}40mm\;to\;250mm{\times}250mm\;symmetric\;field\;and\;40mm{\times}20mm\;to\;250mm{\times}125mm$ asymmetrical fields. The measurements of percent depth dose (PDD) and subsequent plotting of their isodose curves were performed from water surface to 250mm dmm from Y-center axis in $100mm{\times}50mm$ field in order to absence the variability of depth dose according to increasing field sizes and their affects to plotted isodose curves. The difference of PDD between symmetric and asymmetric field was maximum $4.1\%\;decrease\;in\;40mm{\times}20mm\;field,\;maximum\;6.6\%\;decrease\;in\;100mm{\times}50mm\;and\;maximum\;10.2\%\;decrease\;200mm{\times}100mm$, the larger decrease difference of PDD as the greater field size and as greater the depth, The difference of PDD between asymmetrical field and equivalent square field showed maximum $2.4\%\;decrease\;in\;60mm{\times}30mm\;field,\;maximum\;4.8\%\;decrease\;in\;150mm{\times}75mm\;and\;maximum\;6.1\%\;decrease\;in\;250mm{\times}125mm$, and the larger decreased differenced PDD as the greater field size and as greater the depth, these differences of PDD were out of $5\%$ of dose calculation as defined by international Commission on radiation unit and Measurements(ICRU). In the dose distribution of asymmetrical field (half beam) the plotted isodose curves were observed to have deviations by decreased PDD as greater as the blocking of the beam moved closer to the central axis, and as the asymmetrical field increased by moving the block 10 mm keeping away from the central axis, the PDD increased and plotted isodose curves were gradually more flattened, due to reduced amount of the primary beam and the fraction of low energy soft radiations by passing thougepth in asymmetrical field by moving independent jaw each 10 h beam flattening filter. As asymmetrical radiation field as half beam radiation technique is used, the radiation dosimetry calculated in utilizing the fundamental data which measured in standard symmetrical field should be converted on bases of nearly measured data in asymmetrical field, measured beam data flies of various asymmetrical field in various energy and be necessary in each institution.

• Women's Health Nursing
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• v.7 no.2
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• pp.169-187
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• 2001

This study was performed to probe the effect of exercise program on muscle strength, endurance, flexibility, pain, disability level and life satisfaction in female teachers of elementary school who complain of low back pain. For this study, 44 female teachers aged 30-50 years with mechanical low back pain of 6 months' duration, who had the structural normalities in the lumbar spine, were recruited from April 1 to July 10 1999. Twenty three out of them were assigned to the experimental group and twenty one to the control group. The exercise program consisted of education on right postures, the etiology and diagnosis of low back pain, and exercise intervention such as muscle relaxation, elongation and strengthening. With 8 weeks program, the subjects received two sessions of education and six sessions of group exercise in the 1st week, while three sessions of group exercise and four sessions of individual exercise weekly and two sessions of education during the later 7 weeks. The muscle strength and endurance were measured by Cybex 770, the flexibility by flexibility measurement machine, the intensity of pain by Visual Analogue Scale (VAS), the level of disability by Oswestry low back pain disability scale, depression by Beck depression inventory (BDI), and life satisfaction by Life satisfaction index-Z. Study measurements were taken before and after 8 weeks exercise program. Data were analyzed using paired t-test, t-test, and ANCOVA. The results were as follows ; 1. The flexors and extensors peak torque and flexors peak torque per body weight of experimental group were significantly increased at test velocities $30^{\circ}$/sec, $60^{\circ}$/sec compared with those of control group. There was no significant difference in extensors peak torque per flexors peak torque at $30^{\circ}/sec$, $60^{\circ}/sec$ between experimental and control group. 2. The flexors and extensors total work and flexors total work per body weight of experimental group were significantly increased at $120^{\circ}/sec$, compared with those of control group. 3.The flexibility of lumbar spine in experimental group was significantly increased compared with that of control group. The pains in anterior, posterior, left lateral and right lateral bending and in rotation of experimental group were significantly increased compared with those of control group. 4. The Oswestry disability scores of experimental and control group were significantly decreased, and there was no difference in the Oswestry disability score change between experimental and control group. 5. The scores of BDI of experimental group were significantly decreased compared with those of control group. Life satisfaction index-Z scores of experimental group were not changed, but those of control group were significantly decreased. There was no difference in the score change of Life satisfaction index-Z between experimental and control group. 6. ANCOVA analysis for the data variables of inhomogeneous baseline represented that there was no significant difference in extensors peak torque and extensors total work at $120^{\circ}/sec$ and extensor total work per body weight at $120^{\circ}/sec$ change between experimental group and control group. These findings indicate that the exercise program could be effective in increasing the muscle strength, endurance, flexibility and decreasing pain, improving depression in female teachers of elementary school with chronic low back pain. It is suggested that the exercise program could be an essential factor for the effective nursing intervention to the patients suffered from chronic low back pain.

• Geophysics and Geophysical Exploration
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• v.8 no.4
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• pp.270-279
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• 2005

Under the research project supported by Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT), we have conducted the development of GPR systems for landmine detection. Until 2005, we have finished development of two prototype GPR systems, namely ALIS (Advanced Landmine Imaging System) and SAR-GPR (Synthetic Aperture Radar-Ground Penetrating Radar). ALIS is a novel landmine detection sensor system combined with a metal detector and GPR. This is a hand-held equipment, which has a sensor position tracking system, and can visualize the sensor output in real time. In order to achieve the sensor tracking system, ALIS needs only one CCD camera attached on the sensor handle. The CCD image is superimposed with the GPR and metal detector signal, and the detection and identification of buried targets is quite easy and reliable. Field evaluation test of ALIS was conducted in December 2004 in Afghanistan, and we demonstrated that it can detect buried antipersonnel landmines, and can also discriminate metal fragments from landmines. SAR-GPR (Synthetic Aperture Radar-Ground Penetrating Radar) is a machine mounted sensor system composed of B GPR and a metal detector. The GPR employs an array antenna for advanced signal processing for better subsurface imaging. SAR-GPR combined with synthetic aperture radar algorithm, can suppress clutter and can image buried objects in strongly inhomogeneous material. SAR-GPR is a stepped frequency radar system, whose RF component is a newly developed compact vector network analyzers. The size of the system is 30cm x 30cm x 30 cm, composed from six Vivaldi antennas and three vector network analyzers. The weight of the system is 17 kg, and it can be mounted on a robotic arm on a small unmanned vehicle. The field test of this system was carried out in March 2005 in Japan.