• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.851-865
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• 2017

Ransomware such as WannaCry is a global issue and methods to defend against malware attacks are important. We have to be able to classify the malware types efficiently in order to minimize the damage from malwares. This study makes models to classify malware properly with various statistical techniques. Several classification techniques such as logistic regression, random forest, gradient boosting, and support vector machine are used to construct models. This study also helps us understand key variables to classify the type of malicious software.

• Journal of Preventive Medicine and Public Health
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• v.40 no.3
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• pp.218-226
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• 2007

Objectives : This study examined the relationship of parent-related negative life events with mental health and delinquent behaviors among Korean adolescents. Methods : A total of 2,976 high school first-grade pupils (1,498 boys & 1,478 girls) taking part in the third wave of Korean Youth Panel Survey completed a self-administered questionnaire regarding parent-related life events, depressive feelings, suicidal ideation, delinquent behaviors, demographic characteristics, parental socioeconomic status, social support, and social capital. Data analyses were conducted using multivariate logistic regression. Results : After adjusting for all covariates, the more parent-related negative life events adolescents experienced throughout their whole life, the more likely adolescent were to have mental and behavioral problems. A significant dose-response relationship between them was more clearly observed in girls than in boys. The experience of parentrelated negative events during childhood was significantly associated with suicidal ideation and delinquent behaviors for boys, and with depressive feelings for girls during adolescence. Indeed, parental social support, social capital, and having a close friend with delinquent behaviors, especially for girls, partially mediated the relationship between parent-related negative life events and both outcomes. Conclusions : The study showed a clear dose-response relationship of frequency of parent-related negative life events with poor mental and behavioral health for both genders. The residual effect of being exposed to parent-related events during childhood on mental health and delinquent behaviors during adolescence still remained.

• 한국항공운항학회:학술대회논문집
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• 2015.11a
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• pp.224-230
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• 2015

항공기 엔진 체계의 신뢰성과 안전성을 분석하는 것은 RAM 분석이라고 한다. RAM은 제작자가 개발한 시스템의 지속가능성 혹은 그 체계의 수명주기비용(Lice Cycle Cost)에 중대한 영향을 줄 수 있는 시스템 설계특성을 의미한다. 또한 RAM은 시스템이 개발될 당시에 의도된 임무를 수행할 수 있는 능력 혹은 임수수행을 성공할 수 있도록 담보하는 중요한 역할을 한다. RAM은 1970년대 이후 미국 군수분야에서는 군수지원성분석(LSA, Logistic Support Analysis) 과 밀접히 연계되어 활용되어 왔으며, RAM과 LSA를 통합하여 종합군수지원체계(ILS, Integrated Logistics Support)라고 부른다. 본 연구에서는 항공기 엔진에 대한 신뢰성 분석에 Weibull 분포를 활용하였다. Weibull 분포는 2개의 변수 적용에서 다음과 같이 정의된다. 변수가 그 계의 어떤 값을 나타내느냐에 따라 분포도 곡선의 특성 의미는 변수에 의존된다. 예를 들어 시험성적, 사회적 특성을 표현하는 인지도, 산업 제품의 고장이나 결함을 나타내는 고장관련 모수(변수) 등에 따라 그 특징을 나타내게 된다. 항공기 엔진의 신뢰성 분석을 통해 항공기 엔진의 수명 판단이 가능해졌으며 임무 불가능 수준의 In-Flight Shut-Down 비율이 현격히 감소됨을 확인할 수 있었다.

• IE interfaces
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• v.20 no.4
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• pp.448-457
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• 2007

The small & micro business has the characteristics of both consumer credit risk and business credit risk. In predicting the bankruptcy for small-micro businesses, the problem is that in most cases, the financial data for evaluating business credit risks of small & micro businesses are not available. To alleviate such problem, we propose a bankruptcy prediction mechanism using the credit card sales information available, because most small businesses are member store of some credit card issuers, which is the main purpose of this study. In order to perform this study, we derive some variables and analyze the relationship between good and bad signs. We employ the new statistical learning technique, support vector machines (SVM) as a classifier. We use grid search technique to find out better parameter for SVM. The experimental result shows that credit card sales information could be a good substitute for the financial data for evaluating business credit risk in predicting the bankruptcy for small-micro businesses. In addition, we also find out that SVM performs best, when compared with other classifiers such as neural networks, CART, C5.0 multivariate discriminant analysis (MDA), and logistic regression.

• Journal of Family Resource Management and Policy Review
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• v.20 no.2
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• pp.165-184
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• 2016

This study was conducted to identify factors which predict and discriminate women' reemployment. 288 married women whose careers had been interrupted for more than 1 year and 287 married women who re-entered into the labor market within 5 years were surveyed. Collected data were analyzed by logistic regression analysis. In the personal factor(reemployment need), proximal context factors(career barriers, family support and expectation for reemployment) and background context factors(SES, family life cycle), background context factors were revealed not to predict significantly women's reemployment. Secondly, in the case of proximal context factors, it was found that 'expectation of family members for reemployment' and 'sharing family care' had strong effects on reemployment. And compared with interrupted women, reemployed women were less likely to perceive career barriers. Specifically, they showed lower expectation to their job and status which they would achieve, less perceived gender/age discrimination in labor market, and had more confidence that they could find a job. Finally, with regard to the personal factor (reemployment need), the lower women had self-actualization need, the higher economic need, and the higher social need, it was highly likely to classify into reemployed women. We discussed the way to improve reemployment of career interrupted women based on above mentioned findings.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.516-525
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• 2005

Case-based reasoning (CBR) has been widely used in various areas due to its convenience and strength in complex problem solving. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. Most prior studies have tried to optimize the weights of the features or selection process of appropriate instances. But, these approaches have been performed independently until now. Simultaneous optimization of these components may lead to better performance than in naive models. In particular, there have been few attempts to simultaneously optimize the weight of the features and selection of the instances for CBR. Here we suggest a simultaneous optimization model of these components using a genetic algorithm (GA). We apply it to a customer classification model which utilizes demographic characteristics of customers as inputs to predict their buying behavior for a specific product. Experimental results show that simultaneously optimized CBR may improve the classification accuracy and outperform various optimized models of CBR as well as other classification models including logistic regression, multiple discriminant analysis, artificial neural networks and support vector machines.

• Korean Journal of Occupational Health Nursing
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• v.28 no.4
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• pp.242-252
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• 2019

Purpose: This study aimed to identify factors influencing depressive symptoms in public and private sector employees. Methods: Survey data on 23,602 workers who had worked in the public or private sector were obtained from the 2014 Korean Working Condition Survey (KWCS). Symptoms of depression were measured using the WHO-5 Well-being Index. Data were analyzed using a $x^2$ test, t-test, and multivariate stepwise logistic regression to determine the factors affecting the symptoms of depression. Results: First, the prevalence of depressive symptoms was 41.1 % in public sector employees and 43.4 % in private sector employees. Second, the factors commonly affecting depressive symptoms in public and private sector employees were residence area, cognitive demands, development opportunities, social support from colleagues, social support from supervisors, social community at work, job rewards, and work-family conflict. In addition, age, company size, atypical work, ergonomic risks, quantitative demands, emotional demands, influence, and job insecurity were found to be predictors of depressive symptoms unique to private sector employees. Conclusion: Mental health programs including the employee assistance program (EAP) should be developed and implemented after considering the risk factors affecting depressive symptoms.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.4
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• pp.775-780
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• 2019

Techniques for predicting the future can be categorized into statistics-based and deep-run-based techniques. Among them, statistic-based techniques are widely used because simple and highly accurate. However, working-level officials have difficulty using many analytical techniques correctly. In this study, we compared the accuracy of prediction by applying multinomial logistic regression, decision tree, random forest, support vector machine, and Bayesian inference to marketing related data. The same marketing data was used, and analysis was conducted by using R. The prediction results of various techniques reflecting the data characteristics of the marketing field will be a good reference for practitioners.

• Journal of Korean Public Health Nursing
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• v.35 no.2
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• pp.224-238
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• 2021

Purpose: This study aimed to identify factors associated with the self-rated health of married immigrant women in South Korea. Methods: Data, collected in August 2018, were derived from the 2018 National Multicultural Family Survey. Study subjects were 9,230 married immigrant women. Data were analyzed using logistic regression. Results: Of the study subjects, 23.2% rated their health as poor. Results showed that individual factors (age, ethnic affiliation, duration in South Korea, and depressive symptoms); social and community networks (relationship with spouse, parenting efficacy, Korean proficiency, perceived discrimination, social support, and social activities); and living and working conditions (life satisfaction and unmet heath needs) were associated with health. Married immigrant women in their 50s or older, living in Korea for more than 15 years, experiencing depressive symptoms, low life satisfaction, and having unmet health needs were especially at high risk of poor health. Conclusion: More detailed health policy that considers age, length of stay, and country of origin. To prevent the rapidly deteriorating health of married immigrant women after middle age, mental health support should be given priority, and systematic improvement is needed to increase accessibility healthcare services.

• Health Policy and Management
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• v.32 no.1
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• pp.14-20
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• 2022

In this study, we distinguished urban and rural areas in Naju-si, Jeollanam-do, grasped the characteristics of those areas, and investigated the depression-related factors in Naju-si based on this. This study used Community Health Survey data from 2017 to 2019. To investigate the factors affecting the depression in Naju-si local residents, the odds ratio was calculated using a complex sample logistic regression model. As a result of confirming the factors affecting the prevalence of depression in Naju-si residents, the risk of depression was significantly higher at 1.59 (95% confidence interval [CI], 1.02-2.50) for women, 2.14 (95% CI, 1.20-3.83) for recipients of basic livelihoods, 2.35 (95% CI, 1.46-3.79) for those who did not practice walking, and 2.00 (95% CI, 1.23-3.26) for those who slept less than 5 hours. It is necessary to select high-risk groups as a regional-specific project to resolve the mental health disparities in Naju-si and to intervene in early depression prevention through support for mental health support services.