• Journal of the korean academy of Pediatric Dentistry
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• v.51 no.1
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• pp.40-54
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• 2024

This study aims to identify the regional distribution in the prevalence of dental caries and related multidimensional factors among 12-year-old children in Korea. Data from the 2018 Child Oral Health Survey were used to calculate the average DMFT index of 12-year-old children in metropolitan cities, and a multi-level regression model was applied to explain the regional distribution of dental caries prevalence and related factors. Factors were divided into two levels by administrative structure. This study finds a significant regional difference in the prevalence of dental caries in 12-year-old Korean children across metropolitan cities. Multilevel analysis showed that district-level factors (average number of pit and fissure-sealed permanent teeth, dental treatment demand rate, preventive treatment rate, sex ratio, and number of dentists per 100,000 people) and metropolitan-level factors (intakes of cariogenic beverages and number of pediatric dental hospitals and clinics per 100,000 people) had a significant effect on dental caries prevalence (p < 0.05). Individual characteristics and local socio-environmental factors influence the prevalence of dental caries. Especially considering the strong dependence on preventive treatment and accessibility to dental care services, it is necessary to provide adequate preventive treatment and expand health care resources in high-risk areas of dental caries.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.1-8
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• 2018

Whole body fatigue detection is an important phenomenon and the factors contributing to whole body fatigue can be controlled if a mathematical model is available for its assessment. This research study aims at developing a model that categorizes whole body exertion into fatigued and non-fatigued states based on physiological and perceived variables. For this purpose, logistic regression was used to categorize the fatigued and non-fatigued subject as dichotomous variable. Normalized mean power frequency of eight muscles from 25 subjects was taken as physiological variable along with the heart rate while Borg scale ratings were taken as perceived variables. The logit function was used to develop the logistic regression model. The coefficients of all the variables were found and significance level was checked. The detection accuracy of the model for fatigued and non-fatigues subjects was 83% and 95% respectively. It was observed that the mean power frequency of anterior deltoid and the Borg scale ratings of upper and lower extremities were significant in predicting the whole body fatigued when evaluated dichotomously (p < 0.05). The findings can help in better understanding of the importance of combined physiological and perceived exertion in designing the rest breaks for workers involved in squat lifting tasks in industrial as well as health sectors.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.534-541
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• 2011

Risk evaluation approaches for bidding on international construction projects are typically partitioned into three stages: country selection, project classification, and bid-cost evaluation. However, previous studies are frequently under attack in that they have several crucial limitations: 1) a dearth of studies about country selection risk tailored for the overseas construction market at a corporate level; 2) no consideration of uncertainties for input variable per se; 3) less probabilistic approaches in estimating a range of cost variance; and 4) less inclusion of covariance impacts. This study thus suggests a three-staged risk evaluation model to resolve these inherent problems. In the first stage, a country portfolio model that maximizes the expected construction market growth rate and profit rate while decreasing market uncertainty is formulated using multi-objective genetic analysis. Following this, probabilistic approaches for screening bad projects are suggested through applying various data mining methods such as discriminant logistic regression, neural network, C5.0, and support vector machine. For the last stage, the cost overrun prediction model is simulated for determining a reasonable bid cost, while considering non-parametric distribution, effects of systematic risks, and the firm's specific capability accrued in a given country. Through the three consecutive models, this study verifies that international construction risk can be allocated, reduced, and projected to some degree, thereby contributing to sustaining stable profits and revenues in both the short-term and the long-term perspective.

• Journal of Korean Society of Transportation
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• v.31 no.2
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• pp.20-32
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• 2013

This study is aimed at identifying the relationship of transit accessibility types to its related travel frequency in the Seoul metropolitan area. A multi-level poisson regression model is employed after categorizing transit accessibility into 18 types based on locations of residential and work workplace. Analysis results offer three policy implications in improving transit use in the Seoul metropolitan area. First, increase in transit ridership can be more effectively attained when policies concerning both competitive and complementary relationships between bus and rail transit are incorporated. Second, transfer system needs to be focused on such two modal perspectives as one travels from Seoul to suburban area and residential areas given the fact that walking accessibility to bus transit is good but that to rail transit is poor. Third, it is more effective to emphasize rail transit priority rather than bus transit, especially for the travel from suburban area to the city of Seoul in order to increase transit ridership.

• Journal of the Korean Data and Information Science Society
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• v.27 no.1
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• pp.1-8
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• 2016

In the recently, violent crime and accidental crime has been generated continuously. Consequently, people anxiety has been heightened. The Closed Circuit Television (CCTV) has been used to ensure the security and evidence for the crimes. However, the video captured from CCTV has being used in the post-processing to apply to the evidence. In this paper, we propose a flexible multi-level models for estimating whether dangerous behavior and the environment and context for pedestrians. The situation analysis builds the knowledge for the pedestrians tracking. Finally, the decision step decides and notifies the threat situation when the behavior observed object is determined to abnormal behavior. Thereby, tracking the behavior of objects in a multi-region, it can be seen that the risk of the object behavior. It can be predicted by the behavior prediction of crime.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.935-946
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• 2016

In this paper, we analyzed wage determinants of the vocational high school graduates utilizing both individual-level and work region-level variables. We formulate the models in the way wage determination has multi-level structure in the sense that individual wage is influenced by individual-level variables (level-1) and work region-level (level-2) variables. To incorporate dependency between individual wages into the model, we utilize hierarchical linear model (HLM). The major results are as follows. First, it is shown that the HLM model is better than the OLS regression models which do not take level-1 and level-2 variables simultaneously into account. Second, random effects on sex, maester dummy and engineering dummy variables are statistically significant. Third, the fixed effects on business hours and mean wage of regular job for level-2 variables are statistically significant effect individual-level wages. Finally, parental education level, parental income, number of licenses and high school grade are statistically significant for higher individual-level wages.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.6
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• pp.2717-2720
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• 2012

Background: The rates of breast cancer have increased over the past two decades, and this raises concern about physical, psychological and social well-being of women with breast cancer. Further, few women really want to do breast cancer screening. We here investigated the socio-demographic correlates of mammography participation among 400 asymptomatic Iranian women aged between 35 and 69. Methods: A cross-sectional survey was conducted at the four outpatient clinics of general hospitals in Tehran during the period from July through October, 2009. Bi-variate analyses and multi-variate binary logistic regression were employed to find the socio-demographic predictors of mammography utilization among participants. Results: The rate of mammography participation was 21.5% and relatively high because of access to general hospital services. More women who had undergone mammography were graduates from university or college, had full-time or part-time employment, were insured whether public or private, reported a positive family history of breast cancer, and were in the middle income level (all P<0.01).The largest number of participating women was in the age range of 41 to 50 years. The results of multivariate logistic regression further showed that education (95%CI: 0.131-0.622), monthly income (95%CI: 0.038-0.945), and family history of breast cancer (95%CI: 1.97-9.28) were significantly associated (all P<0.05) with mammography participation. Conclusions: The most important issue for a successful screening program is participation. Using a random sample, this study found that the potential predictor variables of mammography participation included a higher education level, a middle income level, and a positive family history of breast cancer for Iranian women, after adjusting for all other demographic variables in the model.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1029-1035
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• 2019

In recent years, artificial intelligence (AI) services have become one of the most essential parts to extend human capabilities in various fields such as face recognition for security, weather prediction, and so on. Various learning algorithms for existing AI services are utilized, such as classification, regression, and deep learning, to increase accuracy and efficiency for humans. Nonetheless, these services face many challenges such as fake news spread on social media, stock selection, and volatility delay in stock prediction systems and inaccurate movie-based recommendation systems. In this paper, various algorithms are presented to mitigate these issues in different systems and services. Convolutional neural network algorithms are used for detecting fake news in Korean language with a Word-Embedded model. It is based on k-clique and data mining and increased accuracy in personalized recommendation-based services stock selection and volatility delay in stock prediction. Other algorithms like multi-level fusion processing address problems of lack of real-time database.

• Journal of Korean society of Dental Hygiene
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• v.11 no.5
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• pp.707-715
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• 2011

Objectives : The objective of this study was to assess a level of oral health related quality of life(OHRQoL) for rural communities elderly and to determine the association between OHRQoL and socio-economic position. Methods : The study population was elderly(60+year-old) residents of PyeongChang county, Jeongseon county, Yeongwol county, Gangwon province. A total of 171 people were invited to participate. Oral health related quality of life was measured using the GOHAI. The data were analyzed with Mann-Whitney U test or Kruskal-Wallis test and to assess socio-economic inequalities in OHRQoL(GOHAI), we used multi-variable logistic regression models. We used models adjusting for age, sex, family status factors(Model I) and compared them to models additionally adjusting for stress level(Model II). Results : There were significant differences in lower score of GOHAI at lower age group and live alone group. And we found that lower SES was significantly associated with lower score of OHRQoL. Social gradient in the score of OHRQoL persisted when adjusted for age, sex, family status, stress level. Conclusions : We recommend that oral health promotion program should be developed after due consideration SES for rural communities elderly because OHRQoL of rural communities elderly was low and association between SES and OHRQoL for rural communities elderly.

• Journal of Biomedical Engineering Research
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• v.31 no.5
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• pp.375-384
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• 2010

The purpose of this study is to determine whether there is a correlation between the segmental bio-impedance measurement with the frequency modulations and the life-style disease of obesity. An obesity is not simply the factor for estimating the life-style disease of obesity, but also the risk factor occurring. There are many methods (BMI, WHR, Waist, CT, DEXA, BIA, etc.) for measuring a degree of obesity; the bio-impedance measurement is more economic and more effective than others. The physical examination, the blood test, the medical imaging diagnosis and the bio-impedancemeasurementswithmultiple frequencies for each body parts have been conducted for 77 people. The estimated value has been calculated through a segmental bio-impedance model based on multi-frequency that was created to reflect the highest correlation by analyzing correlation with linear regression analysis method for the measured bio-impedance and the risk factors. Then we compared with the clinical diagnosis. In case of high level cholesterol, low HDL-C and high LDL-C for life-style disease, the sensitivity is 80~100%and the specificity is 83~100%. This study has shown conclusively that bio-impedance can be a possible predictor to analyze the disease risk rate of population and individual health maintenance. And also the multi-frequency segmental bio-impedance can be used as early predictor to estimate the life-style disease of obesity.