• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.186-191
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• 2023

Data processing techniques play a critical role in decision-making, including handling missing and outlier data, prediction, and recommendation models. This requires a clear explanation of the validity, reliability, and accuracy of all processes and results. In addition, it is necessary to solve data problems through explainable models using decision trees, inference, etc., and proceed with model lightweight by considering various types of learning. The multi-layer mining classification method that applies the sixth principle is a method that discovers multidimensional relationships between variables and attributes that occur frequently in transactions after data preprocessing. This explains how to discover significant relationships using mining on transactions and model the data through regression analysis. It develops scalable models and logistic regression models and proposes mining techniques to generate class labels through data cleansing, relevance analysis, data transformation, and data augmentation to make explanatory decisions.

• Journal of Korean Academy of Nursing
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• v.50 no.2
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• pp.191-199
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• 2020

Purpose: The purpose of this study was to compare sociodemographic characteristics of a normal cognitive group and mild cognitive impairment group, and establish prediction models of Mild Cognitive Impairment (MCI). Methods: This study was a secondary data analysis research using data from "the 4th Korea Longitudinal Study of Ageing" of the Korea Employment Information Service. A total of 6,405 individuals, including 1,329 individuals with MCI and 5,076 individuals with normal cognitive abilities, were part of the study. Based on the panel survey items, the research used 28 variables. The methods of analysis included a χ²-test, logistic regression analysis, decision tree analysis, predicted error rate, and an ROC curve calculated using SPSS 23.0 and SAS 13.2. Results: In the MCI group, the mean age was 71.4 and 65.8% of the participants was women. There were statistically significant differences in gender, age, and education in both groups. Predictors of MCI determined by using a logistic regression analysis were gender, age, education, instrumental activity of daily living (IADL), perceived health status, participation group, cultural activities, and life satisfaction. Decision tree analysis of predictors of MCI identified education, age, life satisfaction, and IADL as predictors. Conclusion: The accuracy of logistic regression model for MCI is slightly higher than that of decision tree model. The implementation of the prediction model for MCI established in this study may be utilized to identify middle-aged and elderly people with risks of MCI. Therefore, this study may contribute to the prevention and reduction of dementia.

• The Journal of Korean Society for School & Community Health Education
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• v.16 no.1
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• pp.51-63
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• 2015

목적: 사하라 이남 아프리카 지역 중저소득국가 모성보건지표의 더딘 개선율은 MDG 5(모자보건향상) 미달성의 주요 원인 중 하나로 꼽힌다. 따라서 본 연구는 우간다 동부 카프초르와 구의 모성사망과 밀접한 산후건강관리(PNC, Postnatal care)서비스 이용결정요인을 파악하는데 있다. 이를 통해 지역건강관리자들에게 PNC 서비스 이용 개선을 위한 정책 수립 및 방안 마련에 기초자료를 제공하고, 궁극적으로는 MDG 5 지표 개선에 일조하고자 한다. 방법: 표본 집단은 카프초르와 구의 15세~49세 여성들 중 최근 1년 내에 출산을 경험한 자들을 대상으로 편의추출 되었다. 조사기간은 2014년 7월부터 10월까지였으며, 구조화된 설문에 총 171명이 응답하였고, 19명의 주요 정보제공자와의 심층면담도 실시하였다. 응답자의 사회인구학적 특성 및 PNC 이용행태를 알아보기 위해 빈도분석을 실시하였으며, 각 독립변수가 PNC 이용에 어떤 영향을 미치는지 파악하기 위해 로지스틱 회귀분석을 실시하였다. 결과: 응답자의 55%만이 의료시설의 PNC 서비스를 받은 것으로 나타났다. 로지스틱 회귀분석을 통해서는 응답자의 연령과 사회적 네트워크, 인지된 건강상태, 산전관리서비스 이용이 PNC 서비스 이용에 긍정적인 영향을 미치는 것으로 나타났으며 의료시설과의 거리, 가족의 규모는 부정적인 영향을 미치는 것으로 나타났다. 결론: PNC 서비스 이용개선을 위해서는 먼저 여성의 사회적 자본 확충 및 개선을 위한 모성보건교육인 소프트 인프라 지원이 지자체 차원에서 실시되어야 할 것이며, 서비스 이용을 가능케 하고 접근성을 높이는 응급후송체계 구축과 같은 물리적 인프라 지원도 도입되어야할 것이다. 또한 가족계획 서비스를 제공하는 등 모성보건관리에 대한 지자체의 민감성을 높이는 것도 필요하겠다.

• Journal of Korean Society of Transportation
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• v.31 no.5
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• pp.79-88
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• 2013

Recently, due to THE MOBILITY ENHANCEMENT FOR THE MOBILITY IMPAIRED ACT, local governments have tired to make various efforts on special transport services(STS), low-flow bus, and installing elevator in subway stations for handicapped people. But in case of STS, insufficient numbers of taxi are raised against the increasing demand of hadicapped people due to the limited budget. This study investigated actual use condition of STS and characteristics of selection of handicapped people on Duribal. In addition, an ordered-logistic model was employed for developing taxi use prediction model considering taxi fare discounts for diverting Duribal demands to taxies. The results can be a significant basic data for transportation policies to improve travel efficiency of the handicapped.

• Land and Housing Review
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• v.13 no.3
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• pp.57-68
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• 2022

This study investigates what influences the 2030 households' intention to utilize housing support policies for the younger generation. Using the logistic regression model, our empirical results show that the 'the recognition of youth housing support projects', 'the housing occupation', 'employment type', 'housing type', and 'age' factors have a significant effect on the intention to use the housing support policies. Specifically, the intention is positively associated with economic activity, one-room residence, monthly rent, employment status during the Covid-19 period, and policy recognition, while negatively related to age. In addition, willingness to use the housing support policies is greater when respondents lived in a studio, lived on a monthly rent, recognized the policy, and improved their employment status. The results suggest that housing support programs need to be expanded and improved. Moreover, information on housing support policies should be efficiently delivered to eligible households, and more sophisticated housing support policies should be provided for young people early in their careers.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.5
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• pp.61-73
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• 2009

Many software reliability growth models(SRGM) have been proposed since the software reliability issue was raised in 1972. The technology to estimate and grow the reliability of developing S/W to target value during testing phase were developed using them. Most of these propositions assumed the S/W debugging testing efforts be constant or even did not consider them. A few papers were presented as the software reliability evaluation considering the testing effort was important afterwards. The testing effort forms which have been presented by this kind of papers were exponential, Rayleigh, Weibull, or Logistic functions, and one of these 4 types was used as a testing effort function depending on the S/W developing circumstances. I propose the methology to evaluate the SRGM using least square estimater and maximum likelihood estimater for those 4 functions, and then examine parameters applying actual data adopted from real field test of developing S/W.

• Journal of the Korean Society for information Management
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• v.23 no.2
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• pp.245-263
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• 2006

Since the beginning of discussions on serial collection management, as budgets have waxed and waned over the ensuing decades, a number of key variables affecting selection/deselection have emerged but without the framework of a coherent and accepted theoretical model. This study is an effort to identify variables which affect the serial collection decision with special attention to selection/deselection in the context of an academic health science library. Based on results from correlation analyses and logistic regression analyses, the serial collection decision can be explained and predicted using various combinations of a reduced set of objective variables. Applications of the results to libraries are discussed, and further research is proposed.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.1 s.35
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• pp.85-91
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• 2006

Recently, slope failures are happen to natural disastrous when they occur in mountainous areas adjoining highways in Korea. The accidents associated with slope failures have increased due to rapid urbanization of mountainous areas. Therefore, Regular maintenance is essential for all slope and needs maintenance of road safety as well as road function. In this study, we take priority of making a database of risk factor of the failure of a slope before assesment and analysis. The purpose of this paper is to recommend a standard of Slope Management Information Sheet(SMIS) like as Hazard Map. The next research, we suggest to pre-estimated model of a road slope using Logistic Regression Model.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.99-100
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• 2022

As the danger of exposure to the asbestos has been revealed, the importance of demolition asbestos in existing buildings has been raised. Extensive body of study has been conducted to evaluate the risk of demolition asbestos, but there were confined types of variables caused by not reflecting categorical information and limitations in collecting quantitative information. Thus, this study aims to derive a model that predicts the risk in workplace of demolition asbestos by collecting categorical and continuous variables. For this purpose, categorical and continuous variables were collected from asbestos demolition reports, and the risk assessment score was set as the dependent variable. In this study, the influence of each variable was identified using logistic regression, and the risk prediction model methodologies were compared through decision tree regression and artificial neural network. As a result, a conditional risk prediction model was derived to evaluate the risk of demolition asbestos, and this model is expected to be used to ensure the safety of asbestos demolition workers.

• Journal of Digital Convergence
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• v.15 no.11
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• pp.245-250
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• 2017

Flash floods is defined as the flooding of intense rainfall over a relatively small area that flows through river and valley rapidly in short time with no advance warning. So that it can cause damage property and casuality. This study is to establish the flash-flood warning system using 38 accident data, reported from the National Disaster Information Center and Land Surface Model(TOPLATS) between 2009 and 2012. Three variables were used in the Land Surface Model: precipitation, soil moisture, and surface runoff. The three variables of 6 hours preceding flash flood were reduced to 3 factors through factor analysis. Decision tree, random forest, Naive Bayes, Support Vector Machine, and logistic regression model are considered as big data methods. The prediction performance was evaluated by comparison of Accuracy, Kappa, TP Rate, FP Rate and F-Measure. The best method was suggested based on reproducibility evaluation at the each points of flash flood occurrence and predicted count versus actual count using 4 years data.