• Journal of Preventive Medicine and Public Health
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• v.53 no.6
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• pp.465-475
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• 2020

Objectives: This study was conducted to examine the association between birth patterns (defined in terms of birth order and interval) with delayed breastfeeding initiation in Indonesia. Methods: A cross-sectional study was carried out using data from the Indonesian Demographic and Health Survey 2017. The weighted number of respondents was 5693 women aged 15-49 years whose youngest living child was less than 2 years old. Multivariable logistic regression was conducted to evaluate associations between birth patterns and delayed breastfeeding initiation after adjusting for other covariates. Results: This study found that 40.2% of newborns in Indonesia did not receive timely breastfeeding initiation. Birth patterns were significantly associated with delayed breastfeeding initiation. Firstborn children had 77% higher odds of experiencing delayed breastfeeding initiation (adjusted odds ratio, 1.77; 95% confidence interval, 1.02 to 3.04; p<0.05) than children with a birth order of 4 or higher and a birth interval ≤ 2 years after adjusting for other variables. Conclusions: Firstborn children had higher odds of experiencing delayed breastfeeding initiation. Steps to provide a robust support system for mothers, especially first-time mothers, such as sufficient access to breastfeeding information, support from family and healthcare providers, and national policy enforcement, will be effective strategies to ensure better practices regarding breastfeeding initiation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.3
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• pp.617-623
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• 2018

This study focuses on detection of malicious code through AndroidManifest permissoion feature extracted based on Android static analysis. Features are built on the permissions of AndroidManifest, which can save resources and time for analysis. Malicious app detection model consisted of SVM (support vector machine), NB (Naive Bayes), Gradient Boosting Classifier (GBC) and Logistic Regression model which learned 1,500 normal apps and 500 malicious apps and 98% detection rate. In addition, malicious app family identification is implemented by multi-classifiers model using algorithm SVM, GPC (Gaussian Process Classifier) and GBC (Gradient Boosting Classifier). The learned family identification machine learning model identified 92% of malicious app families.

• Journal of Home Health Care Nursing
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• v.9 no.2
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• pp.129-137
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• 2002

Purpose: This study was aimed to investigate predictors of job satisfaction of home visiting nurses at the 16 public health centers in Busan. Method: There are two groups of independent factors: non-work related (age. educational level. working duration as nurses in hospitals. certificate). and work related (working duration as nurses in public health centers. working duration as home visiting nurse. position. number of households visited per week, workload, cooperation among staff, support by supervisors, supply of vehicles, supply of materials). The participants were 88 home visiting nurses from 16 public health centers in Busan. Data were collected with self-administrated questionnaires and analysed using an univariate logistic regression and multiple logistic regression analysis. Result: support by supervisors (good vs not-good, OR=3.70. p=0.025), and supply of materials (good vs not-good, OR=3.33, p=0.038) had significants effects on job satisfaction. Conclusion: The results were similar to those of other studies on the predictors of job satisfaction of clinical nurse at hospitals, and were helpful in developing nursing interventions to increase job satisfaction among home visiting nurses in public health centers. Busan.

• Journal of Environmental Science International
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• v.25 no.4
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• pp.603-613
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• 2016

Fog may have a significant impact on road conditions. In an attempt to improve fog predictability in Jeju, we conducted machine learning with various data mining techniques such as tree models, conditional inference tree, random forest, multinomial logistic regression, neural network and support vector machine. To validate machine learning models, the results from the simulation was compared with the fog data observed over Jeju(184 ASOS site) and Gosan(185 ASOS site). Predictive rates proposed by six data mining methods are all above 92% at two regions. Additionally, we validated the performance of machine learning models with WRF (weather research and forecasting) model meteorological outputs. We found that it is still not good enough for operational fog forecast. According to the model assesment by metrics from confusion matrix, it can be seen that the fog prediction using neural network is the most effective method.

• Journal of Family Resource Management and Policy Review
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• v.17 no.3
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• pp.39-58
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• 2013

The purpose of this study was to identify the factors determining the participation of the poor elderly in economic activity. This study analyzed secondary data of the second wave of Korean Longitudinal Study of Ageing. Binary logistic regression was used to identify the factors that are associated with the economic activity of the poor elderly. The results of the analyses showed that age, gender, region, public assistance, education, health status, chronic illness, contacts with acquaintances, and support from children were associated with participation in economic activity. The study's findings have several implications for policies and services. The study identified the need for an age- and gender-specific approach to promoting participation in economic activity among the poor elderly. Regional differences should also be considered in the creation of work opportunities for older adults. In terms of human capital, the positive effect of good health indicates that strategies are needed to address the needs of older adults with health issues. In addition, there is a need for more jobs for elderly job seekers with high levels of education. Finally, policy makers and practitioners should explore interventions for enhancing the social network involvement and community support for the elderly living in poverty.

• Korean Journal of Health Education and Promotion
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• v.27 no.3
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• pp.33-47
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• 2010

Objectives: The purpose of this study was to identify personal and social environmental factors associated with physical activity and sedentary behavior among elementary school students. Methods: Cross-sectional self-reported data were collected from a conveniently clustering sample population of 1538 grade 5 to 6 students attending 19 elementary schools in Seoul metropolitan city and Gyeonggi province. Data were statistically analyzed using Chi-square test and multiple logistic regression analysis. Results: In multiple logistic regression analyses, significant factors that were associated with schoolchildren's physical activity were gender, father's job, social support for physical activity, friend support, participation in school physical education class. Father's education level, mother's job, family functioning and urban residents were significantly associated with TV viewing and gender, age, BMI(obesity), mother's job, family functioning and urban residents were significantly associated with playing computer games among elementary schoolchildren. These results showed that physical activity among elementary school students was most associated with social environmental factors and sedentary behavior among school students was most associated with personal and family environment factors. Conclusion: Health care providers should develop interventions to improve these family and social environmental factors to increase physical activity levels and to decrease sedentary behavior among elementary schoolchildren.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.281-291
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• 2017

Nowadays, as traffic jams are a daily elementary problem in both developed and developing countries, systems to monitor, predict, and detect traffic conditions are playing an important role in research fields. Comparing them, researchers have been trying to solve problems by applying many kinds of technologies, especially roadside sensors, which still have some issues, and for that reason, any one particular method by itself could not generate sufficient traffic prediction results. However, these sensors have some issues that are not useful for research. Therefore, it may not be best to use them as stand-alone methods for a traffic prediction system. On that note, this paper mainly focuses on predicting traffic conditions based on a hybrid prediction approach, which stands on accuracy comparison of three prediction models: multinomial logistic regression, decision trees, and support vector machine (SVM) classifiers. This is aimed at selecting the most suitable approach by means of integrating proficiencies from these approaches. It was also experimentally confirmed, with test cases and simulations that showed the performance of this hybrid method is more effective than individual methods.

• Nuclear Engineering and Technology
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• v.47 no.5
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• pp.624-632
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• 2015

A self-diagnostic monitoring system is a system that has the ability to measure various physical quantities such as temperature, pressure, or acceleration from sensors scattered over a mechanical system such as a power plant, in order to monitor its various states, and to make a decision about its health status. We have developed a self-diagnostic monitoring system for an air-operated valve system to be used in a nuclear power plant. In this study, we have tried to improve the self-diagnostic monitoring system to increase its reliability. We have implemented three different machine learning algorithms, i.e., logistic regression, an artificial neural network, and a support vector machine. After each algorithm performs the decision process independently, the decision-making module collects these individual decisions and makes a final decision using a majority vote scheme. With this, we performed some simulations and presented some of its results. The contribution of this study is that, by employing more robust and stable algorithms, each of the algorithms performs the recognition task more accurately. Moreover, by integrating these results and employing the majority vote scheme, we can make a definite decision, which makes the self-diagnostic monitoring system more reliable.

• Korean Journal of Agricultural Science
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• v.48 no.4
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• pp.987-1001
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• 2021

One of the most critical issues in the dairy industry, alongside the low birth rate and the aging population, is the decrease in demand for milk. In this study, the consumption trends of 12 major dairy products distributed in Korea were predicted using a logistic model, the Gompertz model, and the Bass diffusion model, which are representative S-shaped growth models. The 12 dairy products are fermented milk (liquid type, cream type), butter, milk powder (modified, whole, skim), liquid milk (market, flavored), condensed milk, cheese (natural, processed), and cream. As a result of the analysis, the growth potential of butter, condensed milk, natural cheese, processed cheese, and cream consumption among the 12 dairy products is relatively high, whereas the growth of the remaining dairy product consumption is expected to stagnate or decrease. However, butter and cream are by-products of the skim milk powder manufacturing process. Therefore, even if the consumption of butter and cream grows, it is difficult to increase the demand of domestic milk unless the production of skim milk powder produced from domestic milk is also increased. Therefore, in order to support the domestic dairy industry, policy support should be focused on increasing domestic milk usage for the production of condensed milk, natural cheese, and processed cheese.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1372-1384
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• 2024

The discrimination of the source for xenon gases' release can provide an important clue for detecting the nuclear activities in the neighboring countries. In this paper, three machine learning techniques, which are logistic regression, support vector machine (SVM), and k-nearest neighbors (KNN), were applied to develop the predictive models for discriminating the source for xenon gases' release based on the xenon isotopic activity ratio data which were generated using the depletion codes, i.e., ORIGEN in SCALE 6.2 and Serpent, for the probable sources. The considered sources for the neighboring countries of South Korea include PWRs, CANDUs, IRT-2000, Yongbyun 5 MWe reactor, and nuclear tests with plutonium and uranium. The results of the analysis showed that the overall prediction accuracies of models with SVM and KNN using six inputs, all exceeded 90%. Particularly, the models based on SVM and KNN that used six or three xenon isotope activity ratios with three classification categories, namely reactor, plutonium bomb, and uranium bomb, had accuracy levels greater than 88%. The prediction performances demonstrate the applicability of machine learning algorithms to predict nuclear threat using ratios of xenon isotopic activity.