• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.741-747
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• 2022

Fatigue lowers an individual's ability and makes it difficult to perform work. As fatigue accumulates, concentration decreases and thus the possibility of causing a safety accident increases. Awareness of fatigue is subjective, but it is necessary to quantitatively measure the level of fatigue in the actual field. In previous studies, it was proposed to measure the level of fatigue by expert judgment by adding objective indicators such as bio-signal analysis to subjective evaluations such as multidisciplinary fatigue scales. However this method is difficult to evaluate fatigue in real time in daily life. This paper is a study on the fatigue classification model that determines the fatigue level of workers in real time using speech data recorded in the field. Machine learning models such as logistic classification, support vector machine, and random forest are trained using speech data collected in the field. The performance evaluation showed good performance with accuracy of 0.677 to 0.758, of which logistic classification showed the best performance. From the experimental results, it can be seen that it is possible to classify the fatigue level using speech signals.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.249-251
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• 2022

In this paper, we present machine learning to control drone flight using EEG signals. We defined takeoff, forward, backward, left movement and right movement as control targets and measured EEG signals from the frontal lobe for controlling using Fp1. Fp2 Fp2 two-channel dry electrode (NeuroNicle FX2) measuring at 250Hz sampling rate. And the collected data were filtered at 6~20Hz cutoff frequency. We measured the motion image of the action associated with each control target open for 5.19 seconds. Using Matlab's classification learner for the measured EEG signal, the triple layer neural network, logistic regression kernel, nonlinear polynomial Support Vector Machine(SVM) learning was performed, logistic regression kernel was confirmed as the highest accuracy for takeoff and forward, backward, left movement and right movement of the drone in learning by class True Positive Rate(TPR).

• Child Health Nursing Research
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• v.29 no.4
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• pp.271-279
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• 2023

Purpose: Adolescent self-harm is a public health problem. Research suggests a link between adverse childhood experiences (ACEs) and self-destructive behaviors. Few studies, however, have examined the effects of ACEs on self-harm among Asian adolescents. This study explored the association between lifetime ACEs and a history of self-harm among Korean children and adolescents in elementary, middle, and high schools. Methods: A cross-sectional, retrospective medical record review was conducted on a dataset of a national psychiatrist advisory service for school counselors who participated in the Wee Doctor Service from January 1 to December 31, 2020. The data were analyzed using multiple logistic regression to predict self-harm. Results: Student cases (n=171) were referred to psychiatrists by school counselors for remote consultation. Multiple logistic regression analyses revealed that the odds of self-harm were higher among high school students (adjusted odds ratio [aOR]=4.97; 95% confidence interval [CI]=1.94-12.76), those with two or more ACEs (aOR=3.27; 95% CI=1.43-7.47), and those with depression (aOR=3.06; 95% CI=1.32-7.10). Conclusion: The study's findings provide compelling evidence that exposure to ACEs can increase vulnerability to self-harm among Korean students. Students with a history of ACEs and depression, as well as high school students, require increased attention during counseling. School counselors can benefit from incorporating screening assessment tools that include questions related to ACEs and depression. Establishing a systematic referral system to connect students with experts can enhance the likelihood of identifying self-harm tendencies and offering the essential support to prevent self-harm.

• The Korean Journal of Food And Nutrition
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• v.37 no.3
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• pp.162-170
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• 2024

The domestic swine industry is currently facing a threat due to the recent increase in pork imports. This study aims to determine what factors influence consumers' intention to consume imported pork and suggest measures to support the domestic pork industry. To achieve this, we analyzed data from the Korea Rural Economic Institute's Food Consumption Behavior Survey using a binary logistic regression model. The results revealed that a higher intention to consume imported pork is linked to a higher intention to consume imported rice, purchasing meat online, frequent purchases of HMR, and procuring U.S. beef, especially among urban residents. On the other hand, a lower intention to consume imported pork is associated with a higher awareness of animal welfare certification, frequently dining out, and older age. Based on these findings, we propose the following response measures for the domestic swine industry: implementing educational programs, marketing, and advertising specifically targeting urban residents to improve their perception of domestic agricultural products; enhancing price competitiveness through distribution optimization; and developing policies to promote the use of domestic pork as an ingredient in processed foods.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.169-185
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• 2024

Due to levitate and rapid growth of E-Commerce, most of the organizations are moving towards cashless transaction Unfortunately, the cashless transactions are not only used by legitimate users but also it is used by illegitimate users and which results in trouncing of billions of dollars each year worldwide. Fraud prevention and Fraud Detection are two methods used by the financial institutions to protect against these frauds. Fraud prevention systems (FPSs) are not sufficient enough to provide fully security to the E-Commerce systems. However, with the combined effect of Fraud Detection Systems (FDS) and FPS might protect the frauds. However, there still exist so many issues and challenges that degrade the performances of FDSs, such as overlapping of data, noisy data, misclassification of data, etc. This paper presents a comprehensive survey on financial fraud detection system using such data mining techniques. Over seventy research papers have been reviewed, mainly within the period 2002-2015, were analyzed in this study. The data mining approaches employed in this research includes Neural Network, Logistic Regression, Bayesian Belief Network, Support Vector Machine (SVM), Self Organizing Map(SOM), K-Nearest Neighbor(K-NN), Random Forest and Genetic Algorithm. The algorithms that have achieved high success rate in detecting credit card fraud are Logistic Regression (99.2%), SVM (99.6%) and Random Forests (99.6%). But, the most suitable approach is SOM because it has achieved perfect accuracy of 100%. But the algorithms implemented for financial statement fraud have shown a large difference in accuracy from CDA at 71.4% to a probabilistic neural network with 98.1%. In this paper, we have identified the research gap and specified the performance achieved by different algorithms based on parameters like, accuracy, sensitivity and specificity. Some of the key issues and challenges associated with the FDS have also been identified.

• Safety and Health at Work
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• v.15 no.3
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• pp.352-359
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• 2024

Background: Organizational downsizing may be significantly linked to depressive symptoms, yet research on this impact in Asian contexts is limited. This study investigates the association between downsizing during the COVID-19 pandemic and depressive symptoms across diverse employment statuses. Methods: This study used the data from 6th Korean Working Conditions Survey. Depressive symptoms were measured using WHO-5 well-being index with a cut-off of 50. Downsizing was defined as decrease in the number of employees during last three years. Multivariable logistic regression adjusted for socio-demographic and occupational factors was used to estimate the adjusted odds ratio (OR) and 95% confidence interval (CI) for depressive symptoms associated with downsizing, including subgroup analyses. Results: Among 26,247 Korean workers (mean age: 43.4, men: 47.5%), the prevalence of depressive symptoms was 29.5% (n = 7,751), and the proportion of downsizing was 15.2% (n = 3,978). The prevalence of depressive symptoms was significantly higher among the downsizing group (36.7%, n = 1,460) than among the no-downsizing group (28.3%, n = 6,291). The result of logistic regression revealed a significant association between downsizing and depressive symptoms (adjusted OR [95% CI]: 1.39 [1.29-1.50]), particularly pronounced among high socioeconomic status workers. Conclusion: This study underscores the significant association between depressive symptoms and organizational downsizing, especially high vulnerability of socioeconomically advantaged and stable workers. These findings highlight the necessity for targeted mental health support and further longitudinal research to clarify the relationship between employment changes and mental health within the Korean workforce.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.1
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• pp.139-149
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• 2019

The purpose of this study is to contribute to the minimization of the social cost due to the insolvency by improving the success rate of the startups by providing useful information to the founders and the start-up support institutions through analysis of non-financial information affecting the failure of the startups. This study is aimed at entrepreneurs. The entrepreneurs that are defined by the credit guarantee institutions generally refer to entrepreneurs within 5 years of establishment. The data used in the study are sampled from the companies that were supported by the start-up guarantee from January 2014 to December 2013 as the end of December 2017. The total number of sampled firms is 2,826, 2,267 companies (80.2%), and 559 non-performing companies (19.8%). The non-financial information of the entrepreneur was divided into the entrepreneur characteristics information, the entrepreneur characteristics information, the entrepreneur asset information and the entrepreneur 's credit information, and cross-tabulations and logistic regression analysis were conducted. As a result of cross-tabulations, univariate analysis showed that personal credit rating, presence in the industry, presence of residential housing, presence of employees, and presence of financial statements were selected as significant variables. As a result of the logistic regression analysis, three variables such as personal credit rating, occupation in the industry, and presence of residential house were found to be important factors affecting the failure of founding companies. This result shows the importance of entrepreneur 's personal credibility and experience and entrepreneur' s assets in business management. The start-up support institutions should reflect these results in the entrepreneur 's credit evaluation system, and the entrepreneurs need training on the importance of the personal credit and the management plan in the entrepreneurial education. The results of this analysis will contribute to the minimization of the incapacity of startups by providing useful non-financial information to founders and start-up support organizations.

• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.375-385
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• 2009

For 2007 Korea presidential election, most polls by telephone surveys indicated Lee Myung-Bak led the second runner-up Jung Dong-Young by certain margin. The margin between two candidates can be estimated accurately by averaging individual poll results, provided there exists no systematic bias in telephone surveys. Most Korean telephone surveys via telephone directory are based on quota samples, with the region, the gender and the age-band as quota variables. Thus the surveys may result in certain systematic bias due to unbalanced factors inherent in quota sampling. The aim of this study is to answer the following questions by the analytic methods adopted in Huh et al. (2004): Question 1. Wasn't there systematic bias in estimates of support rates. Question 2. If yes, what was the source of the bias? To answer the questions, we collected eighteen surveys administered during the election campaign period and applied the iterated proportional weighting (the rim weighting) to the last eleven surveys to obtain the balance in five factors - region, gender, age, occupation and education level. We found that the support rate of Lee Myung-Bak was over-estimated consistently by 1.4%P and that of Jung Dong-Young was underestimated by 0.6%P, resulting in the over-estimation of the margin by 2.0%P. By investigating the Lee Myung-Bak bias with logistic regression models, we conclude that it originated from the under-representation of less educated class and/or the over-representation of house wives in telephone samples.

• Journal of Digital Contents Society
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• v.16 no.4
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• pp.595-604
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• 2015

This study aimed to analyze service internship participation determinant contents of undergraduate students who major in airline service to influence their career. To accomplish this purpose, data collected from 211 students were analysed using logistic regression analysis. Personal characteristic, big 5character factors, social support were analysed as service internship determinant contents. The research results showed grade, GPA, broad experience, flight experience among personal characteristics and extraversion among big 5factors personality as significant service internship determinant contents of students. Also, the research results showed career preparation, career decision making self-efficacy and social support as non-significant service internship determinants of students. It was concluded that active participation with service internship is imperative for undergraduate students in airline service major to enhance career competence upon graduation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.209-214
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• 2017

The purpose of this study is to identify the factors that led greenhouse grape farms to delay their heating periods after the coming into force of the Korea-Chile Free Trade Agreement (FTA). Panel data on the cropping (system) changes from 2004 through 2016 were used for the analysis. According to the panel logistic model, the estimated coefficient of the cultivation area was 0.0002, which was statistically significant at the 10% significance level, the estimated coefficient of grape imports was 1.4258, which was statistically significant at the 1% significance level, and the estimated coefficient of the regional dummy was 0.808, which was statistically significant at the 5% significance level. The results indicated that the use of wider cultivation areas, increase in grape imports, and colder climate(in the mid-northern part of Korea) increased the likelihood of delayed heating. The Korean government is offering direct payment programs and business closure support to the greenhouse grape farmers. While these actions can relieve the damage caused by the increase in grape imports, they will not provide the ultimate solution. Various support measures are needed, such as renewing the varieties to meet the changing demand of grape consumers, providing agricultural materials to reduce the heating expenses, and modernizing greenhouse facilities to improve the energy efficiency and reduce the costs.