• Journal of Korean Society for Quality Management
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• v.49 no.1
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• pp.17-29
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• 2021

Purpose: During 2015-2019, the average amount of political donation to the national assembly members in Korea was 1,000 won per person. Despite its benefits such as receiving tax credits, the donation system has not been actively practiced. This paper aims to promote political donations by suggesting a recommendation system of national assembly members by analysing the bills they proposed. Methods: In this paper, we propose a recommendation system based on two aspects: how similar the newly proposed or ammended bills are to the sponsors' interest (similarity index) and how much effort national assembly members put into those bills (intensity index). More than 25,000 bills were used to measure the recommendation quality index consisted with both the similarity and the intensity indices. Word2vec was used to calculate the similarity index of the bills proposed by the national assembly member to the sponsor's interest. The intensity index is calculated by diving the number of newly proposed or entirely revised bills with the number of senators who took part in those bills. Subsequently, we multiply the similarity index by the intensity index to obtain the recommendation quality index that can assist sponsors to identify potential assembly members for their donation. Results: We apply the proposed recommendation system to personas for illustration. The recommendation system showed an average f1 score about 0.69. The analysis results provide insights in recommendation for donation. Conclusion: n this study, the recommendation system was proposed to promote a political donation for national assembly members by creating the recommendation quality index based on the similarity and the intensity indices. We expect that the system presented in this paper will lower user barriers to political information, thereby boosting political sponsorship and increasing political participation.

• Journal of Ginseng Research
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• v.45 no.1
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• pp.191-198
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• 2021

Background: Most clinical studies of immune responses activated by Korean Red Ginseng (KRG) have been conducted exclusively in patients. However, there is still a lack of clinical research on immune-boosting benefits of KRG for healthy persons. This study aims to confirm how KRG boosts the immune system of healthy subjects. Methods: A total of 100 healthy adult subjects were randomly divided into two groups that took either a 2 g KRG tablet or a placebo per day for 8 weeks. The primary efficacy evaluation variables included changes in T cells, B cells, and white blood cells (WBCs) before and after eight weeks of KRG ingestion. Cytokines (TNF-α, INF-γ, IL-2 and IL-4), WBC differential count, and incidence of colds were measured in the secondary efficacy evaluation variables. Safety evaluation variables were used to identify changes in laboratory test results that incorporated adverse reactions, vital signs, hematological tests, blood chemistry tests, and urinalysis. Results: Compared to the placebo group, the KRG intake group showed a significant increase in the number of T cells (CD3) and its subtypes (CD4 and CD8), B cells, and the WBC count before and after eight weeks of the intake. There were no clinically significant adverse reactions or other notable results in the safety evaluation factors observed. Conclusion: This study has proven through its eight-week intake test and subsequent analysis that KRG boosts the immune system through an increase in T cells, B cells, and WBCs, and that it is safe according to the study's safety evaluation.

• The Journal of Pediatrics of Korean Medicine
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• v.35 no.1
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• pp.48-62
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• 2021

Objectives The purpose of this study is to obtain knowledge from clinical studies conducted in China to examine the effectiveness of herb medicine in childhood acute leukemia. Methods We searched the randomized controlled trials (RCTs) with herbal medicine treatment on childhood acute leukemia from the 'CAJ', Chinese Academic Journal from China National Knowledge Infrastructure (CNKI). And then, demographic data, duration of illness, intervention, treatment period, outcome, adverse events, and composition of herbal medicine were analyzed for this study. Result 10 RCT studies were selected and analyzed. The control group were given western medicine therapy, the treatment group was given herbal medicine on the basis of the control group. The most commonly used herbal medicine were 淸熱解毒藥, 補氣藥, 補陰藥, 活血祛瘀藥 in 'boosting vital force and driving out evil spirit (扶正祛邪)' way to 'relieve heat (熱毒)' and 'assist the vital force (正氣)'. In the treatment group, complete remission was significantly higher than control group and the decrease in TCM syndrome scores also showed significant effects. Adverse events were significantly lower in the treatment group. Conclusions Herbal medicine treatment on childhood acute leukemia can be suggested as a new treatment for children who have less response to the conventional therapy, and can supplement the limitations of the western medicine by increasing complete remission and reducing adverse events.

• The Journal of Information Systems
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• v.30 no.4
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• pp.21-42
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• 2021

Purpose In order for companies to continue to grow, they should properly manage human resources, which are the core of corporate competitiveness. Employee turnover means the loss of talent in the workforce. When an employee voluntarily leaves his or her company, it will lose hiring and training cost and lead to the withdrawal of key personnel and new costs to train a new employee. From an employee's viewpoint, moving to another company is also risky because it can be time consuming and costly. Therefore, in order to reduce the social and economic costs caused by employee turnover, it is necessary to accurately predict employee turnover intention, identify the factors affecting employee turnover, and manage them appropriately in the company. Design/methodology/approach Prior studies have mainly used logistic regression and decision trees, which have explanatory power but poor predictive accuracy. In order to develop a more accurate prediction model, XGBoost is proposed as the classification technique. Then, to compensate for the lack of explainability, SHAP, one of the XAI techniques, is applied. As a result, the prediction accuracy of the proposed model is improved compared to the conventional methods such as LOGIT and Decision Trees. By applying SHAP to the proposed model, the factors affecting the overall employee turnover intention as well as a specific sample's turnover intention are identified. Findings Experimental results show that the prediction accuracy of XGBoost is superior to that of logistic regression and decision trees. Using SHAP, we find that jobseeking, annuity, eng_test, comm_temp, seti_dev, seti_money, equl_ablt, and sati_safe significantly affect overall employee turnover intention. In addition, it is confirmed that the factors affecting an individual's turnover intention are more diverse. Our research findings imply that companies should adopt a personalized approach for each employee in order to effectively prevent his or her turnover.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.390-399
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• 2022

Cyber security and resilience are phrases that describe safeguards of ICTs (information and communication technologies) from cyber-attacks or mitigations of cyber event impacts. The sole purpose of Risk models are detections, analyses, and handling by considering all relevant perceptions of risks. The current research effort has resulted in the development of a new paradigm for safeguarding services offered online which can be utilized by both service providers and users. customers. However, rather of relying on detailed studies, this approach emphasizes task selection and execution that leads to successful risk treatment outcomes. Modelling intelligent CSGs (Cyber Security Games) using MLTs (machine learning techniques) was the focus of this research. By limiting mission risk, CSGs maximize ability of systems to operate unhindered in cyber environments. The suggested framework's main components are the Threat and Risk models. These models are tailored to meet the special characteristics of online services as well as the cyberspace environment. A risk management procedure is included in the framework. Risk scores are computed by combining probabilities of successful attacks with findings of impact models that predict cyber catastrophe consequences. To assess successful attacks, models emulating defense against threats can be used in topologies. CSGs consider widespread interconnectivity of cyber systems which forces defending all multi-step attack paths. In contrast, attackers just need one of the paths to succeed. CSGs are game-theoretic methods for identifying defense measures and reducing risks for systems and probe for maximum cyber risks using game formulations (MiniMax). To detect the impacts, the attacker player creates an attack tree for each state of the game using a modified Extreme Gradient Boosting Decision Tree (that sees numerous compromises ahead). Based on the findings, the proposed model has a high level of security for the web sources used in the experiment.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.13-20
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• 2022

It's proposed and analyzed ML(Machine Learning) models to predict vehicle FC(Fuel Consumption) in real-time. The test driving was done for a car to measure vehicle speed, acceleration, road gradient and FC for training dataset. The various ML models were trained with feature data of speed, acceleration and road-gradient for target FC. There are two kind of ML models and one is regression type of linear regression and k-nearest neighbors regression and the other is classification type of k-nearest neighbors classifier, logistic regression, decision tree, random forest and gradient boosting in the study. The prediction accuracy is low in range of 0.5 ~ 0.6 for real-time FC and the classification type is more accurate than the regression ones. The prediction error for total FC has very low value of about 0.2 ~ 2.0% and regression models are more accurate than classification ones. It's for the coefficient of determination (R²) of accuracy score distributing predicted values along mean of targets as the coefficient decreases. Therefore regression models are good for total FC and classification ones are proper for real-time FC prediction.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.55 no.4
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• pp.431-436
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• 2022

For the safety assessment of microbiological and chemical hazards in katsuobushi, fifteen samples of katsuobushi were purchased from supermarkets. The contamination levels of total viable bacteria, coliforms, Escherichia coli, and nine pathogenic bacteria [Staphylococcus aureus, Salmonella spp., Listeria monocytogenes, Bacillus cereus, Vibrio parahaemolyticus, Clostridium perfringens, Enterohemorrhagic E. coli (EHEC), Yersinia enterocolitica and Campylobacter jejuni/coli] were quantitatively or qualitatively assessed. Additionally, the heavy metals (total and methyl mercury) content, radioactivity (¹³¹ I, ¹³⁴ Cs⁺ and ¹³⁷ Cs) were quantitatively assessed. Microbial and chemical analyses were performed using standard methods in Korean food code. The contamination level of total viable bacteria was 2.70 (1.18-4.42) log CFU/g. Coliforms, E. coli and S. aureus were not detected in any samples. Other eight pathogenic bacteria were negative in all samples. The contamination levels of total and methyl mercury were 0.366 (0.227-0.481) and 0.120 (0.002-0.241) mg/kg, respectively. In addition, radioactivity was not detected in any samples. The results will be helpful in revitalizing domestic use and boosting exports of katsuobushi because the microbiological and chemical safety of katsuobushi has been assured. Furthermore, the results may be used as a basis for performing chemical and microbial risk assessments of katsuobushi.

• The korean journal of orthodontics
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• v.53 no.5
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• pp.328-335
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• 2023

Objective: The aim of this study was to determine whether an association between study quality, other study characteristics, and Altmetric Attention Scores (AASs) existed in orthodontic studies. Methods: The Scopus database was searched to identify orthodontic studies published between January 1, 2017, and December 31, 2019. Articles that satisfied the eligibility criteria were included in this study. Study characteristics, including study quality were extracted and entered into a pre-pilot data collection sheet. Descriptive statistics were calculated. On an exploratory basis, random forest and gradient boosting machine learning algorithms were used to examine the influence of article characteristics on AAS. Results: In total, 586 studies with an AAS were analyzed. Overall, the mean AAS of the samples was 5. Twitter was the most popular social media platform for publicizing studies, accounting for 53.7%. In terms of study quality, only 19.1% of the studies were rated as having a high level of quality, with 41.8% of the studies deemed moderate quality. The type of social media platform, number of citations, impact factor, and study type were among the most influential characteristics of AAS in both models. In contrast, study quality was one of the least influential characteristics on the AAS. Conclusions: Social media platforms contributed the most to the AAS for orthodontic studies, whereas study quality had little impact on the AAS.

• Journal of Preventive Medicine and Public Health
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• v.56 no.1
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• pp.41-49
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• 2023

Objectives: Rwanda reported a stunting rate of 33% in 2020, decreasing from 38% in 2015; however, stunting remains an issue. Globally, child deaths from malnutrition stand at 45%. The best options for the early detection and treatment of stunting should be made a community policy priority, and health services remain an issue. Hence, this research aimed to develop a model for predicting stunting in Rwandan children. Methods: The Rwanda Demographic and Health Survey 2019-2020 was used as secondary data. Stratified 10-fold cross-validation was used, and different machine learning classifiers were trained to predict stunting status. The prediction models were compared using different metrics, and the best model was chosen. Results: The best model was developed with the gradient boosting classifier algorithm, with a training accuracy of 80.49% based on the performance indicators of several models. Based on a confusion matrix, the test accuracy, sensitivity, specificity, and F1 were calculated, yielding the model's ability to classify stunting cases correctly at 79.33%, identify stunted children accurately at 72.51%, and categorize non-stunted children correctly at 94.49%, with an area under the curve of 0.89. The model found that the mother's height, television, the child's age, province, mother's education, birth weight, and childbirth size were the most important predictors of stunting status. Conclusions: Therefore, machine-learning techniques may be used in Rwanda to construct an accurate model that can detect the early stages of stunting and offer the best predictive attributes to help prevent and control stunting in under five Rwandan children.

• Convergence Security Journal
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• v.23 no.1
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• pp.89-95
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• 2023

In order to help reduce the medical expenses of patients with auto insurance accidents, this study predicted the treatment period, which is the most important factor in the medical expenses of patients in their 40s and 50s, and analyzed the factors affecting the treatment period. To this end, a mechine learning model using five algorithms such as Decision Tree was created, and its performance was compared and analyzed between models. There were three algorithms that showed good performance including Decison Tree, Gradient Boost, and XGBoost. In addition, as a result of analyzing the factors affecting the prediction of the treatment period, the type of hospital, the treatment area, age, and gender were found. Through these studies, easy research methods such as the use of AutoML were presented, and we hope that the results of this study will help policies to reduce medical expenses for automobile insurance accidents.