• Journal of Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.11-24
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• 2022

For the questions asked for LMS-based online evaluation the professor directly set exam questions, or use the automatic question-taking method according to the level of difficulty using the question bank divided by category. Among them, it is important to manage the difficulty of questions in an objective and efficient way, above all, in the automatic question-taking method according to difficulty. Because the questions presented to the evaluators may be different. In this paper, we propose an difficulty re-adjustment algorithm that considers not only the correct rate of a problem but also the time taken to solve the problem. For this, a logistic regression classification algorithm was used of machine learning, and a reference threshold was set based on the predicted probability value of the learning model and used to readjust the difficulty of each item. As a result, it was confirmed that there were many changes in the difficulty of each item that depended only on the existing correct rate. Also, as a result of performing group evaluation using the adjustment difficulty problem, it was confirmed that the average score improved in most groups compared to the difficulty problem based on the percentage of correct answers.

• Journal of Marine Life Science
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• v.7 no.1
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• pp.37-44
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• 2022

This study was performed to obtain information on the sex ratio, size at sexual group maturity, and main spawning period of Glyptocephalus stelleri. The sex ratio (female: male) was 1:0.54 (n=189:103, 64.7% female), and the frequency of females in the population tended to increase with total length. The oocyte development pattern was group synchronous development, in which oocyte groups at different stages were identified within the same ovary. The total length at 50% sexual group maturity was analyzed using a logistic regression model, and was determined to be 28.51 (female) and 30.49 cm (male). The gonadosomatic index (GSI) displayed the highest values in April (female) and March (male), and the main spawning period being in April to May.

• 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.

• 한국노년학
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• v.31 no.1
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• pp.171-188
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• 2011

The purpose of this study is to examine the factors associated with the probability and levels of the health service utilization among the disabled elderly in Korea. The sample includes 2,111 people older than 65 who are extracted from the 2008 National Survey on People with Disabilities. More than half (54.3%) of the sample experienced at least 1 outpatient physician visit within two weeks and 26.7% were hospitalized within a year. The key factors associated with the outpatient visits were health insurance status, the existence of chronic disease, self-rated health, the Activities of Daily Living (ADLs), as well as renal impairment. Similarly, the utilization of inpatient care was related to health insurance status along with the existence of the internal organ disabilities such as cardiac or respiratory disorders. The study implies the need for the health care policies regarding the prevention of chronic diseases, dependency for daily activities of the elderly, and a management system that specifically targets those with internal organ disabilities. Moreover, the study suggests that financial supports for the low-income group would be helpful to increase their access to health service utilization.

• Journal of The Geomorphological Association of Korea
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• v.27 no.2
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• pp.1-17
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• 2020

This study evaluated a debris landform distribution potential area map in the southwest region of the Korean peninsula. A GIS spatial integration technique and logistic regression method were used to produce a distribution potential area map. Seven topographic and environmental factors were considered for analysis and 28 different data set were combined and used to get most effective results. Moreover, in an accuracy assessment, the extracted results of the Distribution Potential area were evaluated by conducting a cross-validation module. Block stream showed the highest accuracy in the combination No. 6, and that DEM (digital elevation model) and TWI (topographic wetness index) have relatively high influences on the production of the Block stream Distribution Potential area map. Talus showed the highest accuracy in the combination No. 13. We also found that slope, TWI and geology have relatively high influences on the production of the Talus Distribution Potential area map. In addition, fieldwork confirmed the accuracy of the input data that were used in this study, and the slope and geology were also similar. It was also determined that these input data were relatively accurate. In the case of angularity, the block stream was composed of sub-rounded and sub-angular systems and Talus showed differences according to the terrain formation. Although the results of the rebound strain measurement using a Schmidt's hammer did not shown any difference in topographic conditions, it is determined that the rebound strain results reflected the underlying geological setting.

• Journal of The Geomorphological Association of Korea
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• v.27 no.2
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• pp.65-80
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• 2020

This paper deals with risk assessment of life in a landslide-prone area by a GIS-based modeling method. Landslide susceptibility maps can provide a probability of landslide prone areas to mitigate or proper control this problems and to take any development plan and disaster management. A landslide inventory map of the study area was prepared based on past historical information and aerial photography analysis. A total of 550 landslides have been counted at the whole study area. The extracted landslides were randomly selected and divided into two different groups, 50% of the landslides were used for model calibration and the other were used for validation purpose. Eleven causative factors (continuous and thematic) such as slope, aspect, curvature, topographic wetness index, elevation, forest type, forest crown density, geology, land-use, soil drainage, and soil texture were used in hazard analysis. The correlation between landslides and these factors, pixels were divided into several classes and frequency ratio was also extracted. Eventually, a landslide susceptibility map was constructed using a logistic regression model based on entire events. Moreover, the landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract a success rate curve. Based on the results, logistic regression produced an 85.18% accuracy, so we believed that the model was reliable and acceptable for the landslide susceptibility analysis on the study area. In addition, for risk assessment, vulnerability scale were added for social thematic data layer. The study area predictive landslide affected pixels 2,000 and 5,000 were also calculated for making a probability table. In final calculation, the 2,000 predictive landslide affected pixels were assumed to run. The total population causalities were estimated as 7.75 person that was relatively close to the actual number published in Korean Annual Disaster Report, 2006.

• 한국벤처창업학회:학술대회논문집
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• 2023.04a
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• pp.93-99
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• 2023

경기 침체와 더불어 고용 불안, 그에 따른 사회의 혼란 속에서 국가의 성장 동력의 대안 중 하나로 창업 활성화의 요구가 높아지고 있다. 우리나라를 비롯한 많은 국가에서 창업 활성화를 중장기 목표로 설정하고 다방면으로 노력하고 있다. 이에 따라 창업의도를 높일 수 있는 요인에 대한 연구가 진행되어 왔고, 특히 자기효능감과 창업기회인식 등의 개인적 역량 요소가 창업의도를 높인다는 연구 결과들이 지속적으로 제시되고 있다. 이러한 창업의도를 높일 수 있는 자기효능감, 창업기회인식을 고취시키기 위해 학계의 연구활동 뿐 아니라 정부의 정책적 접근 또한 활발하게 이루어지고 있다. 창업교육 활성화부터 사회적 환경 조성을 위한 창업 롤모델 활용, 미디어를 통한 창업 활동 홍보 등 긍정적인 창업 경험을 공유하도록 하기 위한 연구 역시 계속되고 있다. 그러나 개인적 역량 요소와 사회적 환경 조성 외에 문화적 특성이 창업의도에 영향을 미치는지에 대한 연구는 부족하였다. 본 연구는 문화적 특성이 창업의도를 높일 수 있는 요인으로 개인적 역량 요소와 사회적 환경 조성과 함께 의미가 있을 것이라는 물음에서 시작하였다. 가설 검증을 위하여 SPSS 26버전을 활용하여 로지스틱 회귀분석 하였고 GEM KOREA의 2017년 데이터를 분석하였으며, 자기효능감과 창업기회인식은 창업의도에 긍정적인 영향을 미친다는 기존 연구 동일한 결과가 나왔다. 본 연구의 특징은 문화적 특성을 집단주의와 관계주의로 구분하여 자기효능감, 창업기회인식과 창업의도에 영향을 미치는 과정에서의 조절효과를 검증하였다는 것이다. 문화적 특성 중 집단주의 특성은 유의하지 않았으나 관계주의 특성이 유의하여 조절효과를 가진다는 결과를 얻어냈다. 이는 국가에 새로운 성장 동력이 필요한 상황에서 창업의도가 없거나 낮은 개인들도 관계주의 특성을 활용하여 창업의도를 높일 수 있다는 연구 결론으로 이어진다. 지금까지 알려진 바와 달리 한국은 집단주의 보다 관계주의가 강하기 때문에 관계주의 문화 특성을 고려하여 선배 창업가 또는 로컬 창업가들과의 관계를 만들고 유지할 수 있도록 하는 등의 정책을 수립할 필요가 있다는 것을 시사한다. 하지만 이미 설계된 GEM 데이터를 활용하였다는 점, 문화적 특성이 각기 다른 국가들과의 비교연구가 필요하다는 점 등은 본 연구의 한계라고 할 수 있다.

• Journal of Practical Engineering Education
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• v.16 no.2
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• pp.203-214
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• 2024

The purpose of this study was to identify the factors influencing vocational training participation among individuals aged 65 and older in order to enhance their employability. According to the research findings, the educational background and economic activity status of the elderly significantly impact their participation in vocational training. It was confirmed that economic activity and vocational training are closely related to the capacity development and increased employability of the elderly. Moreover, a considerable number of elderly individuals express a continued desire to work, and this group tends to participate more in vocational training. This underscores the importance of promoting vocational training among the elderly and developing suitable models, which holds significant policy implications. Logistic regression analysis revealed that gender, education, economic activity, desire to work, and pension income affect participation in vocational training. This highlights the necessity of formulating specific strategies in government support policies, particularly for those with lower educational backgrounds. Additionally, the study emphasizes the importance of approaches that encourage vocational training participation, especially among those with lower pension income.

• Information Systems Review
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• v.21 no.2
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• pp.69-89
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• 2019

Recently, consumers are increasingly interested in the sharing economy, which utilizes various resources by sharing unused or under-used products/services with others. This study focuses on Airbnb, a representative sharing economy platform, to identify the success factors of the sharing accommodation services. The key properties of sharing accommodation services are extensively surveyed from extant literature and are classified them into the three important factors (economic, convenience, and trust) that influence the success of room-sharing services. The research data include 1,673 Airbnb hosts who offered accommodations in New York City, USA, in June 2018. The research variables of economic-, convenience-, and trust-related factors are utilized in the empirical analyses. The results of this study show that the number of available facilities, flexibility of refunds, the response rate and time to customer requests, and the status of Super host are positively associated with guest satisfaction from sharing accommodation services. This study bears significant managerial implications by suggesting a set of practical guidelines to participants in sharing accommodation services.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.127-137
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• 2019

Recidivism prediction has been a subject of constant research by experts since the early 1970s. But it has become more important as committed crimes by recidivist steadily increase. Especially, in the 1990s, after the US and Canada adopted the 'Recidivism Risk Assessment Report' as a decisive criterion during trial and parole screening, research on recidivism prediction became more active. And in the same period, empirical studies on 'Recidivism Factors' were started even at Korea. Even though most recidivism prediction studies have so far focused on factors of recidivism or the accuracy of recidivism prediction, it is important to minimize the prediction misclassification cost, because recidivism prediction has an asymmetric error cost structure. In general, the cost of misrecognizing people who do not cause recidivism to cause recidivism is lower than the cost of incorrectly classifying people who would cause recidivism. Because the former increases only the additional monitoring costs, while the latter increases the amount of social, and economic costs. Therefore, in this paper, we propose an XGBoost(eXtream Gradient Boosting; XGB) based recidivism prediction model considering asymmetric error cost. In the first step of the model, XGB, being recognized as high performance ensemble method in the field of data mining, was applied. And the results of XGB were compared with various prediction models such as LOGIT(logistic regression analysis), DT(decision trees), ANN(artificial neural networks), and SVM(support vector machines). In the next step, the threshold is optimized to minimize the total misclassification cost, which is the weighted average of FNE(False Negative Error) and FPE(False Positive Error). To verify the usefulness of the model, the model was applied to a real recidivism prediction dataset. As a result, it was confirmed that the XGB model not only showed better prediction accuracy than other prediction models but also reduced the cost of misclassification most effectively.