• Journal of Practical Engineering Education
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• v.14 no.1
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• pp.165-177
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• 2022

The purpose of this study is to present the direction of employment guidance for long-term service through the analysis of the cause of employment of employed students who graduated from specialized high school. In particular, the purpose is to present student guidance plans for long-term service by analyzing personal reasons for students graduating from commercial high schools and policy factors for individual, school, company, and government service after employment. To this end, a survey was conducted for graduates of commercial high schools nationwide, and the validity, reliability, and causality of the survey data were analyzed by applying Exploratory Factor Analysis, Cronbach's Alpha, and decision tree analysis techniques. We found that personal goal setting for employment is an important factor for working for more than 1 year, personal relationships at work and personal characteristics are important factors for working for more than 3 years. In addition, we found that the reason for getting a job is that personal reasons and school recommendations are great, special lectures on employment, camps, and 'advice from seniors and teachers' programs are helpful in finding a job, and accounting and computer related subjects are helpful for long-term employment. Accordingly, in specialized high schools, it is required to prepare specific instructional measures for education such as setting personal goals and the formation of human relationships that are the basis of social life, and to actively operate the above subjects and programs to help with employment and longevity.

• Journal of Service Research and Studies
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• v.13 no.3
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• pp.70-81
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• 2023

The choice of business method is a necessary decision at the last moment of life, and to this end, we use several criteria. Our funeral methods were dominated by ancestral worship culture and religion, not nature. It is only recently that nature was used as a means from a human perspective, but natural field methods such as consideration for nature and symbiosis with nature have emerged. The recent high public preference for natural fields is today's strong zeitgeist and nature-friendly values. Based on statistics in 2021, Korea's national cremation rate exceeded 92%, and compared to less than 20% of the cremation rate just 20 years ago, our business method has changed rapidly. As the cremation promotion movement and government policies, which began in the early 90s, were systematically developed, the enshrinement facility was established next to us. However, while this was also subject to criticism of national damage, the Jang Act called natural field was introduced into the system in 2008, and about 15 years have passed, but the revitalization of natural field is slower than expected. One of the reasons for the stagnation of development is to forget the basic spirit of the natural field (once you return to the forest), and to think like a graveyard grave. Accordingly, this study aims to identify the background of the introduction and current operation of natural fields and present development measures to improve memorial services to make natural fields loved by the people.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1283-1293
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• 2022

The purpose of this study is to predict residual chlorine in order to maintain stable residual chlorine concentration in sedimentation basin by using artificial intelligence algorithms in water treatment process employing pre-chlorination. Available water quantity and quality data are collected and analyzed statistically to apply into mathematical multiple regression and artificial intelligence models including multi-layer perceptron neural network, random forest, long short term memory (LSTM) algorithms. Water temperature, turbidity, pH, conductivity, flow rate, alkalinity and pre-chlorination dosage data are used as the input parameters to develop prediction models. As results, it is presented that the random forest algorithm shows the most moderate prediction result among four cases, which are long short term memory, multi-layer perceptron, multiple regression including random forest. Especially, it is result that the multiple regression model can not represent the residual chlorine with the input parameters which varies independently with seasonal change, numerical scale and dimension difference between quantity and quality. For this reason, random forest model is more appropriate for predict water qualities than other algorithms, which is classified into decision tree type algorithm. Also, it is expected that real time prediction by artificial intelligence models can play role of the stable operation of residual chlorine in water treatment plant including pre-chlorination process.

• Design Convergence Study
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• v.15 no.6
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• pp.195-214
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• 2016

Predictive analysis is based on a probabilistic learning algorithm called pattern recognition or machine learning. Therefore, if users want to extract more information from the data, they are required high statistical knowledge. In addition, it is difficult to find out data pattern and characteristics of the data. This study conducted statistical data analyses and visual data analyses to supplement prediction analysis's weakness. Through this study, we could find some implications that haven't been found in the previous studies. First, we could find data pattern when adjust data selection according as splitting criteria for the decision tree method. Second, we could find what type of data included in the final prediction model. We found some implications that haven't been found in the previous studies from the results of statistical and visual analyses. In statistical analysis we found relation among the multivariable and deducted prediction model to predict high box office performance. In visualization analysis we proposed visual analysis method with various interactive functions. Finally through this study we verified final prediction model and suggested analysis method extract variety of information from the data.

• Journal of Korean Library and Information Science Society
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• v.54 no.2
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• pp.23-42
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• 2023

The library tour program is a new type of cultural program that was first introduced and operated by J City, and library tourists travel to specialized libraries in the city according to a set course and experience various experiences. This study aims to build a customized course recommendation model that considers the characteristics of individual participants in addition to the existing fixed group travel format so that more users can enjoy the opportunity to participate in library tours. To this end, the characteristics of library travelers were categorized to establish traveler personas, and library evaluation items and evaluation criteria were established accordingly. We selected 22 libraries targeted by the library travel program and measured library data through actual visits. Based on the collected data, we derived the characteristics of suitable libraries and developed a persona-based library tour course recommendation model using a decision tree algorithm. To demonstrate the feasibility of the proposed recommendation model, we build a mobile application mockup, and conducted user evaluations with actual library users to identify satisfaction and improvements to the developed model.

• Journal of the Korea Society for Simulation
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• v.32 no.4
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• pp.19-26
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• 2023

The increase in the dropout rate of college students nationwide has a serious negative impact on universities and society as well as individual students. In order to proactive identify students at risk of dropout, this study built a decision tree, random forest, logistic regression, and deep learning-based dropout prediction model using academic data that can be easily obtained from each university's academic management system. Their performances were subsequently analyzed and compared. The analysis revealed that while the logistic regression-based prediction model exhibited the highest recall rate, its f-1 value and ROC-AUC (Receiver Operating Characteristic - Area Under the Curve) value were comparatively lower. On the other hand, the random forest-based prediction model demonstrated superior performance across all other metrics except recall value. In addition, in order to assess model performance over distinct prediction periods, we divided these periods into short-term (within one semester), medium-term (within two semesters), and long-term (within three semesters). The results underscored that the long-term prediction yielded the highest predictive efficacy. Through this study, each university is expected to be able to identify students who are expected to be dropped out early, reduce the dropout rate through intensive management, and further contribute to the stabilization of university finances.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.547-560
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• 2023

Recently, research on predicting ground classification using machine learning techniques, TBM excavation data, and ground data is increasing. In this study, a multi-classification prediction study for uniaxial compressive strength (UCS) was conducted by applying random forest model based on a decision tree among machine learning techniques widely used in various fields to machine data and ground data acquired at three slurry shield TBM sites. For the classification prediction, the training and test data were divided into 7:3, and a grid search including 5-fold cross-validation was used to select the optimal parameter. As a result of classification learning for UCS using a random forest, the accuracy of the multi-classification prediction model was found to be high at both 0.983 and 0.982 in the training set and the test set, respectively. However, due to the imbalance in data distribution between classes, the recall was evaluated low in class 4. It is judged that additional research is needed to increase the amount of measured data of UCS acquired in various sites.

• Information Systems Review
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• v.20 no.4
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• pp.79-96
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• 2018

This study started by focusing on the internalization of the technology appraisal model into the credit rating model to increase the discriminative power of the credit rating model not only for SMEs but also for all companies, reflecting the items related to the financial stability of the enterprises among the technology appraisal items. Therefore, it is aimed to verify whether the technology appraisal model can be applied to identify high-stability SMEs in advance. We classified companies into industries (manufacturing vs. non-manufacturing) and the age of company (initial vs. non-initial), and defined as a high-stability company that has achieved an average debt ratio less than 1/2 of the group for three years. The C5.0 was applied to verify the discriminant power of the model. As a result of the analysis, there is a difference in importance according to the type of industry and the age of company at the sub-item level, but in the mid-item level the R&D capability was a key variable for discriminating high-stability SMEs. In the early stage of establishment, the funding capacity (diversification of funding methods, capital structure and capital cost which taking into account profitability) is an important variable in financial stability. However, we concluded that technology development infrastructure, which enables continuous performance as the age of company increase, becomes an important variable affecting financial stability. The classification accuracy of the model according to the age of company and industry is 71~91%, and it is confirmed that it is possible to identify high-stability SMEs by using technology appraisal items.

• Journal of Korean Neurosurgical Society
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• v.67 no.1
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• pp.94-102
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• 2024

Objective : The spontaneous intracerebral hemorrhage (ICH) remains a significant cause of mortality and morbidity throughout the world. The purpose of this retrospective study is to develop multiple models for predicting ICH outcomes using machine learning (ML). Methods : Between January 2014 and October 2021, we included ICH patients identified by computed tomography or magnetic resonance imaging and treated with surgery. At the 6-month check-up, outcomes were assessed using the modified Rankin Scale. In this study, four ML models, including Support Vector Machine (SVM), Decision Tree C5.0, Artificial Neural Network, Logistic Regression were used to build ICH prediction models. In order to evaluate the reliability and the ML models, we calculated the area under the receiver operating characteristic curve (AUC), specificity, sensitivity, accuracy, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR). Results : We identified 71 patients who had favorable outcomes and 156 who had unfavorable outcomes. The results showed that the SVM model achieved the best comprehensive prediction efficiency. For the SVM model, the AUC, accuracy, specificity, sensitivity, PLR, NLR, and DOR were 0.91, 0.92, 0.92, 0.93, 11.63, 0.076, and 153.03, respectively. For the SVM model, we found the importance value of time to operating room (TOR) was higher significantly than other variables. Conclusion : The analysis of clinical reliability showed that the SVM model achieved the best comprehensive prediction efficiency and the importance value of TOR was higher significantly than other variables.

• The Journal of the Korean Institute of Forest Recreation
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• v.22 no.4
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• pp.11-22
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• 2018

This study analyzes the economic feasibility on the transition of production structure to increase income for a local forest village in Laos PDR. The study area was the Nongboua village in Sangthong district where the primary product is rice from rice paddy. Possible strategies were considered to increase the villagers' revenue, and Noni (Morinda citrifolia) was production in the short-term. We assumed that the project period was for 20 years for the analysis, and a total of 1,100 Noni tree was planted in 1 ha by $3m{\times}3m$ spacing. This study classified basic scenario one, scenario two, scenario three by the survival rate and purchase pirce of Noni. Generally Noni grows well. However, the seedlings' average survival rate (= production volume) was set up conservatively in this study to consider potential risks such as no production experience of Noni and tree disease. The scenario one assumed that the survival rate of Noni seedlings was 50% for 0-1 years, 60% for 0-2 years, and 70% for 3-20 years; the scenario two, 10% less, i.e., 40%, 50%, and 60%; and the scenario three, 10% less, i.e., 40%, 50%, 60% and purchase price 10% less, i.e., $0.29 to $0.26, respectively. Our analysis showed that all 3 scenarios resulted in economically-feasible IRR (internal rate of return) of 24.81%, 19.02%, and 16.30% of with a discounting rate of 10%. The B/C (benefit/cost) ratio for a unit area (1ha) was also analyzed for the three scenarios with a discounting rate of 10%, resutling in the B/C ratio of 1.71, 1.47, and 1.31. The study results showed that the Nongboua village would have a good opportunity to improve its low-income structure through planting and managing alternative crops such as Noni. Also the results can be used as useful decision-making information at a preliminary analysis level for planning other government and public investment projects for the Nonboua village.