• The Journal of the Korea Contents Association
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• v.15 no.12
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• pp.407-420
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• 2015

This study analyzes the effect of CoP(Community of Practice) influence factors on satisfaction and learning culture activation in R&D groups. Research model and hypothesis is designed the relationship the effect factors for CoP which are consist of personal factor, interacting factor, support factor and environmental factor and satisfaction and the learning culture activation focused on comparing between maturity and immaturity CoP member group. It conducted an analysis based on 371 survey responses significantly. Hence, interacting, supporting and personal factor have a significant positive effect on satisfaction but environmental factor was negative effect to it. CoP Satisfaction has a positive effect on the learning culture activation. However average between two groups has not a statistically significant difference in all of the factors. At the result, interacting between members is the most important factor to the successful CoP development of R&D groups.

• Korean Journal of Community Nutrition
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• v.4 no.4
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• pp.504-511
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• 1999

The purpose of this survey was to investigate the relationship among obese children, dietary intake and environmental factors. Therefore, this survey compared obese children with normal children on dietary intake and environmental factors. The survey were 110 obese children and 110 normal children whose age, height, and sex were same as the obese children of 21 elementary school in Inchon. The statistical analysis of data was completed using SAS program. The results were summarized as follows : 1) The student's obesity was related to parent's obesity and number of their siblings. There were significant differences between obese groups and normal group for these two factors. 2) Meal time of the obese group showed more irregularity than that of the normal group. Otherwise, the normal group were more "piclity" about special food than the obese group(p<0.05). Also obese children showed unconsious eating while reading or watching TV(p<0.01). The normal group attended physical education class more eagerily the obese group(p<0.001). There was no significant difference between obese children and normal children for learning habits. 3) Obese children shoed higher intake of nutrients compared to normal children. Among all the nutrients, minerals and vitamins showed significant differences. Therefore, further study on obese children and their intake of minerals and vitamin is needed. Also, in order to prevent factors which influence obesity, nutrition education at home as well as school was needed.as needed.

• Hwankyungkyoyuk
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• v.22 no.1
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• pp.1-11
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• 2009

Environmental Literacy is needed to establish a sustainable society and can be very well developed through Outdoor Environmental Education(OEE). However, establishing OEE in Korea is a very difficult. Thus, it is very important to find out which factors influence the sustainability of OEE. The purpose of this study is to identify those factors. Participatory observation research and some interviews were used in "The Whale Class" of the University of Georgia in the United States. Major findings and recommendations were as follows: (1) OEEs give participants good experiences about the environment; (2) Program operators of OEEs are enthusiastic about education and environmental conservation; (3) Good educational practices such as cooperative education and participation in conversation foster learning; (4) Good organizations with guest lectures from various environmental fields would be beneficial; (5) Public information about environmental programs would be helpful; (6) Administrative support for those organizations connected to environmental programs would be useful; and (7) OEE provide reflection activities to foster Environmental Literacy.

• Journal of Environmental Impact Assessment
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• v.33 no.2
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• pp.74-83
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• 2024

This study aims to estimate roadkill occurrences and investigate influential factors in Chungcheongnam-do, contributing to the establishment of roadkill prevention measures. By comprehensively considering weather, road, and environmental information, machine learning was utilized to estimate roadkill incidents and analyze the importance of each variable, deriving primary influencing factors. The Gradient Boosting Machine (GBM) exhibited the best performance, achieving an accuracy of 92.0%, a recall of 84.6%, an F1-score of 89.2%, and an AUC of 0.907. The key factors affecting roadkill included average local atmospheric pressure (hPa), average ground temperature (℃), month, average dew point temperature (℃), presence of median barriers, and average wind speed (m/s). These findings are anticipated to contribute to roadkill prevention strategies and enhance traffic safety, playing a crucial role in maintaining a balance between ecosystems and road development.

• Journal of Digital Convergence
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• v.15 no.12
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• pp.35-45
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• 2017

This study aims to explore the effect of quality factors and learning styles on users' satisfaction in e-Learning context. For this purpose, statistical methods such as reliability test, factor analysis, ANOVA, regression analysis were carried out using the survey data from university students. The quality factors of e-Learning were classified into contents, system, service, and interpersonal activities while learning styles were classified into positive-cooperative, self-directed, environmental-dependent, and passive styles. The results showed that each quality factors of e-Learning has a strong positive effect on user satisfaction, and self-directed group has higher satisfaction than other groups. Learning styles have moderating effects on the quality-satisfaction relationship, and especially, the group of passive learning style has a strong moderating effect on the interpersonal activities. Theoretical and practical implications and future research directions are drawn from these findings.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.151-152
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• 2021

The sale price of apartment buildings is a key factor in the success or failure of apartment projects, and the factors that affect the sale price of apartments vary widely, including location, environmental factors, and economic conditions. Existing methods of predicting the sale price do not reflect the nonlinear characteristics of apartment prices, which are determined by the complex impact factors of reality, because statistical analysis is conducted under the assumption of a linear model. To improve these problems, a new analysis technique is needed to predict apartment sales prices by complex nonlinear influencing factors. Using machine learning techniques that have recently attracted attention in the field of engineering, it is possible to predict the sale price reflecting the complexity of various factors. Therefore, this study aims to conduct a basic study for the development of a machine learning-based prediction model for apartment sale prices.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.89-104
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• 2024

Cloud-based e-learning (CBEL) represents a promising technological frontier. Existing literature has presented a diverse array of findings regarding the determinants that influence the adoption of CBEL. The primary objective of this study is to conduct an exhaustive examination of the available literature, aiming to determine the key predictors of CBEL utilization by employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. A comprehensive review of 35 articles was undertaken, shedding light on the status of CBEL as an evolving field. Notably, there has been a discernible downturn in related research output during the COVID-19 pandemic, underscoring the temporal dynamics of this subject. It is noteworthy that a significant portion of this research has emanated from the Asian continent. Furthermore, the dominance of the technology acceptance model (TAM) in research frameworks is affirmed by our findings. Through a rigorous thematic analysis, our study identified five overarching themes, each encompassing a diverse range of sub-themes. These themes encompass 1) technological factors, 2) individual factors, 3) organizational factors, 4) environmental factors, and 5) security factors. This categorization provides a structured framework for understanding the multifaceted nature of CBEL adoption determinants. Our study serves as a compass, guiding future research endeavours in this domain. It underscores the imperative for further investigations utilizing diverse theoretical frameworks, contextual settings, research methodologies, and variables. This call for diversity and expansion in research efforts reflects the dynamic nature of CBEL and the evolving landscape of e-learning technologies.

• Computers and Concrete
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• v.33 no.4
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• pp.409-423
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• 2024

Using an extensive database, a sensitivity analysis across fifteen machine learning (ML) classifiers was conducted to evaluate the impact of various data manipulation techniques, evaluation metrics, and explainability tools. The results of this sensitivity analysis reveal that the examined models can achieve an accuracy ranging from 72-93% in predicting the fire-induced spalling of concrete and denote the light gradient boosting machine, extreme gradient boosting, and random forest algorithms as the best-performing models. Among such models, the six key factors influencing spalling were maximum exposure temperature, heating rate, compressive strength of concrete, moisture content, silica fume content, and the quantity of polypropylene fiber. Our analysis also documents some conflicting results observed with the deep learning model. As such, this study highlights the necessity of selecting suitable models and carefully evaluating the presence of possible outcome biases.

• Journal of Environmental Science International
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• v.30 no.12
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• pp.1053-1065
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• 2021

In this study, the prediction technology of Hydrological Quantitative Precipitation Forecast (HQPF) was improved by optimizing the weather predictors used as input data for machine learning. Results comparison was conducted using bias and Root Mean Square Error (RMSE), which are predictive accuracy verification indicators, based on the heavy rain case on August 21, 2021. By comparing the rainfall simulated using the improved HQPF and the observed accumulated rainfall, it was revealed that all HQPFs (conventional HQPF and improved HQPF 1 and HQPF 2) showed a decrease in rainfall as the lead time increased for the entire grid region. Hence, the difference from the observed rainfall increased. In the accumulated rainfall evaluation due to the reduction of input factors, compared to the existing HQPF, improved HQPF 1 and 2 predicted a larger accumulated rainfall. Furthermore, HQPF 2 used the lowest number of input factors and simulated more accumulated rainfall than that projected by conventional HQPF and HQPF 1. By improving the performance of conventional machine learning despite using lesser variables, the preprocessing period and model execution time can be reduced, thereby contributing to model optimization. As an additional advanced method of HQPF 1 and 2 mentioned above, a simulated analysis of the Local ENsemble prediction System (LENS) ensemble member and low pressure, one of the observed meteorological factors, was analyzed. Based on the results of this study, if we select for the positively performing ensemble members based on the heavy rain characteristics of Korea or apply additional weights differently for each ensemble member, the prediction accuracy is expected to increase.