• The Journal of Bigdata
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• v.5 no.2
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• pp.231-240
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• 2020

With the development in the IT industry and the growth in the game industry, user's game data is recorded in seconds according to various plays and options, and a vast amount of game data can be analyzed based on Bigdata. Combined with business, Bigdata is used to discover new values for profit creation in various fields, but it is utilized in the game industry in insufficient ways. In this study, considering the characteristics of the subdivided lines, we constructed a win-loss prediction model for each line using the game data of League of Legends, and derived the importance of variables. This study can contribute to planning of strategies for general game users to get information about team members in advance and increase the win rate by using the record search sites.

• Herbal Formula Science
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• v.30 no.4
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• pp.293-304
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• 2022

Objectives : Sagunja-Tang is a famous prescription used in Korean medicine for the purpose of promoting vital energy, and there are few studies using Sagunja-Tang on cerebrovascular diseases yet. As previous studies confirmed that Sagunja-tang is highly likely to be used effectively for stroke, this study was intended to predict the mechanism through which Sagunja-tang would act effectively on stroke. Methods : In this study, a network pharmacology analysis method was used, and oral bioavailability (OB), drug likeness (DL), Caco-2 and BBB permeability were utilized to select compounds with potential activity. For the values of each variable used in this study, OB ≥ 30%, DL ≥ 0.18, Caco-2 ≥ 0, and BBB ≥ 0.3 were applied. Using the above variables, the relations between target genes and diseases that are presumed to be involved in the selected bioavailable compounds were constructed in a network format, and proteins thought to play a major role were identified. Results : Among the compounds included in Sagunja-Tang, 26 bioavailable compounds were selected and it was confirmed that these compounds can be effectively used in cerebrovascular diseases such as Alzheimer's disease and stroke. These compounds are considered to act on proteins related in cell death and growth. The most important mechanism of action was predicted to be apoptosis, and the protein that is thought to play the most key action in this mechanism was caspase-3. Conclusions : In our future study, Sagunja-Tang will be used in an ischemic stroke mouse model, and the mechanism of action will be explored focusing on apoptosis and cell proliferation.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.19-33
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• 2022

With the explosive growth of social media, its abundant text-based data generated by web users has become an important source for data analysis. For example, we often witness online movie reviews from the 'Naver Movie' affecting the general public to decide whether they should watch the movie or not. This study has conducted analysis on the Naver Movie's text-based review data to predict the actual ratings. After examining the distribution of movie ratings, we performed semantics analysis using Korean Natural Language Processing. This research sought to find the best review rating prediction model by comparing machine learning and deep learning models. We also compared various regression and classification models in 2-class and multi-class cases. Lastly we explained the causes of review misclassification related to movie review data characteristics.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.134-138
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• 2023

This study focuses on the analysis of the results of computational fluid dynamics simulations of mist-chemical vapor deposition for the growth of an epitaxial wafer in power semiconductor technology using artificial intelligence techniques. The conventional approach of predicting the uniformity of the deposited layer using computational fluid dynamics and design of experimental takes considerable time. To overcome this, artificial intelligence method, which is widely used for optimization, automation, and prediction in various fields, was utilized to analyze the computational fluid dynamics simulation results. The computational fluid dynamics simulation results were analyzed using a supervised deep neural network model for regression analysis. The predicted results were evaluated quantitatively using Euclidean distance calculations. And the Bayesian optimization was used to derive the optimal condition, which results obtained through deep neural network training showed a discrepancy of approximately 4% when compared to the results obtained through computational fluid dynamics analysis. resulted in an increase of 146.2% compared to the previous computational fluid dynamics simulation results. These results are expected to have practical applications in various fields.

• Journal of Environmental Science International
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• v.17 no.6
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• pp.611-621
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• 2008

This study intends to provide the necessary basic data needed for predicting the water quality and examining changes in water quality on the basis of the hydrological changes: an outflow or the character of a flow by investigating the interaction of the parameters through the estimation of optimal parameters need for predicting the water quality of the dam basin and the sensitivity among those estimated parameters. Im-Ha Dam in the upstream area of the Nakdong River was selected for analysis, and the water quality survey data necessary for parameter estimation was based on the monthly water quality data (water temperature, BOD, T-N and T-P) between December 1, $2005{\sim}$November 31, 2006. K1C(the saturated growth rate of plant plankton), K1RC (endogenous respiratory quotient of plankton), KDC(deoxidized ratio), K71C(minealized ratio of dissolved organic phosphorus), K83C(mineralized ratio of dissolved organic nitrogen) have been considered as the factors of the water quality performed in this water quality simulation, that is, the most effective parameters on BOD, T-N and T-P. In the result of the analysis of the sensitivity, KDC(deoxidized ratio) was the most sensitively reacted parameter on BOD and it was K71C(mineralized ratio of dissolved organic phosphorus) and K83C(mineralized ratio of dissolved organic nitrogen) on T-N and T-P. It is considered that it will be possible to apply the most optimal parameter to an analysis of the water quality simulation at Im-Ha Ho basin in the goal year by examining the interaction of the parameters through the parameters sampling which are able to applicable to prediction of the water quality and the analysis of the its sensitivity, in the future, also the analysis on the basis of the hydrological conditions: an outflow or the character of a flow will be needed.

• The Journal of the Korea Contents Association
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• v.20 no.8
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• pp.685-691
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• 2020

Standard values for fetal bio index measurements should be applied differently depending on the past present and general characteristics of the target population. Therefore, we tried to predict the number of gestational week(GA) and analyze the differences by country based on the measurements of Korean fetal bio index. 480 fetal bio index measurements between 15~38 weeks of pregnancy using ultrasound were compared retrospectively with USA ad Japanese data. One Way ANOVA was used for the analysis of differences by country, and quadratic regression model was designed to predict the GA of fetal bio index in order to predict the standard pregnancy number of Korean fetuses(p<0.005). Mean difference in the 95% confidence interval is BPD was Korea and USA 0.17, Korea and Japan 0.11, AC was Korea and USA -0.35, Korea and Japan 0.42, FL was Korea and USA -0.18, Korea and Japan 0.14. Therefore, fetal bio index for GA predict is considered to be the standard of the fetal growth assessment by applying the country specific standard in consideration of differences between races.

• Journal of Bio-Environment Control
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• v.26 no.4
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• pp.411-417
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• 2017

Environmental and growth factors such as light intensity, vapor pressure deficit, and leaf area index are important variables that can change the transpiration rate of plants. The objective of this study was to compare the transpiration rates estimated by modified Penman-Monteith model and artificial neural network. The transpiration rate of paprika (Capsicum annuum L. cv. Fiesta) was obtained by using the change in substrate weight measured by load cells. Radiation, temperature, relative humidity, and substrate weight were collected every min for 2 months. Since the transpiration rate cannot be accurately estimated with linear equations, a modified Penman-Monteith equation using compensated radiation (Shin et al., 2014) was used. On the other hand, ANN was applied to estimating the transpiration rate. For this purpose, an ANN composed of an input layer using radiation, temperature, relative humidity, leaf area index, and time as input factors and five hidden layers was constructed. The number of perceptons in each hidden layer was 512, which showed the highest accuracy. As a result of validation, $R^2$ values of the modified model and ANN were 0.82 and 0.94, respectively. Therefore, it is concluded that the ANN can estimate the transpiration rate more accurately than the modified model and can be applied to the efficient irrigation strategy in soilless cultures.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.4
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• pp.179-188
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• 2012

A kiwifruit cultivar 'Hayward' has been grown in Jeju Island where the current climate is suitable for growth and development of this crop. Prediction of the geographical shift in the phenology can help the kiwifruits growers to adapt to the local climate change in the future. Two phenology models (i.e., chill-day and DVS) were parameterized to estimate flowering date of kiwifruits 'Hayward' based on the data collected from field plots and chamber experiments in the southern coastal and island locations in South Korea. Spatio-temporally independent datasets were used to evaluate performance of the two models in predicting flowering date of 'Hayward'. Chill-day model showed better performance than DVS model (2.5 vs. 4.0 days in RMSE). Daily temperature data interpolated at a higher spatial resolution over Jeju Island were used to predict flowering dates of 'Hayward' in 2021-2100 under the A1B scenario. According to the model calculation under the future climate condition, the flowering of kiwifruits shall accelerate and the area with poor flowering might increase due to the warmer winter induced insufficient chilling. Optimal land area for growing 'Hayward' could increase for a while in the near future (2021-2030), whereas such areas could decrease to one half of the current areas by 2100. The geographic locations suitable for 'Hayward' cultivation would migrate from the current coastal area to the elevated mountain area by 250 m.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.119-133
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• 2022

One of the most important current features of food related industry is the growth of food delivery service. Another notable food related culture is, with the advent of Youtube, the popularity of Mukbang, which refers to content that records eating. Based on these background, this study intended to focus on two things. First, we tried to see the impact of Youtube Mukbang and the sentiments of Mukbang comments on the number of related food deliveries. Next, we tried to set up the predictive modeling of chicken delivery order with machine learning method. We used Youtube Mukbang comments data as well as weather related data as main independent variables. The dependent variable used in this study is the number of delivery order of fried chicken. The period of data used in this study is from June 3, 2015 to September 30, 2019, and a total of 1,580 data were used. For the predictive modeling, we used machine learning methods such as linear regression, ridge, lasso, random forest, and gradient boost. We found that the sentiment of Youtube Mukbang and comments have impacts on the number of delivery orders. The prediction model with Mukban data we set up in this study had better performances than the existing models without Mukbang data. We also tried to suggest managerial implications to the food delivery service industry.

• Journal of Navigation and Port Research
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• v.47 no.3
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• pp.167-173
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• 2023

The manufacturing industry is the backbone of the Korean economy. Among them, the petrochemical industry is a strategic growth industry, which makes a profit through reexports based on eminent technology in South Korea which imports all of its crude oil. South Korea imports whole amount of crude oil, which is the raw material for many manufacturing industries, by sea transportation. Therefore, it must respond swiftly to a highly volatile tanker freight market. This study aimed to make an early warning model of crude oil shipping market using a signal approach. The crisis of crude oil shipping market is defined by BDTI. The overall leading index is made of 38 factors from macro economy, financial data, and shipping market data. Only leading correlation factors were chosen to be used for the overall leading index. The overall leading index had the highest correlation coefficient factor of 0.499 two months ago. It showed a significant correlation coefficient five months ago. The QPS value was 0.13, which was found to have high accuracy for crisis prediction. Furthermore, unlike other previous time series forecasting model studies, this study quantitatively approached the time lag between economic crisis and the crisis of the tanker ship market, providing workers and policy makers in the shipping industry with an framework for strategies that could effectively deal with the crisis.