• Journal of Korean Dental Science
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• v.16 no.1
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• pp.63-73
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• 2023

Purpose: Hyperdontia is a developmental disorder of the oral cavity. Mesiodens refers to the hyperdontia located between the maxillary central incisors. During the surgical procedure, the anesthetic method for pain control should be considered along with factors related to the surgery itself. The purpose of this study was to evaluate the effect of the impacted position of the mesiodens on the selection of sedation method and to suggest incisive foramen as a brief reference. Materials and Methods: This study included 126 patients who were scheduled for extraction of mesiodens. The selection criteria included patients with one palatally impacted inverted mesiodens accessible from the palatal gingival margin, and those with good cooperation potential in order to control for clinical information. Using cone beam computed tomography, vertical, horizontal, and palatal positional factors were measured, and the anesthetic method was determined by two examiners. The patients were grouped into vertical and horizontal groups based on the position of the incisive foramen. Data were statistically analyzed using the Mann-Whitney test, the chi-square test, and logistic regression analysis. Result: All positional factors differed between the outpatient and inpatient anesthetic groups. The vertical minimum distance from the alveolar ridge to the mesiodens (Va) and the minimum distance from the palatal surface to the crown tip of the mesiodens (Tc) were factors affecting the choice of anesthetic method. The distribution of the vertical and horizontal positional groups differed between the outpatient and inpatient anesthetic groups. Conclusion: The incisive foramen can be used as a brief reference to determine the appropriate anesthetic method. Referral for inpatient anesthesia may be a priority if they are in the V₂H₂ group with Va ≥5 mm, and Tc ≥6 mm, and outpatient sedation may be considered if they are in the V₁H₁ group with Va ≤1.5 mm, and Tc ≤2.5 mm.

• Journal of Powder Materials
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• v.30 no.3
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• pp.217-222
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• 2023

Machine learning-based data analysis approaches have been employed to overcome the limitations in accurately analyzing data and to predict the results of the design of Nb-based superalloys. In this study, a database containing the composition of the alloying elements and their room-temperature tensile strengths was prepared based on a previous study. After computing the correlation between the tensile strength at room temperature and the composition, a material science analysis was conducted on the elements with high correlation coefficients. These alloying elements were found to have a significant effect on the variation in the tensile strength of Nb-based alloys at room temperature. Through this process, a model was derived to predict the properties using four machine learning algorithms. The Bayesian ridge regression algorithm proved to be the optimal model when Y, Sc, W, Cr, Mo, Sn, and Ti were used as input features. This study demonstrates the successful application of machine learning techniques to effectively analyze data and predict outcomes, thereby providing valuable insights into the design of Nb-based superalloys.

• Animal Bioscience
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• v.37 no.4
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• pp.622-630
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• 2024

Objective: Pig breeders cannot obtain phenotypic information at the time of selection for sow lifetime productivity (SLP). They would benefit from obtaining genetic information of candidate sows. Genomic data interpreted using deep learning (DL) techniques could contribute to the genetic improvement of SLP to maximize farm profitability because DL models capture nonlinear genetic effects such as dominance and epistasis more efficiently than conventional genomic prediction methods based on linear models. This study aimed to investigate the usefulness of DL for the genomic prediction of two SLP-related traits; lifetime number of litters (LNL) and lifetime pig production (LPP). Methods: Two bivariate DL models, convolutional neural network (CNN) and local convolutional neural network (LCNN), were compared with conventional bivariate linear models (i.e., genomic best linear unbiased prediction, Bayesian ridge regression, Bayes A, and Bayes B). Phenotype and pedigree data were collected from 40,011 sows that had husbandry records. Among these, 3,652 pigs were genotyped using the PorcineSNP60K BeadChip. Results: The best predictive correlation for LNL was obtained with CNN (0.28), followed by LCNN (0.26) and conventional linear models (approximately 0.21). For LPP, the best predictive correlation was also obtained with CNN (0.29), followed by LCNN (0.27) and conventional linear models (approximately 0.25). A similar trend was observed with the mean squared error of prediction for the SLP traits. Conclusion: This study provides an example of a CNN that can outperform against the linear model-based genomic prediction approaches when the nonlinear interaction components are important because LNL and LPP exhibited strong epistatic interaction components. Additionally, our results suggest that applying bivariate DL models could also contribute to the prediction accuracy by utilizing the genetic correlation between LNL and LPP.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.147-170
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• 2022

Although the number of patent which is one of the core outputs of technological innovation continues to increase, the number of low-value patents also hugely increased. Therefore, efficient evaluation of patents has become important. Estimation of patent lifespan which represents private value of a patent, has been studied for a long time, but in most cases it relied on a linear model. Even if machine learning methods were used, interpretation or explanation of the relationship between explanatory variables and patent lifespan was insufficient. In this study, patent lifespan (number of renewals) is predicted based on the idea that patent lifespan represents the value of the patent. For the research, 4,033,414 patents applied between 1996 and 2017 and finally granted were collected from USPTO (US Patent and Trademark Office). To predict the patent lifespan, we use variables that can reflect the characteristics of the patent, the patent owner's characteristics, and the inventor's characteristics. We build four different models (Ridge Regression, Random Forest, Feed Forward Neural Network, Gradient Boosting Models) and perform hyperparameter tuning through 5-fold Cross Validation. Then, the performance of the generated models are evaluated, and the relative importance of predictors is also presented. In addition, based on the Gradient Boosting Model which have excellent performance, Accumulated Local Effects Plot is presented to visualize the relationship between predictors and patent lifespan. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the evaluation reason of individual patents, and discuss applicability to the patent evaluation system. This study has academic significance in that it cumulatively contributes to the existing patent life estimation research and supplements the limitations of existing patent life estimation studies based on linearity. It is academically meaningful that this study contributes cumulatively to the existing studies which estimate patent lifespan, and that it supplements the limitations of linear models. Also, it is practically meaningful to suggest a method for deriving the evaluation basis for individual patent value and examine the applicability to patent evaluation systems.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.809-825
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• 2017

The number of TV entertainment shows is increasing. Competition among programs in the entertainment market is intensifying since cable channels air many entertainment TV shows. There is now a need for research on program ratings and the number of episodes. This study presents predictive models for entertainment TV show ratings and number of episodes. We use various data mining techniques such as linear regression, logistic regression, LASSO, random forests, gradient boosting, and support vector machine. The analysis results show that the average program ratings before the first broadcast is affected by broadcasting company, average ratings of the previous season, starting year and number of articles. The average program ratings after the first broadcast is influenced by the rating of the first broadcast, broadcasting company and program type. We also found that the predicted average ratings, starting year, type and broadcasting company are important variables in predicting of the number of episodes.

• Korean Journal of Agricultural and Forest Meteorology
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• v.10 no.1
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• pp.9-16
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• 2008

In this study we investigated the effects of the microclimatic conditions on tree growth in different site types for natural deciduous forests in Korea. First, we classified all the sites into 36 types according to their aspect (east, west, south, and north), elevation (higher than 1,000 m, 700$\sim$1,000 m, and lower than 700 m), and topographical conditions (ridge, slope, and valley). For each site type, we measured diameter growth with increment borer, and then estimated periodic annual increment of diameter, height and volume. We applied a topoclimatological technique for estimating microclimatic conditions, and produced monthly climatic estimates from which 17 weather variables (including indices of warmth, coldness, and aridity) were computed for each site type. The periodic annual increments of diameter, height, and volume were then correlated by regression analysis with those weather variables to examine effects of microclimate on tree growth by site type. We found that the correlation of diameter growth by site type was significantly correlated with most weather variables except daily photoperiod. Water condition was the most important factor for the height growth. For volume growth, on the other hand, the conditions such as relatively high temperature and low humidity provided favorable environment. Our regression analysis shows that aridity index is a good predictor for tree growth including diameter, height and volume increments.

• Journal of the Korean GEO-environmental Society
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• v.12 no.9
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• pp.55-61
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• 2011

This study investigate the use of ultraviolet(UV) light with hydrogen peroxide($H_2O_2$) for Methyl Tert Butyl Ether(MTBE) degradation in photolysis reactor. The process in general demands the generation of OH radicals in solution at the presence of UV light. These radicals can then attack the MTBE molecule and it is finally destroyed or converted into a simple harmless compound. The MTBE removal by photolysis were mathematically described as the independent variables such as irradiation intensity, initial concentration of MTBE and $H_2O_2$/MTBE ratio, and these were modeled by the use of response surface methodology(RSM). These experiments were carried out as a Box-Behnken Design(BBD) consisting of 15 experiments. Regression analysis term of Analysis of Variance(ANOVA) shows significantly p-value(p<0.05) and high coefficients for determination values($R^2$=94.60%) that allow satisfactory prediction of second-order regression model. And Canonical analysis yields the stationery point for response, with the estimate ridge of maximum responses and optimal conditions for Y(MTBE removal efficiency, %) are $x_1$=25.75 W of irradiation intensity, $x_2$=7.69 mg/L of MTBE concentration and $x_3$=11.04 of $H_2O_2$/MTBE molecular ratio, respectively. This study clearly shows that RSM is available tool for optimizing the operating conditions to maximize MTBE removal.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.219-239
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• 2019

As the importance of providing customized services to individuals becomes important, researches on personalized recommendation systems are constantly being carried out. Collaborative filtering is one of the most popular systems in academia and industry. However, there exists limitation in a sense that recommendations were mostly based on quantitative information such as users' ratings, which made the accuracy be lowered. To solve these problems, many studies have been actively attempted to improve the performance of the recommendation system by using other information besides the quantitative information. Good examples are the usages of the sentiment analysis on customer review text data. Nevertheless, the existing research has not directly combined the results of the sentiment analysis and quantitative rating scores in the recommendation system. Therefore, this study aims to reflect the sentiments shown in the reviews into the rating scores. In other words, we propose a new algorithm that can directly convert the user 's own review into the empirically quantitative information and reflect it directly to the recommendation system. To do this, we needed to quantify users' reviews, which were originally qualitative information. In this study, sentiment score was calculated through sentiment analysis technique of text mining. The data was targeted for movie review. Based on the data, a domain specific sentiment dictionary is constructed for the movie reviews. Regression analysis was used as a method to construct sentiment dictionary. Each positive / negative dictionary was constructed using Lasso regression, Ridge regression, and ElasticNet methods. Based on this constructed sentiment dictionary, the accuracy was verified through confusion matrix. The accuracy of the Lasso based dictionary was 70%, the accuracy of the Ridge based dictionary was 79%, and that of the ElasticNet (${\alpha}=0.3$) was 83%. Therefore, in this study, the sentiment score of the review is calculated based on the dictionary of the ElasticNet method. It was combined with a rating to create a new rating. In this paper, we show that the collaborative filtering that reflects sentiment scores of user review is superior to the traditional method that only considers the existing rating. In order to show that the proposed algorithm is based on memory-based user collaboration filtering, item-based collaborative filtering and model based matrix factorization SVD, and SVD ++. Based on the above algorithm, the mean absolute error (MAE) and the root mean square error (RMSE) are calculated to evaluate the recommendation system with a score that combines sentiment scores with a system that only considers scores. When the evaluation index was MAE, it was improved by 0.059 for UBCF, 0.0862 for IBCF, 0.1012 for SVD and 0.188 for SVD ++. When the evaluation index is RMSE, UBCF is 0.0431, IBCF is 0.0882, SVD is 0.1103, and SVD ++ is 0.1756. As a result, it can be seen that the prediction performance of the evaluation point reflecting the sentiment score proposed in this paper is superior to that of the conventional evaluation method. In other words, in this paper, it is confirmed that the collaborative filtering that reflects the sentiment score of the user review shows superior accuracy as compared with the conventional type of collaborative filtering that only considers the quantitative score. We then attempted paired t-test validation to ensure that the proposed model was a better approach and concluded that the proposed model is better. In this study, to overcome limitations of previous researches that judge user's sentiment only by quantitative rating score, the review was numerically calculated and a user's opinion was more refined and considered into the recommendation system to improve the accuracy. The findings of this study have managerial implications to recommendation system developers who need to consider both quantitative information and qualitative information it is expect. The way of constructing the combined system in this paper might be directly used by the developers.

• Korean Journal of Food Science and Technology
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• v.29 no.2
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• pp.309-313
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• 1997

Maillard reaction products (MRPs) added into a culture and the resultant bacterial growth were investigated using response surface methodology. The coefficients of determination $(R^{2})$ of response surface regression equations for bacteria were 0.9544 and 0.9578 in Bacillus subtilis and Bacillus natto, respectively. The MRPs produced at higher reaction temperature and for longer reaction time showed greater antimicrobial effect for Bacillus subtilis. Especially, the MRPs produced at temperature above $150^{\circ}C$ for 8 to 12 hrs showed the strongest antimicrobial effect. The MRPs produced at lower reaction temperature and for shorter reaction time showed greater microbial growth effect for Bacillus natto, but those produced at the reaction temperature higher than $160^{\circ}C$ showed the greatest antimicrobial effect. In the ridge analysis, the growth of Bacillus subtilis was the most significantly inhibited in the presence of MRPs prepared at $159.10^{\circ}C$ and pH 12.21 for 9.67 hrs, and the growth of Bacillus natto was the most significantly inhibited in the presence of MRPs prepared at $169.94^{\circ}C$ and pH 9.66 for 9.22 hrs.

• Journal of Korean Society of Environmental Engineers
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• v.33 no.3
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• pp.149-156
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• 2011

This experimental design and response surface methodology (RSM) have been applied to the investigation of the electro-UV-ultrasonic complex process for the disinfection of E. coli in the water. The disinfection reactions of electro-UV-ultrasonic process were mathematically described as a function of parameters power of electrolysis ($X_1$), UV ($X_2$), and ultrasonic process ($X_3$) being modeled by use of the Box-Behnken technique, which was used for fitting 2nd order response surface model. The application of RSM yielded the following regression equation, which is empirical relationship between the residual E. coli number (Ln CFU) in water and test variables in coded unit: residual E. coli number (Ln CFU) = 23.69 - 3.75 Electrolysis - 0.67 UV - 0.26 Ultrasonic - 0.16 Electrolysis UV + 0.05 Electrolysis Ultrasonic + 0.27 $Electrolysis^2$ + 0.14 $UV^2$ - 0.01 $Ultrasonic^2$). The model predictions agreed well with the experimentally observed result ($R^2$ = 0.983). Graphical 2D contour and 3D response surface plots were used to locate the optimum range. The estimated ridge of maximum response and optimal conditions for residual E. coli number (Ln CFU) using 'numerical optimization' of Design-Expert software were 1.47 Ln CFU/L and 6.94 W of electrolysis, 6.72 W of UV and 14.23 W of ultrasonic process. This study clearly showed that response surface methodology was one of the suitable methods to optimize the operating conditions and minimize the residual E. coli number of the complex disinfection.