• Analytical Science and Technology
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• v.24 no.6
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• pp.533-543
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• 2011

The hERG (human ether-a-go-go related gene) ion channel is a main factor for cardiac repolarization, and the blockade of this channel could induce arrhythmia and sudden death. Therefore, potential hERG ion channel inhibitors are now a primary concern in the drug discovery process, and lots of efforts are focused on the minimizing the cardiotoxic side effect. In this study, $IC_{50}$ data of 202 organic compounds in HEK (human embryonic kidney) cell from literatures were used to develop predictive 2D-QSAR model. Multiple linear regression (MLR), Support Vector Machine (SVM), and artificial neural network (ANN) were utilized to predict inhibition concentration of hERG ion channel as machine learning methods. Population based-forward selection method with cross-validation procedure was combined with each learning method and used to select best subset descriptors for each learning algorithm. The best model was ANN model based on 14 descriptors ($R^2_{CV}$=0.617, RMSECV=0.762, MAECV=0.583) and the MLR model could describe the structural characteristics of inhibitors and interaction with hERG receptors. The validation of QSAR models was evaluated through the 5-fold cross-validation and Y-scrambling test.

• Journal of Digital Convergence
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• v.12 no.7
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• pp.261-266
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• 2014

When the number of parameters in the time series model are diverse, it is hard to forecast because of the increasing error by a parameter estimation. If the homogeneity hypothesis which was obtained from the same model about severeal data for the time series is selected, it is easy to get the predictive value better. Nonlinear time-series panel data for each parameter for each time series, since there are so many parameters that are present, and the large number of parameters according to the parameter estimation error increases the accuracy of the forecast deteriorated. Panel present in the time series of multiple independent homogeneity is satisfied by a comprehensive time series to estimate and to test of the parameters. For studying about the homogeneity test for the m independent non-linear of the time series panel data, it needs to set the model and to make the normal conditions for the model, and to derive the homogeneity test statistic. Finally, it shows to obtain the limit distribution according to ${\chi}^2$ distribution. In actual analysis,, we can examine the result for the homogeneity test about nonlinear time series panel data which are 2 groups of stock price data.

• The Journal of Bigdata
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• v.4 no.1
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• pp.63-72
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• 2019

For power energy, optimal generation and distribution plans based on accurate demand forecasts are necessary because it is not recoverable after they have been delivered to users through power generation and transmission processes. Failure to predict power demand can cause various social and economic problems, such as a massive power outage in September 2011. In previous studies on forecasting power demand, ARIMA, neural network models, and other methods were developed. However, limitations such as the use of the national average ambient air temperature and the application of uniform criteria to distinguish seasonality are causing distortion of data or performance degradation of the predictive model. In order to improve the performance of the power demand prediction model, we divided Korea into five major regions, and the power demand prediction model of the linear regression model and the neural network model were developed, reflecting seasonal characteristics through regional characteristics and migration period learning techniques. With the proposed approach, it seems possible to forecast the future demand in short term as well as in long term. Also, it is possible to consider various events and exceptional cases during a certain period.

• Journal of Trauma and Injury
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• v.25 no.4
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• pp.139-144
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• 2012

Purpose: Hypovolemic shock is the leading cause of death in multiple trauma patients with pelvic bone fracures. The purpose of this study was to develop a simple prediction method for injury severity and amount of hemorrhage via an analysis of the correlation between the site of pelvic bone fracture and the amount of transfusion and to verify the usefulness of the such a simple scoring system. Methods: We analyzed retrospectively the medical records and radiologic examination of 102 patients who had been diagnosed as having a pelvic bone fracture and who had visited the Emergency Department between January 2007 and December 2011. Fracture sites in the pelvis were confirmed and re-classified anatomically as pubis, ilium or sacrum. A multiple linear regression analysis was performed on the amount of transfusion, and a simplified scoring system was developed. The predictive value of the amount of transfusion for the scoring system as verified by using the receiver operating characteristics (ROC). The area under the curve of the ROC was compared with the injury severity score (ISS). Results: From among the 102 patients, 97 patients (M:F=68:29, mean $age=46.7{\pm}16.6years$) were enrolled for analysis. The average ISS of the patients was $16.2{\pm}7.9$, and the average amount of packed RBC transfusion for 24 hr was $3.9{\pm}4.6units$. The regression equation resulting from the multiple linear regression analysis was 'packed RBC units=1.40${\times}$(sacrum fracture)+1.72${\times}$(pubis fracture)+1.67${\times}$(ilium fracture)+0.36' and was found to be suitable (p=0.005). We simplified the regression equation to 'Pelvic Bleeding Score=sacrum+pubis+ilium.' Each fractured site was scored as 0(no fracture) point, 1(right or left) point, or 2(both) points. Sacrum had only 0 or 1 point. The score ranged from 0 to 5. The area under the curve (AUC) of the ROC was 0.718 (95% CI: 0.588-0.848, p=0.009). For an upper Pelvis Bleeding Score of 3 points, the sensitivity of the prediction for a massive transfusion was 71.4%, and the specificity was 69.9%. Conclusion: We developed a simplified scoring system for the anatomical fracture sites in the pelvis to predict the requirement for a transfusion (Pelvis Bleeding Score (PBS)). The PBS, compared with the ISS, is considered a useful predictor of the need for a transfusion during initial management.

• Journal of Conservation Science
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• v.34 no.5
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• pp.387-396
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• 2018

Exposure to the natural environment can cause damage to domestic wooden cultural assets, such as temples. Deterioration is accelerated by biological damage and various environmental factors. In this study, meteorological factors were monitored by equipment installed at Beopjusa temple of Boeun province and Seonamsa temple of Suncheon province. A statistical model was applied to these data to predict the meteorological factors and to compare the predictive performance of each meteorological factor. The resulting correlation coefficient between air and dew point temperatures was highest, at 0.95, while the correlation coefficient for relative humidity had a moderate value(0.65) at both the Beopjusa and Seonamsa temples. Thus, a general linear model was found to be suitable for predicting air and dew point temperatures. An analysis of correlation between meteorological factors showed that there was strong positive correlation between air temperature and dew point temperature, and between solar radiation and evaporation at both sites. There was a weak positive correlation between air temperature and evaporation at Beopjusa temple. Wind speed was negatively correlated with both air temperature and relative humidity at Seonamsa temple. The wind speed at this location is higher than average in winter and lower than average in summer, and it was hypothesized that the low wind speed plays a role in reducing water evaporation in summer, when both air temperature and relative humidity are high. As a result, damage to the wooden buildings of Seonamsa temple is accelerated.

• Korean Journal of Clinical Laboratory Science
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• v.36 no.2
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• pp.199-204
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• 2004

Height has become one of the most important factors to determine the pulmonary function test index, and there is a high correlation between them, so that they have been utilized for evaluating pulmonary function test predictive value or nomogram. Therefore, we have tried to find out that difference and if there is any correlation and linear relationship between height and forced expiratory flow curve. There were a total of 163 subjects, male 93 and female 70. This study was done at the Department of Pulmonary Function Test of Jeon-Ju Presbyterian Hospital and we measured the index at the forced expiratory flow curve of FVC, $FEV_{1.0}$, $FEV_{1.0}$/FVC, $FEF_{25-75%}$, and $FEF_{200-1200m{\ell}}$. When we subjected the group of height more than 160cm, there were gradual increments at FVC(p<0.001), $FEV_{1.0}$(p<0.001), $FEF_{25-75%}$(p<0.05) and $FEF_{200-1200m{\ell}}$(p<0.001), but no changes at $FEV_{1.0}$/FVC in terms of forced expiratory flow curve index. We have analyzed the relationship between height and forced expiratory flow curve, there was a close relationship at FVC(r=0.670, p<0.01), $FEV_{1.0}$(r=0.491, p<0.01), $FEF_{25-75%}$ (r=0.175, p<0.05) and $FEF_{200-1200m{\ell}}$(r=0.370, p<0.01) but there was reciprocal relationship at $FEV_{1.0}$/FVC(r=-0.215, p<0.01). We have tried simple regression analysis to see if height affects forced expiratory flow curve index as a sector, and the result was $FVC(\ell)=0.0642{\times}height(cm)-7.2978$(p<0.01, $R^2=0.449$), $FEV_{1.0}(\ell)=0.0407{\times}height(cm)-4.2774$ (p<0.01, $R^2=0.2411$), $FEV_{1.0}/FVC(%)=-0.2892{\times}height(cm)+121.44$(p<0.01, $R^2=0.0464$), $FEF_{25-75%}(\ell/sec)=0.0176{\times}height(cm)-0.7876$(p<0.05, $R^2=0.0237$), $FEF_{200-1200m{\ell}}(\ell/sec)=0.0967{\times}height(cm)-11.037$(p<0.01, $R^2=0.1214$) this was approved statistically. According to this study, if height is taller than average, forced expiratory flow curve index were increased, there was a close relationship between height and forced expiratory flow curve, and there was a linear relationship as sector between height and forced expiratory flow curve index. Therefore, researches that study other factors such as sex, age, weight, body surface area, and obesity indexes other than height should be done to see if there are any further relationships.

• Safety and Health at Work
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• v.11 no.1
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• pp.118-124
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• 2020

Background: The poultry industry in Pakistan has flourished since the 1960s; however, there are scarce data regarding the impact of occupational exposure on the pulmonary health of farm workers in terms of years working in the industry. The objective of the present study was to assess the effect of poultry environment on the health of occupationally exposed poultry farmers in countries of warm climatic regions, such as Pakistan. This study will also show the effect of exposure to poultry facilities on the health of poultry farmers in the context of low-income countries with a relatively inadequate occupational exposure risk management. Materials and methods: The lung function capacity of 79 poultry workers was measured using a spirometer. Along with spirometry, a structured questionnaire was also administrated to obtain information about age, height, weight, smokers/nonsmokers, years of working experience, and pulmonary health of farm workers. The workers who were directly involved in the care and handling of birds in these intensive facilities were considered and divided into four groups based on their years of working experience: Group I (3-10 months), Group II (1-5 years), Group III (6-10 years), and Group IV (more than 11 years). The forced vital capacity (FVC), forced expiratory volume in one second (FEV1) and the FEV1/FVC ratio were considered to identify lung function abnormalities. Statistical analysis was carried out using independent sample t test, Chi-square test, Pearson's correlation, and linear regression. Results: Based on the performed spirometry, 68 (86 %) of workers were found normal and healthy, whereas 11 (14 %) had a mild obstruction. Of the 11 workers with mild obstruction, the highest number with respect to the total was in Group IV (more than 11 years of working experience) followed by Group III and Group II. Most of the workers were found healthy, which seems to be because of the healthy survivor effect. For the independent sample t test, a significant difference was noticed between healthy and nonhealthy farmers, whereas Chi-square test showed a significant association with height, drugs, and working experience. Linear regression that was stratified by respiratory symptoms showed for workers with symptoms, regression models for all spirometric parameters (FVC, FEV1, and FEV1/FVC) have better predictive power or R square value than those of workers without symptoms. Conclusion: These findings suggest that lung function capacity was directly related to years of working experience. With increasing number of working years, symptoms of various respiratory problems enhanced in the poultry workers. It should be noted that most of the poultry workers were healthy and young, the rationale being that there is a high turnover rate in this profession. The mobility in this job and our finding of 86% of the healthy workers in the present study also proposed healthy worker survivor effect.

• Tuberculosis and Respiratory Diseases
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• v.80 no.4
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• pp.385-391
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• 2017

Background: Men with chronic obstructive pulmonary disease, have reduced endogenous testosterone levels, but the relationship between pulmonary function and endogenous testosterone levels, is inconsistent. Testicular volume is a known indicator of endogenous testosterone levels, male fertility, and male potency. In the present study, the authors investigated the relationship, between testicular volume and lung function. Methods: One hundred and eighty-one South Korean men age 40-70, hospitalized for urological surgery, were retrospectively enrolled, irrespective of the presence of respiratory disease. Study subjects underwent pulmonary function testing, prior to procedures, and testicular volumes were measured by orchidometry. Testosterone levels of patients in blood samples collected between $7{\small{AM}}$ and $11{\small{AM}}$, were measured by a direct chemiluminescent immunoassay. Results: The 181 study subjects were divided into two groups, by testicular volume (${\geq}35mL$ vs. <35 mL), the larger testes group, had better lung functions (forced vital capacity [FVC]: $3.87{\pm}0.65L$ vs. $3.66{\pm}0.65L$, p=0.037; forced expiratory volume in 1 second [$FEV_1$]: $2.92{\pm}0.57L$ vs. $2.65{\pm}0.61L$, p=0.002; FVC % predicted: $98.2{\pm}15.2%$ vs. $93.8{\pm}13.1%$, p=0.040; $FEV_1$ % predicted: $105.4{\pm}19.5%$ vs. $95.9{\pm}21.2%$, p=0.002). In addition, the proportion of patients with a $FEV_1/FVC$ of <70%, was lower in the larger testes group. Univariate analysis conducted using linear regression models, revealed that testicular volume was correlated with FVC (r=0.162, p=0.029), $FEV_1$ (r=0.218, p=0.003), $FEV_1/FVC$ (r=0.149, p=0.046), and $FEV_1$ % predicted (r=0.178, p=0.017), and multivariate analysis using linear regression models, revealed that testicular volume was a significant predictive factor for $FEV_1$ % predicted (${\beta}=0.159$, p=0.041). Conclusion: Larger testicular volume was independently associated, with favorable indices of lung function. These results suggest that androgens, may contribute to better lung function.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.59-76
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• 2023

Since the COVID-19 era, the rise in apartment prices has been unconventional. In this uncertain real estate market, price prediction research is very important. In this paper, a model is created to predict the actual transaction price of future apartments after building a vast data set of 870,000 from 2015 to 2020 through data collection and crawling on various real estate sites and collecting as many variables as possible. This study first solved the multicollinearity problem by removing and combining variables. After that, a total of five variable selection algorithms were used to extract meaningful independent variables, such as Forward Selection, Backward Elimination, Stepwise Selection, L1 Regulation, and Principal Component Analysis(PCA). In addition, a total of four machine learning and deep learning algorithms were used for deep neural network(DNN), XGBoost, CatBoost, and Linear Regression to learn the model after hyperparameter optimization and compare predictive power between models. In the additional experiment, the experiment was conducted while changing the number of nodes and layers of the DNN to find the most appropriate number of nodes and layers. In conclusion, as a model with the best performance, the actual transaction price of apartments in 2021 was predicted and compared with the actual data in 2021. Through this, I am confident that machine learning and deep learning will help investors make the right decisions when purchasing homes in various economic situations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.87-96
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• 2022

The traffic vulnerable, including elderly pedestrians, have a relatively low walking speed and slow cognitive response time due to reduced physical ability. Although a smart crossing system has been developed and operated to improve problem, it is difficult to operate a signal that reflects the appropriate walking speed for each pedestrian. In this study, a neural network model and a multiple regression model-based traversing speed estimation model were developed using image information collected in an area with a high percentage of traffic vulnerability. to support the provision of optimal walking signals according to real-time traffic weakness. actual traffic data collected from the urban traffic network of Paju-si, Gyeonggi-do were used. The performance of the model was evaluated through seven selected indicators, including correlation coefficient and mean absolute error. The multiple linear regression model had a correlation coefficient of 0.652 and 0.182; the neural network model had a correlation coefficient of 0.823 and 0.105. The neural network model showed higher predictive power.