• Journal of Korean Society for Quality Management
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• v.45 no.1
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• pp.117-128
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• 2017

Purpose: This study analyzes automobile quality review data to develop alternative analytical method of informal data. Existing methods to analyze informal data are based mainly on the frequency of informal data, however, this research tries to use correlation information of each informal data. Method: After sentimental analysis to acquire the user information for automobile products, three classification methods, that is, $na{\ddot{i}}ve$ Bayes, random forest, and support vector machine, were employed to accurately classify the informal user opinions with respect to automobile qualities. Additionally, Word2vec was applied to discover correlated information about informal data. Result: As applicative results of three classification methods, random forest method shows most effective results compared to the other classification methods. Word2vec method manages to discover closest relevant data with automobile components. Conclusion: The proposed method shows its effectiveness in terms of accuracy and sensitivity on the analysis of informal quality data, however, only two sentiments (positive or negative) can be categorized due to human errors. Further studies are required to derive more sentiments to accurately classify informal quality data. Word2vec method also shows comparative results to discover the relevance of components precisely.

• Journal of Biomedical Engineering Research
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• v.33 no.3
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• pp.148-154
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• 2012

Hemorrhagic shock is a primary cause of deaths resulting from injury in the world. Although many studies have tried to diagnose accurately hemorrhagic shock in the early stage, such attempts were not successful due to compensatory mechanisms of humans. The objective of this study was to construct a survival prediction model of rats in acute hemorrhagic shock using a random forest (RF) model. Heart rate (HR), mean arterial pressure (MAP), respiration rate (RR), lactate concentration (LC), and peripheral perfusion (PP) measured in rats were used as input variables for the RF model and its performance was compared with that of a logistic regression (LR) model. Before constructing the models, we performed 5-fold cross validation for RF variable selection, and forward stepwise variable selection for the LR model to examine which variables were important for the models. For the LR model, sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (ROC-AUC) were 0.83, 0.95, 0.88, and 0.96, respectively. For the RF models, sensitivity, specificity, accuracy, and AUC were 0.97, 0.95, 0.96, and 0.99, respectively. In conclusion, the RF model was superior to the LR model for survival prediction in the rat model.

• Computers and Concrete
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• v.19 no.3
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• pp.275-282
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• 2017

The aim of this study is to build Machine Learning models to evaluate the effect of blast furnace slag (BFS) and waste tire rubber powder (WTRP) on the compressive strength of cement mortars. In order to develop these models, 12 different mixes with 288 specimens of the 2, 7, 28, and 90 days compressive strength experimental results of cement mortars containing BFS, WTRP and BFS+WTRP were used in training and testing by Random Forest, Ada Boost, SVM and Bayes classifier machine learning models, which implement standard cement tests. The machine learning models were trained with 288 data that acquired from experimental results. The models had four input parameters that cover the amount of Portland cement, BFS, WTRP and sample ages. Furthermore, it had one output parameter which is compressive strength of cement mortars. Experimental observations from compressive strength tests were compared with predictions of machine learning methods. In order to do predictive experimentation, we exploit R programming language and corresponding packages. During experimentation on the dataset, Random Forest, Ada Boost and SVM models have produced notable good outputs with higher coefficients of determination of R2, RMS and MAPE. Among the machine learning algorithms, Ada Boost presented the best R2, RMS and MAPE values, which are 0.9831, 5.2425 and 0.1105, respectively. As a result, in the model, the testing results indicated that experimental data can be estimated to a notable close extent by the model.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.33-47
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• 2018

City gas pipelines are buried underground. Because of this, pipeline is hard to manage, and can be easily damaged. This research proposes a real time prediction system that helps experts can make decision about pressure anomalies. The gas pipline pressure data of Jungbu City Gas Company, which is one of the domestic city gas suppliers, time variables and environment variables are analysed. In this research, regression models that predicts pipeline pressure in minutes are proposed. Random forest, support vector regression (SVR), long-short term memory (LSTM) algorithms are used to build pressure prediction models. A comparison of pressure prediction models' preformances shows that the LSTM model was the best. LSTM model for Asan-si have root mean square error (RMSE) 0.011, mean absolute percentage error (MAPE) 0.494. LSTM model for Cheonan-si have RMSE 0.015, MAPE 0.668.

• KIISE Transactions on Computing Practices
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• v.22 no.9
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• pp.455-460
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• 2016

Recently, with more severe types felonies such as robbery and sexual violence, the importance of crime prediction and prevention is emphasized. For accurate and prompt crime prediction and prevention, both a classification model of crime with high accuracy based on past criminal records and well-designed system interface are required. However previous studies on the analysis of crime factors have limitations in terms of accuracy due to the difficulty of data preprocessing. In addition, existing crime monitoring systems merely offer a vast amount of crime analysis results, thereby they fail to provide users with functions for more effective monitoring. In this paper, we propose a classification model for types of crime based on random-forest algorithms and system design factors for real-time crime prediction. From our experiments, we proved that our proposed classification model is superior to others that only use criminal records in terms of accuracy. Through the analysis of existing crime monitoring systems, we also designed and developed a system for real-time crime monitoring.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.10
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• pp.309-316
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• 2020

We predict the mortality of the elderly patients visiting the emergency department who are over 65 years old using Feed Forward Neural Network (FFNN) and Convolutional Neural Network (CNN) respectively. Medical data consist of 99 features including basic information such as sex, age, temperature, and heart rate as well as past history, various blood tests and culture tests, and etc. Among these, we used random forest to select features by measuring the importance of features in the prediction of mortality. As a result, using the top 80 features with high importance is best in the mortality prediction. The performance of the FFNN and CNN is compared by using the selected features for training each neural network. To train CNN with images, we convert medical data to fixed size images. We acquire better results with CNN than with FFNN. With CNN for mortality prediction, F1 score and the AUC for test data are 56.9 and 92.1 respectively.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.395-406
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• 2017

The purpose of this study is to identify the pattern of daily electricity demand through clustering and classification. The hourly data was collected by KPS (Korea Power Exchange) between 2008 and 2012. The time trend was eliminated for conducting the pattern of daily electricity demand because electricity demand data is times series data. We have considered k-means clustering, Gaussian mixture model clustering, and functional clustering in order to find the optimal clustering method. The classification analysis was conducted to understand the relationship between external factors, day of the week, holiday, and weather. Data was divided into training data and test data. Training data consisted of external factors and clustered number between 2008 and 2011. Test data was daily data of external factors in 2012. Decision tree, random forest, Support vector machine, and Naive Bayes were used. As a result, Gaussian model based clustering and random forest showed the best prediction performance when the number of cluster was 8.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.6
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• pp.100-105
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• 2017

This work presents an ECG biometric recognition system for the purpose of biometric authentication. ECG biometric approaches are divided into two major categories, fiducial-based and non-fiducial-based methods. This paper proposes a new non-fiducial framework using discrete cosine transform and a Random Forest classifier. When using DCT, most of the signal information tends to be concentrated in a few low-frequency components. In order to apply feature vector of Random Forest, DCT feature vectors of ECG heartbeats are constructed by using the first 40 DCT coefficients. RF is based on the computation of a large number of decision trees. It is relatively fast, robust and inherently suitable for multi-class problems. Furthermore, it trade-off threshold between admission and rejection of ID inside RF classifier. As a result, proposed method offers 99.9% recognition rates when tested on MIT-BIH NSRDB.

• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.213-219
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• 2020

In this paper, we propose a classification analysis method for diagnosing and predicting problematic smartphone use in order to provide policy data on problematic smartphone use, which is getting worse year after year. Attempts have been made to identify key variables that affect the study. For this purpose, the classification rates of Decision Tree, Random Forest, and Support Vector Machine among machine learning analysis methods, which are artificial intelligence methods, were compared. The data were from 25,465 people who responded to the '2018 Problematic Smartphone Use Survey' provided by the Korea Information Society Agency and analyzed using the R statistical package (ver. 3.6.2). As a result, the three classification techniques showed similar classification rates, and there was no problem of overfitting the model. The classification rate of the Support Vector Machine was the highest among the three classification methods, followed by Decision Tree and Random Forest. The top three variables affecting the classification rate among smartphone use types were Life Service type, Information Seeking type, and Leisure Activity Seeking type.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.1
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• pp.8-17
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• 2014

In this research, we employed various data mining techniques to build predictive models for win-loss prediction in Korean professional baseball games. The historical data containing information about players and teams was obtained from the official materials that are provided by the KBO website. Using the collected raw data, we additionally prepared two more types of dataset, which are in ratio and binary format respectively. Dividing away-team's records by the records of the corresponding home-team generated the ratio dataset, while the binary dataset was obtained by comparing the record values. We applied seven classification techniques to three (raw, ratio, and binary) datasets. The employed data mining techniques are decision tree, random forest, logistic regression, neural network, support vector machine, linear discriminant analysis, and quadratic discriminant analysis. Among 21(= 3 datasets${\times}$7 techniques) prediction scenarios, the most accurate model was obtained from the random forest technique based on the binary dataset, which prediction accuracy was 84.14%. It was also observed that using the ratio and the binary dataset helped to build better prediction models than using the raw data. From the capability of variable selection in decision tree, random forest, and stepwise logistic regression, we found that annual salary, earned run, strikeout, pitcher's winning percentage, and four balls are important winning factors of a game. This research is distinct from existing studies in that we used three different types of data and various data mining techniques for win-loss prediction in Korean professional baseball games.