• Journal of Digital Convergence
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• v.17 no.1
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• pp.239-247
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• 2019

An accurate prediction of emotion is a very important issue for the sake of patient-centered medical device development and emotion-related psychology fields. Although there have been many studies on emotion prediction, no studies have applied the heart rate variability and neuro-fuzzy approach to emotion prediction. We propose ANFEP(Adaptive Neuro Fuzzy System for Emotion Prediction) HRV. The ANFEP bases its core functions on an ANFIS(Adaptive Neuro-Fuzzy Inference System) which integrates neural networks with fuzzy systems as a vehicle for training predictive models. To prove the proposed model, 50 participants were invited to join the experiment and Heart rate variability was obtained and used to input the ANFEP model. The ANFEP model with STDRR and RMSSD as inputs and two membership functions per input variable showed the best results. The result out of applying the ANFEP to the HRV metrics proved to be significantly robust when compared with benchmarking methods like linear regression, support vector regression, neural network, and random forest. The results show that reliable prediction of emotion is possible with less input and it is necessary to develop a more accurate and reliable emotion recognition system.

• The Journal of Bigdata
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• v.3 no.2
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• pp.71-78
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• 2018

The study is based on machine learning techniques to increase the accuracy of the forest fire predictive model. It used 14 years of data from 2003 to 2016 in Gang-won-do where forest fire were the most frequent. To reduce weather data errors, Gang-won-do was divided into nine areas and weather data from each region was used. However, dividing the forest fire forecast model into nine zones would make a large difference between the date of occurrence and the date of not occurring. Imbalance issues can degrade model performance. To address this, several sampling methods were applied. To increase the accuracy of the model, five indices in the Canadian Frost Fire Weather Index (FWI) were used as derived variable. The modeling method used statistical methods for logistic regression and machine learning methods for random forest and xgboost. The selection criteria for each zone's final model were set in consideration of accuracy, sensitivity and specificity, and the prediction of the nine zones resulted in 80 of the 104 fires that occurred, and 7426 of the 9758 non-fires. Overall accuracy was 76.1%.

• Journal of Yeungnam Medical Science
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• v.35 no.2
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• pp.171-178
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• 2018

Background: To evaluate mid-term oncological and functional outcomes in patients with prostate cancer treated by robot-assisted laparoscopic radical prostatectomy (RALP) at our institution. Methods: We retrospectively reviewed the medical records of 128 patients with prostate cancer who underwent RALP at our institution between February 2008 and April 2010. All patients enrolled in this study were followed up for at least 5 years. We analyzed biochemical recurrence (BCR)-free survival using a Kaplan-Meier survival curve analysis and predictive factors for BCR using multivariate Cox regression analysis. Continence recovery rate, defined as no use of urinary pads, was also evaluated. Results: Based on the D'Amico risk classification, there were 30 low-risk patients (23.4%), 47 intermediaterisk patients (38.8%), and 51 high-risk patients (39.8%), preoperatively. Based on pathological findings, 50.0% of patients (64/128) showed non-organ confined disease (${\geq}T3a$) and 26.6% (34/128) had high grade disease (Gleason score ${\geq}8$). During a median follow-up period of 71 months (range, 66-78 months), the frequency of BCR was 33.6% (43/128) and the median BCR-free survival was 65.9 (0.4-88.0) months. Multivariate Cox regression analysis revealed that high grade disease (Gleason score ${\geq}8$) was an independent predictor for BCR (hazard ratio=4.180, 95% confidence interval=1.02-17.12, p=0.047). In addition, a majority of patients remained continent following the RALP procedure, without the need for additional intervention for post-prostatectomy incontinence. Conclusion: Our study demonstrated acceptable outcomes following an initial RALP procedure, despite 50% of the patients investigated demonstrating high-risk features associated with non-organ confined disease.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.3201-3209
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• 2018

The purpose of this study was to explore the factors predicting increased usage hours of smartphone among adolescents. Secondary data was analyzed to be collected from a nationally representative sample of 2017 Korean adolescents. This study sample included 54,601 students in middle or high schools of Korea. The collected data were analyzed SPSS version 23.0 program for frequency, percentage, mean, standard deviation, t-test, ANOVA, Pearson's correlation coefficient and binary logistic regression analysis. In the results, the mean usage hour of smartphone among the adolescents was 28.42 (SD 23.30) per week. Analyses of the differences in usage hours of smartphone according to research variables were found that the groups of lower level of study (F=1361.067, p<.001) and sociality content type (F=761.549, p<.001) spent more time, as compared to the other groups. The logistic analysis showed the predictive factors for increased hour of using smartphone were smartphone usage for sociality (OR: 2.44, 95% CI: 2.26-2.64) and peer group counselor (OR: 1.49, 95% CI: 1.49). Conclusionally, the findings of this study suggests that it needs to understand cause or purpose of smartphone using of adolescent and to cope and educate on the cause.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.723-734
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• 2021

Wind energy is one of the rapidly developing renewable energies which is being developed and invested in response to climate change. As renewable energy policies and power plant installations are promoted, the supply of wind power in Korea is gradually expanding and attempts to accurately predict demand are expanding. In this paper, the ARIMA and ARIMAX models which are Time series techniques and the SVR, Random Forest and XGBoost models which are machine learning models were compared and analyzed to predict wind power generation in the Jeonnam and Gyeongbuk regions. Mean absolute error (MAE) and mean absolute percentage error (MAPE) were used as indicators to compare the predicted results of the model. After subtracting the hourly raw data from January 1, 2018 to October 24, 2020, the model was trained to predict wind power generation for 168 hours from October 25, 2020 to October 31, 2020. As a result of comparing the predictive power of the models, the Random Forest and XGBoost models showed the best performance in the order of Jeonnam and Gyeongbuk. In future research, we will try not only machine learning models but also forecasting wind power generation based on data mining techniques that have been actively researched recently.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.745-760
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• 2021

Prediction of a stock price has been a subject of interest for a long time in financial markets, and thus, many studies have been conducted in various directions. As the efficient market hypothesis introduced in the 1970s acquired supports, it came to be the majority opinion that it was impossible to predict stock prices. However, recent advances in predictive models have led to new attempts to predict the future prices. Here, we summarize past studies on the price prediction by evaluation measures, and predict the direction of stock prices of Samsung Electronics, LG Chem, and NAVER by applying various machine learning models. In addition to widely used technical indicator variables, accounting indicators such as Price Earning Ratio and Price Book-value Ratio and outputs of the hidden Markov Model are used as predictors. From the results of our analysis, we conclude that no models show significantly better accuracy and it is not possible to predict the direction of stock prices with models used. Considering that the models with extra predictors show relatively high test accuracy, we may expect the possibility of a meaningful improvement in prediction accuracy if proper variables that reflect the opinions and sentiments of investors would be utilized.

• Investigative Magnetic Resonance Imaging
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• v.25 no.3
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• pp.164-171
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• 2021

Purpose: Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD). Brain atrophy in this disease spectrum begins in the medial temporal lobe structure, which can be recognized by magnetic resonance imaging. To overcome the unsatisfactory inter-observer reliability of visual evaluation, quantitative brain volumetry has been developed and widely investigated for the diagnosis of MCI and AD. The aim of this study was to assess the prediction accuracy of quantitative brain volumetry using a fully automated segmentation software package, NeuroQuant^®, for the diagnosis of MCI. Materials and Methods: A total of 418 subjects from the Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer's Disease cohort were included in our study. Each participant was allocated to either a cognitively normal old group (n = 285) or an MCI group (n = 133). Brain volumetric data were obtained from T1-weighted images using the NeuroQuant software package. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to investigate relevant brain regions and their prediction accuracies. Results: Multivariate logistic regression analysis revealed that normative percentiles of the hippocampus (P < 0.001), amygdala (P = 0.003), frontal lobe (P = 0.049), medial parietal lobe (P = 0.023), and third ventricle (P = 0.012) were independent predictive factors for MCI. In ROC analysis, normative percentiles of the hippocampus and amygdala showed fair accuracies in the diagnosis of MCI (area under the curve: 0.739 and 0.727, respectively). Conclusion: Normative percentiles of the hippocampus and amygdala provided by the fully automated segmentation software could be used for screening MCI with a reasonable post-processing time. This information might help us interpret structural MRI in patients with cognitive impairment.

• Korean Journal of Psychosomatic Medicine
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• v.29 no.1
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• pp.11-16
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• 2021

Objectives : QTc prolongation due to antipsychotics is of major concern because it may lead to fatal ventricular arrhythmia such as torsade de pointes. However, few studies have been conducted on QTc prolongation due to antipsychotics, especially in South Korea. This study aimed to investigate how demographic and clinical variables affect QTc interval in patients with schizophrenia. Methods : By retrospectively reviewing medical records, we assessed QTc interval, demographic data and clinical features of 441 (175 males) patients with schizophrenia who admitted to the psychiatric ward of a general hospital. To explore the predictive factors for QTc interval, hierarchical regression analysis was performed with QTc interval as the dependent variable. Results : The mean QTc interval was 417.2±28.4 ms. In the hierarchical regression analysis, the use of short-acting antipsychotic injection was the strongest predictor of the QTc prolongation. Conclusions : This study demonstrated that the use of short-acting antipsychotic injection may affect QTc prolongation in patients with schizophrenia. This result suggests that more attention should be paid to the use of short-acting antipsychotic injection in the treatment of schizophrenia.

• Journal of the Korean Wood Science and Technology
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• v.49 no.3
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• pp.213-225
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• 2021

In this paper, we investigated the carbonization characteristics of lignin hydrochar prepared by hydrothermal carbonization and established a model for predicting the carbonization degree using near-infrared spectroscopy and partial least squares regression. The carbon content of the hydrothermally carbonized lignin at the temperature of 200 ℃ was higher by approximately 3 wt% than that of the untreated sample, and the carbon content tended to gradually increase as the heating time increased. Hydrothermal carbonization made lignin more carbon-intensive and more homogeneous by eliminating the microparticles. The discriminant and predictive models using near-infrared spectroscopy and partial least squares regression approppriately determined whether hydrothermal carbonization has been applied and predicted the carbon content of hydrothermal carbonized lignin with high accuracy. In this study, we confirmed that we can quickly and nondestructively predict the carbonization characteristics of lignin hydrochar manufactured by hydrothermal carbonization using a partial least squares regression model combined with near-infrared spectroscopy.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.48-58
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• 2021

For securing technology and business competences of companies that is the engine of domestic industrial growth, government-supported policy programs for the creation of commercialization results in various forms such as 『Technology Transaction Market Vitalization』 and 『Technology Finance-based R&D Commercialization Support』 have been carried out since 2014. So far, various studies on technology valuation theories and evaluation variables have been formalized by experts from various fields, and have been utilized in the field of technology commercialization. However, Their practicality has been questioned due to the existing constraint that valuation results are assessed lower than the expectation in the evaluation sector. Even considering that the evaluation results may differ depending on factors such as the corporate situation and investment environment, it is necessary to establish a reference infrastructure to secure the objectivity and reliability of the technology valuation results. In this study, we investigate the evaluation infrastructure built by each institution and examine whether the latest artificial neural networks and deep learning technologies are applicable for performing predictive simulation of technology values based on principal variables, and predicting sales estimates and qualitative evaluation scores in order to embed onto the technology valuation system.