• Journal of Korean Society for Geospatial Information Science
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• v.25 no.1
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• pp.37-46
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• 2017

Recently, disaster caused by social factors is frequently occurring in Korea. Prediction about what crisis could happen is difficult, raising the citizen's concern. In this study, we developed a program to acquire tweet data by applying Python language based Tweepy plug-in, regarding social disasters such as 'Nonspecific motive crimes' and 'Oxy' products. These data were used to evaluate psychological trauma and anxiety of citizens through the text clustering analysis and the opinion mining analysis of the R Studio program after natural language processing. In the analysis of the 'Oxy' case, the accident of Sewol ferry, the continual sale of Oxy products of the Oxy had the highest similarity and 'Nonspecific motive crimes', the coping measures of the government against unexpected incidents such as the 'incident' of the screen door, the accident of Sewol ferry and 'Nonspecific motive crime' due to misogyny in Busan, had the highest similarity. In addition, the average index of the Citizens sentiment score in Nonspecific motive crimes was more negative than that in the Oxy case by 11.61%p. Therefore, it is expected that the findings will be utilized to predict the mental health of citizens to prevent future accidents.

• Journal of the Korean earth science society
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• v.32 no.3
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• pp.249-264
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• 2011

A multi-Gaussian kriging approach extended to space-time domain is presented for uncertainty modeling as well as time-series mapping of environmental variables. Within a multi-Gaussian framework, normal score transformed environmental variables are first decomposed into deterministic trend and stochastic residual components. After local temporal trend models are constructed, the parameters of the models are estimated and interpolated in space. Space-time correlation structures of stationary residual components are quantified using a product-sum space-time variogram model. The ccdf is modeled at all grid locations using this space-time variogram model and space-time kriging. Finally, e-type estimates and conditional variances are computed from the ccdf models for spatial mapping and uncertainty analysis, respectively. The proposed approach is illustrated through a case of time-series Particulate Matter 10 ($PM_{10}$) concentration mapping in Incheon Metropolitan city using monthly $PM_{10}$ concentrations at 13 stations for 3 years. It is shown that the proposed approach would generate reliable time-series $PM_{10}$ concentration maps with less mean bias and better prediction capability, compared to conventional spatial-only ordinary kriging. It is also demonstrated that the conditional variances and the probability exceeding a certain thresholding value would be useful information sources for interpretation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.9
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• pp.243-250
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• 2018

This descriptive study was conducted to investigate the factors affecting the mental health of university students. Data were collected from 312 university students by questionnaires and analyzed by t-test, ANOVA, Scheffe's test, correlation analysis, and multiple regression analysis. The mean scores were 1.69, 1.87 and 2.21 out of 5 on Likert scales for mental health, campus life stress and employment stress, respectively. The mean score for self-esteem was 2.27 out of 4 on a Likert scale. Gender and number of close friends affected mental health significantly. Moreover, there was a negative correlation between mental health and self-esteem(r=-.426, p<0.001), while a positive correlation was observed between mental health and campus life stress (r=0.660, p<0.001), and mental health and employment stress(r=.517, p<0.001). Multiple regression analysis showed that campus life stress (${\beta}=.545$), self-esteem(${\beta}=-.145$), and employment stress (${\beta}=0.067$) affected mental health in order, and the three research variables led to a 45.2% prediction for mental health of university students. Based on the results of this study, effective systematic plans for decreasing campus life stress and employment stress and increasing self-esteem are needed to improve the mental health of university students.

• The Korean Journal of Pain
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• v.24 no.2
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• pp.81-86
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• 2011

Background: Facet joint disease plays a major role in axial low-back pain. Few diagnostic tests and imaging methods for identifying this condition exist. Single photon emission computed tomography (SPECT) is reported that it has a high sensitivity and specificity in diagnosing facet disease. We prospectively evaluated the use of bone scintigraphy with SPECT for the identification of patients with low back pain who would benefit from medial branch block. Methods: SPECT was performed on 33 patients clinically suspected of facet joint disease. After SPECT, an ultrasound guided medial branch block was performed on all patients. On 28 SPECT-positive patients, medial branch block was performed based on the SPECT findings. On 5 negative patients, medial branch block was performed based on clinical findings. For one month, we evaluated the patients using the visual analogue scale (VAS) and Oswestry disability index. SigmaStat and paired t-tests were used to analyze patient data and compare results. Results: Of the 33 patients, the ones who showed more than 50% reduction in VAS score were assigned 'responders'. SPECT positive patients showed a better response to medial branch blocks than negative patients, but no changes in the Oswestry disability index were seen. Conclusions: SPECT is a sensitive tool for the identification of facet joint disease and predicting the response to medial branch block.

• Radiation Oncology Journal
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• v.36 no.2
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• pp.139-146
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• 2018

Purpose: IBTR! 2.0 nomogram is web-based nomogram that predicts ipsilateral breast tumor recurrence (IBTR). We aimed to validate the IBTR! 2.0 using an external data set. Materials and Methods: The cohort consisted of 2,206 patients, who received breast conserving surgery and radiation therapy from 1992 to 2012 at our institution, where wide surgical excision is been routinely performed. Discrimination and calibration were used for assessing model performance. Patients with predicted 10-year IBTR risk based on an IBTR! 2.0 nomogram score of <3%, 3%-5%, 5%-10%, and >10% were assigned to groups 1, 2, 3, and 4, respectively. We also plotted calibration values to observe the actual IBTR rate against the nomogram-derived 10-year IBTR probabilities. Results: The median follow-up period was 73 months (range, 6 to 277 months). The area under the receiver operating characteristic curve was 0.607, showing poor accordance between the estimated and observed recurrence rate. Calibration plot confirmed that the IBTR! 2.0 nomogram predicted the 10-year IBTR risk higher than the observed IBTR rates in all groups. High discrepancies between nomogram IBTR predictions and observed IBTR rates were observed in overall risk groups. Compared with the original development dataset, our patients had fewer high grade tumors, less margin positivity, and less lymphovascular invasion, and more use of modern systemic therapies. Conclusions: IBTR! 2.0 nomogram seems to have the moderate discriminative ability with a tendency to over-estimating risk rate. Continued efforts are needed to ensure external applicability of published nomograms by validating the program using an external patient population.

• Journal of the Korean Society of Food Science and Nutrition
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• v.26 no.4
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• pp.588-594
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• 1997

In this study, the shelf-life of Kochujang during storage was predicted through physicochemical and sensory analyses. Amino nitrogen, lightness, characteristics of surface color, pH and number of viable cell counts in Kochujang decreased during storage, while ammonia nitrogen, titratable acidity and viscosity increased. Among the physicochemical analyses, amino nitrogen content exhibited the highest correlation with sensory score. The marginal amounts of amino nitrogen was 170.6mg%. Degradation rate of amino nitrogen was a first order reaction. Q$_{10}$-value and the activation energy of Kochujang during storage were 1.80 and 8.6kca1/mol, respectively. The shelf-life Predicted of Kochujang at each storage temperature was calculated. The shelf-life predicted was 467 days at 1$0^{\circ}C$, 261 days at 2$0^{\circ}C$ and 133 days at 35$^{\circ}C$.

• Journal of Korean Society of Disaster and Security
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• v.14 no.3
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• pp.17-27
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• 2021

In this study, classification models were built using machine learning techniques that can classify the soil creep risk into three classes from A to C (A: risk, B: moderate, C: good). A total of six machine learning techniques were used: K-Nearest Neighbor, Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting and then their classification accuracy was analyzed using the nationwide soil creep field survey data in 2019 and 2020. As a result of classification accuracy analysis, all six methods showed excellent accuracy of 0.9 or more. The methods where numerical data were applied for data training showed better performance than the methods based on character data of field survey evaluation table. Moreover, the methods learned with the data group (R1~R4) reflecting the expert opinion had higher accuracy than the field survey evaluation score data group (C1~C4). The machine learning can be used as a tool for prediction of soil creep if high-quality data are continuously secured and updated in the future.

• The Journal of Bigdata
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• v.5 no.2
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• pp.53-67
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• 2020

This study employees a supervised learning prediction model to detect nonconformity in advance of processed food manufacturing and processing businesses. The study was conducted according to the standard procedure of machine learning, such as definition of objective function, data preprocessing and feature engineering and model selection and evaluation. The dependent variable was set as the number of supervised inspection detections over the past five years from 2014 to 2018, and the objective function was to maximize the probability of detecting the nonconforming companies. The data was preprocessed by reflecting not only basic attributes such as revenues, operating duration, number of employees, but also the inspections track records and extraneous climate data. After applying the feature variable extraction method, the machine learning algorithm was applied to the data by deriving the company's risk, item risk, environmental risk, and past violation history as feature variables that affect the determination of nonconformity. The f1-score of the decision tree, one of ensemble models, was much higher than those of other models. Based on the results of this study, it is expected that the official food control for food safety management will be enhanced and geared into the data-evidence based management as well as scientific administrative system.

• Journal of Veterinary Clinics
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• v.39 no.5
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• pp.199-206
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• 2022

Postpartum diseases should be predicted to prevent productivity loss before calving especially in organic dairy farms. This study was aimed to investigate the incidence of postpartum metabolic diseases in an organic dairy farm in Korea, to confirm the association between diseases and prepartum blood biochemical parameters, and to evaluate the accuracy of these parameters with a receiver operating characteristic (ROC) analysis for identifying vulnerable cows. Data were collected from 58 Holstein cows (16 primiparous and 42 multiparous) having calved for 2 years on an organic farm. During a transition period from 4 weeks prepartum to 4 weeks postpartum, blood biochemistry was performed through blood collection every 2 weeks with a physical examination. Thirty-one (53.4%) cows (9 primiparous and 22 multiparous) were diagnosed with at least one postpartum disease. Each incidence was 27.6% for subclinical ketosis, 22.4% for subclinical hypocalcemia, 12.1% for retained placenta, 10.3% for displaced abomasum and 5.2% for clinical ketosis. Between at least one disease and no disease, there were significant differences in the prepartum levels of parameters like body condition score (BCS), non-esterified fatty acid (NEFA), total bilirubin (T-bil), direct bilirubin (D-bil) and NEFA to total cholesterol (T-chol) ratio (p < 0.05). The ROC analysis of each of these prepartum parameters had the area under the curve (AUC) <0.7. However, the ROC analysis with logistic regression including all these parameters revealed a higher AUC (0.769), sensitivity (71.0%), and specificity (77.8%). The ROC analysis with logistic regression including the prepartum BCS, NEFA, T-bil, D-bil, and NEFA to T-chol ratio can be used to identify cows that are vulnerable to postpartum diseases with moderate accuracy.

• Journal of the Korean Geosynthetics Society
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• v.22 no.2
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• pp.1-12
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• 2023

Research on the causes of landslides and prediction of vulnerable areas is being conducted globally. This study aims to predict the effective soil depth, a critical element in analyzing and forecasting landslide disasters, using topographic information. Topographic data from various institutions were collected and assigned as attribute information to a 100 m × 100 m grid, which was then reduced through data grading. The study predicted effective soil depth for two cases: three depths (shallow, normal, deep) and five depths (very shallow, shallow, normal, deep, very deep). Three classification models, including K-Nearest Neighbor, Random Forest, and Deep Artificial Neural Network, were used, and their performance was evaluated by calculating accuracy, precision, recall, and F1-score. Results showed that the performance was in the high 50% to early 70% range, with the accuracy of the three classification criteria being about 5% higher than the five criteria. Although the grading criteria and classification model's performance presented in this study are still insufficient, the application of the classification model is possible in predicting the effective soil depth. This study suggests the possibility of predicting more reliable values than the current effective soil depth, which assumes a large area uniformly.