• Journal of the Korean Society for information Management
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• v.36 no.4
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• pp.83-105
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• 2019

This study aims to suggest an effective method for the automatic classification of keywords with similar patterns by calculating pattern similarity of temporal data. For this, large scale news on the Web were collected and time series data composed of 120 time segments were built. To make training data set for the performance test of the proposed model, 440 representative keywords were manually classified according to 8 types of trend. This study introduces a Dynamic Time Warping(DTW) method which have been commonly used in the field of time series analytics, and proposes an application model, MA-DTW based on a Moving Average(MA) method which gives a good explanation on a tendency of trend curve. As a result of the automatic classification by a k-Nearest Neighbor(kNN) algorithm, Euclidean Distance(ED) and DTW showed 48.2% and 66.6% of maximum micro-averaged F1 score respectively, whereas the proposed model represented 74.3% of the best micro-averaged F1 score. In all respect of the comprehensive experiments, the suggested model outperformed the methods of ED and DTW.

• Korean Journal of Cognitive Science
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• v.21 no.4
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• pp.669-696
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• 2010

Assuming that the whole meaning of a document is a composition of the meanings of each part, this paper proposes to study the automatic grading of movie reviews which contain sentimental expressions. This will be accomplished by calculating the values of semantic segments and performing data classification for each review. The ARSSA(The Automatic Rating System for Sentiment analysis using an Appraisal dictionary) system is an effort to model decision making processes in a manner similar to that of the human mind. This aims to resolve the discontinuity between the numerical ranking and textual rationalization present in the binary structure of the current review rating system: {rate: review}. This model can be realized by performing analysis on the abstract menas extracted from each review. The performance of this system was experimentally calculated by performing a 10-fold Cross-Validation test of 1000 reviews obtained from the Naver Movie site. The system achieved an 85% F1 Score when compared to predefined values using a predefined appraisal dictionary.

• Korean Journal of Clinical Pharmacy
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• v.34 no.1
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• pp.21-29
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• 2024

Background: Daytime sleepiness, a common phenomenon among adolescents focused on academics, has negative effects on aspects such as growth and overall learning. However, research on various drugs and diseases affecting daytime sleepiness is lacking in the reality. Therefore, this study aims to investigate the factors influencing daytime sleepiness in adolescents with daytime sleepiness. Methods: This study was conducted through a survey of 2,432 middle and high school students, aged 14 to 19. The questionnaire consisted of information on socio-demographic characteristics, overall health status, and sleep patterns. The Pediatric Daytime Sleepiness Scale (PDSS), translated into Korean, was used to assess daytime sleepiness. Daytime sleepiness was measured by calculating the total score for each item of the PDSS, and divided into two groups based on the cutoff value of 19, which was the upper quartile. Results: We analyzed a total of 1,770 students including 799 boys and 971 girls. Students with a PDSS score of 19 or higher made up 33.3% of boys and 66.7% of girls. In multivariate analyses, females, smoking, poor self-reported health level, sleep after 12 am, not feeling refreshed in the morning, headache, muscle pain, and scoliosis increased the risk of daytime sleepiness significantly. The AUROC of PDSS, including significant factors in multivariate analyses, was 0.751 (95% CI 0.725~0.776). Conclusions: Daytime sleepiness in adolescents affects growth, academic performance, and emotional stability. Therefore, it is important to manage medications, diseases, and other factors that affect daytime sleepiness on a social level.

• Journal of Korean Academy of Nursing
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• v.29 no.4
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• pp.817-828
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• 1999

This is a descriptive study on quality of life(QOL) and related factors of ostomates to provide a basic data for development of nursing interventions. The subjects were 110 ostomates who were members of the Daegu or Daejon branches of the Korean Ostomy Association. Data collection was performed between March 1st and April 6th, 1998. Measurements of QOL, self-care, family support, self-esteem, and hope were used as the study tools. Data were analyzed with the SAS program by using t-test, ANOVA Pearson correlation and stepwise multiple regression. The results are as follows : 1) The score on the QOL scale ranged from 97 to 226 with a mean of 164.53($\pm$28.29). 2) The score of QOL on the general and ostomate-related characteristics showed significant differences according to monthly income, monthly participation in ostomate meetings, combined treatments after operation, types of evacuation management, problems of ostomy, help in caring for the ostomy, length of time since ostomate surgery, and presence or absence of readmission after discharge. 3) There were significant positive correlations between quality of life and other factors : self-esteem(r＝0.7107, P＜0.001), hope(r＝0.6584, P＜0.001) family support(r＝0.6191, P＜0.001), perception of health condition(r＝0.6017, P＜0.001), and self-care (r＝0.2286, P＜0.05). 4) The variables that affected the quality of life of the subjects were self-esteem, level of family support, perception of health condition, monthly participation in ostomate meetings, combined treatments after operation, monthly income, level of hope, and age in that order. The variance of quality of life was $R^2$＝77.20 percent by calculating the sum of those variables. In conclusion, it is necessary for nursing intervention to promote self-esteem, family support, and hope in the care of ostomates in order to improve QOL.

• Imaging Science in Dentistry
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• v.52 no.4
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• pp.351-357
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• 2022

Purpose: Convolutional neural networks (CNNs) have rapidly emerged as one of the most promising artificial intelligence methods in the field of medical and dental research. CNNs can provide an effective diagnostic methodology allowing for the detection of early-staged diseases. Therefore, this study aimed to evaluate the performance of a deep CNN algorithm for apical lesion segmentation from panoramic radiographs. Materials and Methods: A total of 1000 panoramic images showing apical lesions were separated into training (n=800, 80%), validation (n=100, 10%), and test (n=100, 10%) datasets. The performance of identifying apical lesions was evaluated by calculating the precision, recall, and F1-score. Results: In the test group of 180 apical lesions, 147 lesions were segmented from panoramic radiographs with an intersection over union (IoU) threshold of 0.3. The F1-score values, as a measure of performance, were 0.828, 0.815, and 0.742, respectively, with IoU thresholds of 0.3, 0.4, and 0.5. Conclusion: This study showed the potential utility of a deep learning-guided approach for the segmentation of apical lesions. The deep CNN algorithm using U-Net demonstrated considerably high performance in detecting apical lesions.

• The Journal of Korean Medicine
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• v.44 no.1
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• pp.76-87
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• 2023

Objectives: The purpose of this study is to draw priorities for candidate projects so that future inter-Korean traditional medicine cooperation can be promoted efficiently and effectively. Methods: This study used the Delphi-AHP method to derive priorities for the inter-Korean traditional medicine cooperation project. First, 33 candidate projects were selected through a data survey. In addition, the priority importance score was calculated through a 2-round mini-Delphi survey of 20 experts. The importance of 33 candidate projects was calculated by three evaluation criteria, and the weights for these three evaluation criteria were derived through the AHP method. Results: As for the weight by item, 'feasibility' was the highest with 0.6749 points, followed by 'social ripple effect' (0.1811) and 'instrumentality' (0.1439). As a result of calculating the importance score by reflecting the weight of the evaluation criteria for each project, the South's sole project, "Understanding the Status of North Korea's Korean Medicine," was the top priority, followed by the "Establishment of Strategy for Inter-Korean Traditional Medicine and Cooperation." Conclusions: As a result of this study, experts now believe that it is important to prioritize the highly feasible South Korean independent project in the field of traditional medicine between the two Koreas. This will serve as the basis for promoting cooperative projects in the event of future changes in the inter-Korean situation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.72-81
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• 2023

This research proposes a novel approach to tackle the challenge of categorizing unstructured customer complaints in the automotive industry. The goal is to identify potential vehicle defects based on the findings of our algorithm, which can assist automakers in mitigating significant losses and reputational damage caused by mass claims. To achieve this goal, our model uses the Word2Vec method to analyze large volumes of unstructured customer complaint data from the National Highway Traffic Safety Administration (NHTSA). By developing a score dictionary for eight pre-selected criteria, our algorithm can efficiently categorize complaints and detect potential vehicle defects. By calculating the score of each complaint, our algorithm can identify patterns and correlations that can indicate potential defects in the vehicle. One of the key benefits of this approach is its ability to handle a large volume of unstructured data, which can be challenging for traditional methods. By using machine learning techniques, we can extract meaningful insights from customer complaints, which can help automakers prioritize and address potential defects before they become widespread issues. In conclusion, this research provides a promising approach to categorize unstructured customer complaints in the automotive industry and identify potential vehicle defects. By leveraging the power of machine learning, we can help automakers improve the quality of their products and enhance customer satisfaction. Further studies can build upon this approach to explore other potential applications and expand its scope to other industries.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1088-1097
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• 2021

Vibration data of mechanical equipment inevitably have noise. This noise adversely af ects the maintenance of mechanical equipment. Accordingly, the performance of a learning model depends on how effectively the noise of the data is removed. In this study, the noise of the data was removed using the Denoising Auto Encoder (DAE) technique which does not include the characteristic extraction process in preprocessing time series data. In addition, the performance was compared with that of the Wavelet Transform, which is widely used for machine signal processing. The performance comparison was conducted by calculating the failure detection rate. For a more accurate comparison, a classification performance evaluation criterion, the F-1 Score, was calculated. Failure data were detected using the One-Class SVM technique. The performance comparison, revealed that the DAE technique performed better than the Wavelet Transform technique in terms of failure diagnosis and error rate.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.4
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• pp.673-689
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• 2022

We propose an APT attack scoring method as a part of the process for detecting and responding to APT attacks. First, unlike previous work that considered inconsistent and subjective factors determined by cyber security experts in the process of scoring cyber attacks, we identify quantifiable factors from components of MITRE ATT&CK^Ⓡ techniques and propose a method of quantifying each identified factor. Then, we propose a method of calculating the score of the unit attack technique from the quantified factors, and the score of the entire APT attack composed of one or more multiple attack techniques. We present the possibility of quantification to determine the threat level and urgency of cyber attacks by applying the proposed scoring method to the APT attack reports, which contains the hundreds of APT attack cases occurred worldwide. Using our work, it will be possible to determine whether actual cyber attacks have occurred in the process of detecting APT attacks, and respond to more urgent and important cyber attacks by estimating the priority of APT attacks.

• Clinics in Shoulder and Elbow
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• v.25 no.1
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• pp.57-64
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• 2022

Background: There are various conservative treatment options for lateral epicondylitis (LE). The aim is to evaluate pain, daily functioning, and complications after ultrasound-standardized autologous blood injections in patients with LE. Methods: For this prospective cohort study, consecutive patients (>18 years) diagnosed with LE were included. Autologous blood was injected using a medical device containing an injection disposable with 12 small needles (Instant Tennis Elbow Cure [ITEC]) device. Patient-Rated Tennis Elbow Evaluation (PRTEE), subjective elbow score (SES), palpation and provocation pain, satisfaction, and complications of treatment were measured at baseline and two months after treatment. Paired t-tests and Fisher's exact tests were used for calculating the difference between pre- and post-treatment outcomes. Results: Fifty-five elbows were analyzed. Mean time between pre- and post-treatment was 11.1 weeks (standard deviation [SD], 8.9 weeks). The mean PRTEE score decreased from 68.2 (SD, 15.7) before surgery to 53.2 (SD, 25.9; p<0.001) after. The mean SES improved from 36.9 (SD, 20.8) to 51.7 (SD, 27.4; p<0.001). Despite this improvement, only 44.7% of patients showed relevant clinical improvement in PRTEE, and 37.3% showed significant clinical improvement based on SES. Four patients reported a complication and the injection disposable failed three times. Conclusions: Ultrasound-standardized autologous blood injection using the ITEC device is not an effective tool in reducing symptoms related to LE. This study showed that only half of all patients experienced a positive effect. In this heterogeneous cohort of patients, we showed no added value of ultrasound standardization.