• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.352-356
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• 2004

Without detailed exact knowledge of a retrieval collection, most users find it difficult to formulate effective queries. A method to overcome this difficulty is to use query expansion that reformulates better query from initial query. In this paper we propose concept based query evaluation method using concept of class that retrieved from initial query.

• Journal of Korean Academy of Nursing
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• v.43 no.1
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• pp.1-10
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• 2013

Purpose: The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method. Methods: A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs. Results: The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease. Conclusion: The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.

• Journal of the Korea Society of Computer and Information
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• v.27 no.6
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• pp.77-86
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• 2022

Recently, various cryptocurrencies (coins) based on block chains have appeared, and interest in privacy coins, which is an anonymity-based cryptocurrency that values personal information protection, is growing. In this paper, we look at coin abuse cases using privacy coins, and analyze the technology that guarantees the anonymity of 8 mainly traded privacy coins (Monero, Dash, Zcash, BEAM, Grin, Horizen, Verge, and Pirate Chain). We would like to analyze the applied technologies for We present the problems that can occur in these privacy coins, check the technology and each element applied to the privacy coin, and analyze the technical difficulty of the anonymity guarantee technology for the mainly traded coins through this, and Appropriate countermeasures and classification of privacy coins for technical difficulty were presented through the problem. Through this, the standard for re-evaluating the value of the coin according to the application of appropriate technology for the privacy coin can be presented.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.53-59
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• 2008

Recently, vision-based hand gesture recognition techniques have been developed for assisting elderly and disabled people to control home appliances. Frequently occurred problems which lower the hand gesture recognition rate are due to the inter-person variation and intra-person variation. The recognition difficulty caused by inter-person variation can be handled by using user dependent model and model selection technique. And the recognition difficulty caused by intra-person variation can be handled by using fuzzy logic. In this paper, we propose multivariate fuzzy decision tree learning and classification method for a hand motion recognition system for multiple users. When a user starts to use the system, the most appropriate recognition model is selected and used for the user.

• Phonetics and Speech Sciences
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• v.16 no.2
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• pp.37-47
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• 2024

This study was conducted on 27 male students majoring in or preparing for popular music vocals to determine whether they were aware of their voice classification and vocal range. Additionally, differences in the fundamental frequency and average speaking fundamental frequency were compared among the voice classifications. Moreover, considering that they may differ in their ability to produce high frequencies depending on the vowel, differences in voice ranges among the cardinal vowels, /a/, /i/, and /u/, were examined, and differences in voice ranges between vowels were compared by voice classification. The results showed that more than half of the male students majoring in or preparing for popular music vocals were not accurately aware of their voice types. In addition, statistically significant differences were found in the maximum fundamental frequency and frequency range among vowels, indicating differences in the voice range that can be produced depending on the vowel type. In particular, the voice range decreased in the following order: /a/>/u/>/i/. This suggests that while the vowel /a/ is easier to articulate in the high register compared to other vowels, vowels /u/ and /i/ as high vowels involve narrowing of the oral cavity due to the raised position of the tongue, accompanied by raising of the larynx, resulting in a decrease in voice range and difficulty in vocalizing in the high register.

• Journal of Internet Computing and Services
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• v.12 no.6
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• pp.117-128
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• 2011

In this paper, we present a novel approach for contents-based medication image retrieval from a medication image database using the shape classification and color information of the medication. One major problem in developing a contents-based drug image retrieval system is there are too many similar images in shape and color and it makes difficult to identify any specific medication by a single feature of the drug image. To resolve such difficulty in identifying images, we propose a hybrid approach to retrieve a medication image based on shape and color features of the medication. In the first phase of the proposed method we classify the medications by shape of the images. In the second phase, we identify them by color matching between a query image and preclassified images in the first phase. For the shape classification, the shape signature, which is unique shape descriptor of the medication, is extracted from the boundary of the medication. Once images are classified by the shape signature, Hue and Saturation(HS) color model is used to retrieve a most similarly matched medication image from the classified database images with the query image. The proposed system is designed and developed especially for specific population- seniors to browse medication images by using visual information of the medication in a feasible fashion. The experiment shows the proposed automatic image retrieval system is reliable and convenient to identify the medication images.

• The Journal of Churna Manual Medicine for Spine and Nerves
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• v.7 no.2
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• pp.1-14
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• 2012

Objectives : The aim of this study was to prepare the validity and relevance for National Health Insurance of Chuna manual therapy through measurement of resource-based relative value scale(RBRVS) scores. Methods : To prepare for National Health Insurance, we studied it about standardized classification of the practice and definition, and speciality by difficulty and safety of Chuna manual therapy. Results : Classification of the practice could be classified to 7 of Chuna manual therapy and 24 traditional manual therapy, it also categorized as one of 3 kinds(basic, simple, special). The RBRVS scores of Chuna manual therapy were measured to 283.28, 566.57 and 1133.14. Conclusions : This study could be used to basis data for National Health Insurance of Chuna manual therapy, but further studies must be needed more objective investigation and data to calculate RBRVS scores.

• Archives of Plastic Surgery
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• v.50 no.5
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• pp.446-451
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• 2023

Background Elderly patients often have complications of blepharoptosis surgery that can result in the appearance or exacerbation of superficial punctate keratopathy (SPK). However, postoperative changes to SPK status have not been previously reported. We used subjective assessment of symptoms and measurement of SPK scale classification to investigate the safety and efficacy of blepharoptosis surgery in elderly patients. Methods Included in this prospective study were 22 patients (44 eyes) with bilateral blepharoptosis that underwent surgery. Patients comprised 8 males and 14 females with a mean (±standard deviation) age of 75.7 ± 8.2 years (range: 61-89). Blepharoptosis surgery consisted of transcutaneous levator advancement and blepharoplasty including resection of soft tissue (skin, subcutaneous tissue, and the orbicularis oculi muscle). Margin reflex distance-1 (MRD-1) measurement, a questionnaire survey of symptoms and SPK scale classification, was administered preoperatively and 3 months postoperatively for evaluation. Results The median MRD-1 was 1 mm preoperatively and 2.5 mm postoperatively, representing a significant postoperative improvement. SPK area and density scores were found to increase when the MRD-1 increase was more than 2.5 mm with surgery. All 10 items on the questionnaire tended have increased scores after surgery, and significant differences were observed in 7 items (poor visibility, ocular fatigue, heavy eyelid, foreign body sensation, difficulty in focusing, headaches, and stiff shoulders). Conclusion Blepharoptosis surgery was found to be a safe and effective way to maintain the increase in MRD-1 within 2.0 mm. Despite the benefits, surgeons must nonetheless be aware that blepharoptosis surgery is a delicate procedure in elderly people.

• Journal of the Korea Society of Computer and Information
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• v.28 no.5
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• pp.17-28
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• 2023

Potholes that occur on paved roads can have fatal consequences for vehicles traveling at high speeds and may even lead to fatalities. While manual detection of potholes using human labor is commonly used to prevent pothole-related accidents, it is economically and temporally inefficient due to the exposure of workers on the road and the difficulty in predicting potholes in certain categories. Therefore, completely preventing potholes is nearly impossible, and even preventing their formation is limited due to the influence of ground conditions closely related to road environments. Additionally, labeling work guided by experts is required for dataset construction. Thus, in this paper, we utilized the Mean Teacher technique, one of the semi-supervised learning-based knowledge distillation methods, to achieve robust performance in pothole image classification even with limited labeled data. We demonstrated this using performance metrics and GradCAM, showing that when using semi-supervised learning, 15 pre-trained CNN models achieved an average accuracy of 90.41%, with a minimum of 2% and a maximum of 9% performance difference compared to supervised learning.

• Journal of Information Technology Services
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• v.11 no.4
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• pp.233-250
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• 2012

In this paper, credit card delinquency means the possibility of occurring bad debt within the certain near future from the normal accounts that have no debt and the problem is to predict, on the monthly basis, the occurrence of delinquency 3 months in advance. This prediction is typical binary classification problem but suffers from the issue of data imbalance that means the instances of target class is very few. For the effective prediction of bad debt occurrence, Support Vector Machine (SVM) with kernel trick is adopted using credit card usage and payment patterns as its inputs. SVM is widely accepted in the data mining society because of its prediction accuracy and no fear of overfitting. However, it is known that SVM has the limitation in its ability to processing the large-scale data. To resolve the difficulties in applying SVM to bad debt occurrence prediction, two stage clustering is suggested as an effective data reduction method and ensembles of SVM models are also adopted to mitigate the difficulty due to data imbalance intrinsic to the target problem of this paper. In the experiments with the real world data from one of the major domestic credit card companies, the suggested approach reveals the superior prediction accuracy to the traditional data mining approaches that use neural networks, decision trees or logistics regressions. SVM ensemble model learned from T2 training set shows the best prediction results among the alternatives considered and it is noteworthy that the performance of neural networks with T2 is better than that of SVM with T1. These results prove that the suggested approach is very effective for both SVM training and the classification problem of data imbalance.