• 한국전자거래학회지
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• 제14권3호
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• pp.107-129
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• 2009

e-비즈니스를 이행하는 사람은 정보기술과 비즈니스 프로세스를 이해하여야 한다. 현행 정책과 시스템은 국내 e-비즈니스 인력양성을 위하여 새로운 변화가 이루어져야 한다. 새로운 차원에서 변화를 충족하기 위하여 현재의 인력양성에 대한 정의와 인력분류를 이행하였다. e-비즈니스 인력의 교육과정에 대한 역량 및 프로그램 개발은 신입사원, 전문가, 관리자, 경영자의 과정으로 분류하였다. 그리고 그 과정은 대학에서 전공과 기업에서 수행하는 업무와 기능에 따라 10가지 세부 직무를 구분하였다. 세부 직무에 필요한 역량을 ASK(Attitude, Skill, Knowledge)모델로 제시하여 프로그램 개발에 대한 초점을 맞추었다.

• 한국직업건강간호학회지
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• 제28권2호
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• pp.75-82
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• 2019

Purpose: This study aimed to determine the influence of injury and/or injured area classification on depression in patients with industrial injuries. Methods: The participants comprised438 patients who consented to participate and completed self-reported questionnaires. Data were analyzed using SPSS/WIN version 22.0 for descriptive statistics, $x^2$ test, fisher's exact test, ANOVA, and post-hoc $Scheff{\acute{e}}$ test. A stepwise multiple regression analysis was used to identify factors influencing depression. Results: The results indicated that the effect of disease classification and injured areas on depression were significantly different in patients with industrial injuries. The results further showed that severe depression was significantly higher in cardiovascular patients and patients with an injured area of the head and waist. The most powerful predictor was age (50~59 years), return to work (reemployment), disease classification (cardiovascular), and injured area (head, including vascular disease). Conclusion: This study showed that the most influential variable of depression in patients with industrial injuries were cardiovascular issues, injury areas of the head and waist, being aged 50~59 years, and reemployment. To reduce depression in these patients, it is important to develop and implement a psychiatric rehabilitation program that helps patients to formulate a concrete plan and goal for recovery, enabling patients to actively engage in their rehabilitation.

• 한국컴퓨터정보학회논문지
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• 제24권1호
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• pp.49-55
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• 2019

The character dialogue created by AI is unnatural when compared with human-made dialogue, and it can not reveal the character's personality properly in spite of remarkable development of AI. The purpose of this paper is to classify characters through the linguistic style and to investigate the relation of the specific linguistic style with the personality. We analyzed the dialogues of 92 characters selected from total 60 movies categorized four movie genres, such as romantic comedy, action, comedy and horror/thriller, using Linguistic Inquiry and Word Count (LIWC), a text analysis software. As a result, we confirmed that there is a unique language style according to genre. Especially, we could find that the emotional tone than analytical thinking are two important features to classify. They were analyzed as very important features for classification as the precision and recall is over 78% for romantic comedy and action. However, the precision and recall were 66% and 50% for comedy and horror/thriller. Their impact on classification was less than romantic comedy and action genre. The characters of romantic comedy deal with the affection between men and women using a very high value of emotional tone than analytical thinking. The characters of action genre who need rational judgment to perform mission have much greater analytical thinking than emotional tone. Additionally, in the case of comedy and horror/thriller, we analyzed that they have many kinds of characters and that characters often change their personalities in the story.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.4105-4121
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• 2021

Recently, malware classification based on Deep Neural Networks (DNN) has gained significant attention due to the rise in popularity of artificial intelligence (AI). DNN-based malware classifiers are a novel solution to combat never-before-seen malware families because this approach is able to classify malwares based on structural characteristics rather than requiring particular signatures like traditional malware classifiers. However, these DNN-based classifiers have been found to lack robustness against malwares that are carefully crafted to evade detection. These specially crafted pieces of malware are referred to as adversarial examples. We consider a clever adversary who has a thorough knowledge of DNN-based malware classifiers and will exploit it to generate a crafty malware to fool DNN-based classifiers. In this paper, we propose a DNN-based malware classifier that becomes resilient to these kinds of attacks by exploiting Generative Adversarial Network (GAN) based data augmentation. The experimental results show that the proposed scheme classifies malware, including AEs, with a false positive rate (FPR) of 3.0% and a balanced accuracy of 70.16%. These are respective 26.1% and 18.5% enhancements when compared to a traditional DNN-based classifier that does not exploit GAN.

• Archives of Plastic Surgery
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• 제50권2호
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• pp.160-165
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• 2023

Background The number of cosmetic and reconstructive surgeries that use breast implants is increasing in Korea. Recently, it has been reported that breast implant-associated anaplastic large-cell lymphoma is related to textured breast implants, and interest in classification according to the texture of breast implants is increasing. However, there is currently no clear and unified classification. In particular, the definition of "microtextured" is highly varied. In this study, we retrospectively investigated and analyzed the clinical outcomes of smooth and microtextured breast implants. Methods A retrospective chart review of all patients who underwent breast augmentation surgery with smooth and microtextured silicone gel implants between January 2016 and July 2020 was performed. We retrospectively analyzed implant manufacturer, age, body mass index (BMI), smoking status, incision location, implant size, follow-up period, complications, and reoperation rate. Results A total of 266 patients underwent breast augmentation surgery, of which 181 used smooth silicone gel implants and 85 used microtextured silicone gel implants. Age, BMI, smoking status, implant size, and follow-up period were not significantly different between the two groups. Similarly, complications and reoperation rates were not significantly different between the two groups. Conclusion It is important to provide information regarding the clinical risks and benefits of breast implants to surgeons and patients through a clear and unified classification according to the texture of the breast implant.

• Child Health Nursing Research
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• 제7권2호
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• pp.191-202
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• 2001

This study was attempted to help in explore new direction about classification of the severity of the pediatric patients visiting emergency center. Data were collected from 276 patients who visited emergency center of E University Hospital during 3 months period from March 1, to May 31,1999. The results were as follows: 1. The degree of severity of the pediatric patients visiting emergency center shown ranged 0-18 and averaged .87. 2. With the respect to the severity of the pediatric patients visiting emergency center, there were statiscally significant difference in patients' visiting time(F=2.607, p=.025), disease classification(F=9.606, p=.000), consciousness level(F=71.499, p=.000), period of symptom manifestation (F=2.262, p=.030), pediatric patients protector's thinking about pediatric patients state (F=16.833, p=.000), treatment outcome (t=5.362, p=.000), duration of stay at emergency center(F=23.944, p=.000).

• Imaging Science in Dentistry
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• 제37권1호
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• pp.1-7
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• 2007

Fibro-osseous lesions are composed of connective tissue and varying amount of mineralized substances, which may be bony or cementum-like structures. It is necessary for oral radiologist to differentiate due to the tendency of these fibro-osseous lesions to show similar histopathologic appearances, while the management of each lesion is different. However we often encounter a little difficulty in judgement because there are some overlaps between concept of each lesions. So recently I suggest, we face a need to review basic concept and classification of several fibro-osseous jaw lesions. In this article, several fibre-osseous lesions, such as fibrous dysplasia, cemento-ossifying fibroma and cemento-osseous dysplasia, will be discussed basing on the review of literature. particular emphasis will be made on the nomenclature revision of WHO's classification in 1992.

• 대한한의학회지
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• 제33권4호
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• pp.37-41
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• 2012

Objectives: This study was aimed to establish the clinical features of the patients suffering from hyperhidrosis, who are willing to visit Oriental clinics. Methods: Forty-six patients with primary hyperhidrosis were enrolled in this study classification, body part of perspiration and its severity, and constitutional differentiation were analyzed. Results: 85.1% of patients were 10 to 39 years old. The body part most complained of hyperhidrosis was the hands and feet at 50%. The portion of Teaeumin, Soumin, and Soyangin was 56.6% 21.1%, and 21.7% respectively. Soumin specifically showed a higher frequency of palmar and plantar hyperhidrosis as 90%. The average score of symptoms was $5.1{\pm}1.7$ by a 10-point self- reporting numeric rating scale (NRS). No statistical difference of NRS score was observed regarding gender, Sasang classification, or hyperhidrosis region. Conclusions: This study provides an overview of hyperhidrosis patients visiting an Oriental clinic, and will be helpful in establishing a strategy for the Korean medicine (KM)-based therapeutic development.

• 동의생리병리학회지
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• 제20권1호
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• pp.268-272
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• 2006

In this study, when physician make a diagnosis of Sasang constitution of patients, anthropometric data are applied to seek the classification function into Sasang constitution. Data used in the analysis are the anthropometric data of 550 patients who had been treated in nine oriental medical hospital, and our data have no missing value in 12 anthropometric variables. In order to improve the accuracy of classification function into Sasang constitution, we consider one method of variable transformation of anthropometric data based on oriental medicine.

• Communications for Statistical Applications and Methods
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• 제30권2호
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• pp.215-226
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• 2023

Deep learning has made great strides in the field of unstructured data such as text, images, and audio. However, in the case of tabular data analysis, machine learning algorithms such as ensemble methods are still better than deep learning. To keep up with the performance of machine learning algorithms with good predictive power, several deep learning methods for tabular data have been proposed recently. In this paper, we review the latest deep learning models for tabular data and compare the performances of these models using several datasets. In addition, we also compare the latest boosting methods to these deep learning methods and suggest the guidelines to the users, who analyze tabular datasets. In regression, machine learning methods are better than deep learning methods. But for the classification problems, deep learning methods perform better than the machine learning methods in some cases.