• Management & Information Systems Review
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• v.36 no.4
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• pp.33-51
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• 2017

With the advent of 'The Forth Industrial Revolution' and the growing demand for quality of life due to economic growth, needs for the quality of medical services are increasing. Artificial intelligence has been introduced in the medical field, but it is rarely used in chronic skin diseases that directly affect the quality of life. Also, atopic dermatitis, a representative disease among chronic skin diseases, has a disadvantage in that it is difficult to make an objective diagnosis of the severity of lesions. The aim of this study is to establish an intelligent severity recognition model of atopic dermatitis for improving the quality of patient's life. For this, the following steps were performed. First, image data of patients with atopic dermatitis were collected from the Catholic University of Korea Seoul Saint Mary's Hospital. Refinement and labeling were performed on the collected image data to obtain training and verification data that suitable for the objective intelligent atopic dermatitis severity recognition model. Second, learning and verification of various CNN algorithms are performed to select an image recognition algorithm that suitable for the objective intelligent atopic dermatitis severity recognition model. Experimental results showed that 'ResNet V1 101' and 'ResNet V2 50' were measured the highest performance with Erythema and Excoriation over 90% accuracy, and 'VGG-NET' was measured 89% accuracy lower than the two lesions due to lack of training data. The proposed methodology demonstrates that the image recognition algorithm has high performance not only in the field of object recognition but also in the medical field requiring expert knowledge. In addition, this study is expected to be highly applicable in the field of atopic dermatitis due to it uses image data of actual atopic dermatitis patients.

• The Korean Journal of Nuclear Medicine Technology
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• v.17 no.1
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• pp.30-35
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• 2013

Purpose: One of the current services, providing information to the patients and their guardians by using promotion materials induces positive responses and contributes to the improvement of the hospital reliability. Therefore, the objective of this study is to evaluate the effectiveness of audio visual materials, one of the means of promotion, as a way to give accurate medical information to resolve patient's curiosity about purpose and procedure of their examination and deplete complains about waiting which attributes negative effect to service quality assessment. Materials and Methods: 60 patients(mean age $53.97{\pm}12.24$, male : female = 26 : 34) who had $^{18}F-FDG PET/CT$ scan from July 2012 to August 2012 in Seoul Asan Medical Center were referred to the study. All of the patients having PET/CT scan were asked to watch an informative video material before the injection of radiopharmaceutical ($^{18}F-FDG$) and to fill in a questionnaire. Results: As a result of analyzing the contents of questionnaire, 52% of 60 patients had PET/CT scan for the first time and 72.4% of the patients read the PET/CT guidebook offered from their outpatient department or inpatient wards before their scan. After we searched the level of previous knowledge of the purpose and method of PET/CT scan, the patients answered 25.1% "know well", 34% "not sure", 40.9% "don't know" respectively. And 84.7% of the patients answered that watching the PET/CT guide video before the injection helps understanding what exam they were having and 15.3% of the patients did not. For the question asking ever the patients have experienced using our homepage or smart phone QR code to see the guide video before they visit out PET center, only 3.3% of them answered "yes". Lastly, the patients answered 60.1% "yes", 31.4% "so so" and 8.5% "no" respectively for the question asking whether watching the video makes the patients to fill the waiting time short. Conclusion: It is found that understanding of objective and method of the PET/CT scan and level of satisfaction was improved after the patients watched the guide video whether they had PET/CT scan before and read the PET/CT guidebook or not. Also, watching the video was effective for the reduction of perceptible waiting time. But while displaying the PET/CT guide video is useful for providing information about the scan and shortening the waiting time as one of the medical service, utilization of service was actually very poor because of the passive promotion and indifference of the patients about their examination. Therefore, from now on, it is necessary to construct the healthcare system which can be offered to more patients through the active promotion.

• Journal of the Korean Geographical Society
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• v.47 no.1
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• pp.60-78
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• 2012

The main objective of this study is to conduct subnational population projections of Korea based on a Myriad Things" (萬物門), which is part of Seongho's representative work entitled $Miscellaneous$ $Explanations$ $of$ $Seongho$ (星湖僿說), has been in this paper in order to understand Seongho's "thinking on geography". To do so, contents related to geography were selected and these were discussed and interpreted in terms of the classification system of today's geographical knowledge. Following is the result of this research. First, information on astronomical geography and natural geography such as uplift, tornado, structure of soil, and the $yut$ board as well as humangeographical topics such as wild $ginseng$, cigarettes, hot pepper, traditional fruits and nuts (chestnuts, jujubes, and persimmons), Goryeo paper (Korean paper), mulberry trees, cotton plants, natural dye, policy about horses, magnetic compass needles, and farming implements for rice transplantation are mentioned in "Myriad Things" in relation to geography. Second, the depth of information described varies from topic to topic, but the topics on tornado and magnetic compass needles, horses, wild ginseng, traditional fruits and nuts, and $yut$ board are described in depth and in detail. Third, authenticity of the contents on these topics are "true" insofar as bibliographical information and citations are provided for support. Fourth, these topics reflect the interests and circumstances that are related to the "economic improvement of common people's livelihood" in those days, such as agriculture, crops, and transportation of goods. Fifth, the bibliography and citations explaining all instances reveal that China (Qing) is a great civilization of the advanced world and that the scholarship of Joseon relied on and accepted it. Sixth, except for horse raising and management, farming implements for rice transplantation, sericulture, and natural dying of cloth, most of the topics are useful even today. In short, theres is a profound aspect to the content that makes it possible to estimate the "geographical thinking". In general, the focus of the content of this book directly linked to the practical agricultural economy of the common people.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.14 no.1
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• pp.12-25
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• 2003

Objectives：As increasing number of new antidepressants have been being introduced in clinical practice, pharmacological understanding has been broadened. These changes mandate new information and theories to be incorporated into the treatment process of children with depressive disorders. In light of newly coming knowledge, this review intended to recapitulate the characteristics of new antidepressants and to consider the pivotal issues to develope guidelines for the treatment of depression in childhood and adolescence. Methods：Searching the Pub-Med online database for the articles with the key words of 'new', 'antidepressants' and 'children' ninety-seven headings of review articles were obtained. The author selected the articles of pertinent subjects in terms of either treatment guideline or psychopharmacology of new antidepressants. When required, articles about the clinical effectiveness of individual antidepressants were separatedly searched. In addition, the safety information of new antidepressants was acquired by browsing the official sites of the United States Food and Drugs Administration and Department of Health and Human Services. Results：1) For the clinical course, treatment phase, and treatment outcome, the reviews or treatment guidelines adopted the information from adult treatment guidelines. 2) Systematic and critical reviews unambiguously concluded that selective serotonin reuptake inhibitors(SSRIs) excelled tricyclic antidepressants( TCAs) for both efficacy and side effect profiles, and were recommend for the first-line choice for the treatment of children with depressive disorders. 3) New antidepressants generally lacked treatment experiences and randomized controlled clinical trials. 4) SSRIs and other new antidepressants, when used together, might result in pharmacokinetic and/or pharmacodynamic drug-to-drug interaction. 5) The difference of the clinical effectiveness of antidepressants between children and adults should be addressed from developmental aspects, which required further evidence. Conclusion：Treatment guidelines for the pharmacological treatment of childhood and adolescence depression could be constructed on the basis of clinical trial findings and practical experiences. Treatment guidelines are to best serve as the frame of reference for a clinician to make reasonable decisions for a particular therapeutic situation. In order to fulfill this role, guidelines should be updated as soon as new research data become available.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.99-120
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• 2010

Due to the wide spread of customers' frequent access of non face-to-face services, there have been many attempts to improve customer satisfaction using huge amounts of data accumulated throughnon face-to-face channels. Usually, a call center is regarded to be one of the most representative non-faced channels. Therefore, it is important that a call center has enough agents to offer high level customer satisfaction. However, managing too many agents would increase the operational costs of a call center by increasing labor costs. Therefore, predicting and calculating the appropriate size of human resources of a call center is one of the most critical success factors of call center management. For this reason, most call centers are currently establishing a department of WFM(Work Force Management) to estimate the appropriate number of agents and to direct much effort to predict the volume of inbound calls. In real world applications, inbound call prediction is usually performed based on the intuition and experience of a domain expert. In other words, a domain expert usually predicts the volume of calls by calculating the average call of some periods and adjusting the average according tohis/her subjective estimation. However, this kind of approach has radical limitations in that the result of prediction might be strongly affected by the expert's personal experience and competence. It is often the case that a domain expert may predict inbound calls quite differently from anotherif the two experts have mutually different opinions on selecting influential variables and priorities among the variables. Moreover, it is almost impossible to logically clarify the process of expert's subjective prediction. Currently, to overcome the limitations of subjective call prediction, most call centers are adopting a WFMS(Workforce Management System) package in which expert's best practices are systemized. With WFMS, a user can predict the volume of calls by calculating the average call of each day of the week, excluding some eventful days. However, WFMS costs too much capital during the early stage of system establishment. Moreover, it is hard to reflect new information ontothe system when some factors affecting the amount of calls have been changed. In this paper, we attempt to devise a new model for predicting inbound calls that is not only based on theoretical background but also easily applicable to real world applications. Our model was mainly developed by the interactive decision tree technique, one of the most popular techniques in data mining. Therefore, we expect that our model can predict inbound calls automatically based on historical data, and it can utilize expert's domain knowledge during the process of tree construction. To analyze the accuracy of our model, we performed intensive experiments on a real case of one of the largest car insurance companies in Korea. In the case study, the prediction accuracy of the devised two models and traditional WFMS are analyzed with respect to the various error rates allowable. The experiments reveal that our data mining-based two models outperform WFMS in terms of predicting the amount of accident calls and fault calls in most experimental situations examined.

• Journal of muscle and joint health
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• v.2 no.1
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• pp.17-40
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• 1995

Rheumatoid arthritis is the one of the chronic diseases, one of its major symptoms is a chronic pain. Despite developing medical treatment and surgical techniques, it is suggested that to control the pain is the goal of the treatment. But pain is an inner experience and even those closest to the patient cannot truly observe its progress or share in its suffering. The National Academy of Sciences Institute of Medicine's report on Pain and Disability concluded that there is no objective measure of pain-(exactly) no pain thermometer-nor can there ever be one, because the experience of pain is inseparable from personal perception and social influence such as culture. To explore chronic pain experience is to understand the process and property of the patient's perception of pain through the response to pain, the coping with pain, and the adaptation to pain. Therefore a qualitative study was conducted in order to gain an understanding of pain experience of patients with RA in korea. I used naturalistic inquiry as a research methodology, which had 5 axioms, the first is that realities are multiple, constructed, and holistic, the second is that knower and known are interactive, inseparable, the third is only time and context bound working hypotheses(idiographic statements) are possible, the forth is all entities are in a state of mutual simultaneous shaping, so that it is impossible to distinguish causes from effects and the last is that inquiry is value-bound. Purposive sampling was conducted as a sampling. 20 subjects who experienced pain over 10 years, lived in middle-sized city and big city in Korea, and 17 women and 3 men. The subject's age was from 32 to 62 (average 48.8), all were married, living with their spouse and children, except two-one divorced and the other widow before they became ill. I collected data using In depth structured interview. I had interviews two or three times with each subject, and the interviews were conducted at each subject's home. Each interview lasted about two hours an average. A recording was taken with the consent of the subject. I used inductive data analysis-such as unitizing and categorizing. unitizing is a process of coding, whereby raw data are systematically transformed and aggregated into units. Categorizing is a process wherby previously unitized data are organized into categories that provide descriptive or inferential information about the context or setting from which the units were derived. This process is used constant comparative method. The pain controlling process is composed of behavior of pain control. The behaviors of pain control are rearranging of ADL, hiddening role conflict, balancing treatment, and changing social relation. Rearranging of ADL includes diet management, sleep management, and the adjustment of daily life activities. The subjects try to rearrange their daily activities by modified style of motions, rearranging time span & range of activities, using auxillary facilities, and getting help in order to keep on the pace of daily life. Hiddening role conflict means to reduce conflicts between sick role and their role as a family member. In this process, the subjects use two modes, one is to control the pain complaints, and the other is to internalize the value which is to stay home is good for caring her children and being a good mother. To control pain complaints is done by 'enduring', 'understanding' the other family members, or making them undersood in order to reduce pain. Balancing treatment is composed of two aspects. One is to keep the pain within the endurable level, the other is to keep in touch with medical personnel in order to get the information of treatment and emotional support. Changing social relation is made by information seeking and sharing, formation of mutual support relation, and finally simplification of social relationships. The subjects simplify their social relationships by refraining from relations with someone who makes them physically and psychologically strained. In particular the subjects are apt to avoid contact with in-laws, and the change of relation to in-laws results in lessening the family boundary. In the course of this process, they confront the crisis of family confict result in family dissolution. This crisis is related to the threat of self-existence. Findings from this study contribute to understanding the chronic pain experience. To advance this study, we should compare this result with other cases in different cultural contexts. I think to interpret these results, korean cultural background should be considered. Especially the different family concept, more broader family members and kinship network, and the traditional medical knowledge influences patients' behavior.

• The Korean Journal of Archival Studies
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• no.8
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• pp.137-180
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• 2003

The term 'Business Archives' is not familiar with us in our society. Some cases can be found that materials are collected for publishing the history of a firm on commemoration of some decades of its foundation. However, the appropriate management of these collected materials doesn't seem to be followed in most of companies. The Records and archives management is inevitable in order to maximize the utility of Information and knowledge in the business world. The interest in records management has been grown, especially in the fields of business management and information technology. However, the importance of business archives hasn't been conceived yet. And also no attention has been paid to the business archives as social resources and the responsibility of the society as a whole for their preservation. The company archives doesn't have a long history in Germany although the archives of the nation, the aristocracy, communes and churches have a long tradition. However the company archives of Krupps which was established in 1905, is regarded as the first business archives in the world, It means that Germany has taken a key role to lead the culture of business archives. This paper focuses on the process of the establishment of business archives in Germany and its characteristics. The business archives in Germany can be categorized in three types: company archives, regional business archives and branch archives. It must be noted here that each type of these was generated in the context of the accumulation of the social resources and its effective use. A company archives is established by an individual company for the preservation of and use of the archives that originated in the company. The holdings in the company archives can be used as materials for decision making of policies, reporting, advertising, training of employees etc. They function not only as sources inside the company, but also as raw sources for the scholars, contributing to the study of the social-economic history. Some archives of German companies are known as a center of research. A regional business archives manages materials which originated m commerce chambers, associations and companies in a certain region. There are 6 regional business archives in Germany. They collect business archives which aren't kept in a proper way or are under pressure of damage in the region for which they are responsible. They are also open to the public offering the sources for the study of economic history, social history like company archives, so that they also play a central role as a research center. Branch business archives appeared relatively late in Germany. The first one is established in Bochum in 1969. Its general duties and goals are almost similar with ones of other two types of archives. It has differences in two aspects. One is that the responsibility of the branch business archives covers all the country, while regional business archives collects archives in a particular region. The other is that a branch business archives collects materials from a single industry. For example, the holdings of Bochum archives are related with the mining industry. The mining industry-specialized Bochum archives is run as an organization in combination with a museum, which is called as German mine museum, so that it plays a role as a cultural center with the functions of exhibition and research. The three types of German business archives have their own functions but they are also closely related each other under the German Association of Business Archivists. They are sharing aims to preserve primary materials with historical values in the field of economy and also contribute to keeping the archives as a social resources by having feed back with the public, which leads the archives to be a center of information and research. The German case shows that business archives in a society should be preserved not only for the interest of the companies, but also for the utilities of social resources. It also shows us how business archives could be preserved as a social resource. It is expected that some studies which approach more deeply on this topic will be followed based on the considerations from the German case.