• Asia pacific journal of information systems
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• v.19 no.2
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• pp.139-155
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• 2009

For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.

• The Journal of Korean Academy of Sensory Integration
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• v.18 no.2
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• pp.55-68
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• 2020

Objective : The purpose of this study is to summarize the best-available intervention evidence for children's sensory integration therapy, drawn from studies published domestically in Korea over the last 10 years. Methods : The articles evaluated in this study were collected from the RISS and DBpia databases using the search terms "sensory integration," "sensory processing," and "Ayres Sensory Integration (ASI)". A total of 19 papers were analyzed. The selected studies were then assessed using the Population, Intervention, Outcomes, and Comparison method, the International Classification of Functioning, Disability and Health (ICF) method, and the modified Evidence Alert Traffic Light Grading System. Results : Development delay was the most commonly applied diagnosis for children's sensory integration therapy and individual sensory integration therapy was the most frequently used intervention method. The intervention effect was 91 percent in the body structure and function of ICF. The areas concentrated on were sensory modulation, sensory processing, fine and gross motor, body scheme, body-self concept, balance, basic movement, postural control and hand function, attention, and self-esteem. Conclusion : This simple overview of the efficacy of children's sensory integration therapy provides a basis for easy understanding and use by therapists, researchers and families with children.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.77-88
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• 2017

Recently, with the advent of knowledge based society where information and knowledge make values, patents which are the representative form of intellectual property have become important, and the number of the patents follows growing trends. Thus, it needs to classify the patents depending on the technological topic of the invention appropriately in order to use a vast amount of the patent information effectively. IPC (International Patent Classification) is widely used for this situation. Researches about IPC automatic classification have been studied using data mining and machine learning algorithms to improve current IPC classification task which categorizes patent documents by hand. However, most of the previous researches have focused on applying various existing machine learning methods to the patent documents rather than considering on the characteristics of the data or the structure of patent documents. In this paper, therefore, we propose to use two structural fields, technical field and background, considered as having impacts on the patent classification, where the two field are selected by applying of the characteristics of patent documents and the role of the structural fields. We also construct multi-label classification model to reflect what a patent document could have multiple IPCs. Furthermore, we propose a method to classify patent documents at the IPC subclass level comprised of 630 categories so that we investigate the possibility of applying the IPC multi-label classification model into the real field. The effect of structural fields of patent documents are examined using 564,793 registered patents in Korea, and 87.2% precision is obtained in the case of using title, abstract, claims, technical field and background. From this sequence, we verify that the technical field and background have an important role in improving the precision of IPC multi-label classification in IPC subclass level.

• The Journal of Korean Physical Therapy
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• v.30 no.6
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• pp.239-245
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• 2018

Purpose: This study was to identify international classification of functioning, disability and health (ICF) categories that could be linked conceptually to disability of arm, shoulder and hand (DASH) items and short form of health survey 36 (SF-36) items for persons with shoulder pain. Methods: Linkage between each item in DASH and SF-36 and the categories in the ICF were assessed. The linking process was performed by ten health professionals following the linking rule. One hundred four patients with shoulder pain were enrolled from 12 private clinic outpatient departments and participated in this study. Pearson correlation coefficients were used to assess the relationships between each scale item and the linked ICF code. Results: Thirty DASH items were able to be linked to 30 ICF codes, whereas the 36 items in SF-36 were only linked to 17 ICF codes. General health items included in SF-36 could not be linked to a relevant ICF concept. There was a high correlation between the two measurement tools and the linked ICF codes, DASH and its ICF code list (r =0.91), SF-36-Physical Health and its code list (r =-0.62), and SF-36-Mental Health and its code list (r =-0.72). Conclusion: The results suggest that concepts within each item in DASH can be linked to ICF codes for patients with shoulder pain, however, the concepts in the SF-36 items had limited linkage to ICF codes. The shoulder-specific functional tool, DASH can be expressed with ICF codes and, therefore, its use can promote data standardization and improve communication between professionals.

• The Journal of the Korea Contents Association
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• v.18 no.2
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• pp.113-123
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• 2018

This study was conducted to construct and test a structural equation model of instrumental activities of daily living(IADL) in community-dwelling elderly. The model was based on ICF(International Classification of Functioning, Disability and Health) model. The participants were 260 elderly who were more than 65 years old. Physical and psychological function, visual-motor integration and social activities had direct effects on IADL. That is, the better the subjective health status, the lower the depression and the less chronic illness, the better IADL. Personal factor, social support and social activities had indirect effect on IADL. This model explained 32% of the variance in IADL.

• Journal of the Korean Society for information Management
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• v.33 no.1
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• pp.53-84
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• 2016

This study clarified the intellectual structure of international collaboration in psychiatry based on analyzing networks in order to vitalize for international collaboration in psychiatry in South Korea. The data set was collected from Web of Science citation database during the period from 2009 to 2013. SU="psychiatry" search formulary (means field of psychiatric medical research) was used through advanced retrieval function and a total of 18,590 articles were selected among international collaborations. A total of 85 different keywords were selected from the 18,590 articles, and the results of analysis were as follows. First, this study examined 8 sub-subject areas focusing on disorders, and found that major subject areas could be divided into a total of 8 sub-subject areas. Second, this study examined 6 keywords that have a strong impact, and extend subject areas by promoting intermediation between other keywords Third, this study examined sub-subject areas by using the Knowledge Classification Scheme of the National Research Foundation of Korea through community analysis, and found a total of 15 clusters and a total of 12 sub-subject areas.

• The Journal of Korean Physical Therapy
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• v.26 no.5
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• pp.344-350
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• 2014

Purpose: The purpose of this study was to translate and culturally adapt the International Classification of Functioning, Disability and Health (ICF) into the Korean language. Methods: The process of translation and adaptation of the ICF used here followed the translation guidelines of WHO. Implementation of this procedure comprised of four steps; forward translation, expert panel back-translation, pre-testing and cognitive interviewing, and final adaptation. The translators included health professionals with knowledge of ICF and non-health professionals blinded to the ICF. Clinical academics with significant experience in the use of disability survey, medical doctors, special educators, related policy makers, clinicians, architecture professionals, and international experts in ICF were invited to integrate all versions of the ICF for testing; 151 clinicians volunteered from 19 medical institutes across the country. Four different core-sets and a questionnaire were used for testing its practical usability and adaptation. Results: All translations were reviewed and a consensus was reached on any discrepancy from the earlier versions. Over 90% of the newly translated version of K-ICF was found to be different from the 2004 K-ICF version in the ICF language. Understanding of K-ICF language was responded difficult and very difficult by 50% of participants, whereas its practical use was responded 'useful' by more than 50% of subjects. Conclusion: It can be suggested that the new version of K-ICF should be widely used for final adaptation in the field of areas. Future studies will be required for implementation of K-ICF.

• Journal of Dental Rehabilitation and Applied Science
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• v.32 no.2
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• pp.102-108
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• 2016

Headache disorders, one of most common disease in general population, have been developed according to many versions of international classifications. The primary headaches are those in which no consistently identified organic cause can be determined. It is divided into the following categories: (1) migraine, (2) tension-type headache, (3) cluster headache and other trigeminal autonomic cephalalgias, (4) other primary headaches. This review described a diagnosis of primary headache disorders based on International Classification of Headache Disorders (ICHD)-3 beta criteria.

• International Journal of Advanced Culture Technology
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• v.12 no.2
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• pp.161-166
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• 2024

As globalization has accelerated, the use of English loanwords is increasing in South Korea. In this paper, we have analyzed news stories from four Korean quality newspapers-Chosun Ilbo, Dong-A Ilbo, KyungHyang Sinmun, and Chung-Ang Ilbo to investigate the usage of English loanwords in news texts. Thirty-eight news stories on life, politics, business and IT were collected from the four newspapers and then analyzed based on the five types of loanwords-Direct, Mixed Code Combination, Clipping and Neologism and Double Notation, partly following Lee's and Rudiger's classification. As a result, the followings were revealed: first, the use of the category Direct was overwhelming the others with 90%, indicating that English loanwords were not translated from its source language and introduced into Korean directly with little modification; second, the use of English loanwords was significantly higher in the sections of business and IT than in other sectors, implying that English loanwords function in a similar way as a lingua franca does within those fields. Furthermore, the linguistic trends can provide a basic guide for translators to make an informed decision between the use of English loanwords and its translated Korean version in English-into Korean translation.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.97-106
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• 2021

Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.