• The Journal of Society for e-Business Studies
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• v.10 no.3
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• pp.85-110
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• 2005

OWL-S has a major leadership in the field of Web Service discovery and is being actively studied in LARKS and METEOR-S projects. These researches do not consider all components of OWL-S standards, and it is needed to enhance their discovery algorithms. In this paper, we propose matching algorithms based on process information such as process structure matching, service classification matching and business pattern matching algorithms. We also improve the QoS matching algorithm of METEOR-S project. Finally, we integrate these two kinds of matching algorithms as accommodate users preferences.

• The Journal of Society for e-Business Studies
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• v.18 no.2
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• pp.95-110
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• 2013

This paper presents a novel feature engineering technique that can improve the conventional machine learning-based text classification systems. The proposed method extends the initial set of features by using hyperlink relationships in order to effectively categorize hypertext web documents. Web documents are connected to each other through hyperlinks, and in many cases hyperlinks exist among highly related documents. Such hyperlink relationships can be used to enhance the quality of features which consist of classification models. The basic idea of the proposed method is to generate a sort of ed concept feature which consists of a few raw feature words; for this, the method computes the semantic similarity between a target document and its neighbor documents by utilizing hierarchical relationships in the WordNet ontology. In developing classification models, the ed concept features are equated with other raw features, and they can play a great role in developing more accurate classification models. Through the extensive experiments with the Web-KB test collection, we prove that the proposed methods outperform the conventional ones.

• The Journal of Society for e-Business Studies
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• v.24 no.2
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• pp.1-14
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• 2019

Because of the characteristics of game software, it is important to quickly identify and reflect users' needs into game software after its launch. However, most sites such as the Google Play Store, where users can download games and post reviews, provide only very limited and ambiguous classification categories for game reviews. Therefore, in this paper, we develop an automatic classification system for game reviews that categorizes reviews into categories that are clearer and more useful for game providers. The developed system converts words in reviews into vectors using word2vec, which is a representative word embedding model, and classifies reviews into the most relevant categories by measuring the similarity between those vectors and each category. Especially, in order to choose the best similarity measure that directly affects the classification performance of the system, we have compared the performance of three representative similarity measures, the Euclidean similarity, cosine similarity, and the extended Jaccard similarity, in a real environment. Furthermore, to allow a review to be classified into multiple categories, we use a threshold-based multi-category classification method. Through experiments on real reviews collected from Google Play Store, we have confirmed that the system achieved up to 95% accuracy.

• The Journal of Society for e-Business Studies
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• v.20 no.3
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• pp.113-125
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• 2015

Information Security Incidents that have recently happen rapidly spread and the scale of that incidents' damage is large. In addition, as it proceeds to the era of converged industry in the future environment and the virtual cyber world expands to the physical world, new types of security threats have occurred. Now, it is time to supply security professionals who have a multi-dimensional security capabilities that can manage the strategies of technological security and physical security from the management point of view, rather than the ones who primarily focus on the traditional technologic-centered strategies to solve new types of security threats. In conclusion, in this paper we try to produce the curriculum of information security featured in the occupational classification system and analyze the subjects that are additionally required for those who move to other occupations to cultivate security professionals who suited to the converged-industrial environment. It is expected that multi-dimensional security professionals who suited to the converged-industrial environment will be cultivated by harmoniously integrating information security subjects from technological and business/managerial perspectives, and education training courses will be developed that effectively provide core knowledges per occupational classification when people moves to other occupations in the areas of information security.

• Information Systems Review
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• v.11 no.3
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• pp.107-123
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• 2009

In this paper, we review digital certificate technologies and their applications in e-commerce. Current digital certificate technologies are evaluated and their importance is explained. The configuration of certificate flows from providers to users through software, hardware, and network technologies is described. These five domains and the configuration of digital certificate flows guide our review of the characteristics of digital certificates. We then develop a framework for the classification of digital certificates that integrate these five domains with VeriSign's types and levels of assurance. In order to demonstrate the adequacy of our digital certificate classification framework, we populated it with VeriSign's digital certificates. Within each domain, VeriSign's classes of digital certificates are classified in accordance with the VeriSign type and level of assurance. The results of our analysis suggest that the framework is a useful step in developing a taxonomy of digital certificate technologies. The strengths and weaknesses of the study are discussed, and opportunities for further research are identified and discussed.

• Proceedings of the CALSEC Conference
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• 1997.11a
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• pp.443-461
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• 1997

ㆍProSTEP, PDES, Inc., and JSTEP announce agreement on PDM Schema ㆍ November 4, 1997 ㆍInteroperable with STEP AP203, AP2l0, AP214, and AP232 ㆍNeutral specification allows for the exchange of the following types - item master data (part identification, approval of part version) - item structure - item classification - item property (mass, costs) - document management (identification, revision… ) - work management (engineering change request, EC order, project) ㆍVendors of PDM systems are asked to join ProSTEP's PDM Round Table and PDES, Inc.'s PDMnet.(omitted)

• Proceedings of the Korea Contents Association Conference
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• 2010.05a
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• pp.542-544
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• 2010

본 논문은 유전 알고리즘과 신경망 알고리즘을 결합하여 내용기반 영상 식별을 하는 연구 방법을 제시한다. 특징벡터로는 색상 정보와 질감 정보를 사용하였다. 추출된 특징벡터의 집합을 제안한 모델을 통해 최적의 유효 특징벡터의 집합을 찾아 영상을 식별하고자 한다.

• Journal of Fashion Business
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• v.12 no.4
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• pp.46-55
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• 2008

The purpose of this study is to provide the effective internet marketing communication strategy as marketing tools by analyzing the web sites of young casual fashion brands. We've selected 19 young casual fashion brands in 3 department stores and made the classification standard - advertising, promotion, public relation(PR), customer management - and analysed the young casual fashion brands according to 4 classification standard on the web sites. As a result of study, it is found that 19 young casual brands' web sites put an emphasis on activity of customer management and promotion in general. However, they did not conduct the PR and advertising actively compared with other parts. Especially, the promotion strategy occupies more parts than any other parts through the variety of membership card's services. Also they are sending e-mails or providing 1:1(FAQ/Q&A) board to the members as a customer management to be able to help to communicate with customer through the web site.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.509-515
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• 2005

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved it more powerful than traditional artificial neural networks (ANNs)(Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al, 2005; Kim, 2003). The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is cost-sensitive. Therefore, it is necessary to convert the output of the classifier into well-calibrated posterior probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create probabilities (Platt, 1999; Drish, 2001). This study applies a method to estimate the probability of outputs of SVM to bankruptcy prediction and then suggests credit scoring methods using the estimated probability for bank's loan decision making.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.97-117
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• 2021

Predicting term deposit subscriptions is one of representative financial marketing in banks, and banks can build a prediction model using various customer information. In order to improve the classification accuracy for term deposit subscriptions, many studies have been conducted based on machine learning techniques. However, even if these models can achieve satisfactory performance, utilizing them is not an easy task in the industry when their decision-making process is not adequately explained. To address this issue, this paper proposes an explainable scheme for term deposit subscription forecasting. For this, we first construct several classification models using decision tree-based ensemble learning methods, which yield excellent performance in tabular data, such as random forest, gradient boosting machine (GBM), extreme gradient boosting (XGB), and light gradient boosting machine (LightGBM). We then analyze their classification performance in depth through 10-fold cross-validation. After that, we provide the rationale for interpreting the influence of customer information and the decision-making process by applying Shapley additive explanation (SHAP), an explainable artificial intelligence technique, to the best classification model. To verify the practicality and validity of our scheme, experiments were conducted with the bank marketing dataset provided by Kaggle; we applied the SHAP to the GBM and LightGBM models, respectively, according to different dataset configurations and then performed their analysis and visualization for explainable term deposit subscriptions.