• Journal of KIISE:Computing Practices and Letters
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• v.13 no.6
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• pp.343-357
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• 2007

In this paper, we propose the techniques for effective management of profiles that necessary to provide adaptation content to an user. The profiles must be configured the related information of the user and can be exchanged according to periodic/aperiodic or event among user device, profile repository, contents adaptation server and contents storage server for providing adaptation content to an user. The profiles also must be supplied the contents services providers that need profiles for the adapted contents services. To support those function, we propose the management technologies of profiles and design profile framework supporting those. The proposed management framework of profiles is supported the profiles that are configurated using the various user's information and is used the Web Services for exchanging and providing regardless of the various devices and platform. The dynamic configuration method, metadata configuration method, and profiles providing methods using weight for effective management in the framework also are applied. The result of evaluation the proposed management techniques show effective profiles processing.

• Asia pacific journal of information systems
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• v.16 no.3
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• pp.159-176
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• 2006

Collaborative filtering is one of the popular techniques for personalized recommendation in e-commerce. In collaborative filtering, user profiles are usually managed per product category in order to reduce data sparsity. Product diversification of Internet storefronts and multiple product category sales of e-commerce portals require cross-product category usage of user profiles in order to overcome the cold start problem of collaborative filtering. In this paper, we study the feasibility of cross-product category usage of user profiles, and suggest a method to improve recommendation performance of cross-product category user profiling. First, we investigate whether user profiles on a product category can be used to recommend products in other product categories. Furthermore, a way of utilizing user profiles selectively is suggested to increase recommendation performance of cross-product category user profiling. The feasibility of cross-product category user profiling and the usefulness of the proposed method are tested with real click stream data of an Internet storefront which sells multiple product categories including books, music CDs, and DVDs. The experiment results show that user profiles on a product category can be used to recommend products in other product categories. Also, the selective usage of user profiles based on correlations between subcategories of two product categories provides better performance than the whole usage of user profiles.

• Journal of Korea Foundry Society
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• v.20 no.2
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• pp.80-88
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• 2000

Continuous casting equipment was designed to cast the metallic uranium rods, and a thermal analysis was carried out to calculate the temperature and solidification profiles. Fluid flow and heat transfer analysis model including the effects of phase change was used to simulate the continuous casting process by finite volume method. In the design of continuous casting equipment, the casting speed, pouring temperature and cooling conditions should be considered as significant factors. In this study, the effects of casting speed, pouring temperature, and air gap between the uranium and mold were investigate. The results represented that the temperature and solidification profiles of continuous casting equipment varied with the casting speed, pouring temperature, and air gap.

• Journal of Information Technology Applications and Management
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• v.12 no.4
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• pp.45-57
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• 2005

In order to provide personalized knowledge recommendation services, most web portals for organizational knowledge management use category or keyword information that portal users explicitly express interests in. However, it is usually difficult to collect correct preference data for all users with this approach, and, moreover, users' preferences may easily change over time, which results In outdated user profiles and impaired recommendation qualify. In order to address this problem, this paper suggests knowledge recommendation methods for portals using user profiles that are automatically constructed from users' activities such as posting or uploading of articles and documents. The result of our experiment shows that the Proposed method can provide equivalent performance with the manual category or keyword selection method.

• Educational Technology International
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• v.18 no.2
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• pp.193-213
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• 2017

This study aimed to explore online learning profiles of in-service teachers in South Korea, focusing on video lecture and discussion activities. A total of 269 teachers took an online professional development course for 14 days, using an online learning platform from which web log data were collected. The data showed the frequency of participation and the initial participation time, which was closely related to procrastinating behaviors. A cluster analysis revealed three online learning profiles of in-service teachers: procrastinating (n=42), passive interaction (n=136), and active learning (n=91) clusters. The active learning cluster showed high-level participation in both video lecture and discussion activities from the beginning of the online course, whereas the procrastinating cluster was seldom engaged in learning activities for the first half of the learning period. The passive interaction cluster was actively engaged in watching video lectures from the beginning of the online course but passively participated in discussion activities. As a result, the active learning cluster outperformed the passive interaction cluster in learning achievements. The findings were discussed in regard to how to improve online learning environments through considering online learning profiles of in-service teachers.

• Industrial Engineering and Management Systems
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• v.9 no.2
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• pp.141-156
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• 2010

Despite all attempts to prevent fraud, it continues to be a major threat to industry and government. Traditionally, organizations have focused on fraud prevention rather than detection, to combat fraud. In this paper we present a role mining inspired approach to represent user behaviour in Enterprise Resource Planning (ERP) systems, primarily aimed at detecting opportunities to commit fraud or potentially suspicious activities. We have adapted an approach which uses set theory to create transaction profiles based on analysis of user activity records. Based on these transaction profiles, we propose a set of (1) anomaly types to detect potentially suspicious user behaviour, and (2) scenarios to identify inadequate segregation of duties in an ERP environment. In addition, we present two algorithms to construct a directed acyclic graph to represent relationships between transaction profiles. Experiments were conducted using a real dataset obtained from a teaching environment and a demonstration dataset, both using SAP R/3, presently the predominant ERP system. The results of this empirical research demonstrate the effectiveness of the proposed approach.

• Journal of Pharmaceutical Investigation
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• v.41 no.5
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• pp.263-272
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• 2011

Tablet dosage forms have been preferred over other formulations for the oral drug administration due to their low manufacturing costs and ease of administrations, especially controlled-release applications. Controlled-release tablets are oral dosage forms from which the active pharmaceutical ingredient (API) is released over an intended or extended period of time upon ingestion. This may allow a decrease in the dosing frequency and a reduction in peak plasma concentrations and hence improves patient compliance while reducing the risk of undesirable side effects. Conventional singlelayered matrix tablets have been extensively utilized to deliver APIs into the body. However, these conventional single-layered matrix tablets present suboptimal delivery properties, such as non-linear drug delivery profiles which may cause higher side effects. Recently, a multi-layered technology has been developed to overcome or eliminate the limitations of the singlelayered tablet with more flexibility. This technology can give a good opportunity in formulating new products and help pharmaceutical companies enhancing their life cycle management. In this review, a brief overview on the multi-layered tablets is given focusing on the various tablet designs, manufacturing issues and drug release profiles.

• Asia pacific journal of information systems
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• v.34 no.1
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• pp.209-225
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• 2024

This research aims to use machine learning technology in human resource management to predict employees' work-life balance. The study utilized a dataset from IBM Watson Analytics in the IBM Community for the machine learning analysis. Multinomial dependent variables concerning workers' work-life balance were examined, categorized into continuous and categorical types using the Generalized Linear Model. The complexity of assessing variable roles and their varied impact based on the type of model used was highlighted. The study's outcomes are academically and practically relevant, showcasing how machine learning can offer further understanding of psychological variables like work-life balance through analyzing employee profiles.

• Industrial Engineering and Management Systems
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• v.2 no.1
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• pp.55-62
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• 2003

This paper deals with the difficulties involved in analyzing and designing a management system to reduce the risks and improve the productivity of new product development. In this paper, a method is described to analyze user information and determine product specifications based on a stimulus-response model, the conjoint measurement of users needs, and product characteristics deployment. The proposed method can analyze the effect of a partial price on the contribution ratio based on the order of preference of product profiles through a smaller number of product profiles. The strengths and weaknesses of this method are examined as the method is applied to the case study of a mobile computer intended for personal use.

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.4
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• pp.460-469
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• 2008

Process mining aims at mining valuable information from process execution results (called "event logs"). Even though process mining techniques have proven to be a valuable tool, the mining results from real process logs are usually too complex to interpret. The main cause that leads to complex models is the diversity of process logs. To address this issue, this paper proposes a trace clustering approach that splits a process log into homogeneous subsets and applies existing process mining techniques to each subset. Based on log profiles from a process log, the approach uses existing clustering techniques to derive clusters. Our approach are implemented in ProM framework. To illustrate this, a real-life case study is also presented.