• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.1
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• pp.159-177
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• 1995

The objective of this paper is to propose a knowledge-based fuzzy post adjustment so that unstructured problems can be solved more realistically by expert systems. Major part of this mechanism forcuses on fuzzily assessing the influence of various external factors and accordingly improving the solutions of unstructured problem being concerned. For this purpose, three kinds of knowledge are used : user knowledge, expert knowledge, and machine knowledge. User knowledge is required for evaluating the external factors as well as operating the expert systems. Machine knowledge is automatically derived from historical instances of a target problem domain by using machine learning techniques, and used as a major knowledge source for inference. Expert knowledge is incorporate dinto fuzzy membership functions for external factors which seem to significantly affect the target problems. We applied this mechanism to a prototyoe expert system whose major objective is to provide expert guidance for stock market timing such as sell, buty, or wait. Experiments showed that our proposed mechanism can improve the solution quality of expert systems operating in turbulent decision-making environments.

• Knowledge Management Research
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• v.11 no.2
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• pp.57-73
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• 2010

This research investigated the effect of human resource practices on knowledge sharing. We developed and tested a mediation model of how human resource practices affect shared vision and codes which facilitates knowledge sharing. A field study of the R&D centers in knowledge-intensive industries showed that commitment-based human resource management systems were positively related to shared vision and codes and knowledge sharing, and the relationship between HR practices and knowledge sharing was fully mediated through shared vision and codes. This study contributed to the extant literature pertaining to the antecedents of knowledge sharing through focusing on the role of HR practices and shared vision and codes.

• Knowledge Management Research
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• v.17 no.1
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• pp.91-111
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• 2016

This study empirically investigated the relationships between servant leadership and key variables (knowledge sharing and individual creativity), and the mediating effect of affective commitment with the survey of 213 Korean employees. Based on the sample of 213 employees, the empirical results are as followings; (1) Servant leadership is positively related to affective commitment, knowledge sharing, and creativity. (2) Affective commitment partially mediates the relationship between servant leadership and knowledge sharing. (3) Affective commitment partially mediates the relationship between servant leadership and creativity. In conclusion, this study confirmed that the servant leader and members with the affective commitment may be best qualified for knowledge sharing and creative performance. When employees recognize that their managers follow the characteristics of servant leadership, then the employees are more likely to absorb in their task, which increases creative performance and knowledge accumulation. Based on the results, this study suggests an ample implication for leaders in any organization to boost their relationships with followers and to enhance their knowledge sharing and creative idea for the growth of organization.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.99-106
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• 2014

Generation and analysis methods have been proposed in recent years, such as using a natural language and formal language processing, artificial intelligence algorithms based knowledge model is effective meaning. its semantic based knowledge model has been used effective decision making tree and problem solving about specific context. and it was based on static generation and regression analysis, trend analysis with behavioral model, simulation support for macroeconomic forecasting mode on especially in a variety of complex systems and social network analysis. In this study, in this sense, integrating knowledge-based models, This paper propose a text mining derived from the inter-Topic model Integrated formal methods and Algorithms. First, a method for converting automatically knowledge map is derived from text mining keyword map and integrate it into the semantic knowledge model for this purpose. This paper propose an algorithm to derive a method of projecting a significant topic map from the map and the keyword semantically equivalent model. Integrated semantic-based knowledge model is available.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.353-359
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• 2005

In this research, we propose a Unified Fuzzy rule-based knowledge Inference Systems (UFIS) to help the expert in cosmetic brand detection. Users' preferred cosmetic product detection is very important in the level of CRM. To this purpose, many corporations trying to develop an efficient data mining tool. In this study, we develop a prototype fuzzy rule detection and inference system. The framework used in this development is mainly based on two different mechanisms such as fuzzy rule extraction and RDB (Relational DB)-based fuzzy rule inference. First, fuzzy clustering and fuzzy rule extraction deal with the presence of the knowledge in data base and its value is presented with a value between 0 -1. Second, RDB and SQL (Structured Query Language)-based fuzzy rule inference mechanism provide more flexibility in knowledge management than conventional non-fuzzy value-based KMS (Knowledge Management Systems).

• Journal of Intelligence and Information Systems
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• v.1 no.2
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• pp.57-71
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• 1995

This paper presents a rule extraction algorithm RE to acquire explicit rules from trained neural networks. The validity of extracted rules has been confirmed using 6 different data sets. Based on experimental results, we conclude that extracted rules from RE predict more accurately and robustly than neural networks themselves and rules obtained from an inductive learning algorithm do. Rule extraction algorithm for neural networks are important for incorporating knowledge obtained from trained networks into knowledge based systems. In lieu of this, the proposed RE algorithm contributes to the trend toward developing hybrid and versatile knowledge-based system including expert systems and knowledge-based decision su, pp.rt systems.

• Journal of Korean Academic Society of Home Health Care Nursing
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• v.22 no.2
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• pp.291-299
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• 2015

Purpose: The purpose of this study was to examine the influences of sex-relatedl knowledge, sex-related attitude and knowledge of cervical cancer on knowledge of human papilloma virus (HPV) among female high school students. Methods: A cross-sectional descriptive study was conducted with a convenience sample of 545 second-grade female high school students of three different schools. Results: Knowledge of HPV was positively correlated with sex-related knowledge (r=.36, p<.001), sex-related attitude (r=.14 p=.001) and knowledge of cervical cancer (r=.62, p<.001). Significant predictors affecting knowledge of HPV among female high school students were knowledge of cervical cancer (${\beta}$=.57) and sex-related knowledge (${\beta}$=.11), explaining 39.6% of the variance in knowledge of cervical cancer among female high school students (F=178.34, p<.001). Conclusion: Based on the outcomes of this study, in order to improve knowledge of HPV among female high school students, school based sexual education linked to HPV and cervical cancer must be included in the curriculum.

• Knowledge Management Research
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• v.16 no.2
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• pp.1-27
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• 2015

This paper presents a knowledge-based view of platform-centered collaborations among multiple organizations. Studies of technological innovation and knowledge creation have broadened beyond their initial emphasis on internal development within an organization or simple exchange of ideas between two parties toward complex collaboration among many organizations at the level of platform-based knowledge ecosystems. Platforms serve as an interface between different groups of producers and consumers in a variety of multi-sided knowledge markets such as smartphone operating systems and video games industries. This study is an exploratory examination to offer theoretical understanding of how the organizational capabilities of platform leaders help expand a network of platform participants. The growth of platform participants is particularly important in the early stage of any platforms as the concept of network effects suggests that the platform with the largest number of participants will capture entire markets. Building upon organization studies and network economics theory on multisided markets, this paper focuses on the role of platform leaders in expanding platform-based collaboration. In our view, platform leaders develop varying levels of three organizational capabilities to discern quality of potential participants, to attract them to actually participate in collaboration, and to maintain long-term exchange relations in the ecosystem. We suggest that the capabilities of platform leaders will have a positive effect on the expansion of platform participants to secure network effects, and also examine several contextual factors that moderate the relationship between a platform leader's capacity and platform expansion.

• Korean Journal of Clinical Pharmacy
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• v.32 no.4
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• pp.328-335
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• 2022

Background: Case-based learning (CBL) is becoming an important approach for improving interprofessional collaboration education. Previous studies have examined learners' satisfaction with interprofessional education (IPE) in medical institutions. However, there are few studies on the implementation of university-led CBL interventions and their direct effects on learning outcomes. The aim of this study was to evaluate the effectiveness of CBL interventions on changes in the participants' perception and knowledge acquisition ability. Methods: The CBL approach consisted of team-based case-based learning, self-directed learning, and post-feedback. It was conducted as a single course for pharmacy students in their 5th year in a university setting. Changes in the participants' perceptions and self-assessments of competence levels were evaluated using survey responses. The effect of the CBL intervention on knowledge acquisition ability was directly evaluated using the exam score. Results: The majority agreed or strongly agreed that team-based case-based learning, and self-directed learning helped them to improve their knowledge and skills to a higher level and to increase the self-assessment of competency level. The average score of knowledge acquisition ability (average score of 75.0, p=0.0098) was significantly higher in the CBL intervention group than the lecture-based learning intervention group (average score of 52.0). Conclusion: The participants positively perceived that CBL intervention helped them to effectively improve their knowledge and the self-assessment of competency level. It also enhanced knowledge acquisition ability. These data, based on the survey responses, suggest that it is necessary to implement CBL interventions in a university-led single professional education.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1996.04a
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• pp.40-46
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• 1996

Three procedures are currently used for the design of deep beams, which are Empirical design method, Nonlinear analysis, and Truss models. The engineering logic and decisions inherent in these design procedures are dependent on the acquired knowledge and experience of the structural engineer. Knowledge-based system is useful to solve problems which require human experties. Therefore, this study presents an application of Knowledge-Based System for design of reinforced concrete deep beams with web openings.