• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.3
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• pp.691-699
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• 2011

Recently, diverse approaches retrieval services based on social network are suggested. Although existing recommendation systems can retrieve experts of specific fields, profiles and evaluations about experts that users want to be recommended are in a system. The proposed expert recommendation system can automatize collection of evaluation to evaluate experts and experts' profiles in separate systems by using the Knowledge Base and XMDR. We also attempt to construct system which can recommend a number of experts by dynamically constructing Social Network by using diverse resources distributed 로컬ly and composed of heterogeneous data sources. To resolve these problems efficiently, there is a need to provide constructed resources between heterogeneous systems with transparency and independence and provide users with a singular interface. Therefore, the proposed system in this paper uses Knowledge Base and XMDR for extracting distributed experts' profiles and designs expert recommendation system connecting Knowledge Base with Social Network.

• Korean Journal of Adult Nursing
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• v.17 no.4
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• pp.622-634
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• 2005

Purpose: The purpose of this study was to evaluate the applicability of the evidence based guideline for prompted voiding by Lyons & Specht (2001) in National Guideline $Clearinghouse^{TM}$ for use in Korea based on the experts' opinions. Method: The target expert group consisted of 8 registered nurses, 6 physicians, and 5 nursing professors who are experts in urinary incontinence. This study used a questionnaire survey. The appropriateness, applicability, and the present application of each recommendation in the guideline were analyzed with descriptive statistics using the SPSS program, with content analysis based on the experts' opinions. Result: The scores on each recommendation's appropriateness showed the high degree of agreement among nurses, physicians, and nursing professors. However, the recommendation for 'use of oxybutinin' showed the lowest score as 5.89. It was notable that the most recommendations scored lower for applicability compared with appropriateness. The reasons for lower scores for applicability were the lack of clinicians' knowledge of assessment and management, and the lack of resources in clinical settings in Korea. Conclusion: This study will augment the understanding of the actual urinary incontinence management in Korean clinical settings and can be used as the baseline data for further study of tailoring international guidelines into local and national clinical settings.

• Korean Management Science Review
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• v.17 no.3
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• pp.19-30
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• 2000

Customization and personalization services are considered as a critical success factor to be a successful Internet store or web service provider. As a representative personalization technique, personalized recommendation techniques are studied and commercialized to suggest products or services to a customer of Internet storefronts based on demographics of the customer or based on an analysis of the past purchasing behavior of the customer. The underlining theories of recommendation techniques are statistics, data mining, artificial intelligence, and/or rule-based matching. In the rule-based approach for personalized recommendation, marketing rules for personalization are usually collected from marketing experts and are used to inference with customers data. however, it is difficult to extract marketing rules from marketing experts, and also difficult to validate and to maintain the constructed knowledge base. In this paper, we proposed a marketing rule extraction technique for personalized recommendation on Internet storefronts using market basket analysis technique, a well-known data mining technique. Using marketing basket analysis technique, marketing rules for cross sales are extracted, and are used to provide personalized advertisement selection when a customer visits in an Internet store. An experiment has been performed to evaluate the effectiveness of proposed approach comparing with preference scoring approach and random selection.

• Journal of Information Technology Applications and Management
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• v.25 no.4
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• pp.145-156
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• 2018

This study proposes a content-based recommendation algorithm (NRA) for pet food. The proposed algorithm tries to recommend appropriate or inappropriate feed by using collective intelligence based on user experience and prior knowledge of experts. Based on the physical and health status of the dogs, this study suggests what kind of nutrients are necessary for the dogs and the most recommended pet food containing these nutrients. Performance evaluation was performed in terms of recall, precision, F1 and AUC. As a result of the performance evaluation, the AUC and F1 value of the proposed NRA was 15% and 42% higher than that of the baseline model, respectively. In addition, the performance of NRA is shown higher for recommendation of normal dogs than disease dogs.

• Journal of Broadcast Engineering
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• v.14 no.3
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• pp.271-279
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• 2009

The Moving Picture Experts Group (MPEG) and Video Coding Experts Group (VCEG) have developed a new standard that promises to outperform the earlier MPEG-4 and H.263 standards. The new standard is called H.264/AVC (Advanced Video Coding) and is published jointly as MPEG-4 Part 10 and ITU-T Recommendation H.264. In particular, the H.264/AVC intra prediction coding provides nine directional prediction modes for every $4{\times}4$ block in order to reduce spatial redundancies. In this paper, an ABS (Adaptive Bit Skip) mode is proposed. In order to achieve coding efficiency, the proposed method can remove the mode bits to represent the prediction mode by using the similarity of adjacent pixels. Experimental results show that the proposed method achieves the PSNR gain of about 0.2 dB in R-D curve and reduces the bit rates about 3.6% compared with H.264/AVC.

• Korean Journal of Health Education and Promotion
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• v.31 no.3
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• pp.83-95
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• 2014

Objectives: This study was to examine the experts perception on the operation of the national health promotion fund and related policies, and to obtain the perspective on the improving governance of the fund. Methods: Experts opinion survey was recruited 120 experts who were public health officials, and members of board in academic societies related to health promotion and health policy, and 60 experts participated in the survey. Results: Most health care experts agreed that the current allocation of health promotion fund was not optimal with its lack of allocation on promoting healthy lifestyle and R&D for health promotion, while the majority of the fund was being spent on supporting national health insurance. Thus, establishing governance system and control tower for the fund was viewed as critical. Also the status of deliberation committee should be raised to higher position where it can hold practical authority to plan and evaluate fund spending. Conclusions: The priority of health promotion fund spending should be more on improving health such as modifying life-style and spreading healthy habits, rather than on disease management or subsidizing health insurance. It is recommended that change from to environment in health promotion policy regime is required to establish effective governance system for the fund operation.

• Journal of Digital Convergence
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• v.10 no.6
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• pp.275-281
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• 2012

This research proposes the method to develop the recommendation system of automobile parts using task ontology technology. The proposed intelligent recommendation system is designed to learn the assembly process of automobile parts and the automobile parts are composed by ontology method for the recommendation of the parts. Using hierarchical taxonomy based on is-a relationship, the relationship between each part that makes up automotive engine was set. Each part has each different weighted value according to the knowledge of automobile experts. The weighted value is created by the number of selection that the users of the automobile recommendation system select while using the system and the final value calculated by the multiplication of the weighted value, which is recorded within the system. As a result, the users can easily identify which factor in which part is important by the output in the order of the priority. The intelligent recommendation system for automobile parts is a system to inform of the assembly, the usage and the importance of automobile parts without any specialized knowledge by expressing the parts that are closely related with the applicable parts when selecting any part on the basis of the generated data for the automobile parts that are difficult to access by users.

• The Journal of the Korea Contents Association
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• v.18 no.1
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• pp.64-75
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• 2018

With the development of location-aware technologies and the activation of smart phones, location based social networks(LBSN) have been activated to allow people to easily share their location. In particular, studies on recommending the location of user interests by using the user check-in function in LBSN have been actively conducted. In this paper, we propose a location recommendation scheme considering time and user activities in LBSN. The proposed scheme considers user preference changes over time, local experts, and user interest in rare places. In other words, it uses the check-in history over time and distinguishes the user activity area to identify local experts. It also considers a rare place to give a weight to the user preferred place. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.1
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• pp.7-11
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• 2013

Taking experts' knowledge to recommend items has shown some promising results in recommender system research. In order to improve the performance of the existing recommendation algorithms, previous researches on expert-based recommender systems have exploited the knowledge of a common expert group for all users. In this paper, we study a problem of identifying personalized experts within a user group, assuming each user needs different kinds and levels of expert help. To demonstrate this idea, we present a framework for using Support Vector Machine (SVM) to find varying expert groups for users; it is shown in an experiment that the proposed SVM approach can identify personalized experts, and that the person-alized expert-based collaborative filtering (CF) can yield better results than k-Nearest Neighbor (kNN) algorithm.

• Journal of Korean Clinical Nursing Research
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• v.18 no.1
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• pp.159-170
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• 2012

Purpose: This study was designed to examine the validity of the evidence-based guideline on pain developed by Registered Nurses Association of Ontario(RNAO) translated into Korean based on the experts' opinions. Methods: The panel consisted of experts of 60 registered nurses in surgical units, medical doctors and nursing managers who were experts in pain assessment and management. The validity of translated RNAO guideline was evaluated in terms of appropriateness, applicability, and utilization in current practice. Appropriateness and applicability of each recommendation were measured with 9 point scale, whereas utilization was measured by yes/no question. Data were analyzed by mean, standard deviation, and percent. The experts' opinions were analyzed by content analysis method. Results: In general, most of the recommendations in the guideline received above 7 point in appropriateness and applicability. However, above 20% of recommendations showed less than 50% of utilization rates in current practice. Conclusion: The reasons for low utilization of recommendations in current practice are in need for investigation. This study can be used for the development of guideline more acceptable in Korean health care settings and improve the quality of care for the abdominal surgery patients suffering from pain in Korea.