• Journal of Digital Convergence
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• v.16 no.10
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• pp.1-9
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• 2018

The purpose of this study is to examine the concepts and types of cross-cultural collaborative online learning that enhance the utilization of advanced ICT in education and contribute to the promotion of educational exchanges between countries, and suggest exchange learning design models necessary for the active introduction. For this study, previous studies related to cross-cultural collaborative online learning were examined. As a result, cross-cultural collaborative online learning is an educational method based on constructivism that explore and construct knowledge by interacting and collaborating with students, teachers, and field experts who are linguistically and culturally heterogeneous based on advanced ICT. The type of cross-cultural collaborative online learning could be divided into synchronous exchange learning centered on remote video classes and asynchronous exchange learning centered on website based tasks. A PPIE learning design model considering the characteristics of each type is presented.

• Journal of Digital Convergence
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• v.17 no.11
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• pp.201-206
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• 2019

While many users searched for insurance information online, there were not many cases of contents recommendation researches on insurance companies' websites. Therefore, this study proposed a page recommendation system with high possibility of preference to users by utilizing page visit history of insurance companies' websites. Data was collected by using client-side storage that occurs when using a web browser. Collaborative filtering was applied to research as a recommendation technique. As a result of experiment, we showed good performance in item-based collaborative (IBCF) based on Jaccard index using binary data which means visit or not. In the future, it will be possible to implement a content recommendation system that matches the marketing strategy when used in a company by studying recommendation technology that weights items.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.410-420
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• 2016

Recently, the R&D paradigm has become oriented toward convergence technology and interdisciplinary research. In this study, to elucidate the characteristics of research groups that promote the performance of convergence research, we take into account the input characteristics, disciplinary characteristics and collaborative characteristics of research groups. For this study purpose, 5,217 SCI papers published from 104 centers in Advanced Research Center Projects are analyzed as research outputs. The research findings are that the disciplinary balance in the interdisciplinary characteristics and the number of partners in the collaborative characteristics are positively related to the convergence of R&D outputs. The research field of a group introduced as a control variable exerts a significant effect on the convergent R&D outputs. In conclusion, it is necessary to organize internally with the same number of each disciplinary researcher in a research group and to activate external collaboration with partners in order to produce more outputs of convergent research.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2006.07a
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• pp.311-311
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• 2006

• Journal of Digital Convergence
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• v.11 no.2
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• pp.235-242
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• 2013

Reputation systems aggregate users' feedback after the completion of a transaction and compute the "reputation" of products, services, or providers, which can assist other users in decision-making in the future. With the rapid growth of online e-Learning content providing services, a suitable reputation system for more credible e-Learning content delivery has become important and is essential if educational content providers are to remain competitive. Most existing reputation systems focus on generating ratings only for user reputation; they fail to consider the reputations of products or services(item reputation). However, it is essential for B2C e-Learning services to have a reliable reputation rating mechanism for items since they offer guidance for decision-making by presenting the ranks or ratings of e-Learning content items. To overcome this problem, we propose a novel collaborative filtering based reputation rating method. Collaborative filtering, one of the most successful recommendation methods, can be used to improve a reputation system. In this method, dual information sources are formed with groups of co-oriented users and expert users and to adapt it to the reputation rating mechanism. We have evaluated its performance experimentally by comparing various reputation systems.

• The Journal of the Korea Contents Association
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• v.11 no.7
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• pp.1-8
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• 2011

In modern society, the amount of information has been significantly increased according to the development of Internet and IT convergence technology and that leads to develop information obtaining and searching technologies from lots of data. Although the system integration for medicare has been largely established and that accumulates large amounts of information, there is a lack of providing and supporting information for nursing activities using such established database. In particular, the judgement for the intervention of pains depends on the experience of individual nurses and that leads to make subjective decisions in usual. In this paper, a pain nursing supporting method that uses the existing medical data and performs collaborative filtering is proposed. The proposed collaborative filtering is a method that extracts some items, which represent a high relativeness level, based on similar preferences. A preference estimation method using a user based collaborative filtering method calculates user similarities through Pearson correlation coefficients in which a neighbor selection method is used based on the user preference.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.123-130
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• 2019

One of the most commonly used methods of web recommendation techniques is collaborative filtering. Many studies on collaborative filtering have suggested ways to improve accuracy. This study proposes a method of movie recommendation using Word2Vec and an ensemble convolutional neural networks. First, in the user, movie, and rating information, construct the user sentences and movie sentences. It inputs user sentences and movie sentences into Word2Vec to obtain user vectors and movie vectors. User vectors are entered into user convolution model and movie vectors are input to movie convolution model. The user and the movie convolution models are linked to a fully connected neural network model. Finally, the output layer of the fully connected neural network outputs forecasts of user movie ratings. Experimentation results showed that the accuracy of the technique proposed in this study accuracy of conventional collaborative filtering techniques was improved compared to those of conventional collaborative filtering technique and the technique using Word2Vec and deep neural networks proposed in a similar study.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.259-266
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• 2023

In this paper, we propose an algorithm that can improve the accuracy performance of collaborative filtering using attribute-based opinion mining (ABOM). For the experiment, a total of 1,227 online consumer review data about smartphone apps from domestic smartphone users were used for analysis. After morpheme analysis using the KKMA (Kkokkoma) analyzer and emotional word analysis using KOSAC, attribute extraction is performed using LDA topic modeling, and the topic modeling results for each weighted review are used to add up the ratings of collaborative filtering and the sentiment score. MAE, MAPE, and RMSE, which are statistical model performance evaluations that calculate the average accuracy error, were used. Through experiments, we predicted the accuracy of online customers' app ratings (APP_Score) by combining traditional collaborative filtering among the recommendation algorithms and the attribute-based opinion mining (ABOM) technique, which combines LDA attribute extraction and sentiment analysis. As a result of the analysis, it was found that the prediction accuracy of ratings using attribute-based opinion mining CF was better than that of ratings implementing traditional collaborative filtering.

• Journal of Engineering Education Research
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• v.21 no.1
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• pp.3-13
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• 2018

The purpose of this study is to propose a teaching and learning model that can effectively manage convergence education, which is one of the concerns of university education, at the level of course. The pre-collaborative instructional design stage is to prepare the operation of the convergence project course. It shares the common goal and establishes a team of relevant professors to set up the actual convergence project topic and establishes cooperation relationships with industry or community as needed. In the convergence project activity, students will be able to understand the learning objectives, learning activities, evaluation methods, and explain the subject of the convergence project by proceeding with the whole orientation. Students organize teams of interest and conduct learning and design activities on convergence technologies and present their results. In the educational improvement activities, professors will share the lesson process and results and discuss improvements through the improvement seminar. As a result of analyzing the effectiveness of the proposed convergence project based teaching and learning model, the convergence project experience has improved the cooperative self - efficacy for the learners and the results were confirmed that students perceived to achieve the expected learning goal and satisfied with their experience.

• Journal of Ginseng Research
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• v.48 no.3
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• pp.310-322
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• 2024

Background: Osteosarcopenia is a common condition characterized by the loss of both bone and muscle mass, which can lead to an increased risk of fractures and disability in older adults. The study aimed to elucidate the response of various mouse strains to treatment with Rg3, one of the leading ginsenosides, on musculoskeletal traits and immune function, and their correlation. Methods: Six Collaborative Cross (CC) founder strains induced muscle atrophy and bone loss with dexamethasone (15 mg/kg) treatment for 1 month, and half of the mice for each strain were orally administered Rg3 (20 mg/kg). Different responses were observed depending on genetic background and Rg3 treatment. Results: Rg3 significantly increased grip strength, running performance, and expression of muscle and bone health-related genes in a two-way analysis of variance considering the genetic backgrounds and Rg3 treatment. Significant improvements in grip strength, running performance, bone area, and muscle mass, and the increased gene expression were observed in specific strains of PWK/PhJ. For traits related to muscle, bone, and immune functions, significant correlations between traits were confirmed following Rg3 administration compared with control mice. The phenotyping analysis was compiled into a public web resource called Rg3-OsteoSarco. Conclusion: This highlights the complex interplay between genetic determinants, pathogenesis of muscle atrophy and bone loss, and phytochemical bioactivity and the need to move away from single inbred mouse models to improve their translatability to genetically diverse humans. Rg3-OsteoSarco highlights the use of CC founder strains as a valuable tool in the field of personalized nutrition.