• Communications of Mathematical Education
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• v.34 no.4
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• pp.545-561
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• 2020

The purpose of this study is to investigate the applications and possibilities of major programs that provide services using artificial intelligence in mathematics education. For this study, related papers, reports, and materials were collected and analyzed, focusing on materials mostly published within the last five years. The researcher searched the keywords of "artificial intelligence", "artificial intelligence", "AI" and "mathematics education" independently or in combination. As a result of the study, artificial intelligence for mathematics education was mostly supporting learners' personalized mathematics learning, defining it as an auxiliary role to support human mathematics teachers, and upgrading the technology of not only cognitive aspects but also affective aspects. As suggestions, the researcher argued that followings are necessary: Research for the establishment of an elaborate artificial intelligence mathematical system, discovery of artificial intelligence technology for appropriate use to support mathematics education, development of high quality of mathematics contents for artificial intelligence, and the establishment and operation of a cloud-based comprehensive system for mathematics education. The researcher proposed that continuous research to effectively help students study mathematics using artificial intelligence including students' emotional or empathetic abilities, and collaborative learning, which is only possible in offline environments. Also, the researcher suggested that more sophisticated materials should be developed for designing mathematics teaching and learning by using artificial intelligence.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1321-1329
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• 2019

With the development of social information networks, the marketing methods are also changing in various ways. Unlike successful marketing methods based on existing celebrities and financial support, Influencer-based marketing is a big trend and very famous. In this paper, we first extract influencer features from more than 54 YouTube channels using the multi-dimensional qualitative analysis based on the meta information and comment data analysis of YouTube, model representative themes to maximize a personalized video satisfaction. Plus, the purpose of this study is to provide supplementary means for the successful promotion and marketing by creating and distributing videos of new items by referring to the existing Influencer features. For that we assume all comments of various videos for each channel as each document, TF-IDF (Term Frequency and Inverse Document Frequency) and LDA (Latent Dirichlet Allocation) algorithms are applied to maximize performance of the proposed scheme. Based on the performance evaluation, we proved the proposed scheme is better than other schemes.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.305-312
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• 2023

In introducing memory methods in various educational fields, programs in a mobile environment can be used for the purpose of increasing accessibility and enhancing the effectiveness of education. It is much easier to remember words with meaning than to remember numerical information such as years. From the standpoint of increasing the educational effect, the part that needs to be supplemented with the help of the application can be said to be numerical information. Most studies related to conventional numerical memory have focused on the form that helps memory by imaging numbers. In the paper on memory-based meta-record algorithms in the mobile environment, the application developed in the previous study attempts to supplement this by discovering and simply modifying the user's mistakes in the entered numerical information. In this study, we aim to increase the memory rate by constructing metadata based on personalized log information and correcting mistakes. To do this, applications suitable for the mobile environment are developed, a structure of meta-record data is proposed, and meta-record application algorithms are implemented and evaluated.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.85-94
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• 2009

It is becoming increasingly difficult for learners to find the lectures they are looking for. In turn, the ability to find the particular lecture sought by the learner in an accurate and prompt manner has become an important issue in e-Learning. To deal this issue, in this paper. we present a collaborative approach to provide personalized recommendations of online video lectures. The proposed approach first identifies candidated video lectures that will be of interest to a certain user. Partitioned collaborative filtering is employed as an approach in order to generate neighbor learners and predict learners'preferences for the lectures. Thereafter, Attribute-based filtering is employed to recommend a final list of video lectures that the target user will like the most.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.223-228
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• 2023

According to the Korea Information Society Development Institute, OTT services grew at a rate of 33.4% in four yearsfrom 2017, when they were first launched.TheKorea Export-Import Bank announced in 2020 that the domestic OTT market was worth 780.1 billionKRW. This growth of the OTT market is expected to stimulate competition among OTT service platforms, and user satisfactionwithconvenience features, such as video recommendations, seems to be acting as an important factor in the competition.Currently, the OTT market uses a variety ofdata for customized recommendations, but the limitationis that it only uses datacollected within the app. Thereby we have proposed the use ofpersonal data collected outside the app for personalized recommendations, and the survey results showed that user satisfaction was 23.72% higher for recommended content based on the proposedmethod thanNetflix recommended content.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.10
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• pp.865-878
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• 2017

To realize the healthy life of human, mental, physical, and environmental factors must be managed continuously and stably. In order to manage human health, the 21st century healthcare field is essential ongoing interactions and convergence with ICT technologies. Such demands have created a convergence of technologies (fusion technology) in combination with the heterogeneous technologies. And, with the convergence of medical technology and ICT technologies, the development of personalized therapy environments is created. Advances in ICT-converged healthcare services are progressing due to the development of diverse wearable devices. Such ICT fusion system is exponentially increasing the complexity of the ICT convergence healthcare system and is resulting in various technical, institutional, environmental, and cultural issues. This study explores the status of developments in ICT healthcare technologies from the past to date, identifies major technology and policy issues to address these challenges. Finally it will recommend healthcare policies and a future road-map.

• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.463-468
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• 2022

The prolonged of the Post Corona made many industry's paradigm. It's become very important In the industries products that customers directly touch and use. To cope with this situation, The Cosmetics industry has recently introduced various untact services. many customers would like to try these new services. Typically, online survey services recommend personalized products. but these services reached its limit later. This paper research how to recommend products and define skine type with AI Image diagnosis module combine with legacy survey system.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.357-359
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• 2023

This paper proposes a hybrid recommendation system (RS) model that overcomes the limitations of traditional approaches such as data sparsity, cold start, and scalability by combining collaborative filtering and context-aware techniques. The objective of this model is to enhance the accuracy of recommendations and provide personalized suggestions by leveraging the strengths of collaborative filtering and incorporating user context features to capture their preferences and behavior more effectively. The approach utilizes a novel method that combines contextual attributes with the original user-item rating matrix of CF-based algorithms. Furthermore, we integrate k-mean++ clustering to group users with similar preferences and finally recommend items that have highly rated by other users in the same cluster. The process of partitioning is the use of the rating matrix into clusters based on contextual information offers several advantages. First, it bypasses of the computations over the entire data, reducing runtime and improving scalability. Second, the partitioned clusters hold similar ratings, which can produce greater impacts on each other, leading to more accurate recommendations and providing flexibility in the clustering process. keywords: Context-aware Recommendation, Collaborative Filtering, Kmean++ Clustering.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1285-1290
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• 2023

Although the environment in virtual space (hereinafter referred to as VR) has the problem of being difficult to access compared to existing PCs and smartphones, it has the advantage of being more realistic and providing endless experiences and functions compared to existing environments. In this VR environment, there is a need to develop technologies that help people handle tasks more conveniently in the virtual world by studying interfaces and interactions using ChatGPT, a recently popular AI technology. The ChatGPT interface and interaction in the VR environment are also studied to provide personalized services. Through this, users can choose the interface that suits them and the secretary interface can also provide customized services optimized for users. Accordingly, in this study, we design a convenient interaction method by linking the ChatGPT system in a VR environment and use it as a previous study for the development of an AI assistant.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.131-141
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• 2024

The aging population and worsening lifestyle habits have increased the risk of chronic diseases. This has heightened the importance of preventive healthcare, particularly through personalized health management services based on individual health data. Despite this, the domestic digital healthcare industry remains underdeveloped. Given the need for acceptance from both consumers and providers, this study uses the Analytic Hierarchy Process (AHP) to identify success factors for health management service platforms. AHP evaluates the relative importance of various factors to aid decision-making. Results show that providers prioritize data analysis and platform design, laws and regulations, and data standardization, while consumers prioritize system stability, laws and regulations, and system security. These findings highlight the need for strategies to bridge the expectation gap to effectively promote health management service platforms.