• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.401-402
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• 2018

콘텐츠 큐레이션 서비스를 위해서 대용량 데이터를 학습하는 과정에서 발생하는 메모리부족 문제, 학습소요시간 문제 등을 해결하기 위한 "대용량 문서학습을 위한 동적학습 파이프라인 생성기술 중 빅데이터 마이닝을 위한 점진적 학습 모델" 기술이 필요하며, 본 논문에서 제안한 콘텐츠 큐레이션 서비스는 온라인상의 수많은 콘텐츠들 중 개인의 주관이나 관점에 따라 관련 콘텐츠들을 수집, 정리하고 편집하여 이용자와 관련이 있거나 좋아할 만한 콘텐츠를 제공하는 서비스이다. 본 논문에서 설계된 모아 큐레이션 서비스는 대용량의 문서를 학습함에 있어서 메모리 부족 문제, 학습 소요시간 문제 등을 해결하기 위해 학습데이터의 용량 제한이 없는 문서를 자유롭게 학습하고 부분적인 자질추가/변경 시에 변경요소만을 추가 반영할 수 있는 범용적이고 일반적인 분류기의 구조설계 방법 등을 제시하였다.

• Cartoon and Animation Studies
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• s.16
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• pp.199-210
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• 2009

The development in information technology resulted in an interest for lifelong education, which caused a rapid growth in the educational web based e-Iearning industry. The spread of e-Iearning industries and educational facilities emphasized the need for educational contents with the purpose of online lecturing along with the rapid development of contents production technology. Flash contents provide multimedia, animation, and etc. with diverse and visual expressions, so the complicated interactions with the learner can be easily materialized. The advantage of having a high quality, user-centered interface is that it provides a trigger of interest and arousal of concentration for the user, but it is difficult for teachers to directly create or manipulate educational flash animation contents. This paper is written to technologically methodize the automation of educational programs that the teachers wish to make by reusing the educational flash animation contents.

• Journal of Digital Contents Society
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• v.18 no.7
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• pp.1249-1257
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• 2017

Due to the 4th industrial revolution and an aged society, many studies are being conducted to apply virtual reality to medical field. Research on dementia is especially active. This paper proposes virtual reality based on cognitive rehabilitation contents using image recognition and clustering method to improve cognitive and physical disabilities caused by dementia. Unlike the existing cognitive rehabilitation system, this paper uses travel photos that reflect the memories of the subjects to be treated. In order to generate automated cognitive rehabilitation contents, we extract face information, food pictures, place information, and time information from photographs, and normalization is performed for clustering. And we present scenarios that can be used as cognitive rehabilitation contents using travel photos in virtual reality space.

• The Journal of the Korea Contents Association
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• v.6 no.12
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• pp.177-185
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• 2006

It is a difficult problem that learner have to need much times and efforts to search informations for problem solving. This is caused that the web based search system used by this time have the searching method of simple keyword matching. The searching method of simple keyword matching search informations by method of whether it is simply matched with keyword. Therefore, Learner have to much times and efforts to search informations, and may lose or be out of his bearing. To solve this problems, We design and implement a ontology based search system. This system is apply to PBL of social studies on middle school students. As a result, This system is more effect than the web based search system used by this time.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1076-1084
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• 2019

In this paper, we introduced a system that extracts metadata by recognizing characters and objects in media using deep learning technology. In the field of broadcasting, multimedia contents such as video, audio, image, and text have been converted to digital contents for a long time, but the unconverted resources still remain vast. Building media archives requires a lot of manual work, which is time consuming and costly. Therefore, by implementing a deep learning-based metadata generation system, it is possible to save time and cost in constructing media archives. The whole system consists of four elements: training data generation module, object recognition module, character recognition module, and API server. The deep learning network module and the face recognition module are implemented to recognize characters and objects from the media and describe them as metadata. The training data generation module was designed separately to facilitate the construction of data for training neural network, and the functions of face recognition and object recognition were configured as an API server. We trained the two neural-networks using 1500 persons and 80 kinds of object data and confirmed that the accuracy is 98% in the character test data and 42% in the object data.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.41-63
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• 2023

As the online e-commerce market growing, the need for a recommender system that can provide suitable products or services to customer is emerging. Recently, many studies using the sentiment score of online review have been proposed to improve the limitations of study on recommender systems that utilize only quantitative information. However, this methodology has limitation in extracting specific preference information related to customer within online reviews, making it difficult to improve recommendation performance. To address the limitation of previous studies, this study proposes a novel recommendation methodology that applies deep learning technique and uses various linguistic factors within online reviews to elaborately learn customer preferences. First, the interaction was learned nonlinearly using deep learning technique for the purpose to extract complex interactions between customer and product. And to effectively utilize online review, cognitive contents, affective contents, and linguistic style matching that have an important influence on customer's purchasing decisions among linguistic factors were used. To verify the proposed methodology, an experiment was conducted using online review data in Amazon.com, and the experimental results confirmed the superiority of the proposed model. This study contributed to the theoretical and methodological aspects of recommender system study by proposing a methodology that effectively utilizes characteristics of customer's preferences in online reviews.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.10
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• pp.5925-5932
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• 2014

The aim of this study was to develop assessment tools for analyzing society focusing on abilities, not an academic clique. The development of a type of occupational qualification, which is called a working and learning system, for Koreans, can be reliable and acceptable to a variety of society members. This study was conducted by searching for a dual system and examining a qualification system in developed countries. To achieve the goal of this study, the management status according to a type of qualification system was analyzed. In addition, a variety of related laws, related department, testing authority, testing institute, terms of applying for tests, way of testing, committee of testing, testing subjects, criterion for passing tests, a status of qualification test administrated in some developed countries, such as Germany, UK, the USA and Australia, were conducted. The occupational duty of the machine field and education contents are examined by analyzing the occupational duty. In addition, the criterion to solve problems and a way of marking in the field of machines were indicated and an example of written and practical tests is presented. This study makes a blueprint for a qualification system of working and learning at the same time in a national dimension.

• Asia pacific journal of information systems
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• v.29 no.4
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• pp.752-770
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• 2019

The purpose of this study is to incorporate telemarketing processes to improve telemarketing performance. For this application, we have attempted to mix the model of machine learning to extract potential customers with personalisation techniques to derive recommended products from actual contact. Most of traditional recommendation systems were mainly in ways such as collaborative filtering, which predicts items with a high likelihood of future purchase, based on existing purchase transactions or preferences for products. But, under these systems, new users or items added to the system do not have sufficient information, and generally cause problems such as a cold start that can not obtain satisfactory recommendation items. Also, indiscriminate telemarketing attempts can backfire as they increase the dissatisfaction and fatigue of customers who do not want to be contacted. To this purpose, this study presented a multi-purpose hybrid recommendation algorithm to achieve two goals: to select customers with high possibility of contact, and to recommend products to selected customers. In addition, we used subscription data from telemarketing agency that handles insurance products to derive realistic applicability of the proposed recommendation system. Our proposed recommendation system would certainly solve the cold start and scarcity problem of existing recommendation algorithm by using contents information such as customer master information and telemarketing history. Also. the model could show excellent performance not only in terms of overall performance but also in terms of the recommendation success rate of the unpopular product.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.5
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• pp.1055-1062
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• 2021

As face-to-face education becomes difficult due to the spread of COVID-19, the use of e-learning content and virtual training is increasing. In the case of information security education, practice to learn response techniques is important, so simulation hacking and vulnerability analysis activities have been supported as virtual training for a long time. In order to increase the educational effect, contents should be designed similar to real situation, and learning activities to achieve the learning goals should be designed. In addition, excellent functions and scalability of the system supporting learning activities are required. The researcher developed an LMS evaluation index that supports non-face-to-face education by considering the key elements of non-face-to-face education and training. The developed evaluation index was applied to the information security education platform to verify its practical utility.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.974-992
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• 2021

Recently, the healthcare field has undergone rapid changes owing to the accumulation of health big data and the development of machine learning. Data mining research in the field of healthcare has different characteristics from those of other data analyses, such as the structural complexity of the medical data, requirement for medical expertise, and security of personal medical information. Various methods have been implemented to address these issues, including the machine learning model and cloud platform. However, the machine learning model presents the problem of opaque result interpretation, and the cloud platform requires more in-depth research on security and efficiency. To address these issues, this paper presents a recent technology for Internet-of-Things-based (IoT-based) health big data processing. We present a cloud-based IoT health platform and health big data processing technology that reduces the medical data management costs and enhances safety. We also present a data mining technology for health-risk prediction, which is the core of healthcare. Finally, we propose a study using explainable artificial intelligence that enhances the reliability and transparency of the decision-making system, which is called the black box model owing to its lack of transparency.