• Journal of Digital Convergence
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• v.12 no.5
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• pp.239-247
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• 2014

Ubiquitous computing environment, as a new paradigm of the digital information era, has been introduced to respond to rapid changes in society and culture. Today, technology research around the world competes for the preoccupancy of the core technology as well as the development of technology for providing context aware system more intelligent, more sophisticated. Research in recent is underway the implementation of intelligent provisioning system using the ontology. Erstwhile provisioning system did not providing a personalized service that provides the service after context aware. Accordingly, there is a need for studies of intelligent context aware provisioning technique considering environments and context necessary to user. In this paper, in order to provide a service that is optimized for the needs of the user, propose an ontology-based intelligent provisioning service method taking into account the usage patterns and the context of the user. The proposed system is recognized the status of the user and demonstrated a process of reasoning techniques for fit service. And it is possible to the expansion of intelligence.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2632-2637
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• 2010

Information in UbiComp Environment are transformed to knowledge by relationship in a spatio-temporal location, and then became intelligent contents with task procedures or application models. The entities in U-City has lots of relationships. It is important in U-City contents to provide intelligent and personalized response to meet the intention of users. We extend the spatial ontology model of SPIRIT to other domain. Domain ontologies are consist of type, relation, and instance ontologies. When the relationship model by shared concepts are not defined, we used the spatio-temporal events to find relationships. So we proposed the methods to recommend semantically related terms, not syntactically.

• KIISE Transactions on Computing Practices
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• v.21 no.9
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• pp.596-603
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• 2015

A concept network is an essential knowledge base for semantic search engines, personalized search systems, recommendation systems, and text mining. Recently, studies of extending concept representation using external ontology have been frequently conducted. We thus propose a new way of building 3-dimensional text model-based concept networks using the world knowledge-level Wikipedia ontology. In fact, it is desirable that 'concepts' derived from text documents are defined according to the theoretical framework of formal concept analysis, since relationships among concepts generally change over time. In this paper, concept networks hidden in a given document collection are extracted more reasonably by representing a concept as a term-by-document matrix.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.730-737
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• 2018

With the increasing number of health consumers aiming for a happy quality of life, the O2O medical marketing market is activated by choosing reliable health care facilities and receiving high quality medical services based on the medical information distributed on web's blog. Because unstructured text data used on the Internet, mobile, and social networks directly or indirectly reflects authors' interests, preferences, and expectations in addition to their expertise, it is difficult to guarantee credibility of medical information. In this study, we propose a blog reading system that provides users with a higher quality medical information service by classifying medical information blogs (medical blog, ad blog) using bigdata and MLP processing. We collect and analyze many domestic medical information blogs on the Internet based on the proposed big data and machine learning technology, and develop a personalized health information recommendation system for each disease. It is expected that the user will be able to maintain his / her health condition by continuously checking his / her health problems and taking the most appropriate measures.

• Journal of the Korean Society for Library and Information Science
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• v.52 no.4
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• pp.51-69
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• 2018

The purpose of this study is to analyze the potential use of the theme collection as a new classification method that reflects the interest of users in terms of classification and categorization. For this purpose, the background of the theme collection was identified based on the discussion of the library resource organization and the introduction of the curation service of bookstore. In addition, based on case analysis, which is building the theme collection, concrete concepts and characteristics of theme collection are derived. Based on the above discussion, the classification and categorization characteristics of public library themes collections were analyzed, and the characteristics and functions as a classification were compared with other categories relatively. Finally, the utility and applicability of the theme collection is presented and it is based on the discussions about the user-centered classification system design of the library in the future.

• Journal of Convergence for Information Technology
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• v.11 no.1
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• pp.28-33
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• 2021

Since the stock price is a measure of the future value of the company, when analyzing the stock price, the company's growth potential, such as sales and profits, is considered and invested in stocks. In order to set the criteria for selecting stocks, institutional investors look at current industry trends and macroeconomic indicators, first select relevant fields that can grow, then select related companies, analyze them, set a target price, then buy, and sell when the target price is reached. Stock trading is carried out in the same way. However, general individual investors do not have any knowledge of investment, and invest in items recommended by experts or acquaintances without analysis of financial statements or growth potential of the company, which is lower in terms of return than institutional investors and foreign investors. Therefore, in this study, we propose a research method to select undervalued stocks by analyzing ROE, an indicator that considers the growth potential of a company, such as sales and profits, and predict the stock price flow of the selected stock through deep learning algorithms. This study is conducted to help with investment.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.203-214
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• 2023

The Korea Customs Service is efficiently handling business with an electronic customs system that can effectively handle one-stop business. This is the case and a more effective method is needed. Import and export require HS Code (Harmonized System Code) for classification and tax rate application for all goods, and item classification that classifies the HS Code is a highly difficult task that requires specialized knowledge and experience and is an important part of customs clearance procedures. Therefore, this study uses various types of data information such as product name, product description, and product image in the item classification request form to learn and develop a deep learning model to reflect information well based on Multimodal representation learning. It is expected to reduce the burden of customs duties by classifying and recommending HS Codes and help with customs procedures by promptly classifying items.

• Journal of Advanced Technology Convergence
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• v.2 no.3
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• pp.33-41
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• 2023

This study aimed to improve the quality of classes through the application experience analysis after applying the adult nursing simulation practice modules with PBL. Quantitative and qualitative data such as from satisfaction, validity, self-reflection, and lecture evaluation in 68 nursing students were analyzed after the semester. Satisfaction was 4.64 points out of 5 points, and 'I want to recommend this class to other friends' was the highest. It was appropriate for the validity as 64.7% to 100% positve answer. From the qualitative data analysis of lecture evaluation, it was categorized into 5 thematic groups : 'increased immersion related to a lively class environment', 'growth of knowledge and skills through learners' active participation', 'improvement of mutual collaboration skills through team-based problem-solving process', 'Improvement of problem-solving ability through situational crisis coping process' and 'Improvement of individual comprehension through close teaching'. The continuous development of PBL learning strategies and development of various scenarios are required in the future.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.13-21
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• 2022

Recent deep learning-based research shows excellent performance in most natural language processing (NLP) fields with pre-trained language models. In particular, the auto-encoder-based language model proves its excellent performance and usefulness in various fields of Korean language understanding. However, the decoder-based Korean generative model even suffers from generating simple sentences. Also, there is few detailed research and data for the field of conversation where generative models are most commonly utilized. Therefore, this paper constructs multi-turn dialogue data for a Korean generative model. In addition, we compare and analyze the performance by improving the dialogue ability of the generative model through transfer learning. In addition, we propose a method of supplementing the insufficient dialogue generation ability of the model by extracting recommended response candidates from external knowledge information through a retrival model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.240-243
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• 2021

Recently, there have been research results of applying Big data and AI technologies to the evaluation and individual learning for education. It is information technology innovations that collect dynamic and complex data, including student personal records, physiological data, learning logs and activities, learning outcomes and outcomes from social media, MOOCs, intelligent tutoring systems, LMSs, sensors, and mobile devices. In addition, e-learning was generated a large amount of learning data in the COVID-19 environment. It is expected that learning analysis and AI technology will be applied to extract meaningful patterns and discover knowledge from this data. On the learner's perspective, it is necessary to identify student learning and emotional behavior patterns and profiles, improve evaluation and evaluation methods, predict individual student learning outcomes or dropout, and research on adaptive systems for personalized support. This study aims to contribute to research in the field of education by researching and classifying machine learning technologies used in anomaly detection and recommendation systems for educational data.