• Journal of Information Science Theory and Practice
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• 제1권1호
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• pp.83-91
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• 2013

• 한국식물학회:학술대회논문집
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• 한국식물학회 1999년도 제13회 식물생명공학심포지움 New Approaches to Understand Gene Function in Plants and Application to Plant Biotechnology
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• pp.95-95
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• 1999

• 한국도서관정보학회지
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• 제33권1호
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• pp.99-120
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• 2002

본 연구는 먼저, 북한의 통신망, 하드웨어, 소프트웨어 등 정보화 기반을 조사하고, 이를 바탕으로 도서관 정보화 현황을 파악하였다. 또한 과학기술정보를 중심으로 한 북한의 학술정보 유통체계를 조사하기 위하여 북한의 대표적인 과학기술통보기관인 중앙과학기술통보사 현황과 이 기관이 개발하여 널리 사용하고 있는 검색프로그램인 ‘광명시스템’을 통해 북한의 과학기술정보시스템을 분석하였다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2003년도 춘계학술발표논문집 (하)
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• pp.1611-1614
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• 2003

본 논문에서는 트랜잭션 기반의 데이터 관리 및 검색 기능을 갖춘 정보 관리 시스템인 KRISTAL-2000을 간략하게 소개하고, JAVA를 구현언어로 하는 사용자가 해당 시스템의 기능을 원활하게 사용할 수 있도록 하기 위한 JAVA 기반의 KRISTAL-2000 사용자 API 선계를 목표로 한다. 이때 사용자와 시스템간의 연결은 텍스트 기반의 소켓 통신을 전제로 한다.

• Journal of Information Science Theory and Practice
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• 제10권spc호
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• pp.1-11
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• 2022

As data-based research activities and outcomes increase and ontact or non-face-to-face activities become common, the demand for easy utilization of resources, tools, functions, and easily accessible information required for research in the R&D sector has increased accordingly. With the rapid increase in the demand for collaborative research based on online platforms, research support institutions strive to provide venues for research activities that merge various information and functions. ScienceON, an integrated science & technology (S&T) knowledge infrastructure service developed and operated by the Korea Institute of S&T Information (KISTI), supports open collaboration by connecting and merging all the information, functions, and infrastructure required for research activities. This paper describes the online research activity support tool provided by ScienceON and the remarkable results achieved through this activity. Specifically, the excellent creation of the following flow of meta-material research activities in the ontact space is elucidated. First, the papers required for a meta-material analysis are retrieved, virtual simulation is conducted with the experimental data extracted from the papers, and research data are accumulated. ScienceON's tools for supporting ontact research activity will play a role as an important service in the era of digital transformation and open science.

• International Journal of Computer Science & Network Security
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• 제23권12호
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• pp.225-234
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• 2023

In this era of information and communication technology (ICT), tremendous improvements have been witnessed in our daily lives. The impact of these technologies is subjective and negative or positive. For instance, ICT has brought a lot of ease and versatility in our lifestyles, on the other hand, its excessive use brings around issues related to physical and mental health etc. In this study, we are bridging these both aspects by proposing the idea of AI based mental healthcare (AIMS). In this regard, we aim to provide a platform where the patient can register to the system and take consultancy by providing their assessment by means of a chatbot. The chatbot will send the gathered information to the machine learning block. The machine learning model is already trained and predicts whether the patient needs a treatment by classifying him/her based on the assessment. This information is provided to the mental health practitioner (doctor, psychologist, psychiatrist, or therapist) as clinical decision support. Eventually, the practitioner will provide his/her suggestions to the patient via the proposed system. Additionally, the proposed system prioritizes care, support, privacy, and patient autonomy, all while using a friendly chatbot interface. By using technology like natural language processing and machine learning, the system can predict a patient's condition and recommend the right professional for further help, including in-person appointments if necessary. This not only raises awareness about mental health but also makes it easier for patients to start therapy.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2014년도 춘계 종합학술대회 논문집
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• pp.269-270
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• 2014

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2014년도 춘계 종합학술대회 논문집
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• pp.247-248
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• 2014

• Asian Journal of Innovation and Policy
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• 제5권3호
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• pp.315-329
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• 2016

Public R&D investment information has diverse implications for researching R&D trends. Also, as it is important for the establishment of R&D policy to grasp the current situation and trends of R&D to improve science and technology level, science and technology information service system, such as NTIS (National Science & Technology Information Service), is operated at a national level in most countries. However, since the data forms provided by current NTIS are raw data, it is necessary to develop the R&D performance indicator or to use additional scientometric methods by analyzing scientific papers or scientific R&D project information for grasping R&D trends or analyzing R&D task results. Thus, this study applied public R&D investment information to investigate and monitor R&D trends in the field of information & communication technology (ICT) of meteorology and climatology by using NTIS data of Korea and NSF (National Science Foundation) data of USA.

• Journal of Information Science Theory and Practice
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• 제10권spc호
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• pp.39-45
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• 2022

For national industrial development in the Fourth Industrial Revolution, it is necessary to provide researchers with appropriate job information. This can be achieved by interconnecting the National Science and Technology Standard Classification System used for management of research activity with the Korean Employment Classification of Occupations used for job information management. In the present study, an automatic linkage model of classification systems is introduced based on a pre-trained language model for interconnecting science and technology information with job information. We propose for the first time an automatic model for linkage of classification systems. Our model effectively maps similar classes between the National Science & Technology Standard Classification System and Korean Employment Classification of Occupations. Moreover, the model increases interconnection performance by considering hierarchical features of classification systems. Experimental results show that precision and recall of the proposed model are about 0.82 and 0.84, respectively.