• 가정과삶의질연구
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• 제27권6호
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• pp.243-253
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• 2009

This study identifies the concept of a digital convergence paradigm, which deals with the relationship among convergence and consumer participation, digital contents services, and the culture of consumption (web 2.0). This research was conducted using a quantitative method involving focus group interviews (FGI) and literature reviews. This research examines the relationship between convergence and consumer participation and the adoption of a concept of "convergence culture", which is categorized as "collective intelligence" and "intellectual community participation". The role of the consumer has changed from one of a "consuming consumer" to that of a "collaborating consumer" and finally to the concept of a consumers as the originator of a new consumption culture. In the study, the consumption culture of web participants indicated the characteristics of funology, self-expression, life-catching, quick-tempered, immersion(arousal), sharing culture, multitasking, and cyber justice. The recognition level of networking on the "digital convergence" of the consumer was identified as that of the combination of IT(information technology); product and multi-services or alliance of industry groups(telecom, broadband, IPTV service); or the use of one source with multiple devices.

• 대한간호학회지
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• 제43권2호
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• pp.203-214
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• 2013

Purpose: The purpose of this project was to develop an international web-based expert system using principals of artificial intelligence and user-centered design for management of mental health by Korean emigrants. Using this system, anyone can access the system via computer access to the web. Methods: Our design process utilized principles of user-centered design with 4 phases: needs assessment, analysis, design/development/testing, and application release. A survey was done with 3,235 Korean emigrants. Focus group interviews were also conducted. Survey and analysis results guided the design of the web-based expert system. Results: With this system, anyone can check their mental health status by themselves using a personal computer. The system analyzes facts based on answers to automated questions, and suggests solutions accordingly. A history tracking mechanism enables monitoring and future analysis. In addition, this system will include intervention programs to promote mental health status. Conclusion: This system is interactive and accessible to anyone in the world. It is expected that this management system will contribute to Korean emigrants' mental health promotion and allow researchers and professionals to share information on mental health.

• 수산해양교육연구
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• 제21권4호
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• pp.543-555
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• 2009

The characteristic of Web 2.0 is openness, participation, share, cooperation and creation. The purpose of this article was to identify learner based knowledge creation strategy through UCC in Web 2.0, to develop UCC by university students and to make systematic UCC process. This article suggested knowledge creation strategy with UCC learning Community of Practice(CoP). UCC was developed by 25 students who registered e-learning in "P" university and conducted interview with students and experts to analyze the contents which related with research questions. Systematic process for developing educational UCC was consisted of sectors such as idea creation, design, development, implementation and evaluation. Main developing process steps were as follows: making subject$\rightarrow$seeking information$\rightarrow$selecting data$\rightarrow$designing contents$\rightarrow$making story board$\rightarrow$planning of filming$\rightarrow$filming$\rightarrow$digitalizing$\rightarrow$editing$\rightarrow$reviewing final product$\rightarrow$implementing$\rightarrow$evaluating. For learner based knowledge creation through UCC, educational institutions have to provide platform for learners' need, and learners create diverse ideas with UCC CoP. This article suggested knowledge creation strategy with sharing collective intelligence through process of UCC design, development, implement and evaluation.

• 한국지능시스템학회논문지
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• 제19권1호
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• pp.115-121
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• 2009

Web 2.0 has become the face next generation Web among the business world and research community. Web 2.0 is instant superficial gratification of people. On the other hand, Semantic Web is deep, meaningful and lasting relationship with data. So, it is difficult to apply the Semantic Web to the real world. In this paper, a platform for educational services using the Semantic Web and Web 2.0 is proposed. The proposed platform is based mix of the Semantic Web and Web 2.0, so it is useful to apply in the real world applications. Two services are presented, one is a semantic email system and the other is a cyber study space. The cyber study space adjusted each student is presented. The study environment is called iStudySpace that has personal scheduler, study status plan table, personalized search engine and several gadgets. Finally characteristics and limitations of the Semantic Web and Web 2.0, the organization and components of the platform, evaluation of iStudySpace are shown.

• Journal of Information Processing Systems
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• 제1권1호
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• pp.14-21
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• 2005

Even though mainly statistical methods have been used in anomaly network intrusion detection, to detect various attack types, machine learning based anomaly detection was introduced. Machine learning based anomaly detection started from research applying traditional learning algorithms of artificial intelligence to intrusion detection. However, detection rates of these methods are not satisfactory. Especially, high false positive and repeated alarms about the same attack are problems. The main reason for this is that one packet is used as a basic learning unit. Most attacks consist of more than one packet. In addition, an attack does not lead to a consecutive packet stream. Therefore, with grouping of related packets, a new approach of group-based learning and detection is needed. This type of approach is similar to that of multiple-instance problems in the artificial intelligence community, which cannot clearly classify one instance, but classification of a group is possible. We suggest group generation algorithm grouping related packets, and a learning algorithm based on a unit of such group. To verify the usefulness of the suggested algorithm, 1998 DARPA data was used and the results show that our approach is quite useful.

• 한국인공지능학회지
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• 제11권4호
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• pp.29-34
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• 2023

The flow from developing a machine learning model to deploying it in a production environment suffers challenges. Efficient and reliable deployment is critical for realizing the true value of machine learning models. Bridging this gap between development and publication has become a pivotal concern in the machine learning community. FastAPI, a modern and fast web framework for building APIs with Python, has gained substantial popularity for its speed, ease of use, and asynchronous capabilities. This paper focused on leveraging FastAPI for deploying machine learning models, addressing the potentials associated with integration, scalability, and performance in a production setting. In this work, we explored the seamless integration of machine learning models into FastAPI applications, enabling real-time predictions and showing a possibility of scaling up for a more diverse range of use cases. We discussed the intricacies of integrating popular machine learning frameworks with FastAPI, ensuring smooth interactions between data processing, model inference, and API responses. This study focused on elucidating the integration of machine learning models into production environments using FastAPI, exploring its capabilities, features, and best practices. We delved into the potential of FastAPI in providing a robust and efficient solution for deploying machine learning systems, handling real-time predictions, managing input/output data, and ensuring optimal performance and reliability.

• The Korean Journal of Physiology and Pharmacology
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• 제28권5호
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• pp.393-401
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• 2024

Large language models (LLMs) are rapidly transforming medical writing and publishing. This review article focuses on experimental evidence to provide a comprehensive overview of the current applications, challenges, and future implications of LLMs in various stages of academic research and publishing process. Global surveys reveal a high prevalence of LLM usage in scientific writing, with both potential benefits and challenges associated with its adoption. LLMs have been successfully applied in literature search, research design, writing assistance, quality assessment, citation generation, and data analysis. LLMs have also been used in peer review and publication processes, including manuscript screening, generating review comments, and identifying potential biases. To ensure the integrity and quality of scholarly work in the era of LLM-assisted research, responsible artificial intelligence (AI) use is crucial. Researchers should prioritize verifying the accuracy and reliability of AI-generated content, maintain transparency in the use of LLMs, and develop collaborative human-AI workflows. Reviewers should focus on higher-order reviewing skills and be aware of the potential use of LLMs in manuscripts. Editorial offices should develop clear policies and guidelines on AI use and foster open dialogue within the academic community. Future directions include addressing the limitations and biases of current LLMs, exploring innovative applications, and continuously updating policies and practices in response to technological advancements. Collaborative efforts among stakeholders are necessary to harness the transformative potential of LLMs while maintaining the integrity of medical writing and publishing.

• Journal of Information Technology Applications and Management
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• 제24권3호
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• pp.1-21
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• 2017

Business Intelligence (BI) system is a strategic tool that presents an analytical perspective about business and external environments. Even though its strategic value was well known, users often avoid using it or adopt it ceremonially. In fact, over 50 per cent of BI projects worldwide are reported to end in failure. Such an unexpectedly lower success rate has been a key issue in BI studies. In order to enhance a proper use of information systems, MIS field provided a number of theoretical constructs. One example is Goodhue & Thompson's Task-Technology Fit (TTF). In addition, internalization, the degree to which people make their own effort to modify behavior, was recently suggested as another important determinant of use. Though in MIS community both TTF and internalization proved to be a key determinant of system use, there has been not much study aiming to discover antecedents influencing these constructs. In this study we assert that user participation should be highlighted in BI projects. Especially, we emphasize user participation at the phase of feasibility study that is mainly conducted to determine whether a BI system is essentially necessary and practicable. Our research model employs participative feasibility study as a major antecedent for TTF and internalization that consequently will lead to user satisfaction and actual use. This model was empirically tested on 121 BI system users. The result shows that user participation in feasibility study is positively associated with TTF and internalization, each being related to user satisfaction and system use. It implies that, if an organization has BI users get involved in strategic feasibility study phase, the BI system would turn out to fit users' tasks and, furthermore, users would put more efforts spontaneously in order to use it properly.

• 산업경영시스템학회지
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• 제47권1호
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• pp.41-50
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• 2024

Korea is facing a significant problem with historically low fertility rates, which is becoming a major social issue affecting the economy, labor force, and national security. This study analyzes the factors contributing to the regional gap in fertility rates and derives policy implications. The government and local authorities are implementing a range of policies to address the issue of low fertility. To establish an effective strategy, it is essential to identify the primary factors that contribute to regional disparities. This study identifies these factors and explores policy implications through machine learning and explainable artificial intelligence. The study also examines the influence of media and public opinion on childbirth in Korea by incorporating news and online community sentiment, as well as sentiment fear indices, as independent variables. To establish the relationship between regional fertility rates and factors, the study employs four machine learning models: multiple linear regression, XGBoost, Random Forest, and Support Vector Regression. Support Vector Regression, XGBoost, and Random Forest significantly outperform linear regression, highlighting the importance of machine learning models in explaining non-linear relationships with numerous variables. A factor analysis using SHAP is then conducted. The unemployment rate, Regional Gross Domestic Product per Capita, Women's Participation in Economic Activities, Number of Crimes Committed, Average Age of First Marriage, and Private Education Expenses significantly impact regional fertility rates. However, the degree of impact of the factors affecting fertility may vary by region, suggesting the need for policies tailored to the characteristics of each region, not just an overall ranking of factors.

• 대한영상의학회지
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• 제83권6호
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• pp.1229-1239
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• 2022

최근 인공지능기술은 자기공명영상(이하 MRI)의 폭넓은 분야에서 임상적 활용가치를 보여주고 있다. 특히, MRI에서 영상획득과정의 효율성 및 복원된 영상의 품질을 향상시키기 위한 목적으로 인공지능모델의 개발이 활발하다. 임상에서 활용되는 다양한 MRI 프로토콜에서 인공지능은 병렬영상기법과 같은 기존 가속화 방법 대비 추가적인 영상획득시간을 가능하게 해줄 수 것으로 기대된다. 또한, 펄스시퀀스 디자인, 영상의 인공물 감소, 자동화된 품질평가와 같은 영역에서도 인공지능모델은 도움을 줄 수 있는 연구 결과들이 소개되고 있다. 또한, 영상분석 과정에서 중요한 장비 및 프로토콜의 영향을 줄여줄 수 있는 방법으로도 인공지능 기반의 접근이 이루어지고 있다. 본 종설에서는 MRI 영상의 획득 과정에서 최근 인공지능기술들이 적용되고 있는 분야 및 해당 분야에서의 인공지능기술의 개발 및 적용과 관련된 현안들을 소개하고자 한다.