• Journal of The Korean Association For Science Education
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• v.43 no.2
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• pp.99-110
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• 2023

In this study, in order to strengthen the AI utilization competency of pre-service secondary science teachers, a project activity in which pre-service teachers directly create an 'AI-based molecular structure customized learning support tool' by using Google's teachable machine was developed and applied. To this end, the program developed for 26 third-grade pre-service teachers enrolled in the Department of Chemistry Education at H University in Chungcheongbuk-do was applied for 14 sessions during extracurricular activities. Then, the perceptions of 'understanding how AI works', 'efficacy of using AI in science classes', and 'plans to utilize AI in science classes' were investigated. As a result of the study, it was found that the program developed in this study was effective in helping pre-service teachers understand the operating principle of AI technology for machine learning at a basic level and learning how to use it. In addition, the program developed in this study was found to be effective in increasing the efficacy of pre-service teachers for the use of AI in science classes. And it was also found that pre-service teachers recognized the aspect of using AI technology as a new teaching·learning strategy and tool that can help students understand science concepts. Accordingly, it was found that the program developed in this study had a positive impact on pre-service teachers' AI utilization competency reinforcement and perception improvement at the basic level. Implications of this were discussed.

• Smart Media Journal
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• v.11 no.2
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• pp.39-52
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• 2022

In the context of the evolution of automation and intelligence, deep learning and machine learning algorithms have been widely applied in aquaculture in recent years, providing new opportunities for the digital realization of aquaculture. Especially, water quality management deserves attention thanks to its importance to food organisms. In this study, we proposed an end-to-end deep learning-based TabNet model for water quality prediction. From major indexes of water quality assessment, we applied novel deep learning techniques and machine learning algorithms in innovative fish aquaculture to predict the number of water cells counting. Furthermore, the application of deep learning in aquaculture is outlined, and the obtained results are analyzed. The experiment on in-house data showed an optimistic impact on the application of artificial intelligence in aquaculture, helping to reduce costs and time and increase efficiency in the farming process.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.3
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• pp.428-434
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• 2020

Recently, due to the development of ICT technology, changes to the convergence service platform of information systems are accelerating. Convergence services expanded to cyber systems with 5G communication, IoT, AI, and cloud are being reflected in the real world. However, the field of cybersecurity audit for responding to cyber attacks and security threats and strengthening security technology is insufficient. In this paper, we analyze the international standard analysis of information security management system, security audit analysis and security of related systems according to the expansion of 5G communication, IoT, AI, Cloud based information system security. In addition, we design and study cybersecurity audit checklists and contents for expanding security according to cyber attack and security threat of information system. This study will be used as the basic data for audit methods and audit contents for coping with cyber attacks and security threats by expanding convergence services of 5G, IoT, AI, and Cloud based systems.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.13-22
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• 2021

The clinical decision support system uses accumulated medical data to apply an AI model learned by machine learning to patient diagnosis and treatment prediction. However, the existing black box-based AI application does not provide a valid reason for the result predicted by the system, so there is a limitation in that it lacks explanation. To compensate for these problems, this paper proposes a system model that applies XAI that can be explained in the development stage of the clinical decision support system. The proposed model can supplement the limitations of the black box by additionally applying a specific XAI technology that can be explained to the existing AI model. To show the application of the proposed model, we present an example of XAI application using LIME and SHAP. Through testing, it is possible to explain how data affects the prediction results of the model from various perspectives. The proposed model has the advantage of increasing the user's trust by presenting a specific reason to the user. In addition, it is expected that the active use of XAI will overcome the limitations of the existing clinical decision support system and enable better diagnosis and decision support.

• Journal of Fashion Business
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• v.27 no.4
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• pp.141-162
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• 2023

With the prospect that integrating creative AI in the fashion design field will become more visible, this study considered the case of creative fashion design development through Human-AI Co-creation (HAIC). Methodologically, this research encompasses a literature review and empirical investigations. In the literature review, the fashion design and creative HAIC processes, and the possibilities of integrating AI in fashion design were considered. In the empirical study, based on the case analysis of generating fashion design through HAIC, the HAIC type according to the role and interaction method, and characteristics of humans and AI was considered, and the HAIC process for fashion design was derived. The results of this study are summarized as follows. First, HAIC types in fashion design are divided into four types: AI-driven passive HAIC, human-driven passive HAIC, flexible interaction-based HAIC, and integrated interaction-based value creation HAIC. Second, the stages of the HAIC process for creative fashion design can be broadly divided into semantic data integration, visual ideation, design creation and expansion, design presentation, and design/manufacturing solution and UX platform creation. Third, in fashion design, HAIC contributes to human ability, enhancement of creativity, achievement of efficient workflow, and creation of new values. This research suggests that HAIC has the potential to revolutionize the fashion design industry by facilitating collaboration between humans and AI; consequently, enhancing creativity, and improving the efficiency of the design process. It also offers a framework for understanding the different types of HAIC and the stages involved in the creative fashion design process.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.369-376
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• 2021

With the development of artificial intelligence (AI) technology, discussions on the use of artificial intelligence are actively taking place in many fields, and various policies for nurturing artificial intelligence talents are being promoted in the field of education. In this study, we propose a robot programming framework using artificial intelligence technology, and based on this, we use Python, which is used frequently in the machine learning field, and an educational robot that is highly utilized in the field of education to provide artificial intelligence. (AI) education program was proposed. The level of autonomous driving (levels 0-5) suggested by the International Society of Automotive Engineers (SAE) is simplified to four levels, and based on this, the camera attached to the robot recognizes and detects lines (objects). The goal was to make a line detector that can move by itself. The developed program is not a standardized form of solving a given problem by simply using a specific programming language, but has the experience of defining complex and unstructured problems in life autonomously and solving them based on artificial intelligence (AI) technology. It is meaningful.

• Korean Journal of Radiology
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• v.25 no.4
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• pp.343-350
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• 2024

Objective: Artificial intelligence-based computer-aided diagnosis (AI-CAD) is increasingly used in mammography. While the continuous scores of AI-CAD have been related to malignancy risk, the understanding of how to interpret and apply these scores remains limited. We investigated the positive predictive values (PPVs) of the abnormality scores generated by a deep learning-based commercial AI-CAD system and analyzed them in relation to clinical and radiological findings. Materials and Methods: From March 2020 to May 2022, 656 breasts from 599 women (mean age 52.6 ± 11.5 years, including 0.6% [4/599] high-risk women) who underwent mammography and received positive AI-CAD results (Lunit Insight MMG, abnormality score ≥ 10) were retrospectively included in this study. Univariable and multivariable analyses were performed to evaluate the associations between the AI-CAD abnormality scores and clinical and radiological factors. The breasts were subdivided according to the abnormality scores into groups 1 (10-49), 2 (50-69), 3 (70-89), and 4 (90-100) using the optimal binning method. The PPVs were calculated for all breasts and subgroups. Results: Diagnostic indications and positive imaging findings by radiologists were associated with higher abnormality scores in the multivariable regression analysis. The overall PPV of AI-CAD was 32.5% (213/656) for all breasts, including 213 breast cancers, 129 breasts with benign biopsy results, and 314 breasts with benign outcomes in the follow-up or diagnostic studies. In the screening mammography subgroup, the PPVs were 18.6% (58/312) overall and 5.1% (12/235), 29.0% (9/31), 57.9% (11/19), and 96.3% (26/27) for score groups 1, 2, 3, and 4, respectively. The PPVs were significantly higher in women with diagnostic indications (45.1% [155/344]), palpability (51.9% [149/287]), fatty breasts (61.2% [60/98]), and certain imaging findings (masses with or without calcifications and distortion). Conclusion: PPV increased with increasing AI-CAD abnormality scores. The PPVs of AI-CAD satisfied the acceptable PPV range according to Breast Imaging-Reporting and Data System for screening mammography and were higher for diagnostic mammography.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.41-55
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• 2021

AI voice speaker has played an important role in forming an early market and development for AI-based goods and service with growing attention from many people. In this context, this research examined factors affecting continuous intention of AI voice speaker based on the integrated adoption model, which combined two factors of perceived playfulness and innovation resistance with extended technology acceptance model. It was also examined whether three perceived anthropomorphic features(i.e., perceived rational support, perceived intimacy, perceived cognitive openness) have influences on continuous intention of AI voice speaker. The data was collected by an online-survey and were responses of those who are in their 20s and 30s and have experienced in using AI voice speaker. They were analyzed by using SEM(Structural Equation Modeling). The results showed that all of perceived ease of use, perceived usefulness, perceived playfulness and innovation resistance had significant influences on continuous intention of AI voice speaker. In addition, all of perceived rational support, perceived intimacy and perceived cognitive openness as perceived anthropomorphic features had significant influences on perceived ease of use, perceived usefulness and perceived playfulness. The implications of found results in this research was also discussed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.951-952
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• 2013

국내 양돈분야에서의 AI(Artificial Insemination)센터는 인공수정 기술의 개발과 보급과 관하여서는 중추적인 역할을 수행하고 있다. 그러나 현재 전국AI센터에서 사용하고 있는 정보관리 시스템은 독립적이고 운영체제에 의존적인 형태로 운영되고 있다. 따라서 현재 전국AI센터 정보관리 체계는 실시간으로 정보관리 시스템의 접근제한과 모바일 서비스 등의 분야에서 그 분명한 한계를 가진다. 이에 본 논문에서는 유무선 통합(FMC) AI PigMoS(Pig Monitoring System, PigMoS) 시스템을 제안하고 구현하였다. 본 논문에서 제안한 FMC AI PigMoS 시스템은 이동성, 실시간 정보관리 등을 지원할 수 있도록 인터넷과 모바일에서 운영할 수 있도록 구현 하였다. 구현된 FMC AI PigMoS 시스템은 이동성과 실시간 정보관리 등에 필요한 모듈 중심으로 설계하고 구현하였다. 이는 원거리 소비자들에게 각 AI센터에서 생성된 AI정보를 실시간으로 제공하여 개별AI센터의 경쟁력 향상을 높일 것으로 기대한다.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.343-347
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• 2022

To date, the use of artificial intelligence has already brought certain results in such areas of art as poetry, painting, and music. The development of AI and its application in the creative process opens up new perspectives, expanding the capabilities of authors and attracting a new audience. The purpose of the article is to analyze the essential, artistic, and technological limitations of AI art. The article discusses the methods of attracting AI to artistic practices, carried out a comparative analysis of the methods of using AI in visual art and in the process of writing music, identified typical features in the creative interaction of the author of a work of art with AI. The basic principles of working with AI have been determined based on the analysis of ways of using AI in visual art and music. The importance of neurobiology mechanisms in the course of working with AI has been determined. The authors conclude that art remains an area in which AI still cannot replace humans, but AI contributes to the further formation of methods for modifying and rethinking the data obtained into innovative art projects.