• 의학교육논단
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• 제18권2호
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• pp.47-50
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• 2016

Development of artificial intelligence is expected to revolutionize today's medicine. In fact, medicine was one of the areas to which advances in artificial intelligence technology were first applied. Recently, state-of-the-art artificial intelligence, especially deep learning technology, has been actively utilized to treat cancer patients and analyze medical image data. Application of artificial intelligence has the potential to fundamentally change various aspects of medicine, including the role of human doctors, the clinical decision-making process, and even overall healthcare systems. Facing such fundamental changes is unavoidable, and we need to prepare to effectively integrate artificial intelligence into our medical system. We should re-define the role of human doctors, and accordingly, medical education should also be altered. In this article, we will discuss the current status of artificial intelligence in medicine and how we can prepare for such changes.

• 정보교육학회논문지
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• 제23권2호
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• pp.189-196
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• 2019

인공지능이 우리 생활의 다양한 곳에 사용되기 시작하였으며, 최근 그 영역 또한 점차 확대되고 있다. 하지만 인공지능에 대한 교육이 초등학생을 대상으로 이루어지고 있지 않기 때문에 학생들이 인공지능 기술에 대해 어렵게 인식하는 경향이 있다. 이에 본 논문에서는 교육용 프로그래밍 언어와 인공지능 교육 방법을 고찰하고, 인공지능에 대한 교육을 실시함으로써 학생들의 인공지능 기술에 대한 태도의 변화를 살펴보았다. 이를 위해 학생들의 수준에 적절한 블록형 프로그래밍 언어 기반 인공지능 기술에 대한 교육을 실시하였다. 그리고 학생들의 인공지능 기술에 대한 태도를 단일집단 사전사후 검사를 통해 태도의 변화를 살펴보았다. 그 결과 인공지능에 대한 흥미, 인공지능 기술에 대한 접근 가능성, 학교에서 인공지능 기술에 대한 교육의 필요성에 있어 유의미한 향상을 가져왔다.

• Journal of Oral Medicine and Pain
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• 제47권2호
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• pp.107-108
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• 2022

Artificial intelligence (AI) refers to the use of machines to mimic intelligent human behavior. It involves interactions with humans in clinical settings, and augmented intelligence is considered as a cognitive extension of AI. The importance of AI in healthcare and medicine has been emphasized in recent studies. Machine learning models, such as genetic algorithms, artificial neural networks (ANNs), and fuzzy logic, can learn and examine data to execute various functions. Among them, ANN is the most popular model for diagnosis based on image data. AI is rapidly becoming an adjunct to healthcare professionals and is expected to be human-independent in the near future. The introduction of AI to the diagnosis and treatment of oral diseases worldwide remains in the preliminary stage. AI-based or assisted diagnosis and decision-making will increase the accuracy of the diagnosis and render treatment more precise and personalized. Therefore, dental professionals must actively initiate and lead the development of AI, even if they are unfamiliar with it.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권6호
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• pp.1518-1539
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• 2024

Artificial intelligence-enabled business models aim to improve decision-making, operational efficiency, innovation, and productivity. The presented systematic literature review is conducted to highlight elucidating the utilization of artificial intelligence (AI) methods and techniques within AI-enabled businesses, the significance and functions of AI-enabled organizational models and frameworks, and the design parameters employed in academic research studies within the AI-enabled business domain. We reviewed 39 empirical studies that were published between 2010 and 2023. The studies that were chosen are classified based on the artificial intelligence business technique, empirical research design, and SLR search protocol criteria. According to the findings, machine learning and artificial intelligence were reported as popular methods used for business process modelling in 19% of the studies. Healthcare was the most experimented business domain used for empirical evaluation in 28% of the primary research. The most common reason for using artificial intelligence in businesses was to improve business intelligence. 51% of main studies claimed to have been carried out as experiments. 53% of the research followed experimental guidelines and were repeatable. For the design of business process modelling, eighteen AI mythology were discovered, as well as seven types of AI modelling goals and principles for organisations. For AI-enabled business models, safety, security, and privacy are key concerns in society. The growth of AI is influencing novel forms of business.

• International Journal of Computer Science & Network Security
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• 제22권11호
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• pp.357-366
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• 2022

Artificial intelligence (AI) is the replication of human intelligence by computer systems and machines using tools like machine learning, deep learning, expert systems, and natural language processing. AI can be applied in administrative settings to automate repetitive processes, analyze and forecast data, foster social communication skills among staff, reduce costs, and boost overall operational effectiveness. In order to understand how AI is being used for administrative duties in various organizations, this paper gives a critical dialogue on the topic and proposed a framework for using artificial intelligence in organizations. Additionally, it offers a list of specifications, attributes, and requirements that organizations planning to use AI should consider.

• 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.

• 전자통신동향분석
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• 제38권6호
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• pp.95-106
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• 2023

As artificial intelligence has become commonplace in various fields, the transparency of AI in its development and implementation has become an important issue. In safety-critical areas, the eXplainable and/or understandable of artificial intelligence is being actively studied. On the other hand, machine learning have been applied to the intelligence of self-organizing network (SON), but transparency in this application has been neglected, despite the critical decision-makings in the operation of mobile communication systems. We describes concepts of eXplainable machine learning (ML), along with research trends, major issues, and research directions. After summarizing the ML research on SON, research directions are analyzed for explainable ML required in intelligent SON of beyond 5G and 6G communication.

• Journal of Platform Technology
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• 제10권1호
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• pp.20-28
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• 2022

인공지능기반의 건강 데이터 검증은 임상 연구에 도움을 줄 뿐만 아니라, 새로운 치료법을 개발하는데 필수 요소가 되었다. 미국 식품의약 관리국이 의학진단 분야 중 인공지능을 이용하여 성인 당뇨병 환자의 경증 이상 당뇨병성 망막증을 감지하는 의료기기 마케팅을 승인한 이래, 인공지능을 이용한 테스트가 증가하고 있다. 본 연구에서는 구글에서 지원하는 Teachable Machine 을 이용하여 이미지 분류 기반의 인공지능모델을 생성하고, 학습을 통한 예측 모델을 완성하였다. 이는 현재 만성질환의 환자들 중 발생하는 안구 질환 중 백내장의 조기 발견하는데 용이하게 할 뿐만 아니라, 눈 건강을 위해 헬스케어 프로그램으로 안 질환 예방을 위한 디지털 개인건강 헬스케어 앱을 개발하기 위한 기초 연구로 진행되었다.

• 한국인공지능학회지
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• 제11권4호
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• pp.15-20
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• 2023

Machine learning is comprised of supervised learning, unsupervised learning and reinforcement learning as the type of data and processing mechanism. In this paper, as input and output are unclear and it is difficult to apply the concrete modeling mathematically, reinforcement learning method are applied for crawling robot in this paper. Especially, Q-Learning is the most effective learning technique in model free reinforcement learning. This paper presents a method to implement a crawling robot that is operated by finding the most optimal crawling method through trial and error in a dynamic environment using a Q-learning algorithm. The goal is to perform reinforcement learning to find the optimal two motor angle for the best performance, and finally to maintain the most mature and stable motion about EV3 Crawling robot. In this paper, for the production of the crawling robot, it was produced using Lego Mindstorms with two motors, an ultrasonic sensor, a brick and switches, and EV3 Classroom SW are used for this implementation. By repeating 3 times learning, total 60 data are acquired, and two motor angles vs. crawling distance graph are plotted for the more understanding. Applying the Q-learning reinforcement learning algorithm, it was confirmed that the crawling robot found the optimal motor angle and operated with trained learning, and learn to know the direction for the future research.

• 한국콘텐츠학회논문지
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• 제21권12호
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• pp.57-65
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• 2021

산업 전 분야에 4차 산업혁명의 신기술인 IoT(Internet of Things), AI(Artificial Intelligence), Bigdata 등이 융합되어 새로운 가치를 창출하는 초연결 지능정보사회가 도래되고 있다. 모든 것이 네트워크에 연결되어 데이터가 폭발적으로 증가하고, 인공지능이 스스로 학습하여 지적 판단 기능까지도 가능하다. 특히 사물인터넷은 언제 어디서나 어느 것과도 연결될 수 있는 새로운 통신환경을 제공함에 따라 모든 것들이 연결되는 초 연결을 가능케 하고 있다. 인공지능 기술은 인간이 가진 지각, 학습, 추론, 자연어처리 등의 능력을 컴퓨터가 실행할 수 있도록 구현되고 있다. 인공지능은 기계학습, 딥러닝(Deep leearning), 자연어처리, 음성인식, 시각인식 등 첨단기술을 개발하는 방향으로 발전되고 있으며, 안전, 의료, 국방, 금융, 복지 등의 다양한 응용 분야에 특화된 소프트웨어와 머신러닝(Machine learning), 클라우드(Cloud) 기술을 포함하고 있다. 이를 통해 인간의 편의와 새로운 가치를 제공하기 위해 산업 전반의 다양한 분야에 활용된다. 하지만, 이와는 반대로 지능적이고 정교해진 사이버 위협들이 증가하고 신기술의 기술적 안전성 확보와 같은 잠재적 역기능들을 동반함에 따라 이에 대한 대응이 필요한 시점이다. 본 논문에서는 이러한 역기능을 해결하기 위한 하나의 방안으로 인공지능기술을 활용하여 IoT 통합보안관제 가능하도록 새로운 데이터모델링(Data modelling) 방안을 제안하였다.