• Advances in Computational Design
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• 제5권4호
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• pp.445-465
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• 2020

This paper presents a study conducted with the aim of developing a model of tendering based on a technique of artificial intelligence by managing and controlling the factors of success or failure of construction projects through the evaluation of the process of invitation to tender. Aiming to solve this problem, analysis of the current environment based on SWOT (Strengths, Weaknesses, Opportunities, and Threats) is first carried out. Analysis was evaluated through a case study of the construction projects in Algeria, to bring about the internal and external factors which affect the process of invitation to tender related to the construction projects. This paper aims to develop a mean to identify threats-opportunities and strength-weaknesses related to the environment of various national construction projects, leading to the decision on whether to continue the project or not. Following a SWOT analysis, novel artificial intelligence models in forecasting the project status are proposed. The basic principal consists in interconnecting the different factors to model this phenomenon. An artificial neural network model is first proposed, followed by a model based on fuzzy logic. A third model resulting from the combination of the two previous ones is developed as a hybrid model. A simulation study is carried out to assess performance of the three models showing that the hybrid model is better suited in forecasting the construction project status than RNN (recurrent neural network) and FL (fuzzy logic) models.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제10권10호
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• pp.285-290
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• 2021

의료분야에서 인공지능 기술을 도입한 질환 진단 및 예측 연구들이 활발하게 진행되고 있다. 의료영상기반의 인공지능 기술 적용에 가장 많이 활용되고 있는 질환 진단 및 예측에 대한 다양한 제품으로 출시되고 있다. 인공지능은 질병에 대한 진단, 양성과 악성으로 구분되는 질환의 구분, 질병의 위험도에 따른 구별이나 판독에 이용하기 위해 질환부위를 분리하는 등에 적용되고 있다. 최근에는 클라우드기술과 연계하여 서비스 제품으로 활용성이 높아지고 있다. 본 논문에서 다루는 질환 중에 간 질환은 통증이 적어 조기진단이 어려워 그 위험도가 매우 높은 질환이다. 이러한 질환 진단에 비침습적인 진단방법으로 의료영상기반으로 인공지능 기술을 도입하였다. 우리는 임상에서 가장 의미 있는 간 경화증 환자의 판독을 돕기 위한 웹 서비스 개발 내용을 기술한다. 그리고 웹서비스 프로세스를 보이고 각 프로세스의 구동 화면과 최종 결과화면을 보인다. 제안한 서비스를 통해 간 경화증을 조기에 진단하고, 빠른 치료를 통해 환자의 회복에 도움을 줄 수 있을 것으로 기대한다.

• 의학교육논단
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• 제22권2호
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• pp.99-114
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• 2020

The rapid development of artificial intelligence (AI), including deep learning, has led to the development of technologies that may assist in the diagnosis and treatment of diseases, prediction of disease risk and prognosis, health index monitoring, drug development, and healthcare management and administration. However, in order for AI technology to improve the quality of medical care, technical problems and the efficacy of algorithms should be evaluated in real clinical environments rather than the environment in which algorithms are developed. Further consideration should be given to whether these models can improve the quality of medical care and clinical outcomes of patients. In addition, the development of regulatory systems to secure the safety of AI medical technology, the ethical and legal issues related to the proliferation of AI technology, and the impacts on the relationship with patients also need to be addressed. Systematic training of healthcare personnel is needed to enable adaption to the rapid changes in the healthcare environment. An overall review and revision of undergraduate medical curriculum is required to enable extraction of significant information from rapidly expanding medical information, data science literacy, empathy/compassion for patients, and communication among various healthcare providers. Specialized postgraduate AI education programs for each medical specialty are needed to develop proper utilization of AI models in clinical practice.

• Asian Journal of Innovation and Policy
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• 제12권2호
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• pp.261-267
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• 2023

In the paper of 'Natural Selection Favors AIs over Humans,' Dan Hendrycks applies principles of Darwinian evolution to forecast potential trajectories of AI development. He proposes that competitive pressures within corporate and military realms could lead to AI replacing human roles and exhibiting self-interested behaviors. However, such claims carry the risk of oversimplifying the complex issues of competition and natural selection without clear criteria for judging whether AI is selfish or altruistic, necessitating a more in-depth analysis and critique. Other studies, such as ''The Threat of AI and Our Response: The AI Charter of Ethics in South Korea,' offer diverse opinions on the natural selection of artificial intelligence, examining major threats that may arise from AI, including AI's value judgment and malicious use, and emphasizing the need for immediate discussions on social solutions. Such contemplation is not merely a technical issue but also significant from an ethical standpoint, requiring thoughtful consideration of how the development of AI harmonizes with human welfare and values. It is also essential to emphasize the importance of cooperation between artificial intelligence and humans. Hendrycks's work, while speculative, is supported by historical observations of inevitable evolution given the right conditions, and it prompts deep contemplation of these issues, setting the stage for future research focused on AI safety, regulation, and ethical considerations.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.95-95
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• 2023

Common hydrological problems of developing countries include poor data management, insufficient measuring devices and ungauged watersheds, leading to small or unreliable data availability. This has greatly affected the adoption of artificial intelligence techniques for flood risk mitigation and damage control in several developing countries. While climate datasets have recorded resounding applications, but they exhibit more uncertainties than ground-based measurements. To encourage AI adoption in developing countries with small ground-based dataset, we propose data augmentation for regression tasks and compare performance evaluation of different AI models with and without data augmentation. More focus is placed on simple models that offer lesser computational cost and higher accuracy than deeper models that train longer and consume computer resources, which may be insufficient in developing countries. To implement this approach, we modelled and predicted streamflow data of the Asa River Watershed located in Ilorin, Kwara State Nigeria. Results revealed that adequate hyperparameter tuning and proper model selection improve streamflow prediction on small water dataset. This approach can be implemented in data-scarce regions to ensure timely flood intervention and early warning systems are adopted in developing countries.

• 대한영상의학회지
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• 제82권1호
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• pp.12-28
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• 2021

유방촬영술은 유방암 검진 및 진단을 위한 기본적인 영상 검사이지만, 판독이 어려우며 높은 숙련도를 필요로 한다고 잘 알려져 있다. 이러한 어려움을 극복하기 위해 최근 몇 년 사이에 인공지능을 이용한 유방암 검출 알고리즘들이 활발히 연구되고 있다. 본 종설에서 저자는 고전적인 computer-aided detection 소프트웨어 대비 최근 많이 사용되는 딥러닝의 특징을 알아보고, 딥러닝 알고리즘의 개발 방법과 임상적 검증 방법에 대해서 기술하였다. 또한 딥러닝 기반의 검진 유방촬영술의 판독 방법 분류, 유방 치밀도 평가, 그리고 유방암 위험도 예측 모델 등을 위한 딥러닝 연구들도 소개하였다. 마지막으로 유방촬영술 관련 인공지능 기술들에 대한 영상의학과 전문의의 관심과 의견의 필요성을 기술하였다.

• International Journal of Internet, Broadcasting and Communication
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• 제12권3호
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• pp.149-155
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• 2020

Air pollution is a problem of environmental health risk in big cities. Recently, researchers have proposed using various artificial intelligence technologies to predict air pollution. The proposed model is Cooperative of Artificial Neural Network (ANN) and Fuzzy Inference System (FIS), to predict air pollution of Korean cities using Python. Data air pollutant variables were collected and the Air Korean Web site air quality index was downloaded. This paper's aim was to predict on the health risks and the very unhealthy values of air pollution. We have predicted the air pollution of the environment based on the air quality index. According to the results of the experiment, our model was able to predict a very unhealthy value.

• Korean Journal of Radiology
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• 제25권4호
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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.

• 융합신호처리학회논문지
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• 제24권4호
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• pp.213-220
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• 2023

낙상은 일상의 활동 중에 예기치 않게 발생하여 생활에 많은 어려움을 초래한다. 본 연구는 고위험 직종 종사자들의 낙상 감지를 위한 시스템을 구성하고 자료를 수집하여 예측 모델에 적용함으로써 그 유효성을 검증하는 것을 목적으로 하였다. 이를 위해 가속도센서와 자이로센서를 통해 가속도 신호와 방위각을 산출하여 낙상 여부를 감지하는 웨어러블 기기를 구성하였다. 그리고 연구 참여자들이 이 기기를 복부에 착용하고 정해진 활동을 수행하는 과정에서 낙상과 관련한 동작으로부터 필요한 데이터를 측정하고 기기 내에 존재하는 블루투스 장치를 통해 컴퓨터로 전송하였다. 이렇게 수집된 데이터를 필터링 등을 통해 처리하여 딥러닝 알고리즘들인 1D CNN, LSTM, CNN-LSTM에 근거한 낙상 감지 예측 모델들에 적용하고 그 결과를 평가하였다.

• 정보보호학회논문지
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• 제34권2호
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• pp.301-321
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• 2024

생성형 인공지능은 현재 빠른 속도로 발전하고 있고, 산업적으로도 확대되고 있다. 생성형 인공지능의발전은 대부분의 산업 분야에서 생산성을 향상시킬 수 있을 것이라 기대되고 있다. 그러나 생성형 인공지능은 악용될 수 있으며, 실제로 범죄까지 이어지는 사례들이 등장하고 있다. 빠르게 발전하는 인공지능의 속도에 비해 이를 규제할 수 있는 법안이 존재하지 않는다. 국내의 경우, 법률제정을 위한 생성형 인공지능 기술과 관련된 범죄 및 위험에 대한 분류가 명확하게 이루어지지 않은 상황이다. 이에 본 연구에서는 생성형 인공지능 관련 범죄를 기존 사이버범죄 분류법에 착안하여 생성형 인공지능 침해범죄 위협, 생성형 인공지능 이용범죄 위협, 기타 인공지능 관련 위협으로 구분하고자 하였다. 또한, 범죄 및 위험에 대한 기술적 대응 방안을 인공지능 개발 단계별로 제시하여 현실성 있는 위협 대응 방안을 다루었다. 법·제도적 개선사항을 통해 생성형 인공지능 범죄에 대한 개발사의 책임과 데이터 수집 방법론의 법제화 등을 제시하였다.