• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.1-20
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• 2021

As the Great Reset is discussed at the World Economic Forum due to the COVID-19 pandemic, artificial intelligence, the driving force of the 4th industrial revolution, is also in the spotlight. However, corporate research in the field of artificial intelligence is still scarce. Since 2000, related research has focused on how to create value by applying artificial intelligence to existing companies, and research on how startups seize opportunities and enter among existing businesses to create new value can hardly be found. Therefore, this study analyzed the cases of startups using the comprehensive framework of the multi-level perspective with the research question of how artificial intelligence based startups, a sub-industry of software, have different innovation patterns from the existing software industry. The target firms are gazelle firms that have been certified as venture firms in South Korea, as start-ups within 7 years of age, specializing in machine learning modeling purposively sampled in the medical, finance, marketing/advertising, e-commerce, and manufacturing fields. As a result of the analysis, existing software companies have achieved process innovation from an enterprise-wide integration perspective, in contrast machine learning technology based startups identified unit processes that were difficult to automate or create value by dismantling existing processes, and automate and optimize those processes based on data. The contribution of this study is to analyse the birth of artificial intelligence-based startups and their innovation patterns while validating the framework of an integrated multi-level perspective. In addition, since innovation is driven based on data, the ability to respond to data-related regulations is emphasized even for start-ups, and the government needs to eliminate the uncertainty in related systems to create a predictable and flexible business environment.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.322-325
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• 2022

삼킴 장애 환자는 뇌졸중, 치매, 외상성 뇌손상, 파킨슨병, 암이 주요 원인으로 급속히 증가하고 있다. 특히 고령화 사회가 되면서 더욱 삼킴 장애 환자는 늘어날 것으로 전망하고 있다. 고령 환자의 삼킴 이상의 진단을 위해 가장 많이 사용하고 있는 검사법으로는 비디오 조영 삼킴 검사(VFSS)이다. VFSS는 진단에 있어서 숙련된 전문의가 필요하기 때문에 대학병원 급에서 주로 시행하며, 고령 환자에게는 분석 결과를 상담받을 때까지 오랜 시간을 소요해야하는 문제점들이 있다. 본 논문에서는 삼킴 장애 진단을 위한 의료영상 관리 및 라벨링 시스템에 대해서 기술한다. 이를 구현하기 위해 서버에서 대용량 멀티프레임 영상을 성능 저하 없이 핸들링 하고 라벨링 데이터 생성을 위한 라벨링 툴을 구현하였다. 차후 라벨링 데이터를 생성하고 학습을 통하여 삼킴 장애 진단을 위한 인공지능 모델을 개발하고자 한다.

• The Journal of Bigdata
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• v.6 no.1
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• pp.51-62
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• 2021

In the field of medical artificial intelligence, there have been a lot of researches on disease prediction and classification algorithms that can help doctors judge, but relatively less interested in artificial intelligence that can help medical consumers acquire and judge information. The fact that more than 150,000 questions have been asked about which hospital to go over the past year in NAVER portal will be a testament to the need to provide medical information suitable for medical consumers. Therefore, in this study, we wanted to establish a classification model that classifies 8 medical subjects for symptom text directly described by patients which was collected from NAVER portal to help consumers choose appropriate medical subjects for their symptoms. In order to ensure the validity of the data involving patients' subject matter, we conducted similarity measurements between objective symptom text (typical symptoms by medical subjects organized by the Seoul Emergency Medical Information Center) and subjective symptoms (NAVER data). Similarity measurements demonstrated that if the two texts were symptoms of the same medical subject, they had relatively higher similarity than symptomatic texts from different medical subjects. Following the above procedure, the classification model was constructed using a ridge regression model for subjective symptom text that obtained validity, resulting in an accuracy of 0.73.

• Journal of the Institute of Convergence Signal Processing
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• v.25 no.1
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• pp.7-14
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• 2024

This study conducted a comparative experiment on the continuity of time series data and the classification performance of artificial intelligence models. In BCI research using EEG signals, the performance of behavior and thought classification improved as the continuity of the data decreased. In particular, LSTM achieved a high performance of 0.8728 on data with low continuity, and DNN showed a performance of 0.9178 when continuity was not considered. This suggests that data without continuity may perform better. Additionally, data without continuity showed better performance in task classification. These results suggest that BCI research based on EEG signals can perform better by showing various data characteristics through shuffling rather than considering data continuity.

• The Journal of Bigdata
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• v.5 no.1
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• pp.139-145
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• 2020

In the era of the 4th Industrial Revolution, the use of information based on AI APIs has a great influence on industry and life. In particular, the use of artificial intelligence data in the medical field will have many changes and effects on society. This paper is to study the necessary components to implement the "Cognitive Health Advisor model (CHA model)" and to implement the "CHA model using chatbot" based on this. It uses the open Cognitive chatbot to analyze and analyze the health status of users changing in their daily lives. The user's health information analyzed by the biometric sensor and chatbot consultation delivers the information to the user through the chatbot. And it implements a cognitive health advisor model that provides educational information for users' health promotion. Through this implementation, it intends to confirm the possibility of future use and to suggest research directions.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.45-46
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• 2021

의료영상이란 의료영상장비로부터 DICOM이라는 의료영상표준에 따라 저장되며, 의료영상관리 시스템인 PACS를 통해 관리된다. 이러한, 의료영상장비 ICT기술이 융합되어 급격하게 발전되고 있으며 다양한 의료영상장치가 개발되어지고 있다. 하지만, 기술력은 높아지고 있으나 개발된 의료영상장비로부터 촬영된 영상품질관리에 대한 문제점이 제기되고 있다. 이와 관련하여 다기관의 의료영상장비 개발과 해당 기기로부터 수집된 의료영상에 대한 품질을 관리할 필요성이 증가하고 있다. 따라서 코로나 19와 같은 상황에서 의료기기 개발 지원과 관리를 비대면 관리서비스 시스템 개발과 의료영상장치 개발 정도를 관리할 수 있을 뿐만 아니라 의료영상에 대한 품질까지 모니터링하여 및 개선 할 수 있는 시스템을 제안하고자 한다.

• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.661-672
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• 2022

After the outbreak of the SARS-CoV2 virus that causes COVID-19, it spreads around the world with the number of infections and deaths rising rapidly caused a shortage of medical resources. As a way to solve this problem, chest X-ray diagnosis using Artificial Intelligence(AI) received attention as a primary diagnostic method. The purpose of this study is to comprehensively analyze the detection of COVID-19 via AI. To achieve this purpose, 292 studies were collected through a series of Classification methods. Based on these data, performance measurement information including Accuracy, Precision, Area Under Cover(AUC), Sensitivity, Specificity, F1-score, Recall, K-fold, Architecture and Class were analyzed. As a result, the average Accuracy, Precision, AUC, Sensitivity and Specificity were achieved as 95.2%, 94.81%, 94.01%, 93.5%, and 93.92%, respectively. Although the performance measurement information on a year-on-year basis gradually increased, furthermore, we conducted a study on the rate of change according to the number of Class and image data, the ratio of use of Architecture and about the K-fold. Currently, diagnosis of COVID-19 using AI has several problems to be used independently, however, it is expected that it will be sufficient to be used as a doctor's assistant.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.198-201
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• 2011

본 연구는 현재 널리 사용되고 있는 소셜네트워크 속에서 일반 사용자들이 의료 도메인의 전문가들과 쉽게 질문과 응답을 주고 받을 수 있게 해주는 서비스 개발을 위한 기초 연구로써, 사용자의 문서를 분석하여 질문을 추출해 내고 어떤 의료 도메인에 해당하는 질문인지 분류하는 연구이다. 한글로 구성된 문서 속에서 질문에 해당하는 형태소 분석 방법을 이용하야 질문을 추출을 한 다음 질문 속의 단어 들을 분석하여 KORLEX를 이용한 단어간의 관계성을 분석하여 도메인을 분류하는 작업을 거친다. 또한 본 연구는 텍스트마이닝 기법과 인공지능의 분류 기법을 응용하여 소셜네트워크 속에서 질문과 응답을 분석하여, 의료 도메인의 전문가들이 볼 수 있게 함으로써, 소셜네트워크를 이용한 양방향의 질의응답 서비스를 제공 한다. 이 같은 양방향 질의응답 서비스를 통해 헬스케어 및 의료 관리 서비스를 받을 수 있다. 본 논문은 소셜네트워크 상에서 사용자들이 올린 헬스케어에 관련된 질문들을 추출하고 분류해 주는 과정에 한정하여 진행된 결과를 기술한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.903-906
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• 2021

인공지능 기술 발전으로, 의료영상 분야에서도 딥러닝 기반 질병 진단 연구가 활발히 진행되고 있다. 딥러닝 모델 개발 시, 학습 데이터 품질은 모델의 성능과 신뢰성에 매우 큰 영향을 미친다. 그러나 의료 분야의 경우 도메인 지식에 대한 진입 장벽이 높아 개발자가 학습에 사용되는 의료영상 데이터의 품질을 평가하기 어렵다. 이로 인해, 많은 의료영상 분야에서는 각 분야의 특성(질병의 종류, 관찰 아나토미 등)에 따른 영상 품질 평가 방법을 제시해왔다. 그러나 기존의 방법은 특정 질병에 초점이 맞춰져, 일반화된 품질 평가 기준을 제시하고 있지 않다. 따라서 본 논문에서는 대부분의 흉부 질환을 진단하기 위한 흉부 X선 영상의 품질을 평가할 수 있는 기준을 제안한다. 우선, 흉부 X선 영상을 대상으로 관찰된 영역인 심장, 횡격막, 견갑골, 폐 등을 분할하여, 3D 히스토그램을 기반으로 각 영역별 통계적인 정밀 품질 평가 기준을 제안한다. 본 연구에서는 JSRT, Chest 14의 오픈 데이터셋을 활용하여 적용 실험을 수행하였으며, 민감도는 97.6%, 특이도는 92.8%의 우수한 성능을 확인하였다.

• Journal of Convergence for Information Technology
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• v.9 no.7
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• pp.8-13
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• 2019

Recently, In line with the recent trend of the fourth industrial revolution, many companies and institutions have been increasingly applying automated technologies using artificial intelligence to various tasks. Particularly, due to the government's 52-hour workweek system, companies are increasingly struggling with manpower management. Therefore, they are interested in RPA (Robotic Process Automation) for office environment automation for efficient manpower management. It is being introduced in the back-office business in credit card companies, bank, insurance. These RPA solutions require AI-based recognition technology, scripting technology, business software API-related technologies, and various solutions such as Automate One, Automation Anywhere, UiPath, and Blue Prism are provided. This paper analyzes and describes the technology of RPA solution, the market trend, and the efficiency of RPA adoption.