• 디지털융복합연구
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• 제18권10호
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• pp.175-180
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• 2020

최근 활성화 되고 있는 인슈어테크(InsurTech) 산업에서의 인공지능(AI)을 활용한 보험서비스 마케팅 사례연구를 통해, 보험산업 생태계에서 혁신적인 기술(예: 인공지능, 기계학습 등)이 어떻게 활용되고 있는지 살펴보았다. 특히, 국내·외 서비스 사례연구를 통해 인공지능기술을 활용하여 파괴적 혁신을 가져온 미국의 레모네이드(Lemonade)사의 챗봇을 이용한 신속하고, 간편한 보험가입 및 보험금 지급 서비스, 국내 AI컴퍼니의 광학 문자 인식(OCR)기반의 진단서 입력을 통해 예상 보험금이 산출되는 보험금 산정서비스를 고찰해 보았다. 사례분석 결과 인공지능 기반의 수많은 고객데이터를 활용한 기계학습을 통해 보험 가입 및 지급 절차에 있어 리드타임을 획기적으로 단축하였고, 고객과 보험사간의 분쟁이 많은 보험금 산정에 있어서도 정확하고 합리적인 보험금을 산출함으로써, 고객만족과 고객가치를 높일 수 있었다.

• The Plant Pathology Journal
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• 제38권4호
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• pp.395-402
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• 2022

To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forward neural network (FFNN) and long short-term memory, with diverse input datasets, and compares their performance. The Blast_Weathe long short-term memory r_FFNN model had the highest recall score (66.3%) for rice blast prediction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.

• 산업경영시스템학회지
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• 제45권2호
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• pp.65-76
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• 2022

Recently, not only traditional statistical techniques but also machine learning algorithms have been used to make more accurate bankruptcy predictions. But the insolvency rate of companies dealing with financial institutions is very low, resulting in a data imbalance problem. In particular, since data imbalance negatively affects the performance of artificial intelligence models, it is necessary to first perform the data imbalance process. In additional, as artificial intelligence algorithms are advanced for precise decision-making, regulatory pressure related to securing transparency of Artificial Intelligence models is gradually increasing, such as mandating the installation of explanation functions for Artificial Intelligence models. Therefore, this study aims to present guidelines for eXplainable Artificial Intelligence-based corporate bankruptcy prediction methodology applying SMOTE techniques and LIME algorithms to solve a data imbalance problem and model transparency problem in predicting corporate bankruptcy. The implications of this study are as follows. First, it was confirmed that SMOTE can effectively solve the data imbalance issue, a problem that can be easily overlooked in predicting corporate bankruptcy. Second, through the LIME algorithm, the basis for predicting bankruptcy of the machine learning model was visualized, and derive improvement priorities of financial variables that increase the possibility of bankruptcy of companies. Third, the scope of application of the algorithm in future research was expanded by confirming the possibility of using SMOTE and LIME through case application.

• 한국정보교육학회:학술대회논문집
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• 한국정보교육학회 2021년도 학술논문집
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• pp.369-376
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• 2021

인공지능(AI) 기술의 발전에 따라 많은 분야에서 인공지능 활용 방안에 대한 논의가 활발하게 일어나고 있으며 교육 분야에서도 인공지능 인재 양성을 위한 각종 정책이 추진되고 있다. 본 연구에서는 인공지능 기술을 활용한 로봇 프로그래밍 프레임워크를 제안하고 이를 기반으로 머신러닝(Machine Learning) 분야에서 높은 빈도로 활용되는 파이썬(Python)과 교육 현장의 활용도가 높은 교육용 로봇을 활용하여 인공지능(AI) 교육 프로그램을 제안하였다. 국제자동차공학회(SAE)에서 제시하는 자율주행자동차 수준(0~5단계)을 4단계로 단순화하고 이를 기반으로 로봇에 부착된 카메라가 선(객체)을 인지(Perception)하고 검출(Object detection)하여 스스로 움직일 수 있는 라인 디텍터(Line Detector)를 만드는 것을 목표로 하였다. 개발된 프로그램은 단순히 특정 프로그래밍 언어를 활용하여 주어진 문제를 해결하는 정형화된 형태가 아니라 생활 속의 복잡하고 비구조화된 문제를 자기주도적으로 정의하고 인공지능(AI) 기술을 기반으로 해결하는 경험을 가지는데 그 의의가 있다.

• 한국전자통신학회논문지
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• 제12권1호
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• pp.115-122
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• 2017

딥 러닝은 사람이 가르치지 않아도 컴퓨터가 스스로 사람처럼 학습할 수 있는 인공지능 기술이다. 딥 러닝은 세상을 이해하고 감지하는 인공지능을 개발하는데 가장 촉망받는 기술이 되고 있으며, 구글, 바이두, 페이스북 등이 가장 앞서서 개발을 하고 있다. 본 논문에서는 딥 러닝을 구현하는 딥 러닝 프레임워크의 종류에 대해 논의하고, 딥 러닝 프레임워크의 영상과 음성 인식 분야의 효율성에 대해 비교, 분석하고자 한다.

• 산업기술연구
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• 제33권A호
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• pp.31-39
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• 2013

There are two methods to make a distinction of deterioration of high-speed railway track. One is that an administrator checks for each attribute value of track induction data represented in graph and determines whether maintenance is needed or not. The other is that an administrator checks for monthly trend of attribute value of the corresponding section and determines whether maintenance is needed or not. But these methods have a weak point that it takes longer times to make decisions as the amount of track induction data increases. As a field of artificial intelligence, the method that a computer makes a distinction of deterioration of high-speed railway track automatically is based on machine learning. Types of machine learning algorism are classified into four type: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. This research uses supervised learning that analogizes a separating function form training data. The method suggested in this research uses SVM classifier which is a main type of supervised learning and shows higher efficiency binary classification problem. and it grasps the difference between two groups of data and makes a distinction of deterioration of high-speed railway track.

• Journal of Korea Artificial Intelligence Association
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• 제1권1호
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• pp.11-16
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• 2023

Accurate hospital case modeling and prediction are crucial for efficient healthcare. In this study, we demonstrate the implementation of regression analysis methods in machine learning systems utilizing mathematical statics and machine learning techniques. The developed machine learning model includes Bayesian linear, artificial neural network, decision tree, decision forest, and linear regression analysis models. Through the application of these algorithms, corresponding regression models were constructed and analyzed. The results suggest the potential of leveraging machine learning systems for medical research. The experiment aimed to create an Azure Machine Learning Studio tool for the speedy evaluation of multiple regression models. The tool faciliates the comparision of 5 types of regression models in a unified experiment and presents assessment results with performance metrics. Evaluation of regression machine learning models highlighted the advantages of boosted decision tree regression, and decision forest regression in hospital case prediction. These findings could lay the groundwork for the deliberate development of new directions in medical data processing and decision making. Furthermore, potential avenues for future research may include exploring methods such as clustering, classification, and anomaly detection in healthcare systems.

• 한국인공지능학회지
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• 제10권2호
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• pp.7-11
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• 2022

OLED displays cannot be used permanently due to burn-in or generation of dark spots due to degradation. Therefore, the time when the display can operate normally is very important. It is close to impossible to physically measure the time when the display operates normally. Therefore, the time that works normally should be predicted in a way other than a physical way. Therefore, if you do computer simulations based on artificial intelligence, you can increase the accuracy of prediction by saving time and continuous learning. Therefore, if we do computer simulations based on artificial intelligence, we can increase the accuracy of prediction by saving time and continuous learning. In this paper, a dataset in the form of development from generation to diffusion of dark spots, which is one of the causes related to the life of OLED, was generated by applying the finite element method. The dark spots were generated in nine conditions, such as 0.1 to 2.0 ㎛ with the size of pinholes, the number was 10 to 100, and 50% with water content. The learning data created in this way may be a criterion for generating an artificial intelligence-based dataset.

• 융합정보논문지
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• 제11권12호
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• pp.168-175
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• 2021

본 연구에서는 인공지능 교육을 활용하여 생물 교육의 파충류와 양서류를 분류에 대한 이해를 높이고, AI(Artificial Intelligence) 역량을 증대할 수 있도록 탈학문적(Transdisciplinary) 융합 교육 프로그램을 개발하였다. 중심 내용으로는 생물교육에서 오랫동안 다루어진 주제인 파충류와 양서류의 분류를 의사결정 트리 및 ML4K(Machine Learnig for Kids)를 활용하여 해결하는 것으로, 총 3차시 분량으로 설계하였다. 개발된 교육 프로그램에 대하여 전문가 검토를 실시하였고, 그 결과 I-CVI 값이 .88~1.00을 나타내어 내용 타당도를 확보하였다. 이 교육 프로그램은 학습자들에게 정보 교육의 인공지능에 관한 학습 내용과 생물 교육의 척추 동물의 분류에 관한 학습 내용에 대해 동시에 학습할 수 있다는 강점이 있다. 또한, 인공지능 활용 부분에서는 인지 부하를 최소로 하도록 구성되어 있기 때문에 모든 교사들이 쉽게 활용할 수 있다는 점이 특징이다.

• 시스템엔지니어링학술지
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• 제15권2호
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• pp.72-78
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• 2019

Pathology is the motor that drives healthcare to understand diseases. The way pathologists diagnose diseases, which involves manual observation of images under a microscope has been used for the last 150 years, it's time to change. This paper is specifically based on tumor detection using deep learning techniques. Pathologist examine the specimen slides from the specific portion of body (e-g liver, breast, prostate region) and then examine it under the microscope to identify the effected cells among all the normal cells. This process is time consuming and not sufficiently accurate. So, there is a need of a system that can detect tumor automatically in less time. Solution to this problem is computational pathology: an approach to examine tissue data obtained through whole slide imaging using modern image analysis algorithms and to analyze clinically relevant information from these data. Artificial Intelligence models like machine learning and deep learning are used at the molecular levels to generate diagnostic inferences and predictions; and presents this clinically actionable knowledge to pathologist through dynamic and integrated reports. Which enables physicians, laboratory personnel, and other health care system to make the best possible medical decisions. I will discuss the techniques for the automated tumor detection system within the new discipline of computational pathology, which will be useful for the future practice of pathology and, more broadly, medical practice in general.