• Journal of Digital Convergence
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• v.18 no.10
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• pp.175-180
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• 2020

Through case studies for insurance service marketing using artificial intelligence(AI) in the insurtech industry, it investigated how innovative technologies(artificial intelligence, machine learning etc.) are being used in the insurance ecosystems. In particular, through domestic and international case studies, it was examined by Lemonade's service of insurance contracts and getting the indemnity and AI company's service of calculating the compensation through a medical certificate image based on OCR, which brought disruptive innovations using artificial intelligence. As a result of the case analysis, these services have drastically shortened the lead time of insurance contracts and payment through machine learning using numerous customer data based on artificial intelligence. And accurate and reasonable compensation was calculated in the estimation of indemnity, which has a lot of disputes between customers and insurance companies. It was able to increase customer satisfaction and customer value.

• The Plant Pathology Journal
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• v.38 no.4
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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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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• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.1
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• pp.115-122
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• 2017

Deep learning is artificial intelligence technology that can teach people like themselves who need machine learning. Deep learning has become of the most promising in the development of artificial intelligence to understand the world and detection technology, and Google, Baidu and Facebook is the most developed in advance. In this paper, we discuss the kind of deep learning frameworks, compare and analyze the efficiency of the image and speech recognition field of it.

• Journal of Industrial Technology
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• v.33 no.A
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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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• v.1 no.1
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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.

• Korean Journal of Artificial Intelligence
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• v.10 no.2
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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.

• Journal of Convergence for Information Technology
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• v.11 no.12
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• pp.168-175
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• 2021

In this study, a transdisciplinary convergence education program was developed to enhance the understanding for classification of reptiles and amphibians in biology education and also to increase AI (Artificial Intelligence) capability by using artificial intelligence education. The main content is to solve the classification of reptiles and amphibians that has been dealt with for a long time in biology education, using a decision tree and ML4K (Machine Learnig for Kids), it was designed for a total of 3 lessons. Experts review was conducted on the developed education program, as a result, the I-CVI(Item Content Validity Index) value was .88~1.00 so that can secure content validity. This education program has the advantage of being able to simultaneously learn about the learning contents of artificial intelligence in informatics and the classification of vertebrates in the biological education. In addition, since it is configured to minimize the cognitive load in the AI using part, it is characterized by the fact that all of any teachers can apply it their lesson easily.

• Journal of the Korean Society of Systems Engineering
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• v.15 no.2
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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.