• 대한방사선기술학회지:방사선기술과학
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• 제42권1호
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• pp.9-17
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• 2019

Although digital mammography is a representative method for breast cancer detection. It has a limitation in detecting and classifying breast tumor due to superimposed structures. Machine learning, which is a part of artificial intelligence fields, is a method for analysing a large amount of data using complex algorithms, recognizing patterns and making prediction. In this study, we proposed a technique to improve the diagnostic accuracy of energy-selective mammography by training data using the machine learning algorithm and using dual-energy measurements. A dual-energy images obtained from a photon-counting detector were used for the input data of machine learning algorithms, and we analyzed the accuracy of predicted tumor thickness for verifying the machine learning algorithms. The results showed that the classification accuracy of tumor thickness was above 95% and was improved with an increase of imput data. Therefore, we expect that the diagnostic accuracy of energy-selective mammography can be improved by using machine learning.

• Asia pacific journal of information systems
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• 제15권2호
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• pp.51-63
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• 2005

There has been substantial research into the bankruptcy prediction. Many researchers used the statistical method in the problem until the early 1980s. Since the late 1980s, Artificial Intelligence(AI) has been employed in bankruptcy prediction. And many studies have shown that artificial neural network(ANN) achieved better performance than traditional statistical methods. However, despite ANN's superior performance, it has some problems such as overfitting and poor explanatory power. To overcome these limitations, this paper suggests a relatively new machine learning technique, support vector machine(SVM), to bankruptcy prediction. SVM is simple enough to be analyzed mathematically, and leads to high performances in practical applications. The objective of this paper is to examine the feasibility of SVM in bankruptcy prediction by comparing it with ANN, logistic regression, and multivariate discriminant analysis. The experimental results show that SVM provides a promising alternative to bankruptcy prediction.

• 정보교육학회논문지
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• 제25권6호
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• pp.995-1003
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• 2021

다가오는 지능정보사회의 핵심이 될 인공지능(AI) 교육의 필요성이 대두되면서, 국가적 차원에서도 교육과정에 인공지능 관련 내용을 포함하는 등 관심을 집중시키고 있다. 본 연구에서는 지도학습 중심의 머신러닝을 통해 생활 속 문제를 해결하는 과정에서 학생들의 창의적 문제해결력을 신장시키기 위해 PASPA 교육 프로그램을 제시하였으며, 학습의 효과를 높이기 위해 피지컬 컴퓨팅 도구인 마이크로비트(Micro:bit)를 활용하였다. PASPA 교육 프로그램에 적용된 교수 학습 과정은 문제 인식(Problem Recoginition), 해결 방법 논의(Argument), 데이터 기준 세우기(Setting data standard), 프로그래밍(Programming), 적용 및 평가(Application and evaluation)의 5단계로 이루어진다. 본 교육 프로그램을 학생들에게 적용한 결과 창의적 문제해결력의 향상을 확인할 수 있었으며, 세부 영역에서는 특정 영역의 지식·사고, 비판적·논리적 사고 영역에서 유의한 차이를 보임이 확인되었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권5호
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• pp.1690-1707
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• 2021

Precipitation prediction during flood season has been a key task of climate prediction for a long time. This type of prediction is linked with the national economy and people's livelihood, and is also one of the difficult problems in climatology. At present, there are some precipitation forecast models for the flood season, but there are also some deviations from these models, which makes it difficult to forecast accurately. In this paper, based on the measured precipitation data from the flood season from 1993 to 2019 and the precipitation return data of CWRF, ANN cycle modeling and a weighted integration method is used to correct the CWRF used in today's operational systems. The MAE and TCC of the precipitation forecast in the flood season are used to check the prediction performance of the proposed algorithm model. The results demonstrate a good correction effect for the proposed algorithm. In particular, the MAE error of the new algorithm is reduced by about 50%, while the time correlation TCC is improved by about 40%. Therefore, both the generalization of the correction results and the prediction performance are improved.

• 한국컴퓨터정보학회논문지
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• 제27권5호
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• pp.29-36
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• 2022

본 논문에서는 DACON에서 제공하는 데이터셋을 기반으로 한 효과적인 농산물 가격 예측 모델을 제안한다. 이 모델은 XGBoost와 CatBoost 이며 Gradient Boosting 계열의 알고리즘으로써 기존의 Logistic Regression과 Random Forest보다 평균정확도 및 수행시간이 우수하다. 이러한 장점들을 기반으로 농산물의 이전 가격들을 기반으로 1주, 2주, 4주뒤 가격을 예측하는 머신러닝 모델을 설계한다. XGBoost 모델은 회귀 방식의 모델링인 XGBoost Regressor 라이브러리를 사용하여 하이퍼 파라미터를 조정함으로써 가장 우수한 성능을 도출할 수 있다. CatBoost 모델은 CatBoost Regressor를 사용하여 모델을 구현한다. 구현한 모델은 DACON에서 제공하는 API를 이용하여 검증하고, 모델 별 성능평가를 실시한다. XGBoost는 자체적인 과적합 규제를 진행하기 때문에 적은 데이터셋에도 불구하고 우수한 성능을 도출하지만, 학습시간, 예측시간 등 시간적인 성능 면에서는 LGBM보다 성능이 낮다는 것을 알 수 있었다.

• 한국인터넷방송통신학회논문지
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• 제21권4호
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• pp.89-96
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• 2021

코로나바이러스-19는 전 세계에서 확진자가 폭증하고 있으며 수많은 사망자를 발생시켰다. 마스크 착용은 감염예방에 매우 중요하다. 버스, 지하철 등 공공장소에서 마스크 착용 권유로 인한 사건·사고가 발생하고 있으며 심각한 사회문제로 대두되고 있다. 이런 문제점을 해결하기 위해서 본 논문에서는 오픈소스 기반 안면 마스크 착용 모니터링 시스템을 제안한다. 오픈소스 소프트웨어인 웹기반 인공지능 툴 티처블머신과 오픈소스 하드웨어 아두이노를 사용하였다. 마스크 착용여부를 판단하여 안내 메시지 및 알람 등 명령을 수행한다. 티처블머신의 학습파라미터 학습횟수 50, 배치크기 32, 학습률 0.001의 최적의 값으로 학습을 하여 정확성 1, 학습오차는 0.03의 결과 값을 얻었다. 웹기반 인공지능 툴 티처블머신과 아두이노를 이용하여 마스크 착용여부를 판단하여 안내 메시지 및 알람 등 명령을 수행할 수 있는 마스크착용 모니터링 시스템을 설계 및 구현하여 타당성을 입증하였다.

• 한국정보통신학회논문지
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• 제26권1호
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• pp.49-57
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• 2022

최근 인공지능의 다양한 활용은 기계학습의 딥 인공신경망 구조를 통해 가능해졌으며 인간과 같은 능력을 보여주고 있다. 불행하게도 딥 구조의 인공신경망은 아직 정확한 해석이 이루어지고 있지 못하고 있다. 이러한 부분은 인공지능에 대한 불안감과 거부감으로 작용하고 있다. 우리는 이러한 문제 중에서 인공신경망의 능력 부분을 해결한다. 인공신경망 구조의 크기를 계산하고, 그 인공신경망이 처리할 수 있는 데이터의 크기를 계산해 본다. 계산의 방법은 수학에서 쓰이는 군의 방법을 사용하여 데이터와 인공신경망의 크기를 군의 구조와 크기를 알 수 있는 Order를 이용하여 계산한다. 이를 통하여 인공신경망의 능력을 알 수 있으며, 인공지능에 대한 불안감을 해소할 수 있다. 수치적 실험을 통하여 데이터의 크기와 딥 인공신경망을 계산하고 이를 검증한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.631-653
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• 2020

Sign language is a natural, visually oriented and non-verbal communication channel between people that facilitates communication through facial/bodily expressions, postures and a set of gestures. It is basically used for communication with people who are deaf or hard of hearing. In order to understand such communication quickly and accurately, the design of a successful sign language translation system is considered in this paper. The proposed system includes object detection and classification stages. Firstly, Single Shot Multi Box Detection (SSD) architecture is utilized for hand detection, then a deep learning structure based on the Inception v3 plus Support Vector Machine (SVM) that combines feature extraction and classification stages is proposed to constructively translate the detected hand gestures. A sign language fingerspelling dataset is used for the design of the proposed model. The obtained results and comparative analysis demonstrate the efficiency of using the proposed hybrid structure in sign language translation.

• 품질경영학회지
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• 제51권1호
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• pp.55-65
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• 2023

Purpose: The purpose of this study is to propose an optimization process to improve product yield in the process using process data. Recently, research for low-cost and high-efficiency production in the manufacturing process using machine learning or deep learning has continued. Therefore, this study derives major variables that affect product defects in the manufacturing process using eXplainable Artificial Intelligence(XAI) method. After that, the optimal range of the variables is presented to propose a methodology for improving product yield. Methods: This study is conducted using the injection molding machine AI dataset released on the Korea AI Manufacturing Platform(KAMP) organized by KAIST. Using the XAI-based SHAP method, major variables affecting product defects are extracted from each process data. XGBoost and LightGBM were used as learning algorithms, 5-6 variables are extracted as the main process variables for the injection process. Subsequently, the optimal control range of each process variable is presented using the ICE method. Finally, the product yield improvement methodology of this study is proposed through a validation process using Test Data. Results: The results of this study are as follows. In the injection process data, it was confirmed that XGBoost had an improvement defect rate of 0.21% and LightGBM had an improvement defect rate of 0.29%, which were improved by 0.79%p and 0.71%p, respectively, compared to the existing defect rate of 1.00%. Conclusion: This study is a case study. A research methodology was proposed in the injection process, and it was confirmed that the product yield was improved through verification.

• International Journal of Computer Science & Network Security
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• 제24권3호
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• pp.59-70
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• 2024

Background: The COVID-19 pandemic (the form of coronaviruses) developed at the end of 2019 and spread rapidly to almost every corner of the world. It has infected around 25,334,339 of the world population by the end of September 1, 2020 [1] . It has been spreading ever since, and the peak specific to every country has been rising and falling and does not seem to be over yet. Currently, the conventional RT-PCR testing is required to detect COVID-19, but the alternative method for data archiving purposes is certainly another choice for public departments to make. Researchers are trying to use medical images such as X-ray and Computed Tomography (CT) to easily diagnose the virus with the aid of Artificial Intelligence (AI)-based software. Method: This review paper provides an investigation of a newly emerging machine-learning method used to detect COVID-19 from X-ray images instead of using other methods of tests performed by medical experts. The facilities of computer vision enable us to develop an automated model that has clinical abilities of early detection of the disease. We have explored the researchers' focus on the modalities, images of datasets for use by the machine learning methods, and output metrics used to test the research in this field. Finally, the paper concludes by referring to the key problems posed by identifying COVID-19 using machine learning and future work studies. Result: This review's findings can be useful for public and private sectors to utilize the X-ray images and deployment of resources before the pandemic can reach its peaks, enabling the healthcare system with cushion time to bear the impact of the unfavorable circumstances of the pandemic is sure to cause