• International Journal of Advanced Culture Technology
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• 제10권4호
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• pp.499-510
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• 2022

This study examines the reporting factors of crime against business in Korea and proposes a corresponding predictive model using machine learning. While many previous studies focused on the individual factors of theft victims, there is a lack of evidence on the reporting factors of crime against a business that serves the public good as opposed to those that protect private property. Therefore, we proposed a crime prevention model for the willingness factor of theft reporting in businesses. This study used data collected through the 2015 Commercial Crime Damage Survey conducted by the Korea Institute for Criminal Policy. It analyzed data from 834 businesses that had experienced theft during a 2016 crime investigation. The data showed a problem with unbalanced classes. To solve this problem, we jointly applied the Synthetic Minority Over Sampling Technique and the Tomek link techniques to the training data. Two prediction models were implemented. One was a statistical model using logistic regression and elastic net. The other involved a support vector machine model, tree-based machine learning models (e.g., random forest, extreme gradient boosting), and a stacking model. As a result, the features of theft price, invasion, and remedy, which are known to have significant effects on reporting theft offences, can be predicted as determinants of such offences in companies. Finally, we verified and compared the proposed predictive models using several popular metrics. Based on our evaluation of the importance of the features used in each model, we suggest a more accurate criterion for predicting var.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권1호
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• pp.27-35
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• 2016

Black-box classifiers, such as artificial neural network and support vector machine, are a popular classifier because of its remarkable performance. They are applied in various fields such as inductive inferences, classifications, or regressions. However, by its characteristics, they cannot provide appropriate explanations how the classification results are derived. Therefore, there are plenty of actively discussed researches about interpreting trained black-box classifiers. In this paper, we propose a method to make a fuzzy logic-based classifier using extracted rules from the artificial neural network and support vector machine in order to interpret internal structures. As an object of classification, an anomalous propagation echo is selected which occurs frequently in radar data and becomes the problem in a precipitation estimation process. After applying a clustering method, learning dataset is generated from clusters. Using the learning dataset, artificial neural network and support vector machine are implemented. After that, decision trees for each classifier are generated. And they are used to implement simplified fuzzy logic-based classifiers by rule extraction and input selection. Finally, we can verify and compare performances. With actual occurrence cased of the anomalous propagation echo, we can determine the inner structures of the black-box classifiers.

• 산업경영시스템학회지
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• 제44권4호
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• pp.227-233
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• 2021

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

• 한국전자통신학회논문지
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• 제15권6호
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• pp.1089-1098
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• 2020

본 연구에서는 적조 Cochlodinium Polykrikoide를 기계학습 방법과 정지궤도 해색위성 영상을 활용하여 탐지하는 방법을 제안한다. 기계학습 모형을 학습시키기 위해 GOCI Level2 자료를 활용하였으며, 국립수산과학원의 적조 속보 자료를 활용하였다. 기계학습 모델은 로지스틱 회귀모형, 의사결정나무 모형, 랜덤포래스트 모형을 사용하였다. 성능 평가 결과 기계학습을 사용하지 않은 전통적인 GOCI 영상 기반 적조 탐지 알고리즘(Son et al.,2012) (75%)과 비교해보았을 때 약 13~22%p (88~98%)의 정확도 향상을 확인할 수 있었다. 또한 기계학습 모형 간 탐지 성능을 비교 분석해본 결과 랜덤 포레스트 모형(98%)이 가장 높은 탐지 정확도를 보였다. 이러한 기계학습 기반 적조 탐지 알고리즘은 향후 적조를 조기에 탐지하고 그 이동과 확산을 추적 모니터링하는데 활용될 수 있을 것이라고 판단된다.

• International Journal of Computer Science & Network Security
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• 제22권8호
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• pp.275-279
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• 2022

The growth of technology nowadays has made many things easy for humans. These things are from everyday small task to more complex tasks. Such growth also comes with the illegal activities that are perform by using technology. These illegal activities can simple as displaying annoying message to big frauds. The easiest way for the attacker to perform such activities is to convenience user to click on the malicious link. It has been a great concern since a decay to classify URLs as malicious or benign. The blacklist has been used initially for that purpose and is it being used nowadays. It is efficient but has a drawback to update blacklist automatically. So, this method is replace by classification of URLs based on machine learning algorithms. In this paper we have use four machine learning classification algorithms to classify URLs as malicious or benign. These algorithms are support vector machine, random forest, n-nearest neighbor, and decision tree. The dataset that is used in this research has 36694 instances. A comparison of precision accuracy and recall values are shown for dataset with and without preprocessing.

• Journal of Periodontal and Implant Science
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• 제53권1호
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• pp.38-53
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• 2023

Purpose: The current Classification of Periodontal and Peri-Implant Diseases and Conditions, published and disseminated in 2018, involves some difficulties and causes diagnostic conflicts due to its criteria, especially for inexperienced clinicians. The aim of this study was to design a decision system based on machine learning algorithms by using clinical measurements and radiographic images in order to determine and facilitate the staging and grading of periodontitis. Methods: In the first part of this study, machine learning models were created using the Python programming language based on clinical data from 144 individuals who presented to the Department of Periodontology, Faculty of Dentistry, Süleyman Demirel University. In the second part, panoramic radiographic images were processed and classification was carried out with deep learning algorithms. Results: Using clinical data, the accuracy of staging with the tree algorithm reached 97.2%, while the random forest and k-nearest neighbor algorithms reached 98.6% accuracy. The best staging accuracy for processing panoramic radiographic images was provided by a hybrid network model algorithm combining the proposed ResNet50 architecture and the support vector machine algorithm. For this, the images were preprocessed, and high success was obtained, with a classification accuracy of 88.2% for staging. However, in general, it was observed that the radiographic images provided a low level of success, in terms of accuracy, for modeling the grading of periodontitis. Conclusions: The machine learning-based decision system presented herein can facilitate periodontal diagnoses despite its current limitations. Further studies are planned to optimize the algorithm and improve the results.

• 스마트미디어저널
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• 제11권2호
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• pp.31-38
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• 2022

하천의 염분 변화를 신속히 예측하는 것은 염분 침투로 인한 농업, 생태계의 피해를 예측하고 재해 방지 대책을 수립하기 위해서 중요한 작업이다. 머신러닝 기법은 물리 기반 수리 모델에 비해 계산량이 훨씬 적기 때문에, 비교적 짧은 시간에 염분농도를 예측 가능하여 물리 기반 수리 모델의 보완 기법으로 연구되고 있다. 해외에서는 머신러닝 기법 기반 염분 예측 연구들이 활발히 연구되고 있으나, 대한민국의 공공데이터에 머신러닝 기법을 적용한 연구는 충분치 않다. 낙동강 하구의 환경 정보에 관한 공공데이터와 함께, 본 연구는 여러 종류의 머신러닝 기법의 염분농도에 대한 예측 성능을 측정하였다. 실험 결과에서, 결정 트리 기반의 LightGBM 알고리즘은 평균 RMSE 0.37의 예측 정확도와 타 알고리즘 대비 2-20배 빠른 학습 속도를 보여주었다. 따라서 국내 하천의 염분농도 예측에도 머신러닝 기법을 적용할 수 있다고 판단된다.

• 한국정보통신학회논문지
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• 제25권10호
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• pp.1296-1301
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• 2021

최근 전 세계적으로 당뇨병 유발률이 증가함에 따라 다양한 머신러닝과 딥러닝 기술을 통해 당뇨병을 예측하려고 는 연구가 이어지고 있다. 본 연구에서는 독일의 Frankfurt Hospital 데이터로 머신러닝 기법을 활용하여 당뇨병을 예측하는 모델을 제시한다. IQR(Interquartile Range) 기법을 이용한 이상치 처리와 피어슨 상관관계 분석을 적용하고 Decision Tree, Random Forest, Knn, SVM, 앙상블 기법인 XGBoost, Voting, Stacking로 모델별 당뇨병 예측 성능을 비교한다. 연구를 진행한 결과 Stacking ensemble 기법의 정확도가 98.75%로 가장 뛰어난 성능을 보였다. 따라서 해당 모델을 이용하여 현대 사회에 만연한 당뇨병을 정확히 예측하고 예방할 수 있다는 점에서 본 연구는 의의가 있다.

• 한국빅데이터학회지
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• 제5권2호
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• pp.155-170
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• 2020

본 연구의 목적은 서울시 중학생의 진로성숙도 예측 요인을 찾기 위해 머신러닝 기법(Decision Tree, Random Forest, XGBoost)을 서울교육종단연구 4~6차년도 데이터에 적용하였다. 적용에 따라 도출된 세 가지 머신러닝 모형의 변수 중요도와 각 지표별 성능을 확인하였다. 또한 XGBoostExplainer 패키지를 활용하여 모형을 해석하였으며, 데이터 전처리와 분석 모두 R과 R Studio를 활용하였다. 그 결과 각 모형별로 변수 중요도 순위는 다소 차이가 있으나 '성취목표', '창의성', '자아개념', '부모자녀와의 관계', '회복탄력성'이 높은 순위를 보였다. 또한 XGBoostExplainer를 활용하여 패널별 진로성숙도에 정적·부적 영향을 주는 요인을 탐색하였고, '성취목표'가 진로성숙도 예측 최우선 요인임을 찾을 수 있었다. 본 연구결과를 바탕으로 머신러닝 및 변수선택 방법의 비교연구와 서울교육종단연구 코호트별 비교연구가 수행되어야 함을 제언하였다.

• 한국수자원학회논문집
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• 제54권12호
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• pp.1255-1263
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• 2021

안개는 대체수자원이 될 수 있으나 교통사고 위험을 높이고 공항 운영에 제약을 가하는 사회적 영향이 큰 기상현상이다. 본 연구에서는 1 km 미만 가시거리(시정)로 정의되는 안개 발생을 기상자료로 예측하는 지역 기계학습모형을 개발하고 그 예측력을 평가하였다. 전라북도 지역의 10개 기상청 지상관측소의 2017-2019년 시정 및 기상관측자료로 앙상블 분류기법인 Extreme Gradient Boosting (XGB), Light Gradient Boosting(LGB), Random Forests (RF)를 학습시켜 지역 안개 모형을 개발하였고 독립적인 2020년 자료로 모형의 사용성을 평가하였다. 그 결과, 학습·검증기간(2017-2019)에는 True Skill Score를 기준으로 가장 높은 예측력을 보인 방법은 LGB 기법이었지만 다른 두 모형에 비해 False Alarm Ratio가 컸다. RF 모형과 XGB 방법 역시 기존 연구에 상응하는 예측성능을 보이는 것으로 확인되었다. 2020년 자료를 입력해 안개 발생을 모의했을 때 세 모형의 예측성능은 2017-2019년 기간보다 떨어졌지만 모두 관측 안개일수의 공간분포와 일관되는 안개 위험을 예측했다. 세 기계학습모형은 안개위험이 상대적으로 높은 지역을 추출하는 기법으로 사용이 가능할 것으로 보인다.