• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.344-345
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• 2018

본 논문에서는 오디오와 레이더 기반의 딥러닝을 활용한 환경 분류 기술을 제안한다. 제안된 환경 분류 기술은 오디오를 이용한 환경 분류 딥러닝 모델과 레이더를 이용한 딥러닝 모델을 앙상블로 결합하여 환경을 분류한다. 특히, 오디오와 레이더 각 성능을 높이기 위해 별도의 모델이 제안된 딥러닝 환경분류 기법은 실내 환경 5 가지를 분류 하였으며, 오디오 또는 레이더 단일 데이터를 활용한 환경 분류에 비해 우수한 성능을 보였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.602-605
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• 2022

본 연구는 대량의 상권 데이터를 바탕으로 머신 러닝과 딥러닝 분석을 이용하여 최적의 상권 입지를 추천하는 시스템 개발을 목표로 한다. 자영업자들의 오프라인 창업에 있어 개개인의 매장 정보에 기반한 입지 조건 판단은 앞으로의 매출에 중요한 시작점이다. 따라서 상권 정보를 기반으로 미래 매출을 예측하여 최적의 상권 입지를 추천하는 기술이 필요하다. 이를 위해 기존에 선행된 다수의 회귀 기법과 더불어 강하게 편향된 데이터를 레이블링 하여 다중 분류 기법으로도 문제를 접근한다. 최종적으로 딥러닝 모델과 합성하여 더 높은 성능을 이끌어내고 이로부터 편향 데이터 처리 방법과 딥러닝 모델과의 앙상블 중요성에 대해 논의하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.935-938
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• 2021

데이터 베이스에 저장된 사용자의 위치, 성격정보를 자동으로 받아서 머신러닝으로 회귀분석하여 방문 장소에 대한 선호도를 예측한다. 사람의 성격 요소로는 BFF 와 다른 기본 요소들을 사용하였다. 이를 위하여 자동화된 시스템을 구성하였고 위치 방문 선호도를 예측하기 위한 머신러닝 기법으로는 앙상블기법을 사용하였다. 예측 결과는 장소 카테고리별로 방문 선호도가 나타나고 이를 사용자 별로 나누어 저장할 예정이다. 데이터의 양이 많아지면서 나타나는 문제들을 해결하여 향후 연구에 도움이 될 것이다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.129-131
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• 2021

본 논문에서는 심층 특징 앙상블을 사용하여 목조 문화재의 변위 현상 중 하나인 배부름 현상을 감지할 수 있는 모델을 제안한다. 우선 총 4개의 서로 다른 사전 학습된 합성 곱 신경망을 사용하여 입력 영상에 대한 심층 특징들을 추출한다. 그 이후 4개의 서로 다른 심층 특징들을 결합하여 하나의 특징 벡터를 생성한다. 그 이후 합쳐진 특징 벡터는 완전 연결 계층의 입력 값으로 들어와서 최종적으로 변위가 존재하는지 아닌지에 대한 예측을 수행하게 된다. 데이터 셋으로는 충주시 근처의 문화재에 방문해서 수집한 목조 문화재 이미지를 가지고 정상 및 비정상으로 구분한 데이터 셋을 사용하였다. 실험 결과 심층 특징 앙상블 기법을 사용한 모델이 앙상블 기법을 사용하지 않은 모델보다 더 좋은 성능을 나타냄을 확인하였다. 이러한 결과로 부터 우리가 제안한 방법이 목재 문화재의 배부름 현상에 대한 변위 검출에 있어서 매우 적합함을 보여준다.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.71-80
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• 2024

In this study, a proposed ensemble learning technique aims to enhance the semantic segmentation performance of images captured by Unmanned Aerial Vehicles (UAVs). With the increasing use of UAVs in fields such as urban planning, there has been active development of techniques utilizing deep learning segmentation methods for land cover segmentation. The study suggests a method that utilizes prominent segmentation models, namely U-Net, DeepLabV3, and Fully Convolutional Network (FCN), to improve segmentation prediction performance. The proposed approach integrates training loss, validation accuracy, and class score of the three segmentation models to enhance overall prediction performance. The method was applied and evaluated on a land cover segmentation problem involving seven classes: buildings,roads, parking lots, fields, trees, empty spaces, and areas with unspecified labels, using images captured by UAVs. The performance of the ensemble model was evaluated by mean Intersection over Union (mIoU), and the results of comparing the proposed ensemble model with the three existing segmentation methods showed that mIoU performance was improved. Consequently, the study confirms that the proposed technique can enhance the performance of semantic segmentation models.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.2
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• pp.57-72
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• 2020

The price of iron ore has continued to fluctuate with high demand and supply from many countries and companies. In this business environment, forecasting the price of iron ore has become important. This study developed the machine learning model forecasting the price of iron ore a one month after the trading events. The forecasting model used distributed lag model and deep learning models such as MLP (Multi-layer perceptron), RNN (Recurrent neural network) and LSTM (Long short-term memory). According to the results of comparing individual models through metrics, LSTM showed the lowest predictive error. Also, as a result of comparing the models using the ensemble technique, the distributed lag and LSTM ensemble model showed the lowest prediction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1296-1301
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• 2021

With the recent increase in diabetes incidence worldwide, research has been conducted to predict diabetes through various machine learning and deep learning technologies. In this work, we present a model for predicting diabetes using machine learning techniques with German Frankfurt Hospital data. We apply outlier handling using Interquartile Range (IQR) techniques and Pearson correlation and compare model-specific diabetes prediction performance with Decision Tree, Random Forest, Knn (k-nearest neighbor), SVM (support vector machine), Bayesian Network, ensemble techniques XGBoost, Voting, and Stacking. As a result of the study, the XGBoost technique showed the best performance with 97% accuracy on top of the various scenarios. Therefore, this study is meaningful in that the model can be used to accurately predict and prevent diabetes prevalent in modern society.

• Journal of Korean Society of Water and Wastewater
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• v.35 no.1
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• pp.83-91
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• 2021

The increased turbidity in rivers during flood events has various effects on water environmental management, including drinking water supply systems. Thus, prediction of turbid water is essential for water environmental management. Recently, various advanced machine learning algorithms have been increasingly used in water environmental management. Ensemble machine learning algorithms such as random forest (RF) and gradient boosting decision tree (GBDT) are some of the most popular machine learning algorithms used for water environmental management, along with deep learning algorithms such as recurrent neural networks. In this study GBDT, an ensemble machine learning algorithm, and gated recurrent unit (GRU), a recurrent neural networks algorithm, are used for model development to predict turbidity in a river. The observation frequencies of input data used for the model were 2, 4, 8, 24, 48, 120 and 168 h. The root-mean-square error-observations standard deviation ratio (RSR) of GRU and GBDT ranges between 0.182~0.766 and 0.400~0.683, respectively. Both models show similar prediction accuracy with RSR of 0.682 for GRU and 0.683 for GBDT. The GRU shows better prediction accuracy when the observation frequency is relatively short (i.e., 2, 4, and 8 h) where GBDT shows better prediction accuracy when the observation frequency is relatively long (i.e. 48, 120, 160 h). The results suggest that the characteristics of input data should be considered to develop an appropriate model to predict turbidity.

• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.153-166
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• 2021

A main goal of pharmacogenomics studies is to predict individual's drug responsiveness based on high dimensional genetic variables. Due to a large number of variables, feature selection is required in order to reduce the number of variables. The selected features are used to construct a predictive model using machine learning algorithms. In the present study, we applied several hybrid feature selection methods such as combinations of logistic regression, ReliefF, TurF, random forest, and LASSO to a next generation sequencing data set of 400 epilepsy patients. We then applied the selected features to machine learning methods including random forest, gradient boosting, and support vector machine as well as a stacking ensemble method. Our results showed that the stacking model with a hybrid feature selection of random forest and ReliefF performs better than with other combinations of approaches. Based on a 5-fold cross validation partition, the mean test accuracy value of the best model was 0.727 and the mean test AUC value of the best model was 0.761. It also appeared that the stacking models outperform than single machine learning predictive models when using the same selected features.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.1-10
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• 2022

Vegetables such as cabbage are greatly affected by natural disasters, so price fluctuations increase due to disasters such as heavy rain and disease, which affects the farm economy. Various efforts have been made to predict the price of agricultural products to solve this problem, but it is difficult to predict extreme price prediction fluctuations. In this study, cabbage prices were analyzed using the ensemble Voting technique, a method of determining the final prediction results through various classifiers by combining a single classifier. In addition, the results were compared with LSTM, a time series analysis method, and XGBoost and RandomForest, a boosting technique. Daily data was used for price data, and weather information and price index that affect cabbage prices were used. As a result of the study, the RMSE value showing the difference between the actual value and the predicted value is about 236. It is expected that this study can be used to select other time series analysis research models such as predicting agricultural product prices