• Title/Summary/Keyword: 머신러닝(기계학습)

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Diabetes prediction mechanism using machine learning model based on patient IQR outlier and correlation coefficient (환자 IQR 이상치와 상관계수 기반의 머신러닝 모델을 이용한 당뇨병 예측 메커니즘)

  • Jung, Juho;Lee, Naeun;Kim, Sumin;Seo, Gaeun;Oh, Hayoung
    • 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.

The Application Method of Machine Learning for Analyzing User Transaction Tendency in Big Data environments (빅데이터 환경에서 사용자 거래 성향분석을 위한 머신러닝 응용 기법)

  • Choi, Do-hyeon;Park, Jung-oh
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.19 no.10
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    • pp.2232-2240
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    • 2015
  • Recently in the field of Big Data, there is a trend of collecting and reprocessing the existing data such as products having high interest of customers and past purchase details to be utilized for the analysis of transaction propensity of users(product recommendations, sales forecasts, etc). Studies related to the propensity of previous users has limitations on its range of subjects and investigation timing and difficult to make predictions on detailed products with lack of real-time thus there exists difficult disadvantages of introducing appropriate and quick sales strategy against the trend. This paper utilizes the machine learning algorithm application to analyze the transaction propensity of users. As a result of applying the machine learning algorithm, it has demonstrated that various indicators which can be deduced by detailed product were able to be extracted.

Comparison of the Machine Learning Models Predicting Lithium-ion Battery Capacity for Remaining Useful Life Estimation (리튬이온 배터리 수명추정을 위한 용량예측 머신러닝 모델의 성능 비교)

  • Yoo, Sangwoo;Shin, Yongbeom;Shin, Dongil
    • Journal of the Korean Institute of Gas
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    • v.24 no.6
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    • pp.91-97
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    • 2020
  • Lithium-ion batteries (LIBs) have a longer lifespan, higher energy density, and lower self-discharge rates than other batteries, therefore, they are preferred as an Energy Storage System (ESS). However, during years 2017-2019, 28 ESS fire accidents occurred in Korea, and accurate capacity estimation of LIB is essential to ensure safety and reliability during operations. In this study, data-driven modeling that predicts capacity changes according to the charging cycle of LIB was conducted, and developed models were compared their performance for the selection of the optimal machine learning model, which includes the Decision Tree, Ensemble Learning Method, Support Vector Regression, and Gaussian Process Regression (GPR). For model training, lithium battery test data provided by NASA was used, and GPR showed the best prediction performance. Based on this study, we will develop an enhanced LIB capacity prediction and remaining useful life estimation model through additional data training, and improve the performance of anomaly detection and monitoring during operations, enabling safe and stable ESS operations.

Optimum Evacuation Route Calculation Using AI Q-Learning (AI기법의 Q-Learning을 이용한 최적 퇴선 경로 산출 연구)

  • Kim, Won-Ouk;Kim, Dae-Hee;Youn, Dae-Gwun
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.24 no.7
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    • pp.870-874
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    • 2018
  • In the worst maritime accidents, people should abandon ship, but ship structures are narrow and complex and operation takes place on rough seas, so escape is not easy. In particular, passengers on cruise ships are untrained and varied, making evacuation prospects worse. In such a case, the evacuation management of the crew plays a very important role. If a rescuer enters a ship at distress and conducts rescue activities, which zones represent the most effective entry should be examined. Generally, crew and rescuers take the shortest route, but if an accident occurs along the shortest route, it is necessary to select the second-best alternative. To solve this situation, this study aims to calculate evacuation routes using Q-Learning of Reinforcement Learning, which is a machine learning technique. Reinforcement learning is one of the most important functions of artificial intelligence and is currently used in many fields. Most evacuation analysis programs developed so far use the shortest path search method. For this reason, this study explored optimal paths using reinforcement learning. In the future, machine learning techniques will be applicable to various marine-related industries for such purposes as the selection of optimal routes for autonomous vessels and risk avoidance.

A Study on the Application of Machine Learning to Improve BIS (Bus Information System) Accuracy (BIS(Bus Information System) 정확도 향상을 위한 머신러닝 적용 방안 연구)

  • Jang, Jun yong;Park, Jun tae
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.21 no.3
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    • pp.42-52
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    • 2022
  • Bus Information System (BIS) services are expanding nationwide to small and medium-sized cities, including large cities, and user satisfaction is continuously improving. In addition, technology development related to improving reliability of bus arrival time and improvement research to minimize errors continue, and above all, the importance of information accuracy is emerging. In this study, accuracy performance was evaluated using LSTM, a machine learning method, and compared with existing methodologies such as Kalman filter and neural network. As a result of analyzing the standard error for the actual travel time and predicted values, it was analyzed that the LSTM machine learning method has about 1% higher accuracy and the standard error is about 10 seconds lower than the existing algorithm. On the other hand, 109 out of 162 sections (67.3%) were analyzed to be excellent, indicating that the LSTM method was not entirely excellent. It is judged that further improved accuracy prediction will be possible when algorithms are fused through section characteristic analysis.

Cancer driver gene using multi-omics data and biological network information (멀티 오믹스 데이터 및 생물학적 네트워크 정보를 이용한 드라이버 유전자 분류)

  • Jeong-Ho Park;Kyuri Jo
    • Proceedings of the Korea Information Processing Society Conference
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    • 2023.05a
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    • pp.490-492
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    • 2023
  • 시퀀싱(sequencing) 기술의 발달로 다양한 오믹스(omics) 데이터의 축적과 인공 지능 기술의 발달로 인하여 다양한 드라이버 유전자 분류기법이 제안되어왔다. 최근에는 암 데이터가 대용량으로 축적되며 기계 학습 기반의 다양한 기법들이 활발히 제안되었다. 특히 다양한 오믹스 데이터를 결합한 고차원 데이터에서 높은 정확도를 확보하기 위한 시도가 활발히 이루어지고 있다. 본 논문에서는 멀티 오믹스와 네트워크 관련 특징을 기반으로 암의 증식 및 발생에 중요한 역할을 하는 드라이버 유전자를 분류하는 딥러닝 모델을 제시한다. 또한 The Cancer Genome Atlas(TCGA) 데이터를 통해서 모델 학습 후 기존 통계 및 머신러닝 기반 기법과 비교하여 성능이 개선되었음을 확인하였다.

Stale Synchronous Parallel Model in Edge Computing Environment (Edge Computing 환경에서의 Stale Synchronous Parallel Model 연구)

  • Kim, Dong-Hyun;Lee, Byung-Jun;Kim, Kyung-Tae;Youn, Hee-Yong
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2018.01a
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    • pp.89-92
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    • 2018
  • 본 논문에서는 Edge computing 환경에서 다수의 노드들로 구성된 네트워크의 디바이스를 효율적으로 관리하기 위한 방법을 제안한다. 기존의 클라이언트-서버 모델은 모든 데이터와 그에 대한 요청을 중심 서버에서 처리하기 때문에, 다수의 노드로부터 생성된 많은 양의 데이터를 처리하는 데 빠른 응답속도를 보장하지 못한다. Edge computing은 분담을 통해 네트워크의 부담을 줄일 수 있는 IoT 네트워크에 적합한 방법으로, 데이터를 전송하고 받는 과정에서 네트워크의 대역폭을 사용하는 대신 서로 연결된 노드들이 협력해서 데이터를 처리하고, 또한 네트워크 말단에서의 데이터 처리가 허용되어 데이터 센터의 부담을 줄일 수 있다. 여러병렬 기계학습 모델 중 본 연구에서는 Stale Synchronous Parallel(SSP) 모델을 이용하여 Edge 노드에서 분산기계 학습에 적용하였다.

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Sensor Data Collection & Refining System for Machine Learning-Based Cloud (기계학습 기반의 클라우드를 위한 센서 데이터 수집 및 정제 시스템)

  • Hwang, Chi-Gon;Yoon, Chang-Pyo
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.25 no.2
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    • pp.165-170
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    • 2021
  • Machine learning has recently been applied to research in most areas. This is because the results of machine learning are not determined, but the learning of input data creates the objective function, which enables the determination of new data. In addition, the increase in accumulated data affects the accuracy of machine learning results. The data collected here is an important factor in machine learning. The proposed system is a convergence system of cloud systems and local fog systems for service delivery. Thus, the cloud system provides machine learning and infrastructure for services, while the fog system is located in the middle of the cloud and the user to collect and refine data. The data for this application shall be based on the Sensitive data generated by smart devices. The machine learning technique applied to this system uses SVM algorithm for classification and RNN algorithm for status recognition.

Gait Analysis and Machine Learning-based Classification Model using Smart Insole for Alzheimer's Disease Severity Classification (스마트인솔 기반 알츠하이머 중증도 분류를 위한 보행 분석 및 기계학습 기반 분류 모델)

  • Jeon, YoungHoon;Ho, Thi Kieu Khanh;Gwak, Jeonghwan;Song, Jong-In
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2021.07a
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    • pp.317-320
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    • 2021
  • 본 연구는 주기적인 알츠하이머 병의 중증도 모니터링을 위해 스마트 인솔을 통한 보행 특징 추출과 머신러닝 기반 중증도 분류의 성능에 대해 살펴보았다. 최근 고령화가 가속화되는 추세에 있어 치매 환자가 급증하고 있으며, 중증도가 심해질수록 필요한 치료 비용 및 노력이 급증하기 때문에 조기 진단이 최선의 치료 전략으로 보여진다. 환자 친화적이고 저비용의 관성 측정 장치가 내장된 스마트 인솔만을 사용하여 다양한 보행 실험 패러다임에서 환자의 보행 특징을 추출하고, 이를 알츠하이머 병의 중증도 진단을 위한 머신러닝 기반 분류기를 훈련시켜 성능을 평가한 결과, 숫자세기와 같이 뇌에 부하를 주는 하위 작업이 포함된 복합 보행을 측정한 데이터셋을 사용하여 훈련된 분류 모델이 일반 걷기 데이터셋을 사용한 모델보다 성능이 높게 나타나는 것이 관찰되었다. 본 연구는 안전하고 환경적 제약이 적은 방법을 사용하여 시기 적절한 진단뿐만 아니라 주기적인 중증도 모니터링 시스템의 일환으로 활용될 수 있을 것이다.

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Store Sales Prediction Using Gradient Boosting Model (그래디언트 부스팅 모델을 활용한 상점 매출 예측)

  • Choi, Jaeyoung;Yang, Heeyoon;Oh, Hayoung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.25 no.2
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    • pp.171-177
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    • 2021
  • Through the rapid developments in machine learning, there have been diverse utilization approaches not only in industrial fields but also in daily life. Implementations of machine learning on financial data, also have been of interest. Herein, we employ machine learning algorithms to store sales data and present future applications for fintech enterprises. We utilize diverse missing data processing methods to handle missing data and apply gradient boosting machine learning algorithms; XGBoost, LightGBM, CatBoost to predict the future revenue of individual stores. As a result, we found that using median imputation onto missing data with the appliance of the xgboost algorithm has the best accuracy. By employing the proposed method, fintech enterprises and customers can attain benefits. Stores can benefit by receiving financial assistance beforehand from fintech companies, while these corporations can benefit by offering financial support to these stores with low risk.