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An Abnormal Pattern Detection Scheme Based on GCN and DBSCAN in a Large-Scale Graph
Christopher Retiti Diop Emane,이현병,최도진,임종태,복경수,유재수 한국콘텐츠학회 2022 International Journal of Contents Vol.18 No.4
In recent decades, anomaly detection has undoubtedly become one of the most important areas of research. This is because applications such as financial transactions, medical fraud, and anomaly detection can be used to solve a wide range of real-life problems. Data from these applications can be modeled using large graphs of many different nodes and edges. Because of the size and heterogeneity of the data contained in the graph, it is a very difficult task to detect abnormal patterns. In this paper, we proposed a method for detecting abnormal patterns in a large homogeneous graph. The proposed method consisted of two steps. In the first step, the graph was transformed into a vector using a semi-supervised graph neural network (GCN). The second step was based on DBSCAN, an unsupervised clustering method. Various performance evaluations were performed to show the superiority of the proposed method. Experimental results showed that the proposed method could detect abnormal nodes with high accuracy in homogeneous static graphs.
자율 기계 학습을 위한 효과적인 스마트 온실 데이터 전처리 시스템
임종태(Jongtae Lim),유재수(Jae-soo Yoo),Christopher RETITI DIOP EMANE(Christopher RETITI DIOP EMANE ),김윤아(Yuna Kim),백정현(Jeong-Hyun Beak) 한국스마트미디어학회 2023 스마트미디어저널 Vol.12 No.1
최근 정보통신기술을 농업과 접목해 새로운 가치를 창출하는 스마트팜 연구가 활발하게 진행되고 있다. 국내 스마트팜 기술이 농업 선진국 수준의 생산성을 가지기 위해서는 기계 학습을 활용한 자동화된 의사결정이 필요하다. 그러나 현재의 스마트 온실 데이터 수집 기술은 빅데이터 분석이나 기계 학습을 수행하기에 충분하지 않다. 본 논문에서는 자율 기계 학습을 위한 스마트 온실 데이터 전처리 시스템을 설계하고 구현한다. 제안하는 시스템은 대상 데이터를 다양한 전처리 기법에 적용하고 평가를 수행하여 최적 전처리 기법을 탐색하고 저장한다. 이렇게 탐색 된 최적 전처리 기법은 새롭게 수집된 데이터에 대하여 전처리를 수행하는데 활용된다. Recently, research on a smart farm that creates new values by combining information and communication technology(ICT) with agriculture has been actively done. In order for domestic smart farm technology to have productivity at the same level of advanced agricultural countries, automated decision-making using machine learning is necessary. However, current smart greenhouse data collection technologies in our country are not enough to perform big data analysis or machine learning. In this paper, we design and implement a smart greenhouse data preprocessing system for autonomous machine learning. The proposed system applies target data to various preprocessing techniques. And the proposed system evaluate the performance of each preprocessing technique and store optimal preprocessing technique for each data. Stored optimal preprocessing techniques are used to perform preprocessing on newly collected data