首页|New Machine Learning Research from Korea Institute of Ocean Science and Technolo gy Discussed (Machine Learning-Based Anomaly Detection on Seawater Temperature D ata with Oversampling)
New Machine Learning Research from Korea Institute of Ocean Science and Technolo gy Discussed (Machine Learning-Based Anomaly Detection on Seawater Temperature D ata with Oversampling)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in artificial intelligence. According to news originating from Geoje, South Korea, by NewsRx correspondents, research stated, “This study deals with a method for a nomaly detection in seawater temperature data using machine learning methods wit h oversampling techniques.” Financial supporters for this research include Ministry of Oceans And Fisheries Korea; Kiost Projects; Korea Government; Artificial Intelligence Convergence Inn ovation Human Resources Development. Our news correspondents obtained a quote from the research from Korea Institute of Ocean Science and Technology: “Data were acquired from 2017 to 2023 using a C onductivity-Temperature-Depth (CTD) system in the Pacific Ocean, Indian Ocean, a nd Sea of Korea. The seawater temperature data consist of 1414 profiles includin g 1218 normal and 196 abnormal profiles. This dataset has an imbalance problem i n which the amount of abnormal data is insufficient compared to that of normal d ata. Therefore, we generated abnormal data with oversampling techniques using du plication, uniform random variable, Synthetic Minority Oversampling Technique (S MOTE), and autoencoder (AE) techniques for the balance of data class, and traine d Interquartile Range (IQR)-based, one-class support vector machine (OCSVM), and Multi-Layer Perceptron (MLP) models with a balanced dataset for anomaly detecti on.”
Korea Institute of Ocean Science and Tec hnologyGeojeSouth KoreaAsiaCyborgsEmerging TechnologiesMachine Learn ing
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