首页|Jinan University Reports Findings in Machine Learning (Prediction of Aureococcus anophageffens using machine learning and deep learning)
Jinan University Reports Findings in Machine Learning (Prediction of Aureococcus anophageffens using machine learning and deep learning)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is the subject of a report. According to news originating from Guangzhou, People’s Republic of China, by NewsRx correspondents, research stated, “The recurrent brown tide phenomenon, attributed to Aureococcus anophagefferens (A. anophagefferens), constitutes a significant threat to the Qinhuangdao sea area in China, leading to pronounced ecological degradation and substantial economic losses. This study utilized machine learning and deep learning techniques to predict A. anophagefferens population density, aiming to elucidate the occurrence mechanism and influencing factors of brown tide.” Our news journalists obtained a quote from the research from Jinan University, “Specifically, Random Forest (RF) algorithm was utilized to impute missing water quality data, facilitating its direct application in subsequent algal population prediction models. The results revealed that all four models-RF, Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN)-exhibited high accuracy in predicting A. anophagefferens population densities, with R values exceeding 0.75. RF, in particular, showed exceptional accuracy and reliability, with an R value surpassing 0.8.” According to the news editors, the research concluded: “Additionally, the study ascertained five critical factors influencing A. anophagefferens population density: ammonia nitrogen, pH, total nitrogen, temper- ature, and silicate.” This research has been peer-reviewed.
GuangzhouPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
