首页|New Findings in Machine Learning Described from Ningbo University(Short-term So lar Eruptive Activity Prediction Models BasedOn Machine Learning Approaches: a Review)
New Findings in Machine Learning Described from Ningbo University(Short-term So lar Eruptive Activity Prediction Models BasedOn Machine Learning Approaches: a Review)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Machine Learning is th e subject of a report. According to newsreporting originating from Ningbo, Peop le’s Republic of China, by NewsRx correspondents, research stated,“Solar erupti ve activities, mainly including solar flares, coronal mass ejections (CME), and solar protonevents (SPE), have an important impact on space weather and our tec hnosphere. The short-term solareruptive activity prediction is an active field of research in the space weather prediction.”Financial supporters for this research include Science and Technology Developmen t Fund (STDF),Orszagos Tudomanyos Kutatasi Alapprogramok (OTKA), ISSI-Beijing, Chinese Academy of Sciences, NationalKey R&D Program of China, Nat ional Natural Science Foundation of China (NSFC).Our news editors obtained a quote from the research from Ningbo University, “Num erical, statistical,and machine learning methods are proposed to build predicti on models of the solar eruptive activities.With the development of space-based and ground-based facilities, a large amount of observational data of the Sun is accumulated, and data-driven prediction models of solar eruptive activities have made asignificant progress.”
NingboPeople’s Republic of ChinaAsiaCyborgsEmergingTechnologiesMachine LearningNingbo University
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