NASA Goddard Space Flight Center Researcher Provides New Data on Machine Learnin g (Noise reduction for solar-induced fluorescence retrievals using machine learn ing and principal component analysis: simulations and applications to GOME-2 ... )
NASA Goddard Space Flight Center Researcher Provides New Data on Machine Learnin g (Noise reduction for solar-induced fluorescence retrievals using machine learn ing and principal component analysis: simulations and applications to GOME-2 ... )
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligen ce have been presented. According to news reporting originating from Greenbelt, Maryland, by NewsRx correspondents, research stated, “We use a spectral-based ap proach that employs principal component analysis along with a relatively shallow artificial neural network (NN) to substantially reduce noise and other artifact s in terrestrial chlorophyll solar-induced fluorescence (SIF) retrievals. SIF is a very small emission at red and far-red wavelengths that is difficult to measu re and is highly sensitive to random errors and systematic artifacts.”
Key words
NASA Goddard Space Flight Center/Greenb elt/Maryland/United States/North and Central America/Cyborgs/Emerging Techn ologies/Machine Learning
NETL
NSTL