Data fusion in data scarce areas using a back-propagation artificial neural network model: a case study of the South China Sea
Sign Up to like & getrecommendations! Published in 2017 at "Frontiers of Earth Science"
DOI: 10.1007/s11707-017-0652-1
Abstract: The cloud cover for the South China Sea and its coastal area is relatively large throughout the year, which limits the potential application of optical remote sensing. A HJ-charge-coupled device (HJ-CCD) has the advantages of… read more here.
Keywords: propagation artificial; south china; neural network; china sea ... See more keywords
Measurement and correlation study of silymarin solubility in supercritical carbon dioxide with and without a cosolvent using semi-empirical models and back-propagation artificial neural networks
Sign Up to like & getrecommendations! Published in 2017 at "Asian Journal of Pharmaceutical Sciences"
DOI: 10.1016/j.ajps.2017.04.004
Abstract: Graphical Abstract In the present work, the solubility data of silymarin (SM) in pure supercritical carbon dioxide (SCCO2) and SCCO2 with added cosolvent was measured at temperatures ranging from 308 to 338 K and pressures from… read more here.
Keywords: models back; semi empirical; propagation artificial; supercritical carbon ... See more keywords
Predictive Models for Compound Binding to Androgen and Estrogen Receptors Based on Counter-Propagation Artificial Neural Networks
Sign Up to like & getrecommendations! Published in 2023 at "Toxics"
DOI: 10.3390/toxics11060486
Abstract: Endocrine-disrupting chemicals (EDCs) are exogenous substances that interfere with the normal function of the human endocrine system. These chemicals can affect specific nuclear receptors, such as androgen receptors (ARs) or estrogen receptors (ER) α and… read more here.
Keywords: counter propagation; neural networks; estrogen receptors; artificial neural ... See more keywords