Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Support Vector Machines have been presented. According to news reporting out of Florianopolis, Brazil, by NewsRx editors, research stated, “Glasses are widely known for their unique properties, but improper disposal poses several environmental challenges. Laser-Induced Breakdown Spectroscopy (LIBS) has emerged as a promising tool for glass characterization.”Financial supporters for this research include Fundo de Defesa de Direitos Difusos-MJSP, Brazil, Con- selho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ). Our news journalists obtained a quote from the research from Federal University Santa Catarina, “This study explored the performance of LIBS, associated with machine learning methods and spectral angle mapper, to overcome the matrix effect in glass waste analysis. Using 10-fold cross-validation, an accuracy of 99.04% was achieved in color differentiation, 98.82% in the differentiation of flint glasses from the other glasses, 96.75% in differentiating particle sizes. When particle size and color were analyzed simultaneously, the accuracy remained high at 97.64%. The analytical accuracy was further improved using the spectral angle mapper method, which allowed us to achieve lower standard deviations, particularly for samples of larger particle sizes.”
NETL
NSTL
Elsevier