3 resultados para kernel classifiers
em Reposit
Resumo:
PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.
Resumo:
Shelled, roasted and salted cashew nut kernels were packaged in three different flexible materials (PP/PE= polypropylene / polyethylene; PETmet/PE= metallized polyethylene terephthalate / polyethylene; PET/Al/LDPE= polyethylene terephthalate / aluminum foil / low density polyethylene ), with different barrier properties. Kernels were stored for one year at 30° C and 80% relative humidity. Quantitative descriptive sensory analysis (QDA) were performed at the end of storage time. Descriptive terms obtained for kernels characterization were brown color, color uniformity and rugosity for appearance; toasted kernel, sweet, old and rancidity for odor; toasted kernel, sweet, old rancidity, salt and bitter for taste, crispness for texture. QDA showed that factors responsible for sensory quality decrease, after one year storage, were increase in old aroma and taste, increase in rancidity aroma and taste, decrease in roasted kernel aroma and taste, and decrease of crispness. Sensory quality decrease was higher in kernels packaged in PP/PE.
Resumo:
Universidade Estadual de Campinas . Faculdade de Educação Física