Application of neural networks for classifying softwood species using near infrared spectroscopy

On automated source selection for transfer learning in convolutional neural networks

(2018) Muhammad Jamal Afridi et al.   PATTERN RECOGNITION

Application of deep transfer learning for automated brain abnormality classification using MR images

(2018) Muhammed Talo et al.   Cognitive Systems Research

Artificial neural network and SIMCA classification in some wood discrimination based on near-infrared spectra

(2017) Silvana Nisgoski et al.   WOOD SCIENCE AND TECHNOLOGY

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

(2016) Hoo-Chang Shin et al.   IEEE TRANSACTIONS ON MEDICAL IMAGING

The use of near Infrared Spectroscopy to Identify solid wood Specimens of Swietenia Macrophylla0 (Cites Appendix II)

(2016)   IAWA JOURNAL

Forensic timber identification: It's time to integrate disciplines to combat illegal logging

(2015) Eleanor E. Dormontt et al.   BIOLOGICAL CONSERVATION

On-line application of near infrared (NIR) spectroscopy in food production

(2015) Jan U. Porep et al.   TRENDS IN FOOD SCIENCE & TECHNOLOGY

Estimating Shrinkage, Microfibril Angle and Density of Eucalyptus Wood Using near Infrared Spectroscopy

(2012) Paulo Ricardo Gherardi Hein   JOURNAL OF NEAR INFRARED SPECTROSCOPY

Near infrared spectroscopy (NIRS) as a potential tool for monitoring trade of similar woods: Discrimination of true mahogany, cedar, andiroba, and curupixá

(2010) Tereza Cristina Monteiro Pastore et al.   HOLZFORSCHUNG

Relationship between near-infrared (NIR) spectra and the geographical provenance of timber

(2010) Anna Sandak et al.   WOOD SCIENCE AND TECHNOLOGY

Eucalyptus camaldulensis density and fiber length estimated by near-infrared spectroscopy

(2010) Tetsuya Inagaki et al.   WOOD SCIENCE AND TECHNOLOGY

Determination of lignin and extractive content of Turkish Pine (Pinus brutia Ten.) trees using near infrared spectroscopy and multivariate calibration

(2010) B. Üner et al.   WOOD SCIENCE AND TECHNOLOGY

A common near infrared—based partial least squares regression model for the prediction of wood density of Pinus pinaster and Larix × eurolepis

(2010) Ana Alves et al.   WOOD SCIENCE AND TECHNOLOGY

Prediction of Lignin and Extractive Content ofPinus nigra Arnold. var. PallasianaTree Using Near Infrared Spectroscopy and Multivariate Calibration

(2009) B. Uner et al.   JOURNAL OF WOOD CHEMISTRY AND TECHNOLOGY

Classification of Red Oak (Quercus Rubra) and White Oak (Quercus Alba) Wood Using a near Infrared Spectrometer and Soft Independent Modelling of Class Analogies

(2008) Oluwatosin Emmanuel Adedipe et al.   JOURNAL OF NEAR INFRARED SPECTROSCOPY