This study investigated the feature importance of near-infrared spectra from random forest
regression models constructed to predict the carbonization characteristics of hydrochars …

The aim of this study is to establish prediction models for the non-destructive evaluation of
the carbonization characteristics of lignin-derived hydrochars as a carbon material in real …

Lumber species identification is an important issue for the wood industry. In this study, three
types of neural networks (artificial neural network (ANN), deep neural network (DNN), and …

In this paper, we investigated the carbonization characteristics of lignin hydrochar prepared
by hydrothermal carbonization and established a model for predicting the carbonization …

This study aimed to investigate the anatomical structure and fiber quality of four lesser-used
wood species namely Benuang (O. sumatrana), Duabanga (D. moluccana), Pisang Merah …

Five different softwoods were used to investigate fast methods for predicting quantitative
chemical information via near-infrared (NIR) spectroscopy. In biomass-related industries …

Three kinds of softwoods (Douglas fir, radiata pine, and Sugi) were used to test the
possibility of their classification via near infrared (NIR) spectroscopy. In a previous study, the …

This study analyzed the effect of organic solvent extractives on the classification of wood
species via near-infrared spectroscopy (NIR). In our previous research, five species of …

In our previous study, we investigated the use of ensemble models based on LeNet and
MiniVGGNet to classify the images of transverse and longitudinal surfaces of five Korean …

This paper examines the classification of five coniferous species, including larch (Larix
kaempferi), red pine (Pinus densiflora), Korean pine (Pinus koraiensis), cedar (Cryptomeria …