Fiber Quality Prediction Using Nir Spectral Data: Tree-Based Ensemble Learning VS Deep Neural Networks-Reference-Cited by-同舟云学术

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Fiber Quality Prediction Using Nir Spectral Data: Tree-Based Ensemble Learning VS Deep Neural Networks

Published:2023-07-23 Issue:1 Volume:55 Page:100-115
ISSN:0735-6161
Container-title:Wood and Fiber Science
language:
Short-container-title:WFS

Author:

Nasir Vahid,Ali Syed Danish,Mohammadpanah Ahmad,Raut Sameen,Nabavi Mohamad,Dahlen Joseph,Schimleck Laurence

Publisher

Society of Wood Science and Technology

Subject

General Materials Science,Forestry

Reference61 articles.

1. Akiba T, Sano S, Yanase T, Ohta T, Koyama M (2019) Optuna: A next-generation hyperparameter optimization framework. Pages 2623-2631inProceedings, 25thACM SIGKDD International Conference on Knowledge Discovery & Data Mining, August 4-8, 2019, Anchorage, AK. Association for Computing Machinery, NewYork, NY.

2. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions;Alzubaidi;J Big Data 8(1),2021

3. Effect of wood surface roughness on prediction of structural timber properties by infrared spectroscopy using ANFIS, ANN and PLS regression;Ayanleye;Eur J Wood Wood Prod,2021

4. Modeling the longitudinal variation in wood specific gravity of planted loblolly pine (Pinus taeda) in the United States;Antony;Can J For Res 40(12),2010

5. Predicting and correlating the strength properties of wood composite process parameters by use of boosted regression tree models;Carty;For Prod J 65(7-8),2015

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