Classification of thermally treated wood using machine learning techniques-Reference-Cited by-同舟云学术

Classification of thermally treated wood using machine learning techniques

Published:2018-12-13 Issue:1 Volume:53 Page:275-288
ISSN:0043-7719
Container-title:Wood Science and Technology
language:en
Short-container-title:Wood Sci Technol

Author:

Nasir Vahid^ORCID,Nourian Sepideh,Avramidis Stavros,Cool Julie

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Plant Science,General Materials Science,Forestry

Link

http://link.springer.com/article/10.1007/s00226-018-1073-3/fulltext.html

Reference36 articles.

1. Al-Aidaroos KM, Bakar AA, Othman Z (2012) Medical data classification with naive Bayes approach. Inf Technol J 11(9):1166–1174

2. ASTM D1037-12 (2012) Standard test methods for evaluating properties of wood-base fiber and particle panel materials. ASTM International, West Conshohocken, PA

3. ASTM D143-14 (2014) Standard test methods for small clear specimens of timber. ASTM International, West Conshohocken, PA

4. ASTM D2244-16 (2016) Standard practice for calculation of color tolerances and color differences from instrumentally measured color coordinates. ASTM International, West Conshohocken, PA

5. ASTM D2395-17 (2017) Standard test methods for density and specific gravity (relative density) of wood and wood-based material. ASTM International, West Conshohocken, PA

Cited by 47 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. In-situ shrinkage /swelling behavior and displacement based on vascular bundle morphological difference;Industrial Crops and Products;2024-10

2. Decoding dynamic bamboo cell shrinkage with time-lapse microscopy and machine-learning;Industrial Crops and Products;2024-10

3. A comprehensive review of the dairy pasteurization process using machine learning models;Food Control;2024-10

4. Strategies for multivariate characterization and classification of pulps and papers by near-infrared spectroscopy;Analytica Chimica Acta;2024-08

5. Performance of ANN, Random Forest and XGBoost methods in predicting the flexural properties of wood beams reinforced with carbon-FRP;Wood Material Science & Engineering;2024-07-25