AIMC Topic: Wood

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Indigenous wood species classification using a multi-stage deep learning with grad-CAM explainability and an ensemble technique for Northern Bangladesh.

PloS one Jul 29, 2025
Wood species recognition has recently emerged as a vital field in the realm of forestry and ecological conservation. Early studies in this domain have offered various methods for classifying distinct wood species found worldwide using data collected ...
Neural Networks, Computer Bangladesh Conservation of Natural Resources Wood Deep Learning Forests
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Combined impact of semantic segmentation and quantitative structure modelling of Southern pine trees using terrestrial laser scanning.

Scientific reports Jul 8, 2025
Southern pine forests play a key role in the ecological function and economic vitality of the southeastern United States. High-resolution terrestrial laser scanning (TLS) has become an indispensable tool for advancing tree structural research and mon...
Pinus Pinus taeda Wood Florida Algorithms Plant Leaves Forests Trees Lasers
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Deep learning-aided preparation and mechanism revaluation of waste wood lignocellulose-based flame-retardant composites.

International journal of biological macromolecules Mar 3, 2025
Wood and its derivatives play a decisive role in traditional Chinese architecture. Waste wood as a major source of garbage in the construction industry represents a valuable source. The efficient recycling of waste wood has become an urgent technical...
Deep Learning Lignin Wood Phytic Acid Tannins Flame Retardants
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Combating trade in illegal wood and forest products with machine learning.

PloS one Jan 24, 2025
Trade in wood and forest products spans the global supply chain. Illegal logging and associated trade in forest products present a persistent threat to vulnerable ecosystems and communities. Illegal timber trade has been linked to violations of tax a...
Conservation of Natural Resources Commerce United States Forests Wood Crime Forestry Humans Machine Learning
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Assessment of fuzzy logic to enhance species distribution modelling of two cryptic wood boring beetle species in Australia.

Scientific reports Nov 13, 2024
Fuzzy logic presents a promising approach for Species Distribution Modelling by generating a value that can be used for comparative purposes termed 'environmental favourability'. In contrast to 'presence probability', 'environmental favourability' re...
Models, Biological Coleoptera Wood Fuzzy Logic Australia Animals
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Performance of convolutional neural network (CNN) and performance influencing factors for wood species classification of Lepidobalanus growing in Korea.

Scientific reports Aug 5, 2024
This study aimed to investigate the performance and factors affecting the species classification of convolutional neural network (CNN) architecture using whole-part and earlywood-part cross-sectional datasets of six Korean Quercus species. The accura...
Wood Republic of Korea Quercus Neural Networks, Computer
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Random forest machine-learning algorithm classifies white- and brown-rot fungi according to the number of the genes encoding Carbohydrate-Active enZyme families.

Applied and environmental microbiology Jun 4, 2024
UNLABELLED: Wood-rotting fungi play an important role in the global carbon cycle because they are the only known organisms that digest wood, the largest carbon stock in nature. In the present study, we used linear discriminant analysis and random for...
Algorithms Cellulose Machine Learning Carbohydrate Metabolism Wood Lignin Fungal Proteins Random Forest Fungi
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Higher heating value estimation of wastes and fuels from ultimate and proximate analysis by using artificial neural networks.

Waste management (New York, N.Y.) May 30, 2024
Higher heating value (HHV) is one of the most important parameters in determining the quality of the fuels. In this study, comparatively large datasets of ultimate and proximate analysis are constructed to be used in HHV estimation of several classes...
Wood Refuse Disposal Solid Waste Waste Management Heating Waste Products Animals Sewage Neural Networks, Computer Manure
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Predictive modeling of copper (II) adsorption from aqueous solutions by sawdust: a comparative analysis of adaptive neuro-fuzzy interference system (ANFIS) and artificial neural network (ANN) approaches.

Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering Apr 12, 2024
Heavy metal ions are considered to be the most prevalent and toxic water contaminants. The objective of thois work was to investigate the effectiveness of employing the adsorption technique in a laboratory-size reactor to remove copper (II) ions from...
Water Pollutants, Chemical Fuzzy Logic Neural Networks, Computer Hydrogen-Ion Concentration Adsorption Models, Chemical Copper Wood Water Purification
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Wood identification based on macroscopic images using deep and transfer learning approaches.

PeerJ Feb 28, 2024
Identifying forest types is vital for evaluating the ecological, economic, and social benefits provided by forests, and for protecting, managing, and sustaining them. Although traditionally based on expert observation, recent developments have increa...
Forests Mental Recall Artificial Intelligence Machine Learning Wood
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