Research graph
References from Explainable artificial intelligence (AI)–based regression models for predicting moisture content in wood chips through transfer learning. Local targets link to admitted publications; unresolved targets remain external evidence.
Determination of moisture content and basic density of poplar wood chips under various moisture conditions by near-infrared spectroscopy
2019 · External reference
Comminution of logging residues with evolution 910R chipper, MOHA chipper truck, and Morbark 1200 tub grinder
1998 · External reference
Chemical composition of wood chips and wood pellets
10.1021/ef300884k · 2012 · External reference
Heating values of wood pellets from different species
10.1016/j.biombioe.2011.02.043 · 2011 · External reference
Variability of normative properties of wood chips and implications to quality control
10.3390/en14133789 · 2021 · External reference
Predicting and controlling moisture content to optimise forest biomass logistics
2012 · External reference
Analysis of wood chip characteristics for energy production in lithuania
10.3390/en14133931 · 2021 · External reference
Drying effects and dry matter losses during seasonal storage of spruce wood chips under practical conditions
10.1016/j.biombioe.2017.03.022 · 2018 · External reference
Book chapter (conference paper)
2008 · External reference
Unresolved reference
External reference
Fungal spores in chips
2014 · External reference
Natural drying treatments during seasonal storage of wood for bioenergy in different European locations
10.1016/j.biombioe.2011.07.011 · 2011 · External reference
Color changes of various wood species in response to moisture
10.22382/wfs-2019-014 · 2019 · External reference
10.1145/1186822.1073254
10.1145/1186822.1073254 · External reference
Wood chips storage & energy
10.15376/biores.10.3.5544-5553 · 2015 · External reference
Unresolved reference
2020 · External reference
Capacitance sensing of moisture content in fuel wood chips
10.1109/jsen.2016.2541081 · 2016 · External reference
A comprehensive review on wood chip moisture content assessment and prediction
10.1016/j.rser.2023.113843 · 2024 · External reference
Electrical capacitance characteristics of wood chips at low frequency ranges: A cheap tool for quality assessment
10.3390/s21103494 · 2021 · External reference
MoistNet: Machine vision-based deep learning models for wood chip moisture content measurement
10.1016/j.eswa.2024.125363 · 2025 · External reference
Moisture content determination in solid biofuels by dielectric and nir reflection methods
10.1016/j.biombioe.2006.06.005 · 2006 · External reference
Prediction of loblolly pine wood properties using transmittance near-infrared spectroscopy
10.1139/x05-161 · 2005 · External reference
Determination of the moisture content in wood chips of scots pine and Norway spruce using mantex desktop scanner based on dual energy X-ray absorptiometry
10.1007/s10086-012-1260-z · 2012 · External reference
Interpretable wood chip moisture content prediction through texture analysis
10.1016/j.eswa.2025.126989 · 2025 · External reference
An interpretable deep learning model for wood chip moisture content prediction
2023 · External reference
Transfer learning techniques for medical image analysis: A review
10.1016/j.bbe.2021.11.004 · 2022 · External reference
Towards a better understanding of transfer learning for medical imaging: a case study
10.3390/app10134523 · 2020 · External reference
Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning
10.1109/tmi.2016.2528162 · 2016 · External reference
A review of deep transfer learning and recent advancements
10.3390/technologies11020040 · 2023 · External reference
How transferable are features in deep neural networks?
2014 · External reference
An effective and fast solution for classification of wood species: A deep transfer learning approach
10.1016/j.ecoinf.2022.101633 · 2022 · External reference
Wood species recognition with small data: A deep learning approach
10.2991/ijcis.d.210423.001 · 2021 · External reference
Guest editorial explainable AI: towards fairness, accountability, transparency and trust in healthcare
10.1109/jbhi.2021.3088832 · 2021 · External reference
Causability and explainability of artificial intelligence in medicine
2019 · External reference
Unresolved reference
2018 · External reference
Estimation of moisture in wood chips by near infrared spectroscopy
2020 · External reference
Unresolved reference
2009 · External reference
A simple time-domain-reflectometry based methodology for wood-chip humidity measurements
2010 · External reference
Multiclass moisture classification in woodchips using iIoT wi-fi and machine learning techniques
10.1016/j.compchemeng.2021.107445 · 2021 · External reference
UWB radio measurement and time-domain analysis of anisotropy in wood chips
10.1109/jsen.2018.2870760 · 2018 · External reference
Machine learning and woody biomasses: Assessing wood chip quality for sustainable energy production
10.1016/j.biombioe.2024.107527 · 2025 · External reference
Modelling the process of water penetration into softwood chips
2003 · External reference
Unresolved reference
2021 · External reference
A comparison of pooling methods for convolutional neural networks
10.3390/app12178643 · 2022 · External reference
A review of convolutional neural networks
2020 · External reference
A review of convolutional neural networks in computer vision
10.1007/s10462-024-10721-6 · 2024 · External reference
Reliable accuracy estimates from k-fold cross validation
10.1109/tkde.2019.2912815 · 2019 · External reference
Unresolved reference
2022 · External reference
Image segmentation using deep learning: A survey
2021 · External reference
Brain tumor classification using deep CNN features via transfer learning
10.1016/j.compbiomed.2019.103345 · 2019 · External reference
Transfer learning: a friendly introduction
10.1186/s40537-022-00652-w · 2022 · External reference
10.1186/s40537-016-0043-6 · 2016 · Admitted local publication
Convolutional neural networks for medical image analysis: Full training or fine tuning?
10.1109/tmi.2016.2535302 · 2016 · External reference
Feature extraction and image recognition with convolutional neural networks
10.1088/1742-6596/1087/6/062032 · 2018 · External reference
Diagnosis of autism spectrum disorder with convolutional autoencoder and structural MRI images
2021 · External reference
Understanding the mechanisms of deep transfer learning for medical images
2016 · External reference
Deep transfer learning for image-based structural damage recognition
10.1111/mice.12363 · 2018 · External reference
Comparison of fine-tuning strategies for transfer learning in medical image classification
10.1016/j.imavis.2024.105012 · 2024 · External reference
Grad-CAM: Visual explanations from deep networks via gradient-based localization
2017 · External reference
Unresolved reference
2025 · External reference
Unresolved reference
2024 · External reference
Unresolved reference
2016 · External reference
Matplotlib: A 2D graphics environment
10.1109/mcse.2007.55 · 2007 · External reference
Brain tumor classification for MR images using transfer learning and fine-tuning
10.1016/j.compmedimag.2019.05.001 · 2019 · External reference
Diagnosis of salivary gland tumors using transfer learning with fine-tuning and gradual unfreezing
10.3390/diagnostics13213333 · 2023 · External reference
Determination of moisture content and basic density of poplar wood chips under various moisture conditions by near-infrared spectroscopy
2019 · External reference
Precision and accuracy in moisture content determination of wood fuel chips using a handheld electric capacitance moisture meter
10.14214/sf.6993 · 2018 · External reference
Review of factors influencing the dielectric properties of cereal grains
1981 · External reference
Microwave dielectric sensing of bulk density of granular materials
10.1088/0957-0233/12/12/322 · 2001 · External reference
Indigenous wood species classification using a multi-stage deep learning with grad-CAM explainability and an ensemble technique for northern Bangladesh
2025 · External reference
Domain adaptation: Challenges, methods, datasets, and applications
10.1109/access.2023.3237025 · 2023 · External reference
A survey of multi-source domain adaptation
10.1016/j.inffus.2014.12.003 · 2015 · External reference
Unresolved reference
2025 · External reference
