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A transferred multitask regularization convolutional neural network (TrMR-CNN) for laser-induced breakdown spectroscopy quantitative analysis
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Transfer‐learning‐based Raman spectra identification
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Investigating the Impact of Data Volume and Domain Similarity on Transfer Learning Applications
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Laser-induced breakdown spectroscopy in China
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Mechanism of signal uncertainty generation for laser-induced breakdown spectroscopy
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Modeling of oil near-infrared spectroscopy based on similarity and transfer learning algorithm
inferredcites →https://doi.org/10.1016/j.aca.2019.01.002openalex-snapshot
DeepSpectra: An end-to-end deep learning approach for quantitative spectral analysis
inferredcites →https://doi.org/10.1016/j.apgeochem.2021.105135openalex-snapshot
Deep learning with laser-induced breakdown spectroscopy (LIBS) for the classification of rocks based on elemental imaging
inferredcites →https://doi.org/10.1016/j.chemolab.2019.103824openalex-snapshot
Cross components calibration transfer of NIR spectroscopy model through PCA and weighted ELM-based TrAdaBoost algorithm
inferredcites →https://doi.org/10.1016/j.eswa.2022.117756openalex-snapshot
Spectral knowledge-based regression for laser-induced breakdown spectroscopy quantitative analysis
inferredcites →https://doi.org/10.1016/j.fuel.2021.121667openalex-snapshot
Industrial at-line analysis of coal properties using laser-induced breakdown spectroscopy combined with machine learning
inferredcites →https://doi.org/10.1016/j.geoderma.2019.01.009openalex-snapshot
Transfer learning to localise a continental soil vis-NIR calibration model
inferredcites →https://doi.org/10.1016/j.jhazmat.2019.02.015openalex-snapshot
Identification and classification of explosives using semi-supervised learning and laser-induced breakdown spectroscopy
inferredcites →https://doi.org/10.1016/j.sab.2015.02.003openalex-snapshot
A study of machine learning regression methods for major elemental analysis of rocks using laser-induced breakdown spectroscopy
inferredcites →https://doi.org/10.1016/j.sab.2021.106183openalex-snapshot
A review of artificial neural network based chemometrics applied in laser-induced breakdown spectroscopy analysis
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A data preprocessing method based on matrix matching for coal analysis by laser-induced breakdown spectroscopy
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Fast measurement of coking properties of coal using laser induced breakdown spectroscopy
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Incorporating domain knowledge into machine learning for laser-induced breakdown spectroscopy quantification
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Recent advances in laser-induced breakdown spectroscopy quantification: From fundamental understanding to data processing
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Domain-Invariant Partial-Least-Squares Regression
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From machine learning to transfer learning in laser-induced breakdown spectroscopy analysis of rocks for Mars exploration
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A multivariate model based on dominant factor for laser-induced breakdown spectroscopy measurements
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Quantitative and classification analysis of slag samples by laser induced breakdown spectroscopy (LIBS) coupled with support vector machine (SVM) and partial least square (PLS) methods
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Improved analysis of inorganic coal properties based on near-infrared reflectance spectroscopy
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An LIBS quantitative analysis method for alloy steel at high temperature based on transfer learning
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Coal analysis by laser-induced breakdown spectroscopy: a tutorial review
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Assessment of the performance of quantitative feature-based transfer learning LIBS analysis of chromium in high temperature alloy steel samples
inferredcites →https://doi.org/10.1039/d0ja00386gopenalex-snapshot
Validated ensemble variable selection of laser-induced breakdown spectroscopy data for coal property analysis
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Transfer learning improves the prediction performance of a LIBS model for metals with an irregular surface by effectively correcting the physical matrix effect
inferredcites →https://doi.org/10.1109/iccv.2013.274openalex-snapshot
Transfer Feature Learning with Joint Distribution Adaptation
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4676333openalex-extracted
A Transfer Learning Approach for Soil Total Nitrogen Content Detection Base on Laser-Induced Breakdown Sensor
inferred← citeshttps://doi.org/10.1007/s10462-023-10590-5openalex-snapshot
Application of deep learning in laser-induced breakdown spectroscopy: a review
inferred← citeshttps://doi.org/10.1016/j.compag.2024.109396openalex-snapshot
Rapid and accurate detection of total nitrogen in the different types for soil using laser-induced breakdown spectroscopy combined with transfer learning
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.113082openalex-snapshot
Cross-modal feature fusion and distillation for enhanced quantification accuracy in laser-induced breakdown spectroscopy and near-infrared spectroscopy
inferred← citeshttps://doi.org/10.1016/j.measurement.2025.119016openalex-snapshot
A novel elemental analysis method combining LIBS with transfer learning for spent fuel reprocessing
inferred← citeshttps://doi.org/10.1016/j.sab.2023.106634openalex-snapshot
Deep learning regression for quantitative LIBS analysis
inferred← citeshttps://doi.org/10.1016/j.trac.2025.118322openalex-snapshot
Non-destructive spectroscopy assisted by machine learning for coal industrial analysis: Strategies, progress, and future prospects
inferred← citeshttps://doi.org/10.1039/d3ay01380dopenalex-snapshot
Accuracy improvement of laser-induced breakdown spectroscopy coal analysis by hybrid transfer learning
inferred← citeshttps://doi.org/10.1039/d3ja00341hopenalex-snapshot
Rapid quantitative analysis of raw rocks by LIBS coupled with feature-based transfer learning
inferred← citeshttps://doi.org/10.1039/d4ja00407hopenalex-snapshot
Laser-induced breakdown spectroscopy chemometrics for ChemCam Mars in situ data analysis based on deep learning and pretrained-model-based transfer learning
inferred← citeshttps://doi.org/10.1039/d4ja00459kopenalex-snapshot
TrCSL: a transferred CNN-SE-LSTM model for high-accuracy quantitative analysis of laser-induced breakdown spectroscopy with small samples
inferred← citeshttps://doi.org/10.1039/d5ja00011dopenalex-snapshot
Calibration and application of a large-scale LIBS project based on transfer learning in the online quantitative analysis of coal
inferred← citeshttps://doi.org/10.1039/d5ja00313jopenalex-snapshot
Semi-supervised graph learning for spatial mapping of heavy metal concentrations in smelter-adjacent soils using a mobile LIBS device
inferred← citeshttps://doi.org/10.1080/19440049.2025.2494199openalex-snapshot
Detection of whey protein concentrate adulteration using laser-induced breakdown spectroscopy combined with machine learning
inferred← citeshttps://doi.org/10.2139/ssrn.5020324openalex-snapshot
Non-Destructive Spectroscopy Assisted by Machine Learning for Coal Industrial Analysis: Recent Advances and Future Perspectives
inferred← citeshttps://doi.org/10.2139/ssrn.7597604openalex-snapshot
Simulation-based pre-training improves small-sample LIBS quantification through correct line–element association
inferred← citeshttps://doi.org/10.3390/app14041606openalex-snapshot
Application of Semi-Supervised Learning Model to Coal Sample Classification
inferred← citeshttps://doi.org/10.3390/en17143559openalex-snapshot
Progress in the Application of Laser-Induced Breakdown Spectroscopy in Coal Quality Analysis
inferred← citeshttps://doi.org/10.5772/intechopen.1004414openalex-snapshot
recent advances in machine learning methodologies for LIBS quantitative analysis
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