Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313397902> ?p ?o ?g. }
Showing items 1 to 83 of
83
with 100 items per page.
- W4313397902 endingPage "122272" @default.
- W4313397902 startingPage "122272" @default.
- W4313397902 abstract "Quick identification of paper types for customs is extremely crucial. Although there are a variety of researches focus on the discrimination of paper, these techniques either require complex preprocessing or large-scale instruments, which are not suitable for customs environments. In this study, we predicted the type of customs paper by using a Micro-NIR spectrometer, and compared the results with Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy (ATR-FTIR). Four different classification algorithms, including linear and non-linear classifiers: K-nearest neighbor (KNN), soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA), and least squares-support vector machine (LS-SVM) were employed to classify the type of paper. 20 groups of datasets were selected by Monte Carlo sampling. For Micro-NIR data, the performances of KNN and LS-SVM were outstanding than SIMCA and PLS-DA, with the average accuracies 96.06% and 98.91%, respectively. The outcome of SIMCA and PLS-DA were similar, with the average accuracies 93.00% and 93.97%. Based on the standard derivation, the best stability of models was LS-SVM (1.06%), followed by PLS-DA (1.12%), KNN (1.22%) and SIMCA (3.07%). Compared with ATR-FTIR, the effects of Micro-NIR were better, which were embodies in the better KNN and SIMCA models, and the comparable LS-SVM model. The result demonstrated that the Micro-NIR combined with machine learning algorithms was an effective method to classify the type of customs paper efficiently and quickly, even better than ATR-FTIR." @default.
- W4313397902 created "2023-01-06" @default.
- W4313397902 creator A5000082239 @default.
- W4313397902 creator A5039373429 @default.
- W4313397902 creator A5061706893 @default.
- W4313397902 date "2023-04-01" @default.
- W4313397902 modified "2023-09-26" @default.
- W4313397902 title "Rapid analysis the type of customs paper using Micro-NIR spectrometers and machine learning algorithms" @default.
- W4313397902 cites W1958754280 @default.
- W4313397902 cites W1963953542 @default.
- W4313397902 cites W1968542804 @default.
- W4313397902 cites W1973542658 @default.
- W4313397902 cites W1983417063 @default.
- W4313397902 cites W1996170512 @default.
- W4313397902 cites W2001892012 @default.
- W4313397902 cites W2023908253 @default.
- W4313397902 cites W2049254925 @default.
- W4313397902 cites W2080186841 @default.
- W4313397902 cites W2084136177 @default.
- W4313397902 cites W2094991617 @default.
- W4313397902 cites W2136132422 @default.
- W4313397902 cites W2471193151 @default.
- W4313397902 cites W2481555887 @default.
- W4313397902 cites W2796869941 @default.
- W4313397902 cites W2894750004 @default.
- W4313397902 doi "https://doi.org/10.1016/j.saa.2022.122272" @default.
- W4313397902 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36592592" @default.
- W4313397902 hasPublicationYear "2023" @default.
- W4313397902 type Work @default.
- W4313397902 citedByCount "1" @default.
- W4313397902 countsByYear W43133979022023 @default.
- W4313397902 crossrefType "journal-article" @default.
- W4313397902 hasAuthorship W4313397902A5000082239 @default.
- W4313397902 hasAuthorship W4313397902A5039373429 @default.
- W4313397902 hasAuthorship W4313397902A5061706893 @default.
- W4313397902 hasConcept C10551718 @default.
- W4313397902 hasConcept C11413529 @default.
- W4313397902 hasConcept C119857082 @default.
- W4313397902 hasConcept C120665830 @default.
- W4313397902 hasConcept C121332964 @default.
- W4313397902 hasConcept C12267149 @default.
- W4313397902 hasConcept C153180895 @default.
- W4313397902 hasConcept C154945302 @default.
- W4313397902 hasConcept C22354355 @default.
- W4313397902 hasConcept C33923547 @default.
- W4313397902 hasConcept C34736171 @default.
- W4313397902 hasConcept C41008148 @default.
- W4313397902 hasConcept C43571822 @default.
- W4313397902 hasConcept C69738355 @default.
- W4313397902 hasConceptScore W4313397902C10551718 @default.
- W4313397902 hasConceptScore W4313397902C11413529 @default.
- W4313397902 hasConceptScore W4313397902C119857082 @default.
- W4313397902 hasConceptScore W4313397902C120665830 @default.
- W4313397902 hasConceptScore W4313397902C121332964 @default.
- W4313397902 hasConceptScore W4313397902C12267149 @default.
- W4313397902 hasConceptScore W4313397902C153180895 @default.
- W4313397902 hasConceptScore W4313397902C154945302 @default.
- W4313397902 hasConceptScore W4313397902C22354355 @default.
- W4313397902 hasConceptScore W4313397902C33923547 @default.
- W4313397902 hasConceptScore W4313397902C34736171 @default.
- W4313397902 hasConceptScore W4313397902C41008148 @default.
- W4313397902 hasConceptScore W4313397902C43571822 @default.
- W4313397902 hasConceptScore W4313397902C69738355 @default.
- W4313397902 hasLocation W43133979021 @default.
- W4313397902 hasLocation W43133979022 @default.
- W4313397902 hasOpenAccess W4313397902 @default.
- W4313397902 hasPrimaryLocation W43133979021 @default.
- W4313397902 hasRelatedWork W1537282076 @default.
- W4313397902 hasRelatedWork W1967587796 @default.
- W4313397902 hasRelatedWork W2122259043 @default.
- W4313397902 hasRelatedWork W2126100045 @default.
- W4313397902 hasRelatedWork W2138603172 @default.
- W4313397902 hasRelatedWork W2376270290 @default.
- W4313397902 hasRelatedWork W2380927352 @default.
- W4313397902 hasRelatedWork W2889453578 @default.
- W4313397902 hasRelatedWork W3162160273 @default.
- W4313397902 hasRelatedWork W3215304286 @default.
- W4313397902 hasVolume "290" @default.
- W4313397902 isParatext "false" @default.
- W4313397902 isRetracted "false" @default.
- W4313397902 workType "article" @default.