Attention improvement for data-driven analyzing fluorescence excitation-emission matrix spectra via...
Attention improvement for data-driven analyzing fluorescence excitation-emission matrix spectra via interpretable attention mechanism
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London: Nature Publishing Group UK
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English
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London: Nature Publishing Group UK
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Analyzing three-dimensional excitation-emission matrix (3D-EEM) spectra through machine learning models has drawn increasing attention, whereas the reliability of these machine learning models remains unclear due to their “black box” nature. In this study, the convolutional neural network (CNN) for classifying numbers of fluorescent components in 3...
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Attention improvement for data-driven analyzing fluorescence excitation-emission matrix spectra via interpretable attention mechanism
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TN_cdi_doaj_primary_oai_doaj_org_article_d626942565d24349b968e9ba6d666476
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_d626942565d24349b968e9ba6d666476
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ISSN
2059-7037
E-ISSN
2059-7037
DOI
10.1038/s41545-024-00367-w
