<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>2441</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>9</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Comparison of Two Classification Methods Trained with FD-FLIM Data to Identify and Distinguish Plastics from Environmental Materials</title>
    <abstract language="eng">Previous research on identifying plastic types and differentiating plastics from environmental material is promising by utilizing the specific fluorescence lifetime, but the evaluation still has to be automated. Therefore, an automated Gaussian analysis is developed for evaluating frequency-domain fluorescence lifetime images of plastics and environmental materials. Furthermore, we applied a “Multilayer Perceptron” and “Random Forest Classifier” to the data resulting from the Gaussian analysis of the frequency domain fluorescence lifetime imaging microscopy data. The classification results show high F1-scores, whereby the best “Multilayer Perceptron” and “Random Forest Classifier” achieved an F1-score of 90%. Thus, identifying and differentiating plastics and environmental materials is possible by applying a “Multilayer Perceptron” or “Random Forest Classifier” to the Gaussian-analyzed imaged fluorescence lifetime data.</abstract>
    <parentTitle language="eng">2023 International Joint Conference on Neural Networks (IJCNN)</parentTitle>
    <identifier type="doi">10.1109/IJCNN54540.2023.10191054</identifier>
    <enrichment key="opus.import.data">@inproceedingswohlschlager_comparison_2023, title = Comparison of Two Classification Methods Trained with FD-FLIM Data to Identify and Distinguish Plastics from Environmental Materials, doi = 10.1109/IJCNN54540.2023.10191054, booktitle = 2023 International Joint Conference on Neural Networks (IJCNN), author = Wohlschläger, Maximilian and Leiter, Nina and Dietlmeier, Maximilian and Löder, Martin G.J. and Versen, Martin and Laforsch, Christian, year = 2023, keywords = Fluorescence, Microscopy, Plastics, FD-FLIM, Neural networks, Environment, Frequency-domain analysis, Gaussian analysis, MLP, Multilayer perceptrons, Random forests, RFC, pages = 1–9,</enrichment>
    <enrichment key="opus.import.dataHash">md5:e9b53ffef54730e6c949ed39d9e70201</enrichment>
    <enrichment key="opus.import.date">2024-05-27T12:38:19+00:00</enrichment>
    <enrichment key="opus.import.file">/tmp/php7IG1AD</enrichment>
    <enrichment key="opus.import.format">bibtex</enrichment>
    <enrichment key="opus.import.id">66547ebb787039.90003661</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>Maximilian Wohlschläger</author>
    <author>Nina Leiter</author>
    <author>Maximilian Dietlmeier</author>
    <author>Martin G.J. Löder</author>
    <author>Martin Versen</author>
    <author>Christian Laforsch</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Fluorescence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Microscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Plastics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>FD-FLIM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Environment</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Frequency-domain analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Gaussian analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MLP</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multilayer perceptrons</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Random forests</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>RFC</value>
    </subject>
  </doc>
</export-example>
