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    <id>2042</id>
    <completedYear>2022</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>6</pageLast>
    <pageNumber>6</pageNumber>
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    <title language="eng">Development of a neural network to identify plastics using Fluorescence Lifetime Imaging Microscopy</title>
    <abstract language="eng">Plastics have become a major part of human’s daily life. An uncontrolled usage of plastic leads to an accumulation in the environment posing a threat to flora and fauna, if not recycled correctly. The correct sorting and recycling of the most commonly available plastic types and an identification of plastic in the environment are important. Fluorescence lifetime imaging microscopy shows a high potential in sorting and identifying plastic types. A data-based and an image-based classification are investigated using python programming language to demonstrate the potential of a neural network based on fluorescence lifetime images to identify plastic types. The results indicate that the data-based classification has a higher identification accuracy compared to the image-based classification.</abstract>
    <parentTitle language="eng">2022 IEEE Sensors Applications Symposium (SAS)</parentTitle>
    <identifier type="doi">10.1109/SAS54819.2022.9881372</identifier>
    <enrichment key="opus.import.data">@INPROCEEDINGS9881372, author=Maniyattu, Georgekutty Jose and Geegy, Eldho and Leiter, Nina and Wohlschläger, Maximilian and Versen, Martin and Laforsch, Christian, booktitle=2022 IEEE Sensors Applications Symposium (SAS), title=Development of a neural network to identify plastics using Fluorescence Lifetime Imaging Microscopy, year=2022, volume=, number=, pages=1-6, doi=10.1109/SAS54819.2022.9881372</enrichment>
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    <author>Georgekutty Jose Maniyattu</author>
    <author>Eldho Geegy</author>
    <author>Nina Leiter</author>
    <author>Maximilian Wohlschläger</author>
    <author>Martin Versen</author>
    <author>Christian Laforsch</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>fluorescence lifetime imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>FLIM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>plastic identification</value>
    </subject>
  </doc>
  <doc>
    <id>2437</id>
    <completedYear>2023</completedYear>
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    <language>eng</language>
    <pageFirst>31</pageFirst>
    <pageLast>37</pageLast>
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    <edition/>
    <issue>3</issue>
    <volume>25</volume>
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    <title language="eng">Multilayer Perceptron Development to Identify Plastics Using Fluorescence Lifetime Imaging Microscopy</title>
    <abstract language="eng">Existing plastic analysis techniques such as Fourier transform infrared spectroscopy and Raman spectroscopy are problematic because samples must be anhydrous and identification can be hindered by additives. This article describes a new approach that has been successfully demonstrated in which plastics can be classified by neural networks that are trained, validated, and tested by frequency domain fluorescence lifetime imaging microscopy measurements.</abstract>
    <parentTitle language="eng">EDFA Technical Articles</parentTitle>
    <identifier type="doi">10.31399/asm.edfa.2023-3.p031</identifier>
    <enrichment key="opus.import.data">@articlemaniyattu_multilayer_2023, title = Multilayer Perceptron Development to Identify Plastics Using Fluorescence Lifetime Imaging Microscopy, volume = 25, issn = 1537-0755, url = https://doi.org/10.31399/asm.edfa.2023-3.p031, doi = 10.31399/asm.edfa.2023-3.p031, abstract = Existing plastic analysis techniques such as Fourier transform infrared spectroscopy and Raman spectroscopy are problematic because samples must be anhydrous and identification can be hindered by additives. This article describes a new approach that has been successfully demonstrated in which plastics can be classified by neural networks that are trained, validated, and tested by frequency domain fluorescence lifetime imaging microscopy measurements., number = 3, journal = EDFA Technical Articles, author = Maniyattu, Georgekutty Jose and Geegy, Eldho and Wohlschläger, Maximilian and Leiter, Nina and Versen, Martin and Laforsch, Christian, month = aug, year = 2023, note = _eprint: https://dl.asminternational.org/edfa-tech/article-pdf/25/3/31/655168/edfa.2023-3.p031.pdf, pages = 31–37,</enrichment>
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    <author>Georgekutty Jose Maniyattu</author>
    <author>Eldho Geegy</author>
    <author>Maximilian Wohlschläger</author>
    <author>Nina Leiter</author>
    <author>Martin Versen</author>
    <author>Christian Laforsch</author>
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