Enhancing microalgae classification accuracy in marine ecosystems through convolutional neural networks and support vector machines

  • Yazar/lar SÖNMEZ, Mesut Ersin
    GÜMÜŞ, Numan Emre
    ECZACIOĞLU, Numan
    EKER DEVELİ, Elif
    YÜCEL, Kamile
    YILDIZ, Hüseyin Bekir
  • Yayın Türü Makale
  • Yayın Tarihi 2024
  • DOI Numarası 10.1016/j.marpolbul.2024.116616
  • Yayıncı Elsevier
  • Dergi Adı MARINE POLLUTION BULLETIN 116616, ( 205 ), pp.1 - 11

Accurately classifying microalgae species is vital for monitoring marine ecosystems and managing the emergence of marine mucilage, which is crucial for monitoring mucilage phenomena in marine environments. Traditional methods have been inadequate due to time-consuming processes and the need for expert knowledge. The purpose of this article is to employ convolutional neural networks (CNNs) and support vector machines (SVMs) to improve classification accuracy and efficiency. By employing advanced computational techniques, including MobileNet and GoogleNet models, alongside SVM classification, the study demonstrates significant advancements over conventional identification methods. In the classification of a dataset consisting of 7820 images using four different SVM kernel functions, the linear kernel achieved the highest success rate at 98.79 %. It is followed by the RBF kernel at 98.73 %, the polynomial kernel at 97.84 %, and the sigmoid kernel at 97.20 %. This research not only provides a methodological framework for future studies in marine biodiversity monitoring but also highlights the potential for real-time applications in ecological conservation and understanding mucilage dynamics amidst climate change and environmental pollution.

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26.06.2024 tarihinden bu yana
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25 Temmuz 2024 05:05
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Detaylı Görünüm
ISSN
(dc.identifier.issn)
1879-3363
Yayıncı
(dc.publisher)
Elsevier
Haklar
(dc.rights)
Open access
Eser Adı
(dc.title)
Enhancing microalgae classification accuracy in marine ecosystems through convolutional neural networks and support vector machines
Özet
(dc.description.abstract)
Accurately classifying microalgae species is vital for monitoring marine ecosystems and managing the emergence of marine mucilage, which is crucial for monitoring mucilage phenomena in marine environments. Traditional methods have been inadequate due to time-consuming processes and the need for expert knowledge. The purpose of this article is to employ convolutional neural networks (CNNs) and support vector machines (SVMs) to improve classification accuracy and efficiency. By employing advanced computational techniques, including MobileNet and GoogleNet models, alongside SVM classification, the study demonstrates significant advancements over conventional identification methods. In the classification of a dataset consisting of 7820 images using four different SVM kernel functions, the linear kernel achieved the highest success rate at 98.79 %. It is followed by the RBF kernel at 98.73 %, the polynomial kernel at 97.84 %, and the sigmoid kernel at 97.20 %. This research not only provides a methodological framework for future studies in marine biodiversity monitoring but also highlights the potential for real-time applications in ecological conservation and understanding mucilage dynamics amidst climate change and environmental pollution.
Yayın Tarihi
(dc.date.issued)
2024
Yayın Dili
(dc.language.iso)
eng
Yayının ilk Sayfa Sayısı
(dc.identifier.startpage)
1
Yayının son sayfa sayısı
(dc.identifier.endpage)
11
Dergi Adı
(dc.relation.journal)
MARINE POLLUTION BULLETIN
Dergi Sayısı
(dc.identifier.issue)
205
Dergi Cilt
(dc.identifier.volume)
116616
Yayın Türü
(dc.type)
Makale
Yazar/lar
(dc.contributor.author)
SÖNMEZ, Mesut Ersin
Yazar/lar
(dc.contributor.author)
GÜMÜŞ, Numan Emre
Yazar/lar
(dc.contributor.author)
ECZACIOĞLU, Numan
Yazar/lar
(dc.contributor.author)
EKER DEVELİ, Elif
Yazar/lar
(dc.contributor.author)
YÜCEL, Kamile
Yazar/lar
(dc.contributor.author)
YILDIZ, Hüseyin Bekir
DOI Numarası
(dc.identifier.doi)
10.1016/j.marpolbul.2024.116616
Atıf Dizini
(dc.source.database)
Wos
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