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

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dc.authoridGUMUS, NUMAN EMRE/0000-0001-8275-3871
dc.contributor.authorSonmez, Mesut Ersin
dc.contributor.authorGumus, Numan Emre
dc.contributor.authorEczacioglu, Numan
dc.contributor.authorDeveli, Elif Eker
dc.contributor.authorYucel, Kamile
dc.contributor.authorYildiz, Huseyin Bekir
dc.date.accessioned2025-01-12T17:19:43Z
dc.date.available2025-01-12T17:19:43Z
dc.date.issued2024
dc.departmentKaramanoğlu Mehmetbey Üniversitesi
dc.description.abstractAccurately 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.
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [121G114]; TUBITAK
dc.description.sponsorshipThis study was supported by Scientific and Technological Research Council of Turkey (TUBITAK) under the Grant Number 121G114. The authors thank to TUBITAK for their supports.
dc.identifier.doi10.1016/j.marpolbul.2024.116616
dc.identifier.issn0025-326X
dc.identifier.issn1879-3363
dc.identifier.pmid38936001
dc.identifier.scopus2-s2.0-85196950111
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.marpolbul.2024.116616
dc.identifier.urihttps://hdl.handle.net/11492/10184
dc.identifier.volume205
dc.identifier.wosWOS:001261522200001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Sceince
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofMarine Pollution Bulletin
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20250111
dc.subjectMicroalgae classification
dc.subjectMucilage monitoring
dc.subjectCNN
dc.subjectSVM
dc.subjectMobileNet
dc.subjectGoogleNet
dc.titleEnhancing microalgae classification accuracy in marine ecosystems through convolutional neural networks and support vector machines
dc.typeArticle

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