Enhancing microalgae classification accuracy in marine ecosystems through convolutional neural networks and support vector machines
Yazarlar (6)
Mesut Ersin Sonmez Karamanoğlu Mehmetbey Üniversitesi, Türkiye
Numan Emre Gumus Karamanoğlu Mehmetbey Üniversitesi, Türkiye
Dr. Öğr. Üyesi Numan ECZACIOĞLU Karamanoğlu Mehmetbey Üniversitesi, Türkiye
Elif Eker Develi Mersin Üniversitesi, Türkiye
Kamile Yucel Karatay Üniversitesi, Türkiye
Huseyin Bekir Yildiz Bartin Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Marine Pollution Bulletin (Q1) | ||
| Dergi ISSN | 0025-326X Dergi Bilgileri (2024) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 08-2024 |
| Cilt / Sayı / Sayfa | 205 / 1 / – | DOI | 10.1016/j.marpolbul.2024.116616 |
| Makale Linki | https://doi.org/10.1016/j.marpolbul.2024.116616 | ||
| UAK Araştırma Alanları |
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| Özet |
| 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 ... |
| Anahtar Kelimeler |
| Marine Pollution |
Science Direct
Enhancing microalgae classification accuracy in marine ecosystems through convolutional neural networks and support vector machines
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Yöksis | 6 |
| Web of Science | 20 |
| Scopus | 29 |
| Google Scholar | 30 |