Su Altı Görüntülerinden Nesne Tespiti

Kilinç, Ekrem Eşref; Metlek, Sedat

doi:10.31590/ejosat.873540

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…[35] By the same researcher, this value for S. sicula dichloromethane extract was recorded as 3.34 � 0.01 μg/ml. [36] These values were lower than our recorded results (IC 50 = 22.98-36.88 μg/ml) for tested Scabiosa methanol extracts. The IC 50 values of S. columbaria subsp.…”

Section: Antioxidant Activity Analysis Resultscontrasting

confidence: 74%

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Investigation of Phytochemical, Antioxidant and Antidiabetic Potentials of Scabiosa L. (Caprifoliaceae) Species with Chemometric Methods

Albayrak,

Aksoy,

Yilmaz

et al. 2024

Chemistry & Biodiversity

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In this research, the total phenolic and flavonoid amounts, phenolic compositions, in vitro antioxidant, antibacterial and antidiabetic properties of the methanol extracts obtained from Scabiosa L. (Caprifoliaceae) species distributed in the flora of Türkiye were investigated using chemometric methods. For this purpose, principal component (PCA) and agglomerative hierarchical clustering analysis were performed as chemometric methods. Chlorogenic acid, quinic acid and cyranoside were determined in the extracts. According to chemometric analysis, S. columbaria subsp. ochroleuca var. ochroleuca and S. triniifolia species were found to be valuable in terms of methanol extract yields, total phenolic and flavonoid contents, antioxidant and antidiabetic activities while S. columbaria subsp. ochroleuca var. webbiana species were found to be valuable in terms of phenolic composition. The methanol extracts of Scabiosa species showed high antioxidant activity, with high phenolic and flavonoid contents. Among the tested 13 bacteria, Scabiosa extracts showed only low activity against Klebsiella pneumoniae, Streptococcus pneumoniae and Pseudomonas aeruginosa. The extracts showed high α‐amylase and α‐glucosidase inhibitory activity. The results show that Scabiosa methanol extracts may be a source of alternative antioxidants that may be beneficial in slowing or preventing the progression of various oxidative stress‐related diseases.

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Section: Antioxidant Activity Analysis Resultscontrasting

confidence: 74%

“…Same authors reported that dichloromethane extract of S. sicula contain luteolin-6-C-glucoside, luteolin-7-O-glucoside, caffeic acid, ursolic acid, 4-O-caffeoylquinic acid and 3,5-O-dicaffeoylquinic acid. [36] The methanol extracts of S. columbaria subsp. columbaria var.…”

Section: Phytochemical Analysis Resultsmentioning

confidence: 99%

Investigation of Phytochemical, Antioxidant and Antidiabetic Potentials of Scabiosa L. (Caprifoliaceae) Species with Chemometric Methods

Albayrak,

Aksoy,

Yilmaz

et al. 2024

Chemistry & Biodiversity

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Prediction of Fish Species With Deep Learning

Kayaalp

Metlek

2021

International Journal of 3D Printing Technologies and Digital Industry

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Beslenmemizde önemli bir yere sahip olan deniz ürünleri, mükemmel bir vitamin ve mineral kaynağıdır. Protein kaynakları içerisinde sindirilmesi oldukça kolay olan deniz mahsulleri, diğer yüksek proteinli kaynaklara göre oldukça az zararlı yağ içermektedir. Balıklarda bulunan omega-3 gibi yağ asitlerinin, insan sağlığını olumsuz etkileyen kalp ve damar hastalıklarından, diyabet ve kanser gibi daha birçok hastalığa iyi geldiği bilinmektedir. Bunun yanı sıra az da olsa insan sağlığını tehdit edebilecek balık türleri de bulunmaktadır. Gerçekleştirilen çalışma ile günümüzün popüler makine öğrenme yöntemlerinden birisi olan derin öğrenme algoritmaları vasıtasıyla, insanoğlunun beslenmesinde önemli bir role sahip olan balıkların, görüntüleri üzerinden türlerinin tahmin edilmesi amaçlanmıştır. Bu amaç doğrultusunda geliştirilen uygulamada, farklı ortamlardan elde edilen 4410 adet balık görüntüsü kullanılmıştır. Kullanılan balık görüntüleri, 483 adet farklı türden oluşmakla beraber, farklı koşullar altında elde edilen gerçek balık görüntüleridir. Çalışmada hazırlanan derin öğrenme algoritmasının eğitim ve test işlemleri için "QUT FISH" veri seti kullanılmıştır. Derin öğrenme yöntemlerinde sıkça kullanılan, Evrişimsel sinir ağları yöntemi ile veri setindeki görüntülerden, balık türlerine ait öznitelikler çıkartılmıştır. Çıkartılan bu öznitelikler çok katmanlı bir yapay sinir ağı modeli ile sınıflandırılmıştır. Yapılan çalışma ile sınıflandırma başarısı olarak %73,72 değeri elde edilmiştir.

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A HOG Feature Extractor and KNN-Based Method for Underwater Image Classification

Demir,

Yaman

2024

Firat University Journal of Experimental and Computational Engineering

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Underwater garbage affects the life of marine creatures and the entire ecosystem. Detecting underwater garbage is an important research area. In this study, a method is proposed to detect underwater garbage. The open-access Trash-ICRA19 dataset was used to implement the proposed method. The data set cropping process was applied and a data set consisting of 11060 images in total was obtained. These images were converted to 200×200 pixels using preprocessing. By applying the Directed Gradient Histogram (HOG) algorithm, 11060×900 feature vectors were obtained. The resulting feature vectors were then calculated using KNN (K Nearest Neighbor Algorithm), DT (Decision Tree), LD (Linear Discriminant), NB (Naive Bayes), and SVM (Support Vector Machine) classifiers. The results obtained showed that 97.78% accuracy was obtained when the KNN classifier was used in this method. The use of only feature extractors and classifiers in the proposed method shows that the method is lightweight. It has low computational complexity compared to existing studies in the literature. Moreover, according to its performance results, it is more successful than the methods in the literature.

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Su Altı Görüntülerinden Nesne Tespiti

Cited by 3 publications

References 13 publications

Investigation of Phytochemical, Antioxidant and Antidiabetic Potentials of Scabiosa L. (Caprifoliaceae) Species with Chemometric Methods

Investigation of Phytochemical, Antioxidant and Antidiabetic Potentials of Scabiosa L. (Caprifoliaceae) Species with Chemometric Methods

Prediction of Fish Species With Deep Learning

A HOG Feature Extractor and KNN-Based Method for Underwater Image Classification

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