DES-Pat: A novel DES pattern-based propeller recognition method using underwater acoustical sounds

Yaman, Orhan; Tuncer, Türker; Taşar, Beyda

doi:10.1016/j.apacoust.2020.107859

Cited by 26 publications

(6 citation statements)

References 26 publications

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“…For this reason, seven different metric values were calculated in this study. These criteria are precision, sensitivity, accuracy, specificity, F1-score, Matthews correlation coefficient, and Kappa [54]. Table 3 contains the formulas and brief explanations of these criteria.…”

Section: Performance Evaluation Methodsmentioning

confidence: 99%

SkinCancerNet: Automated Classification of Skin Lesion Using Deep Transfer Learning Method

Taşar¹

2023

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Skin cancer has become one of the most common diseases due to the depletion of the ozone layer and the decrease in its protection. Detection and classification of skin cancer in the early stages of its development allows patients to receive appropriate treatment quickly. In this article, a modified CNN framework based on transfer learning is proposed for the classification of skin lesions from skin dermoscopy images. In the proposed framework, pretrained CNN architectures are used. VGG16, ResNet50, DeneNet121, MobileNet, and Xception models were pre-trained using ImageNet images and training weights. In the study training and tests were performed on the HAM10000 skin lesions data set. The classification accuracy of the modified DenseNet121, VGGNet16, ResNet50, MobileNet, and Xception models were calculated as 94.29%, 93.28%, 87.10%, 83.10%, and 80.05% respectively. It was observed that the accuracy success of the proposed transfer learning framework in skin lesion type classification surpasses classical deep learning architectures.

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Section: Performance Evaluation Methodsmentioning

confidence: 99%

SkinCancerNet: Automated Classification of Skin Lesion Using Deep Transfer Learning Method

Taşar¹

2023

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“…Decision trees are one of the most extensively used methods in classification models. Because it is a simpler technique to configure and comprehend, gives model transparency, and has a visual presentation [23].…”

Section: B Classification Methodsmentioning

confidence: 99%

“…The confusion matrix is a numerical table used to demonstrate the classification model output effects on the test data known from the goal labels. Performance evaluation metrics used in this study is given in Table 5 [23].…”

Section: Performance Evaluation Methods (Pem)mentioning

confidence: 99%

Çelik Levha Arıza Tespiti için Makine Öğrenimi Algoritmalarının Karşılaştırmalı Analizi

TAŞAR

2022

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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Metaller, modern zamanların en önemli yapı malzemelerinden biridir. Özellikle yassı metal sacın üretim ve işleme süreci oldukça hassastır. Üretim sürecinin kontrolü sadece ara ürünlerin değil, aynı zamanda son ürünlerinde kalitesini etkiler. Çelik levha yüzeylerinde oluşan hataların erken tespiti, endüstriyel üretimde önemli bir görevdir. Geleneksel olarak süreç kontrolü ve hata tespiti uzman kişiler tarafından manuel olarak yapılmaktadır. Ancak bu yöntem hem zaman hem de maliyet açısından uygun değildir. Sanayi devrimi IR 4.0 ile ürünlerde hata tespit problemlerini çözmek için makine öğrenimi (ML) teknikleri geliştirilmiştir. Bu çalışma, çelik yüzeyde üretim esnasında oluşabilecek altı farklı hata sınıfının tespiti için temel makine öğrenme yöntemleri geliştirmeye odaklanmıştır. Sınıflandırma problemi için beş standart ML modeli: LD, KNN, DT, SVM, RF ve bir derin öğrenme (DNN) modeli: tek boyutlu DNN geliştirilmiştir. Deneysel veri seti olarak UCI çelik plaka deformasyon veri seti kullanılmıştır. Yöntemlerin başarısını tespit etmek için beş performans kriteri: Doğruluk, Duyarlılık, Özgüllük, Kesinlik, F1 değeri kullanılmıştır. LD, KNN, DT, SVM, RF ve DNN sınıflandırma yöntemlerinin başarı oranları sırasıyla 90.136%, 91.780%, 93.013%, 93.287%, 95.479%, 96.986% olarak elde edilmiştir. Sonuçlar, makina öğrenmesi yaklaşımının çelik levha arıza teşhis problemindeki önemli etkisini gösterilmiştir.

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“…However, ReliefF cannot select the best feature vector without a trial-and-error method. Therefore, IRF was presented by Tuncer et al [ 39 ] in 2021. IRF can select the best feature vector automatically, and it is a parametric selector.…”

Section: Methodsmentioning

confidence: 99%

Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images

Barua

Chan

Doğan

et al. 2021

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Optical coherence tomography (OCT) images coupled with many learning techniques have been developed to diagnose retinal disorders. This work aims to develop a novel framework for extracting deep features from 18 pre-trained convolutional neural networks (CNN) and to attain high performance using OCT images. In this work, we have developed a new framework for automated detection of retinal disorders using transfer learning. This model consists of three phases: deep fused and multilevel feature extraction, using 18 pre-trained networks and tent maximal pooling, feature selection with ReliefF, and classification using the optimized classifier. The novelty of this proposed framework is the feature generation using widely used CNNs and to select the most suitable features for classification. The extracted features using our proposed intelligent feature extractor are fed to iterative ReliefF (IRF) to automatically select the best feature vector. The quadratic support vector machine (QSVM) is utilized as a classifier in this work. We have developed our model using two public OCT image datasets, and they are named database 1 (DB1) and database 2 (DB2). The proposed framework can attain 97.40% and 100% classification accuracies using the two OCT datasets, DB1 and DB2, respectively. These results illustrate the success of our model.

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DES-Pat: A novel DES pattern-based propeller recognition method using underwater acoustical sounds

Cited by 26 publications

References 26 publications

SkinCancerNet: Automated Classification of Skin Lesion Using Deep Transfer Learning Method

SkinCancerNet: Automated Classification of Skin Lesion Using Deep Transfer Learning Method

Çelik Levha Arıza Tespiti için Makine Öğrenimi Algoritmalarının Karşılaştırmalı Analizi

Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images

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