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    An effective Turkey marble classification system: Convolutional neural network with genetic algorithm -wavelet kernel - Extreme learning machine
    (IIETA International Information and Engineering Technology Association, 2021) Avcı, Derya; Sert, Eser
    Marble is one of the most popular decorative elements. Marble quality varies depending on its vein patterns and color, which are the two most important factors affecting marble quality and class. The manual classification of marbles is likely to lead to various mistakes due to different optical illusions. However, computer vision minimizes these mistakes thanks to artificial intelligence and machine learning. The present study proposes the Convolutional Neural Network- (CNN-) with genetic algorithm- (GA) Wavelet Kernel- (WK-) Extreme Learning Machine (ELM) (CNN–GA-WK-ELM) approach. Using CNN architectures such as AlexNet, VGG-19, SqueezeNet, and ResNet-50, the proposed approach obtained 4 different feature vectors from 10 different marble images. Later, Genetic Algorithm (GA) was used to optimize adjustable parameters, i.e. k, 1, and m, and hidden layer neuron number in Wavelet Kernel (WK) – Extreme Learning Machine (ELM) and to increase the performance of ELM. Finally, 4 different feature vector parameters were optimized and classified using the WK-ELM classifier. The proposed CNN–GA-WK-ELM yielded an accuracy rate of 98.20%, 96.40%, 96.20%, and 95.60% using AlexNet, SequeezeNet, VGG-19, and ResNet-50, respectively.
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    Ensemble Residual Network Features and Cubic-SVM Based Tomato Leaves Disease Classification System
    (International Information and Engineering Technology Association, 2022) Özyurt, Fatih; Sert, Eser; Avcı, Derya
    The need for automatic disease detection applications that can help farmers in the detection of agricultural product diseases is increasing day by day. Convolutional Neural Network (CNN) is a very popular field in image processing, recognition, and classification. It is seen that CNN architectures are used in the determination of agricultural products. In this study, 3 different ResNet architectures of the features automatically are used in the detection of tomato diseases. The most efficient features obtained from these architectures have been obtained by the NCA algorithm again. The features obtained have been trained with the Cubic SVM machine learning algorithm. Tomato leaves belonging to a total of 10 classes have been trained at 80% and a test performance rate of 98.2% has been achieved.
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    Hurricane-Faster R-CNN-JS: Hurricane detection with faster R-CNN using artificial Jellyfish Search (JS) optimizer
    (SPRINGER, 2022) Kızıloluk, Soner; Sert, Eser
    A hurricane is a type of storm called tropical cyclone (TC) and is likely to lead to severe storms and heavy rains. An early detection of hurricanes using satellite images can alarm people about upcoming disasters and thus minimize any casualties and material losses. Faster R-CNN is one of the most popular and recent object detection approaches. In the present study, AlexNet hyperparameters, which is a CNN model used as a feature extractor in Faster R-CNN, were optimized using artificial Jellyfish Search (JS), which is a recent algorithm, in order to propose a Faster R-CNN with a higher performance. The proposed approach is called Hurricane-Faster R-CNN-JS, since it is used as an early hurricane detection approach on satellite images before these hurricanes reach the land. The results of the present study demonstrated that hyperparameter optimization increased the detection performance of the proposed approach by 10% compared to AlexNet without optimized hyperparameters. As feature extractors of Faster R-CNN, the present study benefited from various architectures such as MobileNet-V2, GoogLeNet, AlexNet, ResNet 18, ResNet 50, VGG-16 and VGG-19 without any optimized hyperparameters to compare them with the proposed approach. It was observed that Average Precision (AP) of Hurricane-Faster R-CNN-JS was 97.39%, which was a remarkably higher AP level compared to other approaches.
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    A new 3D segmentation approach using extreme learning machine algorithm and morphological operations
    (Elsevier, 2020) Kaya, Ertuğrul; Sert, Eser
    Segmentation is one of the most crucial steps of image processing. Because 3D images contain depth information, they have gradually gained importance for numerical systems in image analysis. In the present study, a new 3D segmentation method based on extreme learning machine and morphological operations (3DS-ELM) is proposed. The present study benefits from extreme learning machine (ELM) algorithm, which is a novel and fast learning algorithm for single-hidden layer feedforward networks (SLFNs), for training objects. Because a 3D model contains many points, direct segmentation on a 3D model is time-consuming and causes problems in the segmentation process, the proposed approach minimizes these problems and offers a quick and high-performance 3D segmentation method that can be used in various industrial fields. The proposed 3DS-ELM was compared with different approaches in order to analyze its 3D segmentation performance. Experimental studies proved that the proposed 3DS-ELM performed better than other approaches.
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    UC-merced image classification with CNN feature reduction using wavelet entropy optimized with genetic algorithm
    (International Information and Engineering Technology Association, 2020) Özyurt, Fatih; Ava, Engin; Sert, Eser
    The classification of high-resolution and remote sensed terrain images with high accuracy is one of the greatest challenges in machine learning. In the present study, a novel CNN feature reduction using Wavelet Entropy Optimized with Genetic Algorithm (GA-WEE-CNN) method was used for remote sensing images classification. The optimal wavelet family and optimal value of the parameters of the Wavelet Sure Entropy (WSE), Wavelet Nom Entropy (WNE), and Wavelet Threshold Entropy (WTE) were calculated, and given to classifiers such as K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). The efficiency of the proposed hybrid method was tested using the UC-Merced dataset. 80% of the data were used as training data, and a performance rate of 98.8% was achieved with SVM classifier, which has been the highest ratio compared to all studies using same dataset so far with only 18 features. These results proved the advantage of the proposed method.