Yazar "Firat, Huseyin" seçeneğine göre listele

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    3D residual spatial-spectral convolution network for hyperspectral remote sensing image classification
    (Springer London Ltd, 2023) Firat, Huseyin; Asker, Mehmet Emin; Bayindir, Mehmet Ilyas; Hanbay, Davut
    Hyperspectral remote sensing images (HRSI) are 3D image cubes that contain hundreds of spectral bands and have two spatial dimensions and one spectral dimension. HRSI analysis are commonly used in a wide variety of applications such as object detection, precision agriculture and mining. HRSI classification purposes to assign each pixel in HRSI to a unique class. Deep learning is seen as an effective method to improve HRSI classification. In particular, convolutional neural networks (CNNs) are increasingly used in remote sensing field. In this study, a hybrid 3D residual spatial-spectral convolution network (3D-RSSCN) is proposed to extract deep spatiospectral features using 3D CNN and ResNet18 architecture. Simultaneously spatiospectral features extraction is provided using 3D CNN. In deeper CNNs, ResNet architecture is used to achieve higher classification performance as the number of layers increases. In addition, thanks to the ResNet architecture, problems such as degradation and vanishing gradient that may occur in deep networks are overcome. The high dimensionality of the HRSIs increases the computational complexity. Thus, most of studies apply dimension reduction as preprocessing. In the proposed study, principal component analysis (PCA) is used as the preprocessing step for optimum spectral band extraction. The proposed 3D-RSSCN method is tested with Indian pines, Pavia University and Salinas datasets and compared against various deep learning-based methods (SAE, RPNet, 2D CNN, 3D CNN, M3D CNN, HybridSN, FC3D CNN, SSRN, FuSENet, S3EResBoF). As a result of the applications, the best classification accuracy among these methods compared in all datasets is obtained with the proposed 3D-RSSCN. The proposed 3D-RSSCN method has the best accuracy and time performance in classifying.
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    Automated efficient traffic gesture recognition using swin transformer-based multi-input deep network with radar images
    (Springer London Ltd, 2025) Firat, Huseyin; Uzen, Huseyin; Atila, Orhan; Sengur, Abdulkadir
    Radar-based artificial intelligence (AI) applications have gained significant attention recently, spanning from fall detection to gesture recognition. The growing interest in this field has led to a shift towards deep convolutional networks, and transformers have emerged to address limitations in convolutional neural network methods, becoming increasingly popular in the AI community. In this paper, we present a novel hybrid approach for radar-based traffic hand gesture classification using transformers. Traffic hand gesture recognition (HGR) holds importance in AI applications, and our proposed three-phase approach addresses the efficiency and effectiveness of traffic HGR. In the initial phase, feature vectors are extracted from input radar images using the pre-trained DenseNet-121 model. These features are then consolidated by concatenating them to gather information from diverse radar sensors, followed by a patch extraction operation. The concatenated features from all inputs are processed in the Swin transformer block to facilitate further HGR. The classification stage involves sequential application of global average pooling, Dense, and Softmax layers. To assess the effectiveness of our method on ULM university radar dataset, we employ various performance metrics, including accuracy, precision, recall, and F1-score, achieving an average accuracy score of 90.54%. We compare this score with existing approaches to demonstrate the competitiveness of our proposed method.
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    Classification of Hyperspectral Images Using 3D CNN Based ResNet50
    (Ieee, 2021) Firat, Huseyin; Hanbay, Davut
    Hyperspectral images are images containing rich spectral and spatial information widely used in remote sensing applications. The development of deep learning techniques has had a significant impact on the classification of hyperspectral images. Different Convolutional Neural Network architectures have been used in many hyperspectral image analysis studies. However, the high dimensions of the hyperspectral images increased the computational complexity. For this reason, dimensionality reduction has been used in the preprocessing stage in many studies. Another difficulty encountered in hyperspectral image classification studies is the need to consider both spectral and spatial features. When deep spatial and spectral features are to be extracted, problems such as loss of gradient properties and degradation due to increased depth arise. In this study, the 3D convolutional neural network (CNN) based ResNet50 method is proposed to solve these problems encountered in hyperspectral studies and to extract sufficient spatial spectral properties from the network. Principal Component Analysis (PCA) was used to reduce spectral band excess. The proposed method has been applied to Pavia University and Salinas data sets. Overall accuracy, average accuracy and kappa values were used to measure the performance of the method. Calculated overall accuracy, average accuracy, and kappa values are 99.99% for the Pavia University data set, and while the overall accuracy and kappa values were 99.99% for the Salinas data set, the average accuracy value was 99.98%.
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    Comparison of 3D CNN based deep learning architectures using hyperspectral images
    (Gazi Univ, Fac Engineering Architecture, 2023) Firat, Huseyin; Hanbay, Davut
    Hyperspectral images (HSI) are 3-dimensional (3D) image cubes with two spatial and one spectral dimensions. The development of deep learning methods has had a significant impact on HSI classification. Especially convolutional neural network (CNN) based methods are getting more attention in this field. In this study, we make use of the deep learning architectures LeNet5, AlexNet, VGG16, GoogleNet and ResNet50, which are among the successful examples of CNN for the HSI classification problem. We use a 3D CNN-based hybrid approach when using these architectures. Because, using 3D CNN, spectral-spatial features are extracted simultaneously. In this case, the classification accuracy of HSIs is increased with the spectral-spatial-based deep learning architecture. However, in the proposed model, principal component analysis (PCA) is used as a preprocessing technique for optimal band extraction from HSIs. After applying PCA, 3D cubes are obtained by neighborhood extraction and given to the input of deep learning architectures. Indian pines, Salinas, Botswana and HyRANK-Loukia datasets were used to compare the classification performances of 3D CNN-based deep learning architectures. As a result of the applications, the best classification accuracy was obtained with VGG16 architectures in Indian pines dataset, ResNet50 in Botswana dataset, VGG16 in HyRANK-Loukia dataset, LeNet5 and VGG16 architectures in Salinas dataset.
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    An effective approach to the two-dimensional rectangular packing problem in the manufacturing industry
    (Pergamon-Elsevier Science Ltd, 2020) Firat, Huseyin; Alpaslan, Nuh
    The rectangle packing area minimization problem (RPAMP) has a wide range of applications in industry. The purpose of RPAMP is to ensure maximum use of the materials used in industries and minimize the trim loss. The yield from materials is significant issue. Because a small change in placement can result in a significant reduction in production costs and material savings. In this study, 2D-RPAMP, which is widely seen in the manufacturing industry, has been investigated. The solution approaches have been developed by using heuristic methods such as no-fit polygon and bottom-left fill and metaheuristic method such as Simulated Annealing (SA). In experimental studies, the bottom-left fill method is used for the placement process, no-fit polygon method is used in order to avoid overlapping between the parts placed, and the SA algorithm is employed for the faster and efficient packing process. As a result of experiments using the hybrid methodology consisting of the SA algorithm can reach reasonable level of the trim loss varies that is between 0,21% and 9,1% for most industry. The experimental results show the efficiency of the packing process with hybrid methodology.
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    Epilepsy Diagnosis from EEG Signals Using Continuous Wavelet Transform-Based Depthwise Convolutional Neural Network Model
    (Mdpi, 2025) Disli, Firat; Gedikpinar, Mehmet; Firat, Huseyin; Sengur, Abdulkadir; Guldemir, Hanifi; Koundal, Deepika
    Background/Objectives: Epilepsy is a prevalent neurological disorder characterized by seizures that significantly impact individuals and their social environments. Given the unpredictable nature of epileptic seizures, developing automated epilepsy diagnosis systems is increasingly important. Epilepsy diagnosis traditionally relies on analyzing EEG signals, with recent deep learning methods gaining prominence due to their ability to bypass manual feature extraction. Methods: This study proposes a continuous wavelet transform-based depthwise convolutional neural network (DCNN) for epilepsy diagnosis. The 35-channel EEG signals were transformed into 35-channel images using continuous wavelet transform. These images were then concatenated horizontally and vertically into a single image (seven rows by five columns) using Python's PIL library, which served as input for training the DCNN model. Results: The proposed model achieved impressive performance metrics on unseen test data: 95.99% accuracy, 94.27% sensitivity, 97.29% specificity, and 96.34% precision. Comparative analyses with previous studies and state-of-the-art models demonstrated the superior performance of the DCNN model and image concatenation technique. Conclusions: Unlike earlier works, this approach did not employ additional classifiers or feature selection algorithms. The developed model and image concatenation method offer a novel methodology for epilepsy diagnosis that can be extended to different datasets, potentially providing a valuable tool to support neurologists globally.
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    Hybrid 3D/2D Complete Inception Module and Convolutional Neural Network for Hyperspectral Remote Sensing Image Classification
    (Springer, 2023) Firat, Huseyin; Asker, Mehmet Emin; Bayindir, Mehmet Ilyas; Hanbay, Davut
    Classification in hyperspectral remote sensing images (HRSIs) is a challenging process in image analysis and one of the most popular topics. In recent years, many methods have been proposed to solve the HRSIs classification problem. Compared to traditional machine learning methods, deep learning, especially convolutional neural networks (CNNs), is commonly used in the classification of HRSIs. Deep learning-based methods based on CNNs show remarkable performance in HRSIs classification and greatly support the development of classification technology. In this study, a method in which the Hybrid 3D/2D Complete Inception module and the Hybrid 3D/2D CNN method are used together has been proposed to solve the HRSIs classification problem. In the proposed method, multi-level feature extraction is performed by using multiple convolution layers with the Inception module. This improves the performance of the network. Conventional CNN-based methods use 2D CNN for feature extraction. However, only spatial features are extracted with 2D CNN. 3D CNN is used to extract spatial-spectral features. However, 3D CNN is computationally complex. Therefore, in the proposed method, a hybrid approach is used by first using 3D CNN and then 2D CNN. This reduces computational complexity and extracts more spatial features. In addition, PCA is used as a preprocessing step for optimum spectral band extraction in the proposed method. The proposed method has been tested using Indian pines, Salinas, University of Pavia, HyRANK-Loukia and Houston datasets, which are frequently used in studies for HRSIs classification. The overall accuracy of the proposed method in these five datasets are 99.83%, 100%, 100%, 90.47% and 98.93%, respectively. These results reveal that the proposed method provides higher classification performance compared to state-of-the-art methods.