Yazar "Acikgoz, Hakan" seçeneğine göre listele

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    An Automated Diagnosis of Parkinson's Disease from MRI Scans Based on Enhanced Residual Dense Network with Attention Mechanism
    (Springer, 2025) Acikgoz, Hakan; Korkmaz, Deniz; Talan, Tarik
    The increasing prevalence of neurodegenerative diseases has recently heightened interest in research on early diagnosis of these diseases. Parkinson's disease (PD), among the most prominent of these conditions, is a neurological disorder causing the loss of nerve cells and significantly affecting movement control. Detection of PD in early stages is of critical importance to prevent the progression of the disease and improve treatment processes. The aim of the current study is to develop a deep learning model that can perform accurate classification for early diagnosis of PD from MRI images. In this study, a densely connected feature fusion network with residual learning is designed to diagnose PD patients. The designed network consists of a serial dense block with skip connections and efficient attention mechanisms. In this architecture, squeeze-excitation (SE) blocks with ResNeXt (SE-ResNeXt block) modules are utilized to extract distinctive and high-level features. In the experiments, a publicly available T2-weighted MRI dataset is used, and an offline augmentation process is applied to limited data to increase the generalization ability and classification performance. The proposed method is evaluated and compared with current state-of-the-art deep learning methods. The obtained results show that the proposed model gives higher classification performance with an overall accuracy of 94.44%, precision of 91.67%, sensitivity of 91.67%, specificity of 95.83%, F1-score of 91.67%, and Matthew's correlation coefficient of 87.50% for the PD and healthy control subjects.
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    Cross-scale fusion network and two-stage decomposition for power forecasting of offshore wind turbines
    (Pergamon-Elsevier Science Ltd, 2025) Korkmaz, Deniz; Acikgoz, Hakan; Ustundag, Mehmet
    This research presents a hybrid forecasting model for offshore wind power, which is based on a two-stage decomposition process and a densely connected convolutional network. Initially, the offshore wind power data is decomposed into several components utilizing an improved complete ensemble empirical mode decomposition with adaptive noise. The high-frequency component is further divided into multiple components through empirical mode decomposition. Following the decomposition, the dataset is transformed into the HSV color space. The proposed model features a sequential and multi-scale convolutional block architecture, inspired by the clique network approach. Furthermore, a squeeze-and-excitation module is incorporated to enhance the network performance. Comparative experiments are conducted against state-of-the-art deep learning models using data from two offshore wind turbines. The results indicate that the proposed model achieves superior performance metrics for WT3 and WT4, with root mean square error, mean absolute error, and mean absolute percentage error values ranging from 4.4796 to 4.8578, 3.2736 to 3.6543, and 0.2127 to 0.2193 for 1-h ahead forecast; 4.9674 to 5.7693, 3.5980 to 4.2028, and 0.2214 to 0.2295 for 3-h ahead forecast; and 5.8889 to 5.6338, 4.4247 to 4.1148, and 0.3064 to 0.2436 for 5-h ahead forecast, respectively. This pioneering two-stage decomposition and cross-scale CNN outperforms benchmarks by up to 74 % in RMSE. The proposed methodology improves short-term offshore wind power prediction by removing irregularities in the datasets.
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    Feature fusion-based hand gesture classification with time-domain descriptors and multi-level deep attention network
    (Elsevier, 2025) Alcin, Omer Faruk; Korkmaz, Deniz; Acikgoz, Hakan
    In conventional human-robot interaction (HRI), it is difficult to provide adaptability by located systems in the human body. Surface Electromyography (sEMG) signals have the potential to meet adaptability in HRI by directly representing movements, and classifying hand gestures with sEMG can be an effective solution to meet the increasing needs of these applications. In this paper, a hybrid and multi-scale convolutional neural network (CNN) model is proposed to obtain an efficient sEMG-based classification approach of human hand gestures. The proposed method includes an effective feature extraction process, including spectral moments, sparseness, irregularity factor, Teager-Kaiser energy, Shannon entropy, Katz fractal dimension, and Higuchi's fractal dimension, and waveform length. The obtained features are then converted to RGB images. The designed network is built on multi-scale convolutional blocks with residual learning and convolutional blocks, including the CBAM to improve the network performance by focusing on channel and spatial features. Furthermore, a pyramid non-pooling local block is utilized at the end of the network to learn more powerful features and their correlations. Five comprehensive publicly available datasets are evaluated in the experiments, and the obtained results are compared with the benchmark CNN models and network variations with different attention mechanisms. In the comparative evaluations, the CBAM achieves a classification accuracy between 84.62 % and 97.56 % while other attention mechanism results give accuracy values between 82.88 % and 97.17 %. The experiments show that the proposed method gives more accurate and robust classification performance compared with other variations and benchmark models.
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    MSRConvNet: Classification of railway track defects using multi-scale residual convolutional neural network
    (Pergamon-Elsevier Science Ltd, 2023) Acikgoz, Hakan; Korkmaz, Deniz
    The development of an automated rail line defect classification system is of great benefit, as railway tracks must be periodically monitored and inspected to guarantee the safety of rail transportation. In this paper, an effective multi-scale residual convolutional network (MSRConvNet) model is proposed to classify the different types of railway track defects. The skip connections with residual learning blocks are used to increase the effectiveness of the network. The multi-scale convolutions are connected with parallel and two skip connections in the structure to distribute detailed feature maps with each other. Therefore, different scale feature maps can be extracted. The data augmentation method is performed to ensure a balanced class distribution and to eliminate the negative effect of the imbalanced dataset. The proposed model is compared with both benchmark deep learning models and the different variations of the designed network. The results verify that the proposed model can reach superior classification fulfillment, and the MSRConvNet provides an overall accuracy of 99.83%, precision of 99.83%, sensitivity of 99.83%, specificity of 99.94%, F1-score of 99.83%, and Matthew's correlation coefficient of 99.78% for four defect classes.
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    Photovoltaic cell defect classification based on integration of residual-inception network and spatial pyramid pooling in electroluminescence images
    (Pergamon-Elsevier Science Ltd, 2023) Acikgoz, Hakan; Korkmaz, Deniz; Budak, Umit
    Electroluminescence (EL) imaging provides high spatial resolution and better identifies micro-defects for in-spection of photovoltaic (PV) modules. However, the analysis of EL images could be typically a challenging process due to complex defect patterns and inhomogeneous background structure. In this study, a deep con-volutional neural network (CNN) model using residual connections and spatial pyramid pooling (SPP) is pro-posed for the efficient classification of PV cell defects. The proposed CNN model is built on the Inception-v3 network. In this way, feature maps in inception modules are shared to reuse in deeper layers and the repre-sentation ability of features is enriched with the pooling process of the SPP in different sizes. Due to the imbalanced class distribution, offline data augmentation strategies are applied and network performance is further improved. The proposed method is evaluated on a publicly available dataset of 8 classes, of which 7 classes are defective and one class is defect-free images. In the comparative evaluation, while other approaches give accuracy values between 76.49% and 89.17%, this value is increased to 93.59% with the proposed method. The experimental results show that the proposed method exhibits more accurate and robust classification per-formance compared with other model combinations and CNN models.
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    PVEL-ViT: Adaptive token selective vision transformer for photovoltaic cell defect classification in electroluminescence imaging
    (Elsevier, 2026) Acikgoz, Hakan; Korkmaz, Deniz; Bal, Cafer; Coteli, Resul; Dandil, Besir
    The rapid expansion of photovoltaic (PV) technology has increased the need for reliable and automated defect detection to ensure the long-term efficiency and durability of PV modules. Electroluminescence (EL) imaging provides detailed visualization of internal defects and is a key modality for data-driven classification systems. This study proposes a vision transformer (ViT)-based framework, named PVEL-ViT, for accurate classification of PV cell defects from EL images. The architecture is constructed on a MetaFormer-based backbone and integrates an embedded multi-scale feature fusion module with an adaptive token selective attention mechanism. The designed PVEL-ViT captures fine-grained local defect patterns and global context while dynamically emphasizing defect-relevant tokens and suppressing redundant background information, thereby improving discriminative feature learning and robustness. The proposed framework is evaluated on an EL imaging dataset and compared against recent convolutional and transformer-based models. Experiments show that EfficientNetV2-m and EfficientViT-b2 achieve accuracy rates of 0.9386 and 0.9359, respectively, whereas PVEL-ViT attains a higher accuracy of 0.9584, corresponding to accuracy gains of 2.11% and 2.40% over these models. The obtained results indicate that PVEL-ViT provides a robust and scalable defect classification performance on EL imagery, enhancing the reliability of automated PV inspection pipelines and supporting monitoring in PV modules.
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    Short-term offshore wind speed forecasting approach based on multi-stage decomposition and deep residual network with self-attention
    (Pergamon-Elsevier Science Ltd, 2025) Acikgoz, Hakan; Korkmaz, Deniz
    Wind energy is one of the widely used renewable energy systems. Wind speed forecasting is used to produce of wind energy and to ensure the sustainability of the power system. However, offshore wind speed forecasting is a challenging task with complex variables and highly nonlinear temporal dynamics of the ocean. This paper proposes a hybrid and robust offshore wind speed forecasting approach based on multi-stage decomposition, deep convolutional neural network (CNN), and extreme learning machine (ELM). Unlike conventional preprocessing for forecasting of renewable energy problems, the proposed approach combines two efficient decomposition methods as complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and ensemble empirical mode decomposition (EEMD). This method can decompose high-frequency and lowfrequency components of the wind speed. While high-frequency components are decomposed with the EEMD, low-frequency components are directly sent to the ELM model. The obtained mode functions from the EEMD are then fed to the designed network for forecasting. The CNN model is constructed with the deep residual network and self-attention (SA) mechanism to improve the network performance. In the comparative evaluations, while other approaches give lower forecasting performance between 0.8233 and 2.1885 for the root mean square error (RMSE), the proposed method presents the lowest RMSE value as 0.5400. The experimental results show that the proposed method exhibits more accurate and robust forecasting performance compared with other model combinations and deep learning models.