Sürdürülebilir Kalkınma Amaçları
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- MakaleAçık Erişim2026Scopus SJRQ1Forked hybrid architecture combining 1D-CNN and quantum convolutional networks for EEG signal classification
Esmeray, Furkan; Gülten, Arif
Detecting epileptic seizures from electroencephalogram (EEG) signals presents a significant challenge due to the nonlinear and non-stationary nature of brain activity. In this study, we propose a hybrid classification framework that integrates quantum-assisted and classical deep learning techniques to enhance detection accuracy and model interpretability. First, discrete wavelet transform (DWT) is applied to extract multiscale time–frequency features from raw EEG signals. Subsequently, three comparative models are developed: a conventional 1D-CNN model, an IQCNN model incorporating a parametric quantum circuit, and a novel Forked Enhanced Model that processes EEG features simultaneously through both 1D-CNN and IQCNN branches and combines them in a fully connected layer. The proposed hybrid model leverages the expressive power of quantum entanglement while preserving the robustness of classical deep learning. Evaluations using 5-fold stratified cross-validation on a real EEG dataset show that the forked architecture outperforms both classical CNNs and quantum-only based models in terms of accuracy, F1 score, precision, recall, number of incorrectly classified subjects and AUC. In this study, the best result was obtained with the Denoise IQCNNNet hybrid model. The accuracy of this model is 97.25%, precision 92.74%, recall 93.66%, F-1 score 93.15%, AUC 99.54%, number of incorrectly classified subjects are 57. This study demonstrates the feasibility and potential of hybrid quantum-classical networks in biomedical signal processing and provides a foundation for real-time clinical seizure monitoring systems.
- BildiriYalnız künye2018Quality lignite coal detection with discrete wavelet transform, discrete fourier transform, and ANN based on k-means clustering method
Korkmaz, Sevcan Aytac; Esmeray, Furkan; Varol, A.; Karabatak, M.; Varol, C.
In this article, the lignite coal datas in the Kalburcayi area of the Sivas-Kangal Basin have been used. This original data obtained from Kalburcayi area of the Sivas-Kangal Basin consists of 66 observations in the lignite coal area, including lignite quality parameters such as moisture content, ash, sulfur content and calorific value. These lignite coal datas have been clustered in two group with k-means method according to calori values. This clustering lignite coal data is classified by the Artifical Neural Network (ANN) classifier. In addition, Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT) have been applied to coal data for ANN classifiers. DFT-ANN, DWT-ANN, and ANN classification success results are compared. The highest classification success rate was found by DWT-ANN method.
AtıfScopus8OpenAlex12
İncele - MakaleAçık Erişim2018Scopus SJRQ3A New Application Based on GPLVM, LMNN, and NCA for Early Detection of the Stomach Cancer
Korkmaz, Sevcan Aytaç; Esmeray, Furkan
In this article, speeded-up robust features (SURF) for each image have been calculated. Discrete Fourier transform (DFT) method has been applied to these SURF. High dimensions of these SURF–DFT feature vectors are reduced to low dimensions with large-margin nearest neighbor (LMNN), Gaussian process latent variable models (GPLVM), and neighborhood component analysis (NCA). When size reduction process was done, effect on the GPLVM, LMNN, and NCA of the 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 feature numbers has been examined. These features are classified by naive Bayes (NB) classifier. Thus, SURF_DFT_GPLVM_NB, SURF_DFT_NCA_NB, and SURF_DFT_LMNN_NB methods for gastric histopathological images have been developed. Classification results obtained with these methods have been compared. According to the obtained results, the highest classification result was obtained as 90.24% by using 4 features by SURF_DFT_GPLVM_NB method for second group images.
AtıfScopus5WoS4OpenAlex7
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