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    Convolutional neural networks predict the onset of paroxysmal atrial fibrillation: Theory and applications
    (American Institute of Physics Inc., 2021) Surucu, M.; Isler, Y.; Perc, M.; Kara, R.
    In this study, we aimed to detect paroxysmal atrial fibrillation episodes before they occur so that patients can take precautions before putting their and others' lives in potentially life-threatening danger. We used the atrial fibrillation prediction database, open data from PhysioNet, and assembled our process based on convolutional neural networks. Conventional heart rate variability features are calculated from time-domain measures, frequency-domain measures using power spectral density estimations, time-frequency-domain measures using wavelet transform, and nonlinear Poincaré plot measures. In addition, we also applied an alternative heart rate normalization, which gave promising results only in a few studies, before calculating these heart rate variability features. We used these features directly and their normalized versions using min-max normalization and z-score normalization methods. Thus, heart rate variability features extracted from six different combinations of these normalizations, in addition to no normalization cases, were applied to the convolutional neural network classifier. We tuned the classifiers' hyperparameters using 90% of feature sets and tested the classifiers' performances using 10% of feature sets. The proposed approach resulted in 87.76% accuracy, 91.30% precision, 80.04% recall, and 87.50% f1-score in heart rate variability with z-score feature normalization. When the heart rate normalization was also utilized, the suggested method gave 100% accuracy, 100% precision, 100% recall, and 100% f1-score in heart rate variability with z-score feature normalization. The proposed method with heart rate normalization and z-score normalization methods resulted in better classification performance than similar studies in the literature. By comparing the existing studies, we conclude that our approach provides a much better tool to determine a near-future paroxysmal atrial fibrillation episode. However, although the achieved benchmarks are impressive, we note that the approach needs to be supported by other studies and on other datasets before clinical trials. © 2021 Author(s).
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    A new SNMP-based algorithm for network traffic balancing in virtual local area networks
    (Gazi Universitesi Muhendislik-Mimarlik, 2019) Kırışoğlu, S.; Kara, R.; Özçelik, İ.
    Virtual local area network (VLAN)’s are being created for improve performance, easy to manage security and ensure address on local area networks. This paper introduces a new approach for load balancing on virtual local area networks. The method which is developed for this approach, is dynamically changing the clients ports VLAN membership according to VLAN’s total traffic of the same security policy. The clients which have to register to security VLAN, can access their permission level source at all physically location of LAN, this is the flexibility of the method. The VLAN count which have to be on the LAN, can adjust parametrically or default constantly. In the algorithm which developed for this approach, the hosts belong to traffic on the network, ensures as much as possible equal or nearest distributes homogeneous on the VLAN’s. In this way the VLAN’s have same or nearest traffic value. A software has developed for testing functionality of this method which using SNMP protocol and reached to the aims by testing on the real network. © 2019 Gazi Universitesi Muhendislik-Mimarlik. All rights reserved.
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    Retrraction note: Leakage detection and localization on water transportation pipelines: a multi-label classification approach (Neural Computing and Applications, (2017), 28, 10, (2905-2914), 10.1007/s00521-017-2872-4)
    (Springer Science and Business Media Deutschland GmbH, 2024) Kayaalp, F.; Zengin, A.; Kara, R.; Zavrak, S.
    The Editor-in-Chief and the publisher have retracted this article. The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation's findings the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article. Fatih Kayaalp and Sultan Zavrak disagree with this retraction. Ahmet Zengin has not clearly stated whether or not they agree with this retraction. Resul Kara did not respond to correspondence from the publisher about this retraction. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.