Browsing by Author "Guzel, Mehmet S."

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    A new framework using deep auto-encoder and energy spectral density for medical waveform data classification and processing
    (2019) Karim, Ahmad M.; Guzel, Mehmet S.; Tolun, Mehmet R.; Kaya, Hilal; Celebi, Fatih V.
    This paper proposes a new framework for medical data processing which is essentially designed based on deep autoencoder and energy spectral density (ESD) concepts. The main novelty of this framework is to incorporate ESD function as feature extractor into a unique deep sparse auto-encoders (DSAEs) architecture. This allows the proposed architecture to extract more qualified features in a shorter computational time compared with the conventional frameworks. In order to validate the performance of the proposed framework, it has been tested with a number of comprehensive medical waveform datasets with varying dimensionality, namely, Epilepsy Serious Detection, SPECTF Classification and Diagnosis of Cardiac Arrhythmias. Overall, the ESD function speeds up the deep auto-encoder processing time and increases the overall accuracy of the results which are compared to several studies in the literature and a promising agreement is achieved. (C) 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
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    A New Generalized Deep Learning Framework Combining Sparse Autoencoder and Taguchi Method for Novel Data Classification and Processing
    (2018) Tolun, Mehmet R.; Karim, Ahmad M.; Guzel, Mehmet S.; Kaya, Hilal; Celebi, Fatih V.; 0000-0002-8478-7220; AAX-2456-2021
    Deep autoencoder neural networks have been widely used in several image classification and recognition problems, including hand-writing recognition, medical imaging, and face recognition. The overall performance of deep autoencoder neural networks mainly depends on the number of parameters used, structure of neural networks, and the compatibility of the transfer functions. However, an inappropriate structure design can cause a reduction in the performance of deep autoencoder neural networks. A novel framework, which primarily integrates the Taguchi Method to a deep autoencoder based system without considering to modify the overall structure of the network, is presented. Several experiments are performed using various data sets from different fields, i.e., network security and medicine. The results show that the proposed method is more robust than some of the well-known methods in the literature as most of the time our method performed better. Therefore, the results are quite encouraging and verified the overall performance of the proposed framework.