Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/11727/4809

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    Deep neural network to differentiate brain activity between patients with euthymic bipolar disorders and healthy controls during verbal fluency performance: A multichannel near-infrared spectroscopy study
    (2022) Alici, Yasemin Hosgoren; Oztoprak, Huseyin; Rizaner, Nahit; Baskak, Bora; Ozguven, Halise Devrimci; 0000-0003-3384-8131; 36088826
    In this study, we aimed to differentiate between euthymic bipolar disorder (BD) patients and healthy controls (HC) based on frontal activity measured by fNIRS that were converted to spectrograms with Convolutional Neural Networks (CNN). And also, we investigated brain regions that cause this distinction. In total, 29 BD patients and 28 HCs were recruited. Their brain cortical activities were measured using fNIRS while performing letter versions of VFT. Each one of the 24 fNIRS channels was converted to a 2D spectrogram on which a CNN architecture was designed and utilized for classification. We found that our CNN algorithm using fNIRS activity during a VFT is able to differentiate subjects with BD from healthy controls with 90% accuracy, 80% sensitivity, and 100% specificity. Moreover, validation performance reached an AUC of 94%. From our individual channel analyses, we observed channels corresponding to the left inferior frontal gyrus (left-IFC), medial frontal cortex (MFC), right dorsolateral prefrontal cortex (DLPFC), Broca area, and right premotor have considerable activity variation to distinguish patients from HC. fNIRS activity during VFT can be used as a potential marker to classify euthymic BD patients from HCs. Activity particularly in the MFC, left-IFC, Broca's area, and DLPFC have a considerable variation to distinguish patients from healthy controls.
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    Detection of multiple sclerosis from photic stimulation EEG signals
    (2021) Karaca, Busra Kubra; Aksahin, Mehmet Feyzi; Ocal, Ruhsen
    Background: Multiple Sclerosis (MS) is characterized as a chronic, autoimmune and inflammatory disease of the central nervous system. Early diagnosis of MS is of great importance for the treatment and course of the disease. In addition to the many methods, cost-effective and non-invasive electroencephalogram signals may contribute to the pre-diagnosis of MS. Objectives: The aim of this paper is to classify male subjects who have MS and who are healthy control using photic stimulation electroencephalogram signals. Methods: Firstly the continuous wavelet transformation (CWT) method was applied to electroencephalogram signals under photic stimulation with 5Hz, 10Hz, 15Hz, 20Hz, and 25Hz frequencies. The sum, maximum, minimum and standard deviation values of absolute CWT coefficients, corresponding to "1-4 Hz" and "4-13 Hz" frequency ranges, were extracted in each stimulation frequency region. The ratios of these values obtained from the frequency ranges "1-4Hz" and "4-13Hz" was decided as features. Finally, various machine learning classifiers were evaluated to test the effectivity of determined features. Results: Consequently, the overall accuracy, sensitivity, specificity and positive predictive value of the proposed algorithm were 80 %, 72.7 %, 88.9 %, and 88.9 %, respectively by using the Ensemble Subspace k-NN classifier algorithm. Conclusions: The results showed how photic stimulation electroencephalogram signals can contribute to the prediagnosis of MS.
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    Investigation of some machine learning algorithms in fish age classification
    (2021) Benzer, Semra; Garabaghi, Farid Hassanbaki; Benzer, Recep; Mehr, Homay Danaei; 0000-0002-5339-0554; A-5050-2014
    Marine and freshwater scientists use fish scales, vertebrae, otoliths and length-weights values to estimate fish age because reliable fish age estimation plays a very important role in fish stock management. The advances in technology and the widespread use of artificial intelligence have revealed the use of traditional observations and techniques in the fishing industry. The aim of this study was to evaluate the effectiveness of three disesteemed machine learning algorithms (NB, J48 DT, RF) in comparison with ANNs which has been widely used in such studies in the literature. In culmination, all three algorithms outperformed ANNs and can be considered as alternatives in case of coming across noisy and non-linear datasets. Moreover, among these three algorithms J48 DT and RF showed exceptional performance where the data for specific fish age groups weren't abundant.
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    Diagnosis of Attention Deficit Hyperactivity Disorder with combined time and frequency features
    (2020) Altinkaynak, Miray; Dolu, Nazan; Guven, Aysegul; Pektas, Ferhat; Ozmen, Sevgi; Demirci, Esra; Izzetoglu, Meltem; 0000-0002-3104-7587; AAG-4494-2019
    The aim of this study was to build a machine learning model to discriminate Attention Deficit Hyperactivity Disorder (ADHD) patients and healthy controls using information from both time and frequency analysis of Event Related Potentials (ERP) obtained from Electroencephalography (EEG) signals while participants performed an auditory oddball task. The study included 23 unmedicated ADHD patients and 23 healthy controls. The EEG signal was analyzed in time domain by nonlinear brain dynamics and morphological features, and in time-frequency domain with wavelet coefficients. Selected features were applied to various machine learning techniques including; Multilayer Perceptron, Naive Bayes, Support Vector Machines, k-nearest neighbor, Adaptive Boosting, Logistic Regression and Random Forest to classify ADHD patients and healthy controls. Longer P300 latencies and smaller P300 amplitudes were observed in ADHD patients relative to controls. In fractal dimension calculation relative to the control group, the ADHD group demonstrated reduced complexity. In addition, certain wavelet coefficients provided significantly different values in both groups. Combining these extracted features, our results indicated that Multilayer Perceptron method provided the best classification with an accuracy rate of 91.3% and a high level of reliability of concurrence (Kappa = 0.82). The results showed that combining time and frequency domain features can be a useful and discriminative for diagnostic purposes in ADHD. The study presents a supporting diagnostic tool that uses EEG signal processing and machine learning algorithms. The findings would be helpful in the objective diagnosis of ADHD. (C) 2020 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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    Evaluation of divided attention using different stimulation models in event-related potentials
    (2019) Batbat, Turgay; Gueven, Aysegul; Dolu, Nazan; 0000-0002-3104-7587; 31352660; AAG-4494-2019
    Divided attention is defined as focusing on different tasks at once, and this is described as one of the biggest problems of today's society. Default examinations for understanding attention are questionnaires or physiological signals, like evoked potentials and electroencephalography. Physiological records were obtained using visual, auditory, and auditory-visual stimuli combinations with 48 participants-18-25-year-old university students-to find differences between sustained and divided attention. A Fourier-based filter was used to get a 0.01-30-Hz frequency band. Fractal dimensions, entropy values, power spectral densities, and Hjorth parameters from electroencephalography and P300 components from evoked potentials were calculated as features. To decrease the size of the feature set, some features, which yield less detail level for data, were eliminated. The visual and auditory stimuli in selective attention were compared with the divided attention state, and the best accuracy was found to be 88.89% on a support vector machine with linear kernel. As a result, it was seen that divided attention could be more difficult to determine from selective attention, but successful classification could be obtained with appropriate methods. Contrary to literature, the study deals with the infrastructure of attention types by working on a completely healthy and attention-high group.
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    Can Computerized Adaptive Testing Work in Students' Admission to Higher Education Programs in Turkey?
    (2017) Berberoglu, Giray; Kalender, Ilker
    Admission into university in Turkey is very competitive and features a number of practical problems regarding not only the test administration process itself, but also concerning the psychometric properties of test scores. Computerized adaptive testing (CAT) is seen as a possible alternative approach to solve these problems. In the first phase of the study, a series of CAT simulations based on real students' responses to science items were conducted in order to determine which test termination rule produced more comparable results with scores made on the paper and pencil version of the test. An average of 17 items was used to terminate the CAT administration for a reasonable reliability level as opposed to the normal 45 items. Moreover, CAT based science scores not only produced similar correlations when using mathematics subtest scores as an external criterion, but also ranked the students similarly to the paper and pencil test version. In the second phase, a live CAT administration was implemented using an item bank composed of 242 items with a group of students who had previously taken the exam the paper and pencil version of the test. A correlation of.76 was found between the CAT and paper and pencil scores for this group. The results seem to support the CAT version of the subtests as a feasible alternative approach in Turkey's university admission system.
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    Sparsity-driven weighted ensemble classifier
    (2018) Erdem, Hamit; Ozgur, Atilla; Nar, Fatih
    In this study, a novel sparsity-driven weighted ensemble classifier (SDWEC) that improves classification accuracy and minimizes the number of classifiers is proposed. Using pre-trained classifiers, an ensemble in which base classifiers votes according to assigned weights is formed. These assigned weights directly affect classifier accuracy. In the proposed method, ensemble weights finding problem is modeled as a cost function with the following terms: (a) a data fidelity term aiming to decrease misclassification rate, (b) a sparsity term aiming to decrease the number of classifiers, and (c) a non-negativity constraint on the weights of the classifiers. As the proposed cost function is non-convex thus hard to solve, convex relaxation techniques and novel approximations are employed to obtain a numerically efficient solution. Sparsity term of cost function allows trade-off between accuracy and testing time when needed. The efficiency of SDWEC was tested on 11 datasets and compared with the state-of-the art classifier ensemble methods. The results show that SDWEC provides better or similar accuracy levels using fewer classifiers and reduces testing time for ensemble.