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Yayın Sınıflandırma için diferansiyel mahremiyete dayalı öznitelik seçimi(Gazi Univ, Fac Engineering Architecture, 2018) Var, Esra; İnan, AliVeri madenciliği ve makine öğrenmesi çözümlerinin en önemli ön aşamalarından biri yapılacak analizde kullanılacak verinin özniteliklerinin uygun bir alt kümesini belirlemektir. Sınıflandırma yöntemleri için bu işlem, bir özniteliğin sınıf niteliği ile ne oranda ilişkili olduğuna bakılarak yapılır. Kişisel gizliliği koruyan pek çok sınıflandırma çözümü bulunmaktadır. Ancak bu yöntemler için öznitelik seçimi yapan çözümler geliştirilmemiştir. Bu çalışmada, istatistiksel veritabanı güvenliğinde bilinen en kapsamlı ve güvenli çözüm olan diferansiyel mahremiyete dayalı özgün öznitelik seçimi yöntemleri sunulmaktadır. Önerilen bu yöntemler, yaygın olarak kullanılan bir veri madenciliği kütüphanesi olan WEKA ile entegre edilmiş ve deney sonuçları ile önerilen çözümlerin sınıflandırma başarımına olumlu etkileri gösterilmiştir.Yayın Biometric identification using fingertip electrocardiogram signals(Springer London Ltd, 2018-07) Güven, Gökhan; Gürkan, Hakan; Güz, ÜmitIn this research work, we present a newly fingertip electrocardiogram (ECG) data acquisition device capable of recording the lead-1 ECG signal through the right- and left-hand thumb fingers. The proposed device is high-sensitive, dry-contact, portable, user-friendly, inexpensive, and does not require using conventional components which are cumbersome and irritating such as wet adhesive Ag/AgCl electrodes. One of the other advantages of this device is to make it possible to record and use the lead-1 ECG signal easily in any condition and anywhere incorporating with any platform to use for advanced applications such as biometric recognition and clinical diagnostics. Furthermore, we proposed a biometric identification method based on combining autocorrelation and discrete cosine transform-based features, cepstral features, and QRS beat information. The proposed method was evaluated on three fingertip ECG signal databases recorded by utilizing the proposed device. The experimental results demonstrate that the proposed biometric identification method achieves person recognition rate values of 100% (30 out of 30), 100% (45 out of 45), and 98.33% (59 out of 60) for 30, 45, and 60 subjects, respectively.Yayın Pseudo-spherical submanifolds with 1-type pseudo-spherical gauss map(Birkhauser Verlag AG, 2016-05-28) Bektaş, Burcu; Canfes, Elif Özkara; Dursun, UğurIn this work, we study pseudo-Riemannian submanifolds of a pseudo-sphere with 1-type pseudo-spherical Gauss map. First, we classify Lorentzian surfaces in a 4-dimensional pseudo-sphere (Formula presented.) with index s, (Formula presented.), and having harmonic pseudo-spherical Gauss map. Then we give a characterization theorem for pseudo-Riemannian submanifolds of a pseudo-sphere (Formula presented.) with 1-type pseudo-spherical Gauss map, and we classify spacelike surfaces and Lorentzian surfaces in the de Sitter space (Formula presented.) with 1-type pseudo-spherical Gauss map. Finally, according to the causal character of the mean curvature vector we obtain the classification of submanifolds of a pseudo-sphere having 1-type pseudo-spherical Gauss map with nonzero constant component in its spectral decomposition.Yayın Design and analysis of classifier learning experiments in bioinformatics: survey and case studies(IEEE Computer Soc, 2012-12) İrsoy, Ozan; Yıldız, Olcay Taner; Alpaydın, Ahmet İbrahim EthemIn many bioinformatics applications, it is important to assess and compare the performances of algorithms trained from data, to be able to draw conclusions unaffected by chance and are therefore significant. Both the design of such experiments and the analysis of the resulting data using statistical tests should be done carefully for the results to carry significance. In this paper, we first review the performance measures used in classification, the basics of experiment design and statistical tests. We then give the results of our survey over 1,500 papers published in the last two years in three bioinformatics journals (including this one). Although the basics of experiment design are well understood, such as resampling instead of using a single training set and the use of different performance metrics instead of error, only 21 percent of the papers use any statistical test for comparison. In the third part, we analyze four different scenarios which we encounter frequently in the bioinformatics literature, discussing the proper statistical methodology as well as showing an example case study for each. With the supplementary software, we hope that the guidelines we discuss will play an important role in future studies.Yayın Theta and Beta1 frequency band values predict dyslexia classification(John Wiley and Sons Ltd, 2025-12-29) Eroğlu, Günet; Harb, Mhd Raja AbouDyslexia, impacting children's reading skills, prompts families to seek cost-effective neurofeedback therapy solutions. Utilising machine learning, we identified predictive factors for dyslexia classification. Employing advanced techniques, we gathered 14-channel Quantitative Electroencephalography (QEEG) data from 200 participants, achieving 99.6% dyslexic classification accuracy through cross-validation. During validation, 48% of dyslexic children's sessions were consistently classified as normal, with a 95% confidence interval of 47.31 to 48.68. Focusing on individuals consistently diagnosed with dyslexia during therapy, we found that dyslexic individuals exhibited higher theta values and lower beta1 values compared to typically developing children. This study pioneers machine learning in predicting dyslexia classification factors, offering valuable insights for families considering neurofeedback therapy investment.
