It is possible to generate personally identifiable random numbers to be used in some particular applications, such as authentication and key generation. This study presents the true random number generation from bioelectrical signals like EEG, EMG, and EOG and physical signals, such as blood volume pulse, GSR (Galvanic Skin Response), and respiration. The signals used in the random number generation were taken from BNCIHORIZON2020 databases. Random number generation was performed from fifteen different signals (four from EEG, EMG, and EOG and one from respiration, GSR, and blood volume pulse datasets). For this purpose, each signal was first normalized and then sampled. The sampling was achieved by using a nonperiodic and chaotic logistic map. Then, XOR postprocessing was applied to improve the statistical properties of the sampled numbers. NIST SP 800-22 was used to observe the statistical properties of the numbers obtained, the scale index was used to determine the degree of nonperiodicity, and the autocorrelation tests were used to monitor the 0-1 variation of numbers. The numbers produced from bioelectrical and physical signals were successful in all tests. As a result, it has been shown that it is possible to generate personally identifiable real random numbers from both bioelectrical and physical signals.

One method aimed at enhancing the performance of meta-heuristic optimization techniques is the incorporation of chaotic systems. Instead of irregular distributions in the search space, chaotic distributions are employed in the initial population of optimization algorithms to improve the efficiency of the search process. This approach enables search agents distributed in a chaotic manner to effectively explore the search space. The initial populations of both the well-established PSO algorithm and the enhanced WSO algorithm, which incorporates advanced search techniques, are distributed in the search space according to the characteristics of Logistic, Chebyshev, Circle, Sine, and Piecewise chaotic maps in this study. The original PSO and WSO algorithms, as well as the resulting chaotically initialized PSO and chaotically initialized WSO algorithms, were tested using 23 benchmark functions. Subsequently, the Otsu method was integrated into the tested optimization algorithms to obtain multi-level thresholding values. These algorithms were applied to five different test images with a manually determined number of thresholds. The results obtained were presented in the study and evaluated using statistical tests.

Makine öğrenmesi, var olan verilerin çıkarımlarını matematiksel ve istatistiksel yöntemlerle yapan ve bilinmeyen bir yöntem paradigmasıdır. Bu çalışmada, denetimli öğrenme algoritması, EEG (elektroensefalografi) verilerine uygulanmış, sınıflandırma algoritması performans analiz sonuçları MATLAB ile incelenmiştir. Bu çalışmada, algoritma olarak en yakın komşu algoritması (k-NN) kullanılmıştır. Bu sınıflandırma, Temel Bileşen Analizinin (TBA) kullanıldığı ve kullanılmadığı durumlar için iki aşamada değerlendirilmiştir. Boyut azaltma, verilerin boyut boyutunu küçültme işlemidir. TBA ile veri kümesinin boyutunun azaltılarak, önemli veri özelliklerini korunması beklenir. KNN, sınıflandırma doğruluğu açısından önemli sayılabilecek sonuçlar vermiştir. Mevcut çalışma, farklı biyoelektriksel sinyaller için uygun özelliklerin uygun bir sınıflandırıcı ile kombine edildiğinde anlamlı bir sınıflandırma yapılabileceğini göstermiştir.