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In this paper, neural network based cryptology is performed. The system consists of two stages. In the first stage, neural network-based pseudo-random numbers (NPRNGs) are generated and the results are tested for randomness using National Institute of Standard Technology (NIST) randomness tests. In the second stage, a neural network-based cryptosystem is designed using NPRNGs. In this cryptosystem, data, which is encrypted by non-linear techniques, is subject to decryption attempts by means of two identical artificial neural networks (ANNs). With the first neural network, non-linear encryption is modeled using relationbuilding functionality. The encrypted data is decrypted with the second neural network using decision-making functionality.

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ÖZETÇEBu çalışmada, konjestif kalp yetmezligi (KKY) olan hastaların ayırt edilmesi için ham EKG verilerinden elde edilen Poincare haritası kullanılmıştır. internet üzerinden ücretsiz olarak sağlanan KKY ve normal EKG verileri incelenmiştir. Poincare haritası ızgaralara bölünmüş ve her hücreye düşen nokta sayısı belirlenerek bu değerler knn(k-enyakın komşu) sınıflandırıcıda kullanılmıştır. Sonuç olarak, normaller ile KKY hastalarının Ham EKG kayıtlarının Poincare haritasından elde edilen öznitelikler ile ayrıştırılabileceği görülmüştür. ABSTRACT In this study, in order to diagnose congestive heart failure patients (CHF), Poincare map obtained from raw ECG data is used. CHF and normal ECG data, which are distributed freely via internet, are analyzed. Poincare map is divided into equal rectangle cells and points in all of the cells are determined. These values are used for knn (k-nearest neighbour) classification. At the result of this study, it is considered that CHF patients and normal people can be separated each other using features obtained from Poincare map of Raw ECG record.