Aydemir, Zübeyr | Erkaymaz, Okan | Ferah, Meryem Akpolat

Proceedings | 2017 | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

In this study, the features of the seminiferous tubule sections were extracted and the presence of the cells and cell stain types detected with the help of the feed forward artificial neural network. By looking at the section view with a small window, 78 features were extracted from the pixels seen by the window and used as an input to the artificial neural network. Artificial neural network outputs are decides presence of the cell and the staining of the cell. The results obtained with the artificial neural network were determined by using the connected component labeling method. The results obtained with the help of the user and t . . .he results obtained with the artificial neural network were compared. It has been shown that the proposed ANN model performs cell counting process comparable to the literature (%76 accuracy). © 2017 IEEE Daha fazlası Daha az

Kaya, Ceren | Erkaymaz, Okan | Ayar, Orhan | Özer, Mahmut

Proceedings | 2017 | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

25th Signal Processing and Communications Applications Conference, SIU 2017 -- 15 May 2017 through 18 May 2017 -- -- 128703

Erkaymaz, Okan | Senyer, İrem | Uzun, Rukiye

Proceedings | 2017 | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

Using surface EMG signals is a non-invasive measurement method obtained as a result of muscle activity. In this study, surface EMG data have been used for classification, taken from healthy individuals or individuals with knee abnormalities in gait position. For this purpose, first feature extraction was realized by discrete wavelet transform from the data. Then, extracted features were classified by artificial neural network approach that is widely used in the literature. In classification process, artificial neural networks were trained by using simple cross-validation algorithm. During training the optimal network topology was de . . .termined. The highest classification performance of proposed model was obtained in rate fiction 80%-20% and 70%-30% of data set. Our results revealed that proposed artificial neural network model is able to detect knee abnormality from surface EMG signals. © 2017 IEEE Daha fazlası Daha az