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Cell counting and recognition of immunohistochemically dyed seminiferous tubules with feed-forward neural network

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

Classification of refractive disorders from electrooculogram (EOG) signals by using data mining techniques

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

Proceedings | 2018 | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 , pp.1 - 4

Refractive disorders are common health problems in the community and they are the most important cause of visual impairment. In this study, it was aimed to classify the individuals who have hypermetropia and myopia refractive disorders or not. For this, horizontal and vertical Electrooculogram (EOG) signal data from the right and left eyes of the individuals were used. The performance of the data was investigated by using Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF) and REP Tree (RT) data mining methods. According to the obtained results, REP Tree method has shown the most successful classification performance to d . . .etect hypermetropia and myopia refractive disorders from Electrooculogram (EOG) signals. © 2018 IEEE Daha fazlası Daha az

The artificial neural network model to estimate the photovoltaic modul efficiency for all regions of the Turkey

Ceylan, İlhan | Gedik, Engin | Erkaymaz, Okan | Gürel, Ali Etem

Article | 2014 | Energy and Buildings84 , pp.258 - 267

Artificial neural network (ANN) is a useful tool that using estimates behavior of the most of engineering applications. In the present study, ANN model has been used to estimate the temperature, efficiency and power of the Photovoltaic module according to outlet air temperature and solar radiation. An experimental system consisted photovoltaic module, heating and cooling sub systems, proportional integral derivative (PID) control unit was designed and built. Tests were realized at the outdoors for the constant ambient air temperatures of photovoltaic module. To preserve ambient air temperature at the determined constant values as 10 . . ., 20, 30 and 40 °C, cooling and heating subsystems which connected PID control unit were used in the test apparatus. Ambient air temperature, solar radiation, back surface of the photovoltaic module temperature was measured in the experiments. Obtained data were used to estimate the photovoltaic module temperature, efficiency and power with using ANN approach for all 7 region of the Turkey. The study dealing with this paper not only will beneficial for the limited region but also in all region of Turkey which will be thought established of photovoltaic panels by the manufacturer, researchers and etc. © 2014 Elsevier B.V. All rights reserved Daha fazlası Daha az

Computerized detection of spina bifida using SVM with Zernike moments of fetal skulls in ultrasound screening

Konur, Umut

Article | 2018 | Biomedical Signal Processing and Control43 , pp.18 - 30

A computer aided detection scheme for the neural tube defect of spina bifida is proposed. Features from Zernike moments of fetal skull regions viewed by ultrasound are utilized in SVM classification. Rotational invariance of magnitudes of Zernike moments and their easy normalization with respect to translation and scale make them attractive for image and shape description. In particular, they are perfect candidates for classifying shapes of fetal skulls that possess markers of spina bifida. The automated detection system may act in decision support to help specialists avoid false negatives. Problems of rarity are handled with combin . . .ations of oversampling and undersampling. A variant of the synthetic minority oversampling technique (SMOTE) and random undersampling (RU) have been applied on training data. Experiments show the trade-off in various performance indicators depending on different sampling choices. The average values of 0.6276 F-measure and 0.6306 GMRP are achieved on non-sampled (original) test sets when training is performed using sampled data after 400% borderline-SMOTE followed by 50% RU with respective accuracy and specificity realizations of 94% and 98%. © 2018 Elsevier Lt Daha fazlası Daha az

Detection of directional eye movements based on the electrooculogram signals through an artificial neural network

Erkaymaz, Hande | Özer, Mahmut | Orak, İlhami Muharrem

Article | 2015 | Chaos, Solitons and Fractals77 , pp.225 - 229

The electrooculogram signals are very important at extracting information about detection of directional eye movements. Therefore, in this study, we propose a new intelligent detection model involving an artificial neural network for the eye movements based on the electrooculogram signals. In addition to conventional eye movements, our model also involves the detection of tic and blinking of an eye. We extract only two features from the electrooculogram signals, and use them as inputs for a feed-forwarded artificial neural network. We develop a new approach to compute these two features, which we call it as a movement range. The res . . .ults suggest that the proposed model have a potential to become a new tool to determine the directional eye movements accurately. © 2015 Elsevier Ltd. All rights reserved Daha fazlası Daha az

Performance of small-world feedforward neural networks for the diagnosis of diabetes

Erkaymaz, Okan | Özer, Mahmut | Perc, Matjaž

Article | 2017 | Applied Mathematics and Computation311 , pp.22 - 28

We investigate the performance of two different small-world feedforward neural networks for the diagnosis of diabetes. We use the Pima Indians Diabetic Dataset as input. We have previously shown than the Watts–Strogatz small-world feedforward neural network delivers a better classification performance than conventional feedforward neural networks. Here, we compare this performance further with the one delivered by the Newman–Watts small-world feedforward neural network, and we show that the latter is better still. Moreover, we show that Newman–Watts small-world feedforward neural networks yield the highest output correlation as well . . . as the best output error parameters. © 2017 Elsevier Inc Daha fazlası Daha az

Detection of knee abnormality from surface EMG signals by artificial neural networks

Erkaymaz, Okan | Senyer, İrem | Uzun, Rukiye

Proceedings | 2017 | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017 , pp.22 - 28

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

EOG controlled direction detect system with neuro-fuzzy approach

Erkaymaz, Hande | Özer, Mahmut | Kaya, Ceren

Conference Object | 2015 | 2015 19TH NATIONAL BIOMEDICAL ENGINEERING MEETING (BIYOMUT) , pp.22 - 28

The eye, which has the most advanced features among sense organs, has a perfect functioning on people. Furthermore, as it is placed in the lead role of vision, shows the importance for people is quite big. Nowadays, biomedical devices developed for patients who have mobility are benefiting from eye movements. Electrooculogram studies are especially designed on the basis of the signal depending on the movement of your eyes. Electrical origin of EOG biological signal, that occur around the eye pupil, makes an attempt to meet the needs of patients by the right, left, up, down and blinking action. In this study, 4 basic differences exis . . .ting in the direction of movement using voltage controlled EOG signal studies have tried to determine the Neuro-Fuzzy model. Determining the direction of Neuro-Fuzzy control system demonstrates how it can be successfully used as. In addition, control algorithms of artificial intelligence systems that use this kind of eye signals benefiting from the input of the detection process is advantageous in the classification of complex environment Daha fazlası Daha az

Comparison of artificial neural network and regression models to diagnose of knee disorder in different postures using surface electromyography

Uzun, Rukiye | Erkaymaz, Okan | Şenyer Yapıcı, İrem

Article | 2018 | Gazi University Journal of Science31 ( 1 ) , pp.100 - 110

The surface electromyography (sEMG) is useful tool to diagnose of knee disorder in clinical environments. It assists in designing the clinical decision support systems based classification. These systems exhibit complex structure because of sEMG data obtained at different postures at this study. In this context, we have researched the classification performance of each posture using artificial neural network (ANN) and logistic regression (LR) models and have showed that the classification success of the model used sitting posture data is higher than other postures (gait and standing). We have promoted this finding by using machine l . . .earning and statistical methods. The results show that the proposed models can classify with over 95% of success, and also the ANN model has higher performance than the LR model. Our ANN model outperforms reported studies in literature. The accuracy results indicate that the models used the only sitting posture data can exhibit successful classification for the knee disorder. Therefore, the usage of complex dataset is prevented for diagnosing knee disorder. © 2018, Gazi University Eti Mahallesi. All rights reserved Daha fazlası Daha az

A new adaptive neuro-fuzzy solution for optimization of the parameters in the digital holography setup

Ustabaş, Kaya Gülhan | Erkaymaz, Okan | Saraç, Zehra

Article | 2019 | Soft Computing23 ( 18 ) , pp.8827 - 8837

In this paper,a fuzzy interference and an adaptive neuro-fuzzy interference system models have been presented in order to accelerate designing of the digital holographic setup without experiment. The setting parameters of experimental holographic setup, which affect the quality of images obtained from reconstructed holograms, are predicted digitally by proposed models before the recording process. Hence, we reduce the required time for designing of digital holographic setup with optimization process. The adaptive neuro-fuzzy interference system model for the optimization of the digital holographic setup is first attempt in the liter . . .ature. The accuracy of the proposed models is examined by comparing the presented models and actual calculated experimental root-mean-square values. As a result, the accuracy of the adaptive neuro-fuzzy interference system shows the better performance than the fuzzy interference system. Moreover, the design of experimental setup can be occurred numerically in a short time by using adaptive neuro-fuzzy interference system models. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature Daha fazlası Daha az

Classification of cervical cancer data and the effect of random subspace algorithms on classification performance

Erkaymaz, Okan | Palabaş, Tuğba

Proceedings | 2018 | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 , pp.1 - 4

Computer assisted automatic diagnostic systems are used for the purpose of speeding up diagnosis and treatment and helping to make the right decision. In this study, cervical cancer is identified using four basic classifiers: Naive Bayes (NB), k-Nearest Neighbor (kNN), Multilayer Perceptron (MLP) and Decision Trees (KA-C4.5) algorithms and random subspaces ensemble algorithm. Gain Ratio Attribute Evaluation (GRAE) feature extraction algorithm is applied to contribute to classification performance. The classification results obtained with all datasets and reduced datasets are compared with respect to performance criteria such as accu . . .racy, Root Mean Square Error (RMSE), Sensitivity, Specificity performance criteria. According to the obtained performance analysis, it is seen that the classification performance with the random subspace ensemble algorithm using the kNN basic classifier on the reduced data set is the highest (%95.51). © 2018 IEEE Daha fazlası Daha az

Impact of small-world network topology on the conventional artificial neural network for the diagnosis of diabetes

Erkaymaz, Okan | Özer, Mahmut

Article | 2016 | Chaos, Solitons and Fractals83 , pp.178 - 185

Artificial intelligent systems have been widely used for diagnosis of diseases. Due to their importance, new approaches are attempted consistently to increase the performance of these systems. In this study, we introduce a new approach for diagnosis of diabetes based on the Small-World Feed Forward Artificial Neural Network (SW- FFANN). We construct the small-world network by following the Watts-Strogatz approach, and use this architecture for classifying the diabetes, and compare its performance with that of the regular or the conventional FFANN. We show that the classification performance of the SW-FFANN is better than that of the . . . conventional FFANN. The SW-FFANN approach also results in both the highest output correlation and the best output error parameters. We also perform the accuracy analysis and show that SW-FFANN approach exhibits the highest classifier performance. © 2015 Elsevier Ltd. All rights reserved Daha fazlası Daha az

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