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Detection of Knee Abnormality from Surface EMG Signals by Artificial Neural Networks

Erkaymaz, Okan | Senyer, Irem | Uzun, Rukiye

Conference Object | 2017 | 2017 25TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU)

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 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

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

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

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 , pp.178 - 185

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

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

Impact of synaptic noise and conductance state on spontaneous cortical firing

Özer, Mahmut | Graham, Lyle J. | Erkaymaz, Okan | Uzuntarla, Muhammet

Article | 2007 | NeuroReport18 ( 13 ) , pp.1371 - 1374

Cortical neurons in-vivo operate in a continuum of overall conductance states, depending on the average level of background synaptic input throughout the dendritic tree. We compare how variability, or fluctuations, in this input affects the statistics of the resulting 'spontaneous' or 'background' firing activity, between two extremes of the mean input corresponding to a low-conductance (LC) and a high-conductance (HC) state. In the HC state, we show that both firing rate and regularity increase with increasing variability. In the LC state, firing rate also increases with input variability, but in contrast to the HC state, firing re . . .gularity first decreases and then increases with an increase in the variability. At high levels of input variability, firing regularity in both states converge to similar values. © 2007 Lippincott Williams & Wilkins, Inc Daha fazlası Daha az

Use of autopsy to determine live or stillbirth: new approaches in decision-support systems

Yılmaz, Rıza | Erkaymaz, Okan | Kara, Erdoğan | Ergen, Kıvanç

Article | 2017 | Journal of Forensic Sciences62 ( 2 ) , pp.468 - 472

Fetal deaths are important cases for forensic medicine, as well as for criminal and civil law. From a legal perspective, the determination of whether a deceased infant was stillborn is a difficult process. Such a determination is generally made during autopsy; however, no standardized procedures for this determination exist. Therefore, new facilitative approaches are needed. In this study, three new support systems based on 10 autopsy parameters were tested for their ability to correctly determine whether deceased infants were alive or stillborn. Performances were analyzed and compared with one another. The artificial neural network . . .s and radial basis function network models had 90% accuracy (the highest among the models tested), 100% sensitivity, and 83.3% specificity. Thus, the models presented here provide additional insights for future studies concerning infant autopsies. © 2016 American Academy of Forensic Science Daha fazlası Daha az

Optimization of digital holographic setup by a fuzzy logic prediction system

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

Article | 2016 | Expert Systems with Applications56 , pp.177 - 185

In this study, the optimization of the digital holography setup is achieved by a using fuzzy logic prediction system. In fact, when this optimization process is experimentally performed, some parameters are changed in the setup. These parameters affect directly the obtained image quality after a reconstruction process, which is determined by normalized root mean square. The aim of this study is to achieve the optimization of digital holographic setup by using both experimental and fuzzy logic prediction systems. Furthermore, the required time during the experimental optimization can be lowered by using a numerical method like the fu . . .zzy logic prediction system. Here, the experimental optimization results and the optimization results obtained by the fuzzy logic prediction system are compared. It is offered that the designed experimental system can be optimized by using an artificial intelligent tool. The applied fuzzy logic prediction model is used the first time for optimization of hologram recording setup. As a result, it is reached a conclusion that the optimization of digital holographic setup can be numerically performed by the fuzzy logic prediction system. Moreover, while digital holographic setup is experimentally designed, the required time for optimization is reduced, as well. © 2016 Elsevier Ltd. All rights reserved Daha fazlası Daha az

Modeling of compressive strength parallel to grain of heat treated scotch pine (Pinus sylvestris L.) wood by using artificial neural network

Yapıcı, Fatih | Esen, Raşit | Erkaymaz, Okan | Baş, Hasan

Article | 2015 | Drvna Industrija66 ( 4 ) , pp.347 - 352

In this study, the compressive strength of heat treated Scotch Pine was modeled using artificial neural network. The compressive strength (CS) value parallel to grain was determined after exposing the wood to heat treatment at temperature of 130, 145, 160, 175, 190 and 205ºC for 3, 6, 9, 12 hours. The experimental data was evaluated by usi ng multiple variance analysis. Secondly, the effect of heat treatment on the CS of samples was modeled by using artificial neural network (ANN). © 2015, Journal Drvna Industrija. All rights reserved.

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.347 - 352

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

Computer-assisted diagnosis of vertebral column diseases by adaptive neuro-fuzzy inference system

Uzun, Rukiye | İşler, Yalçın | Erkaymaz, Okan | Kocadayı, Yasemin

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

In this study, a clustering algorithm based on adaptive neural fuzzy inference system (ANFIS) was used for computer-assisted diagnosis of the vertebral column disorder from machine learning databases of UCI (University of California Irvine). Features of pelvic incidence, pelvic tilt and lumbar lordosis angle given in this dataset was applied to the inputs of the algorithm. The performance of algorithm was evaluated using mean square error and regression coefficient criteria to discriminate patients with vertebral column disease from healthy subjects. As a result, the classification performance of 90.82% was obtained by using the gen . . .erated model of ANFIS. © 2018 IEEE Daha fazlası Daha az

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