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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

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.

Prediction of the optimal threshold value in DF relay selection schemes based on artificial neural networks

Kara, Ferdi | Kaya, Hakan | Erkaymaz, Okan | Öztürk, Ertan

Proceedings | 2016 | Proceedings of the 2016 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2016 , pp.347 - 352

In wireless communications, the cooperative communication (CC) technology promises performance gains compared to traditional Single-Input Single Output (SISO) techniques. Therefore, the CC technique is one of the nominees for 5G networks. In the Decode-And-Forward (DF) relaying scheme which is one of the CC techniques, determination of the threshold value at the relay has a key role for the system performance and power usage. In this paper, we propose prediction of the optimal threshold values for the best relay selection scheme in cooperative communications, based on Artificial Neural Networks (ANNs) for the first time in literatur . . .e. The average link qualities and number of relays have been used as inputs in the prediction of optimal threshold values using Artificial Neural Networks (ANNs): Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) networks. The MLP network has better performance from the RBF network on the prediction of optimal threshold value when the same number of neurons is used at the hidden layer for both networks. Besides, the optimal threshold values obtained using ANNs are verified by the optimal threshold values obtained numerically using the closed form expression derived for the system. The results show that the optimal threshold values obtained by ANNs on the best relay selection scheme provide a minimum Bit-Error-Rate (BER) because of the reduction of the probability that error propagation may occur. Also, for the same BER performance goal, prediction of optimal threshold values provides 2dB less power usage, which is great gain in terms of green communication. © 2016 IEEE Daha fazlası Daha az

Classification of diabetic retinopathy disease from Video-Oculography (VOG) signals with feature selection based on C4.5 decision tree

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

Proceedings | 2017 | 2017 Medical Technologies National Conference, TIPTEKNO 20172017-January , pp.1 - 4

'Diabetes Mellitus (Diabetes)' is a disease based on insulin hormone disorders secreted from the pancreas gland. Clinical findings find out that diabetes causes some diseases in vital organs. 'Diabetic Retinopathy' is one of the most common eye diseases based on diabetes, and it is the leading cause of visual loss resulting from structural changes in the retinal vessels. Recent researches show that signals from vital organs can be used to diagnose diseases in the literature. In this study, the features of horizontal and vertical Video-Oculography (VOG) signals from right and left eye are used to classify non-proliferative and prolif . . .erative diabetic retinopathy disease. 25 statistical features are obtained using discrete wavelet transform with VOG signals from 24 subjects. Feature selection is performed using C4.5 decision tree algorithm from 25 features obtained. The statistical features obtained from C4.5 decision tree and discrete wavelet transform are applied as input to artificial neural networks and the classification performance of the 'Diabetic Retinopathy' disease are compared according to these two methods. Our results show that feature selection by C4.5 decision tree algorithm (96.87%) provides better classification performance than feature extraction with discrete wavelet transform (93.75%). © 2017 IEEE 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

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

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

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 diabetic retinopathy disease from Video-Oculography (VOG) signals by artificial neural networks

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

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

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

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

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