Browsing by Author "Kocyigit, Y"

Now showing 1 - 8 of 8
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    Classification of EEG recordings by using fast independent component analysis and artificial neural network
    Kocyigit, Y; Alkan, A; Erol, H
    Since there is no definite decisive factor evaluated by the experts, visual analysis of EEG signals in time domain may be inadequate. Routine clinical diagnosis requests to analysis of EEG signals. Therefore, a number of automation and computer techniques have been used for this aim. In this study we aim at designing a MLPNN classifier based on the Fast ICA that accurately identifies whether the associated subject is normal or epileptic. By analyzing a data set consisting of 100 normal and 100 epileptic EEG time series, we have found that the MLPNN classifier based on the Fast ICA achieved and sensitivity rate of 98%, and specificity rate of 90.5%. The results demonstrate that the testing performance of the neural network diagnostic system is found to be satisfactory and we think that this system can be used in clinical studies. Since the time series analysis of EEG signals is unsatisfactory and requires specialist clinicians to evaluate, this application brings objectivity to the evaluation of EEG signals.
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    Hybrid imbalanced data classifier models for computational discovery of antibiotic drug targets
    Kocyigit, Y; Seker, H
    Identification of drug candidates is an important but also difficult process. Given drug resistance bacteria that we face, this process has become more important to identify protein candidates that demonstrate antibacterial activity. The aim of this study is therefore to develop a bioinformatics approach that is more capable of identifying a small but effective set of proteins that are expected to show antibacterial activity, subsequently to be used as antibiotic drug targets. As this is regarded as an imbalanced data classification problem due to smaller number of antibiotic drugs available, a hybrid classification model was developed and applied to the identification of antibiotic drugs. The model was developed by taking into account of various statistical models leading to the development of six different hybrid models. The best model has reached the accuracy of as high as 50% compared to earlier study with the accuracy of less than 1% as far as the proportion of the candidates identified and actual antibiotics in the candidate list is concerned.
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    Early stage diabetes prediction by features selection with metaheuristic methods
    Özmen, T; Kuzu, Ü; Kocyigit, Y; Sarnel, H
    Diabetes is a metabolic disease that is common worldwide. The number of people suffering from diabetes is expected to increase every year around the world. This means a negative impact on both the comfort of life of individuals and the health system. In this respect, it is important to diagnose the disease at an early stage. The high dimensionality of the data used for diagnostic purposes has a negative effect on the cost and time of the calculation. To avoid this, it is important to select the most valuable features for diagnosis. In this study, feature selection was made using Salp Swarm Algorithm, Artificial Bee Colony Algorithm, Whale Optimization Algorithm and Ant Colony Algorithm using the samples in the UCI (UCI Machine Learning Repository) data store. In order to evaluate the selected features, accuracy, sensitivity and specificity parameters were calculated using k-Nearest Neighborhood (KNN), Naive Bayes (NB), Support Vector Machine (SVM) and Artificial Neural Networks (ANN) methods. In the calculations for the probability of having diabetes, an accuracy rate of 99.04% was obtained with the k-Nearest Neighborhood method.
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    An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines
    Ata, R; Kocyigit, Y
    This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) model to predict the tip speed ratio (TSR) and the power factor of a wind turbine. This model is based on the parameters for LS-1 and NACA4415 profile types with 3 and 4 blades. In model development, profile type, blade number, Schmitz coefficient, end loss, profile type loss, and blade number loss were taken as input variables, while the TSR and power factor were taken as output variables. After a successful learning and training process, the proposed model produced reasonable mean errors. The results indicate that the errors of ANFIS models in predicting TSR and power factor are less than those of the ANN method. (C) 2010 Elsevier Ltd. All rights reserved.
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    A Novel Feature Extraction Method for Heart Sounds Classification
    Kocyigit, Y
    The experimental results showed that the proposed method efficiently classifies heart sounds. Heart sound analysis is a basic method for heart examination, which may suggest the presence of a cardiac pathology and also provide diagnostic information. In this study, a novel feature extraction method based on Independent Component Analysis is applied to classify nine different heart sound categories. The extracted features are subjected to classification by Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) using 5 Cross Validation and by Artificial Neural Network. The experimental results showed that the proposed method efficiently classifies heart sounds.
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    Thermal performance and SVM-based regression of natural convection in a 3D cavity filled with nanofluids as two phase mixture under combined effects of magnetic field and inner conductive hollow rotating conic object
    Selimefendigil, F; Kocyigit, Y; Öztop, HF
    In this study, a conductive hollow rotating conic object (H-RCO) is developed for convection control and thermal management in a 3D partially heated enclosure under uniform magnetic field with nanofluid considering two phase mixture formulation. Analysis is conducted for different parameters of interest as: Rayleigh number (Ra between 10(4) and 10(6)), angular rotational speed of the H-RCO (Omega between -60 and 60), Hartmann number (Ha between 0 and 50), expansion ratio (r1 between 1.1 and 2.5) and conductivity ratio (KR between 0.01 and 50). The rotational speed and expansion ratio of the object contributes significantly to the overall performance improvements. At the highest speed of the H-RCO, the average Nusselt number (Nu) rises up to 38% when compared to cases of non-rotating object. When object with highest expansion ratio is used at rotational speed of Omega = -40, the average Nu rises by about 36%. The impacts of using magnetic field on the reduction of convective effects are stronger when rotations are active while up to 69% reduction of average Nu is seen at the highest strength. Thermal conductivity of the object at higher speeds contributes slightly to the overall heat transfer. Support vector machine based regression model is used for thermal performance predictions while model with third order polynomial kernel gives the best results as compared to high fidelity 3D computational results.
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    Differentiating types of muscle movements using a wavelet based fuzzy clustering neural network
    Karlik, B; Kocyigit, Y; Korürek, M
    The electromyographic signals observed at the surface of the skin are the sum of many small action potentials generated in the muscle fibres. After the signals are processed, they can be used as a control source of multifunction prostheses. The myoelectric signals are represented by wavelet transform model parameters. For this purpose, four different arm movements (elbow extension, elbow flexion, wrist supination and wrist pronation) are considered in studying muscle contraction. Wavelet parameters of myoelectric signals received from the muscles for these different movements were used as features to classify the electromyographic signals in a fuzzy clustering neural network classifier model. After 1000 iterations, the average recognition percentage of the test was found to be 97.67% with clustering into 10 features. The fuzzy clustering neural network programming language was developed using Pascal under Delphi.