Browsing by Author "Kocyigit Y."
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Item Classification of EEG recordings by using fast independent component analysis and artificial neural network(2008) 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. © 2007 Springer Science+Business Media, LLC.Item An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines(2010) 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. © 2010 Elsevier Ltd. All rights reserved.Item Imbalanced data classifier by using ensemble fuzzy c-means clustering(2012) Kocyigit Y.; Seker H.Pattern classifiers developed with the imbalanced data set tend to classify an object to the class with the highest number of samples, resulting in higher overall classifier accuracy but lower sensitivity. A new approach based on a dynamic under-sampling procedure is therefore proposed to improve the classification of imbalanced datasets that are quite common in bio-medicine. To overcome a class imbalance, the dataset is resampled by using the ensemble fuzzy c-means clustering method. The under-sampling procedure is then applied to the majority class to balance the size of the classes. Compared to the existing classifiers, the proposed method yields not only higher classification accuracy and sensitivity but also more stable classification performance under different data sets, classifiers and their parameters, indicating that it is independent of particular clustering or classification methods. © 2012 IEEE.Item Hybrid imbalanced data classifier models for computational discovery of antibiotic drug targets(Institute of Electrical and Electronics Engineers Inc., 2014) 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. © 2014 IEEE.Item Landing sequencing modelling with fuzzy logic: Opportunistic approach for unmanned aerial systems(Institute of Electrical and Electronics Engineers Inc., 2016) Oren A.; Kocyigit Y.From the beginning of 21th century, the quantities and types of Unmanned Aerial Systems (UAS) have grown enormously and they become to boost substitution for the manned systems. UAS are creating advancement with a massive potential to change military operations and also enabling the new civilian applications. The vital issue for the airspace designers and managers is how to integrate manned and unmanned systems to the interoperability airspace. It is global arrangement that UAS operation in the integrated airspace must meet in any operational standards, procedures and safety issues as manned aircraft. For today, Air Traffic Management (ATM) is a dynamic and integrated environment including both manned and unmanned systems. Air Traffic Control (ATC) systems have the obligation to sustain an efficient and safe airspace utilization of manned and unmanned systems together. On the other hand, tendency for civilian and military applications about future is substituting unmanned aerial systems for manned aerial systems. In this paper, we present an analytic approach for UAS landing sequencing modelling in the dynamic airspace including different aerodynamic specifications or mission types for both military and civilian UAS via fuzzy logic modelling. During the designing model, the MATLAB Fuzzy FIS (Fuzzy Inference System) is used with realistic data and the user friendly interface is created via MATLAB/GUI. © 2016 IEEE.Item THE DIRECTION OF THE INTERACTIVE RELATION BETWEEN ORGANIZATIONAL FLEXIBILITY AND TRANSFORMATIONAL LEADERSHIP BEHAVIOURS: A COMPARATIVE ANALYSIS(Allied Business Academies, 2020) Akkaya B.; Kocyigit Y.Leadership and organizational flexibility are closely related. Each component of the organizational flexibility and transformational leadership affects the adapting of organization to environmental changes. But, which of them is antecedent of the other is still a question in literature. To answer this question, in this research two models were tested and goodness of fit of models was compared by using SEM (Structural Equation Modelling). The data were collected form 217 participants who work as bottom/middle level managers in SMEs in Turkey. The results showed that organizational flexibility is the antecedent of the transformational leadership. © 2020. All rights reserved.Item 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(Elsevier Ltd, 2023) Selimefendigil F.; Kocyigit Y.; Öztop H.F.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 104 and 106), angular rotational speed of the H-RCO (Ω 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 Ω=−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. © 2023 Elsevier Ltd