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Sarac U.,Bartn University | Baykul M.C.,Eskiehir Osmangazi University | Uguz Y.,Eskiehir Osmangazi University
Journal of Superconductivity and Novel Magnetism | Year: 2015

Electrodeposited Ni–Co nanocrystalline thin films were grown onto indium tin oxide (ITO)-coated glass substrates from an electrolyte consisting of their sulfate salts and boric acid without stirring at ambient temperature. The effect of applied current density on the microstructural, compositional, magnetic, and morphological properties was investigated using different characterization techniques such as X-ray diffraction (XRD), energy dispersive X-ray (EDX) spectroscopy, vibrating sample magnetometer (VSM), and scanning electron microscopy (SEM). It was observed that the Ni content within the films increases as the applied current density increases. X-ray diffraction (XRD) analyses of Ni–Co films showed the formation of single phase face-centered cubic (FCC) structure and <111>crystallographic orientation. Morphological characterizations revealed that the applied current density affects the surface morphology of the films. The film electrodeposited at high current density has smaller grains than those prepared at lower current densities. Magnetic measurements showed that the coercivity field and remanence ratio of the films decrease as the applied current density increases. Consequently, Ni–Co thin films exhibited different microstructural, compositional, magnetic, and morphological properties according to current density applied during electroplating process. © 2014, Springer Science+Business Media New York. Source


Polat K.,Bartn University | Durduran S.S.,Selcuk University
Neural Computing and Applications | Year: 2012

In this study, the traffic accidents recognizing risk factors related to the environmental (climatological) conditions that are associated with motor vehicles accidents on the Konya-Afyonkarahisar highway with the aid of Geographical Information Systems (GIS) have been determined using the combination of K-means clustering (KMC)-based attribute weighting (KMCAW) and classifier algorithms including artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS). The dynamic segmentation process in ArcGIS9. 0 from the traffic accident reports recorded by District Traffic Agency has identified the locations of the motor vehicle accidents. The attributes obtained from this system are day, temperature, humidity, weather conditions, and month of occurred traffic accidents. The traffic accident dataset comprises five attributes (day, temperature, humidity, weather conditions, and month of occurred traffic accidents) and 358 observations including 179 without accident and 179 with accident. The proposed comprises two stages. In the first stage, the all attributes of dataset have been weighted using KMCAW method. The aims of this weighting method are both to increase the classification performance of used classifier algorithm and to transform from linearly non-separable traffic accidents dataset to a linearly separable dataset. In the second stage, after weighting process, ANN and ANFIS classifier algorithms have been separately used to determine the case of traffic accidents as with accident or without accident. In order to evaluate the performance of proposed method, the classification accuracy, sensitivity, specificity and area under the ROC (Receiver Operating Characteristic) curves (AUC) values have been used. While ANN and ANFIS classifiers obtained the overall prediction accuracies of 53. 93 and 38. 76%, respectively, the combination of KMCAW and ANN and the combination of KMCAW and ANFIS achieved the overall prediction accuracies of 74. 15 and 55. 06% on the prediction of traffic accidents. The experimental results have demonstrated that the proposed attribute weighting method called KMCAW is a robust and effective data pre-processing method in the prediction of traffic accidents on Konya-Afyonkarahisar highway in Turkey. © 2011 Springer-Verlag London Limited. Source


OZTuRK M.,Bartn University
Plant Biosystems | Year: 2015

Leaf area index (LAI) analysis of deciduous forest trees is usually restricted to seasonal monitoring involving the assessment of distinct leaf phenological stages within definite time intervals of the year. However, continuous LAI monitoring that includes entire leaf periods is necessary to define the ecophysiological characteristics of deciduous trees. Therefore, this study investigated the intra-annual cycle of the LAI for a Platanus orientalis L. stand in the Bartın watershed of Turkey. A complete cycle involves three periods: foliation, stable, and defoliation. The foliation period comprises budburst, leaf emergence and flushing sessions, whereas the defoliation period consists of leaf senescence and leaf fall sessions. The stable period is in between these two periods when LAI values are at a climax around maximum. Eight points were determined in the field for the analysis of LAI by a hemispherical photography technique. Over a relatively frequent schedule, photographs were taken almost weekly during the foliation period. Both weekly and approximate monthly photographs were applied during the stable period. Finally, near-monthly photographs were taken for the defoliation period. The foliation period lasted for about 1.5 months from mid-April to May with the mean LAI reaching from 0.16 up to 2.38. Mean LAI was between 2.38 and 2.47 for a stable period over 2 months (June and July). For the defoliation period, mean LAI dropped from 2.42 down to 0.35 over 5 months from August to December. The total foliated period was more than 8 months, which is relatively long for a temperate forest. In addition, correlations between mean LAI and maximum, mean and minimum temperatures were highly significant (P < 0.01) with coefficients (r) of 0.79, 0.90 and 0.93, respectively. By describing the intra-annual LAI pattern, this study fills a gap in the literature on the phenology of Platanus orientalis L. © 2015 Società Botanica Italiana Source


Polat K.,Bartn University
International Journal of Systems Science | Year: 2012

This study presents the application of fuzzy c-means (FCM) clustering-based feature weighting (FCMFW) for the detection of Parkinson's disease (PD). In the classification of PD dataset taken from University of California - Irvine machine learning database, practical values of the existing traditional and non-standard measures for distinguishing healthy people from people with PD by detecting dysphonia were applied to the input of FCMFW. The main aims of FCM clustering algorithm are both to transform from a linearly non-separable dataset to a linearly separable one and to increase the distinguishing performance between classes. The weighted PD dataset is presented to k-nearest neighbour (k-NN) classifier system. In the classification of PD, the various k-values in k-NN classifier were used and compared with each other. Also, the effects of k-values in k-NN classifier on the classification of Parkinson disease datasets have been investigated and the best k-value found. The experimental results have demonstrated that the combination of the proposed weighting method called FCMFW and k-NN classifier has obtained very promising results on the classification of PD. © 2012 Copyright Taylor and Francis Group, LLC. Source


Polat K.,AbantzzetBaysal University | Krmac V.,Bartn University
International Journal of Refrigeration | Year: 2011

This paper focused on the determining of gas types in counter flow type vortex tubes. In the present study, four different gas types including air, oxygen, nitrogen, and argon in the vortex tube with different inlet pressures and nozzle numbers have been used. The main aims of this paper are to investigate the correlations between gas types and input parameters comprising nozzle numbers, inlet pressures, inlet mass flow rate, temperature of cold outlet, temperature of hot outlet, and cold mass fraction and to select the most important attributes using correlation based attribute reduction and pairwise fisher score attribute reduction (PFSAR). After attribute reduction methods applied to dataset, k-nearest neighbor and C4.5 decision tree classifiers have been used to determine the gas type in the RHVT. The results have demonstrated that the PFSAR is a robust and efficient method in the reduction of attributes belonging to vortex tube. © 2011 Elsevier Ltd and IIR. All rights reserved. Source

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