Yazar "Bayir, S." seçeneğine göre listele

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    Detection of railroad networks in SAR images
    (Science and Information Organization, 2018) Açar, S.A.; Bayir, S.
    In this study, a railroad networks detection method for synthetic aperture radar (SAR) images is proposed. Proposed method consists of three steps. Firstly, railroad segments are detected. An existing line detector is modified by describing some rules for this process. Then segments are connected by utilizing perceptual grouping. Finally, a new line analysis algorithm is applied to determine real parts of railroad networks. A software is developed to achieve and evaluate proposed method. Completeness and correctness values which are obtained after different steps are computed to evaluate proposed method. Two different TerraSAR-X images are used in experiments and obtained results are discussed in detail. © 2018 International Journal of Advanced Computer Science and Applications.
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    A hierarchical view of a national stock market as a complex network
    (Bucharest University of Economic Studies, 2017) Baydilli, Y.Y.; Bayir, S.; Türker, I.
    We created a financial network for Borsa Istanbul 100 Index (BIST–100) which forms of N=100 stocks that bargained during T=2 years (2011– 2013). We analyzed the market via minimum spanning tree (MST) and hierarchical tree (HT) by using filtered correlation matrix. While using hierarchical methods in order to investigate factors that affecting grouping of stocks, we have taken account the other statistical and data mining methods to examine success of stock correlation network concept for portfolio optimization, risk management and crisis analysis. We observed that financial stocks, especially Banks, are central position of the network and control information flow. Besides the sectoral and sub-sectoral behavior, corporations play role at grouping of stocks. Finally, this technique provided important tips for determining risky stocks in market. © 2017, Bucharest University of Economic Studies. All rights reserved.
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    Placement score estimation of secondary education transition system (SETS) using artificial neural networks
    (2012) Ucar, E.; Sen, B.; Bayir, S.
    This study offers an approach based on artificial neural networks for predicting the placement score of secondary education transition system (SETS). Artificial neural networks have recently become a very important method in the classification and prediction of the problems. Therefore, Multilayer Perceptron (MLP) and Radial Basis Function (RBF) which are among the most preferred artificial neural network architectures were used in this study. Created expert system was trained and tested on a database including 25000 randomly selected records of primary education 8th grade students. Results of this training and testing are comparatively presented within the study. © Sila Science.
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    Pre-processes for urban areas detection in SAR images
    (International Society for Photogrammetry and Remote Sensing, 2017) Altay, Açar, S.; Bayir, S.
    In this study, pre-processes for urban areas detection in synthetic aperture radar (SAR) images are examined. These pre-processes are image smoothing, thresholding and white coloured regions determination. Image smoothing is carried out to remove noises then thresholding is applied to obtain binary image. Finally, candidate urban areas are detected by using white coloured regions determination. All pre-processes are applied by utilizing the developed software. Two different SAR images which are acquired by TerraSAR-X are used in experimental study. Obtained results are shown visually. © Authors 2017.