Yazar "Luy M." seçeneğine göre listele

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    Öğe
    Böbrek Tümör Segmentasyonu İçin Unet ve Unet-ResNet Modellerinin Karşilaştirilmasi
    (Institute of Electrical and Electronics Engineers Inc., 2019) Turk F.; Luy M.; Barisci N.
    Kidney cancer is one of the types of cancer that can be difficult to diagnose and can be very complicated for physicians to diagnose. Especially in recent years, many new treatment methods for kidney cancer have been developed and some of them are still under development by scientists. These studies enable new treatment modalities for kidney cancer patients. In addition, renal tumors are one of the most insidious progressive tumor types. Many times it can be mistaken for other diseases. Especially until the last stage, patients may not even have a serious complaint. Therefore, conducting such studies is very important for early diagnosis. In this study, it is tried to segmentation with deep learning methods in order to help people who are dealing with difficulties of kidney cancer diagnosis. For this reason, Unet and Unet-ResNet models were compared. The Unet-ResNet model achieved 90.2% success for renal tumor segmentation, while the Unet model achieved 44.3% success for renal tumor segmentation. These results shed light on how successful and necessary the Unet-ResNet model can be in particular in studies on image segmentation. © 2019 IEEE.
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    Öğe
    Short-term fuzzy load forecasting model using genetic-fuzzy and ant colony-fuzzy knowledge base optimization
    (MDPI AG, 2018) Luy M.; Ates V.; Barisci N.; Polat H.; Cam E.
    The estimation of hourly electricity load consumption is highly important for planning short-term supply-demand equilibrium in sources and facilities. Studies of short-term load forecasting in the literature are categorized into two groups: classical conventional and artificial intelligence-based methods. Artificial intelligence-based models, especially when using fuzzy logic techniques, have more accurate load estimations when datasets include high uncertainty. However, as the knowledge base-which is defined by expert insights and decisions-gets larger, the load forecasting performance decreases. This study handles the problem that is caused by the growing knowledge base, and improves the load forecasting performance of fuzzy models through nature-inspired methods. The proposed models have been optimized by using ant colony optimization and genetic algorithm (GA) techniques. The training and testing processes of the proposed systems were performed on historical hourly load consumption and temperature data collected between 2011 and 2014. The results show that the proposed models can sufficiently improve the performance of hourly short-term load forecasting. The mean absolute percentage error (MAPE) of the monthly minimum in the forecasting model, in terms of the forecasting accuracy, is 3.9% (February 2014). The results show that the proposed methods make it possible to work with large-scale rule bases in a more flexible estimation environment. © 2018 by the authors.