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Yalcin B.,Yildirim Beyazit University
Experimental Oncology | Year: 2013

Breast cancer is the most common female malignant disease in the western countries where a woman's lifetime risk of developing the disease is more than 10%. Nulliparity or use of hormonal replacement therapy, strong family history, or a history of therapeutic thoracic radiation are considerable high risk factors for the development of breast cancer. Nowadays more new effective therapeutic agents have been developed for the intervention of the breast cancer, but prognosis is still remained poor in the metastatic disease. For the general population, screening mammography in women older than 40-45 years has been shown to be effective in identifying early-stage breast cancer and in decreasing the mortality rate. In randomized screening mammography trials for breast cancer, it has been established that screening mammograms reduced breast cancer mortality in women older than 50 years of age by 25 to 30%. This review article summarizes the risk factors for developing breast cancer, methods for risk assessment and the accepted screening guidelines. Copyright © Experimental Oncology, 2013. Source

Hiziroglu A.,Yildirim Beyazit University
Expert Systems with Applications | Year: 2013

Segmentation has been taken immense attention and has extensively been used in strategic marketing. Vast majority of the research in this area focuses on the usage or development of different techniques. By means of the internet and database technologies, huge amount of data about markets and customers has now become available to be exploited and this enables researchers and practitioners to make use of sophisticated data analysis techniques apart from the traditional multivariate statistical tools. These sophisticated techniques are a family of either data mining or machine learning research. Recent research shows a tendency towards the usage of them into different business and marketing problems, particularly in segmentation. Soft computing, as a family of data mining techniques, has been recently started to be exploited in the area of segmentation and it stands out as a potential area that may be able to shape the future of segmentation research. In this article, the current applications of soft computing techniques in segmentation problem are reviewed based on certain critical factors including the ones related to the segmentation effectiveness that every segmentation study should take into account. The critical analysis of 42 empirical studies reveals that the usage of soft computing in segmentation problem is still in its early stages and the ability of these studies to generate knowledge may not be sufficient. Given these findings, it can be suggested that there is more to dig for in order to obtain more managerially interpretable and acceptable results in further studies. Also, recommendations are made for other potentials of soft computing in segmentation research. © 2013 Elsevier Ltd. All rights reserved. Source

Celebi N.,Yildirim Beyazit University
Optoelectronics and Advanced Materials, Rapid Communications | Year: 2013

Mid infrared quantum cascade lasers (QCLs) emitting in the 3-4 micron wavelength range have many potential applications in addition to sensitive gas detection since some gases have their strongest absorption features in this region. In this study, this type of QCL is intelligently modelled as a function of characteristic quantities (gain, refractive index change with injection current, linewidth enhancement factor) in terms of radial basis function network (RBFN). The single model results well matched with the experimental data reported elsewhere. Source

Akkemik K.A.,Kadir Has University | Goksal K.,Yildirim Beyazit University
Energy Economics | Year: 2012

Existing studies examining the Granger causality relationship between energy consumption and GDP use a panel of countries but implicitly assume that the panels are homogeneous. This paper extends the Granger causality relationship between energy consumption and GDP by taking into account panel heterogeneity. For this purpose, we use a large panel of 79 countries for the period 1980-2007. Specifically, we examine four different causal relationships: homogeneous non-causality, homogeneous causality, heterogeneous non-causality, and heterogeneous causality. The results show that roughly seven-tenths of the countries exhibit bi-directional Granger causality, two-tenths exhibit no Granger causality, and one-tenths exhibit uni-directional Granger causality. © 2012 Elsevier B.V. Source

Cay Y.,Karabuk University | Korkmaz I.,Duzce University | Cicek A.,Yildirim Beyazit University | Kara F.,Duzce University
Energy | Year: 2013

This study investigates the use of ANN (artificial neural networks) modelling to predict BSFC (break specific fuel consumption), exhaust emissions that are CO (carbon monoxide) and HC (unburned hydrocarbon), and AFR (a. ir-fuel ratio) of a spark ignition engine which operates with methanol and gasoline. To obtain training and testing data, a number of experiments were performed with a four-cylinder, four-stroke test engine operated at different engine speeds and torques. The experimental results reveal that the methanol improved the emission characteristics compared with the gasoline. For the ANN modelling, the standard back-propagation algorithm was found to be the optimum choice for training the model. In the building of the network structure, four different learning algorithms were used such as BFGS (Quasi-Newton back propagation), LM (Levenberg-Marquardt learning algorithm). It was found that the ANN model is able to predict the engine performance and exhaust emissions with a correlation coefficient of 0.998621, 0.977654, 0.998382 and 0.996075 for the BSFC, CO, HC and AFR for testing data, respectively. It was obvious that the developed ANN model is fairly powerful for predicting the brake specific fuel consumption and exhaust emissions of internal combustion engines. © 2012 Elsevier Ltd. Source

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