WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Article Citation - WoS: 5Citation - Scopus: 4Uv-Visible Spectrophotometric Quantitative Analysis of Ternary Mixture Using Multivariate Calibration Methods Optimized by a Genetic Algorithm(Syscom 18 SRL, 2010) Özdemir, Durmuş; Dinç, Erdal; Baleanu, DimutruSimultaneous determination of ternary mixtures of caffeine, paracetamol and metamizol in commercial tablet formulations using UV-visible spectrophotometry combined with classical least squares (CLS) and genetic algorithm (GA) based multivariate calibration methods were demonstrated. The three genetic multivariate calibration methods are named as Genetic Classical Least Squares (GCLS), Genetic Inverse Least Squares (GILS) and Genetic Regression (GR). The GR method is based on a genetic algorithm based wavelength selection followed by a simple linear regression step whereas the GCLS and GILS are multivariate calibration methods modified by a wavelength selection principle using a genetic algorithm. The sample data set contains the UV-visible spectra of 47 synthetic mixtueres (4 to 48 μg/mL) and 16 tablets containing these components from two different producers. The spectra cover the range from 200 to 330 nm in 0.1 nm intervals. Several calibration models were built with the four methods for the three components. Overall, the standard error of calibration (SEC) and the standard error of prediction (SEP) for the synthetic data were in the range of 0.04 and 2.34 μg/mL for all the four methods. Predictive ability of the calibration models generated with synthetic samples was tested with actual tablet samples and results obtained from four methods were compared. The SEP values for the tablets were in the range of 0.31 and 15.44 mg/tablets.Conference Object Yazılım Tanımlı Ağlar için Güç Verimli Yol Atama(Institute of Electrical and Electronics Engineers Inc., 2016) Aydoğmuş, Yiğitcan; Özbek, Berna; Koyuncu, Onur; Ulusoy, Kazım; Karakaya, ÖzgürMobil uygulamaların gittikçe yaygınlaşması ile artan trafik çeşitliliği ve hacmi, ağlarda taşınan trafiğin yönetilmesi ihtiyacını kuvvetlendirdi. Yazılım tanımlı ağlar, trafik yönetimini kullanarak belirlenen gereksinimleri karşılarken, verimi maksimuma çıkararak ağları yönetebilir. Bu bildiride, ağdaki aktif anahtar sayısına dayanan güç tüketimini minimuma indiren bir yol atama algoritması öneriyoruz. Bağlantı kapasitesi kısıtlamalarını göz önüne alarak, akışların veri hacmi gereksinimlerini karşılayan en iyi yolu bulmak için genetik algoritma kullanıp, düşük karmaşıklıklı yeni bir yol atama yaklaşımı öneriyoruz. Önerilen algoritmanın verilen ağ topolojisinde çeşitli akış veri hacmi kısıtlamalarına göre performans değerlendirmelerini sunuyoruz.Article Citation - WoS: 2Citation - Scopus: 2Ampirik Yöntemlerle Gediz Nehri için Askıda Katı Madde Yükü Tahmini(Turkish Chamber of Civil Engineers, 2011) Ülke, Aslı; Özkul, Sevinç; Tayfur, GökmenIt is essential to predict suspended sediment load for understanding river morphology, design of dams, water supply problems, management of reservoirs and determination of pollution levels in rivers. The suspended sediment load can be determined by means of several methods such as direct measurements at the sediment gauging stations, sediment rating curve, son modeling methods, and empirical methods which are based on experimental works. The objective of this study is first to determine the best empirical method for Gediz river and then to improve the determined method by genetic algorithm (GA). It is seen that the GA improved Brooks method can be used for Gediz River Basin. In addition, this method was compared with other soft computing (ANN, ANFIS) methods and its performance is found to be as good as them.Conference Object Citation - WoS: 1Citation - Scopus: 6Genetic Algorithm-Artificial Neural Network Model for the Prediction of Germanium Recovery From Zinc Plant Residues(Taylor and Francis Ltd., 2002) Akkurt, Sedat; Özdemir, Serhan; Tayfur, GökmenA multi-layer, feed-forward, back-propagation learning algorithm was used as an artificial neural network (ANN) tool to predict the extraction of germanium from zinc plant residues by sulphuric acid leaching. A genetic algorithm (GA) was used for the selection of training and testing data and a GA-ANN model of the germanium leaching system was created on the basis of the training data. Testing of the model yielded good error levels (r2 = 0.95). The model was employed to predict the response of the system to different values of the factors that affect the recovery of germanium and the results facilitate selection of the experimental conditions in which the optimum recovery will be achieved.