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Öğe Forecasting local mean sea level by generalized behavioral learning method(Springer Nature, 2017-03-13) Ertuğrul, Ömer Faruk; Tağluk, Mehmet EminDetermining and forecasting the local mean sea level (MSL), which is a major indicator of global warming, is an essential issue to set public policies to save our future. Owing to its importance, MSL values are measured and shared periodically by many agencies. It is not easy to model or forecast MSL because it depends on many dynamic sources such as global warming, geophysical phenomena, and circulations in the ocean and atmosphere. Several of researchers applied and recommended employing artificial neural network (ANN) in the estimation of MSL. However, ANN does not take into account the order of samples, which may consist essential information. In this study, the generalized behavioral learning method (GBLM), which is based on behavioral learning theories, was employed in order to achieve higher accuracies by using samples in the training dataset and the order of samples. To evaluate and validate GBLM, MSL of seven stations around the world was picked up. These datasets were employed to forecast the local MSL for the future. Obtained results were compared with the ones obtained by ANN that is trained by extreme learning machine and the literature. The GBLM is found to be successful in terms of the achieved high accuracies and the ability to tracking trends and fluctuations of a local MSL.Öğe A novel approach for extracting ideal exemplars by clustering for massive time-ordered datasets(TÜBİTAK, 2017-07-30) Ertuğrul, Ömer FarukThe number and length of massive datasets have increased day by day and this yields more complex machine learning stages due to the high computational costs. To decrease the computational cost many methods were proposed in the literature such as data condensing, feature selection, and filtering. Although clustering methods are generally employed to divide samples into groups, another way of data condensing is by determining ideal exemplars (or prototypes), which can be used instead of the whole dataset. In this study, first the efficiency of traditional data condensing by clustering approach was confirmed according to obtained accuracies and condensing ratios in 9 different synthetic or real batch datasets. This approach was then improved to be employed in time-ordered datasets. In order to validate the proposed approach, 23 different real time-ordered datasets were used in experiments. Achieved mean RMSEs were 0.27 and 0.29 by employing the condensed (mean condensed ratio was 97.17%) and the whole datasets, respectively. Obtained results showed that higher accuracy rates and condensing ratios were achieved by the proposed approach.Öğe Imine containing C2-Symmetric chiral half sandwich h6-p-cymene-Ru(II)- phosphinite complexes: Investigation of their catalytic activityin the asymmetric transfer hydrogenation of ketones(Journal of Molecular Structure, 2020-01-15) Aral, Tarık; Paşa, Salih; Kayan, Cezmi; Meriç, Nermin; Sünkür, Murat; Aydemir, Murat; Baysal, Akın; Durap, Feyyaz; Saleh, Najmuldain AbdullahNew chiral C2-symmetric bis(phosphinite) ligands containing imine group were synthesized from 1-({[(1R,2R)-2-{[(2-hydroxynaphthalen-1-yl)methylidene] amino}cyclohexyl]- imino}methyl)- naphthalen-2-ol and two equivalents of Ph2PCl, (i-Pr)2PCl or (Cy)2PCl, in high yields. Binuclear C2-symmetric half sandwich η6-p-cymene-Ru(II) complexes of the chiral phosphinite ligands were synthesized by treating of [Ru(η6-p-cymene)(μ-Cl)Cl]2 with the phosphinites in 1:1 M ratio in CH2Cl2. Their catalytic activities in asymmetric transfer hydrogenation (ATH) were investigated for the reaction of acetophenone derivatives with isopropyl alcohol. The corresponding optically active secondary alcohols were obtained in excellent levels of conversion (96–99%) and moderate enantioselectivity (up to 60% ee). Among three complexes investigated, complex 4 was the most efficient one.Öğe Al-Makrizi’s Khitat and the markets in Cairo during the Mamluks era(Türk Tarih Kurumu, 2017-08) Ağır, Abdullah MesutThis study examines the markets in Cairo during the reign of the Mamlūks in the light of al-Makrīzī’s Chronicle al-Khitat. Besides those which were built during the Mamlūks era the commercial life were ongoing at the markets dating back to the Fatimids and the Ayyubids periods. The marketplaces generally occupied in al-Qasaba which was between Bāb al-Futūh in the north and Bāb al-Zuwayla in the south was the trading center of the city. Al-Qasaba is al-Mu’izz Street today which takes its name from the Fatimid Caliph al-Mu’izz li-Dinillah (341-364/953-975). The economic and social decline especially seen during the second half of the Mamlūks in the 15th century af-fected also the domestic markets stability and most of the sûqs disappeared depending on these conditions.Öğe A fast feature selection approach based on extreme learning machine and coefficient of variation(TÜBİTAK, 2017-07-30) Ertuğrul, Ömer Faruk; Tağluk, Mehmet EminFeature selection is the method of reducing the size of data without degrading their accuracy. In this study, we propose a novel feature selection approach, based on extreme learning machines (ELMs) and the coefficient of variation (CV). In the proposed approach, the most relevant features are identified by ranking each feature with the coefficient obtained through ELM divided by CV. The achieved accuracies and computational costs, obtained with the use of features selected via the proposed approach in 9 classification and 26 regression benchmark data sets, were compared to those obtained with all features, as well as those obtained with the features selected by a wrapper and a filtering method. The achieved accuracy values obtained with the proposed approach were generally higher than when using all features. Furthermore, high feature reduction ratios were obtained with the proposed approach, including the achieved feature reduction ratios in epilepsy, liver, EMG, shuttle, and abalone. Stock data sets were 90.48%, 90%, 70.59%, 66.67%, 75%, and 77.78%, respectively. This approach is an extremely fast process that is independent of the employed machine-learning methods.
