Yazar "Karlik B." seçeneğine göre listele
Listeleniyor 1 - 4 / 4
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe Comparison of type-2 fuzzy clustering-based cascade classifier models for ECG arrhythmias(World Scientific Publishing Co. Pte Ltd, 2014) Ceylan R.; Özbay Y.; Karlik B.The aim of this study is to present a comparison of the novel cascade classifier models based on fuzzy clustering and feature extraction techniques according to efficiency. These models are composed of three subsystems: The first subsystem is constituted by fuzzy clustering technique to choose the best patterns that ideally show its class attributes in dataset. The second subsystem consists of discrete wavelet transform (DWT) which realizes feature extraction procedure on selected patterns by using fuzzy c-means clustering. The last subsystem implements the classification of extracted features for each pattern using classification algorithm. In this paper, type-2 fuzzy c-means (T2FCM) clustering is used in the first subsystem of the proposed classification models and the new training set is obtained. In the second subsystem, the features of the obtained new training set are extracted with DWT; hence, three different feature sets along with different number of features are formed using Daubechies-2 wavelet function. In the last subsystem of the model, the feature sets are classified using classification algorithm. Here, two different classification algorithms, neural network (NN) and support vector machine (SVM), are used for comparison. Thus, two classification models are implemented and named as T2FCWNN (classifier is NN) and T2FCWSVM (classifier is SVM), respectively. This proposed classifier models have been applied to classify electrocardiogram (ECG) signals. One of the goals of this study is to present a fast and efficient classifier. For this reason, high accuracy rate is been aimed for classification of RR intervals in ECG signal. So, we have utilized T2FCM and WTs to improve the performance of the classification algorithms. Both training and testing set for classifier models have included 12 ECG signal classes. Well-known back propagation algorithm has been used for training of neural networks (NNs). The best testing results have been obtained with 99% recognition accuracy with T2FCWNN-2. © 2014 National Taiwan University.Öğe Determination of drilling points with artificial neural networks [Sondaj noktalari{dotless}ni{dotless}n yapay sinir a?lari{dotless} ile belirlenmesi](Chamber of Mining Engineers of Turkey, 2013) Özdeniz A.H.; Karlik B.Drilling operations are widely used method in mine exploration. Mine exploration using this method is required a significant work to determine the mine characteristics such as reserves, grade and quality. In addition, drilling operations is an important outcome for mine companies. Evaluation of mines is so difficult however it is so necessary. Data obtained from exploration drilling is used to determine the new drilling points to direct the drilling work. In this study, drilling data was used obtained from 28 drilling well in a coal mine located at Ermenek region of Konya, Turkey. Artificial neural networks (ANN) software has developed to determine new drilling location points at this coal mine. A supervised neural network learning algorithm (Back-propagation) was used to train and test for a drilling data obtained from coal mine. Moreover, experimental and test results of ANN were compared. The results showed that determination of a new drilling points using ANN are provided great benefit in terms of both time and cost.Öğe A new sparse convex combination of ZA-LLMS and RZA-LLMS algorithms [ZA-LLMS ve RZA-LLMS Algoritmalarinin Seyrek Sistemlerdeki Dişbükey Birleşimi](Institute of Electrical and Electronics Engineers Inc., 2015) Salman M.S.; Hameed A.A.; Turan C.; Karlik B.In the last decade, several algorithms have been proposed for performance improvement of adaptive filters in sparse system identification. In this paper, we propose a new convex combination of two different algorithms as zero-attracting leaky least-mean-square (ZA-LLMS) and reweighted zero-attracting leaky-least-mean square (RZA-LLMS) algorithms in sparse system identification setting. The performances of the aforementioned algorithms has been tested and compared to the result of the new combination. Simulations show that the proposed algorithm has a good ability to track the MSD curves of the other algorithms in additive white Gaussian noise (AWGN) and additive correlated Gaussian noise (ACGN) environments. © 2015 IEEE.Öğe Soft computing methods in bioinformatics: A comprehensive review(2013) Karlik B.Applications of genomic and proteomic, epigenetic, pharmacogenomics, and systems biology have shown increased a lot, resulting in an explosion in the amount of highly dimensional and complicated data being generated. The data of bioinformatics fields are always with high-dimension and small samples. Genome-wide investigations generate in large numbers of data and there is a need for soft computing methods (SCMs) such as artificial neural networks, fuzzy systems, evolutionary algorithms, metaheuristic and swarm intelligence algorithms, statistical model algorithms etc. that can deal with this amount of data. The use of soft computing methods has been increased to a variety of bioinformatics applications. It is used to inquire the underlying mechanisms and interactions between biological molecules in a lot of diseases, and it is a main tool in any biological (or biomarker) discovery process. The aim of this article is to introduce soft computing methods for bioinformatics. These methods present supervised or unsupervised classification, clustering and statistical or stochastic heuristics models for knowledge discovery. In this article, the current problems and the prospects of SCMs in the application of bioinformatics is also discussed.
