Yazar "Arslan, Ahmet" seçeneğine göre listele
Listeleniyor 1 - 20 / 65
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe The Adiponectin variants contribute to the genetic background of type 2 diabetes in Turkish population(ELSEVIER, 2014) Arikoglu, Hilal; Ozdemir, Hulya; Kaya, Dudu Erkoc; Ipekci, Suleyman Hilmi; Arslan, Ahmet; Kayis, Seyit Ali; Gonen, Mustafa SaitAdiponectin, an adipose tissue specific protein encoded by the Adiponectin gene, modulates insulin sensitivity and plays an important role in regulating energy homeostasis. Many studies have shown that single nucleotide polymorphisms (SNPs) in the Adiponectin gene are associated with low plasma Adiponectin levels, insulin resistance and an increased risk of type 2 diabetes mellitus. The aim of the present study was to evaluate the contribution of the Adiponectin gene polymorphisms in genetic background of type 2 diabetes in a Turkish population. In total, 169 unrelated and non-obese diabetic patients and 119 age- and BMI-matched nondiabetic individuals with no family history of diabetes were enrolled in this study. We detected a significant association between type 2 diabetes and two SNPs: SNP - 11391G>A. which is located in the promoter region of the Adiponectin gene, and SNP + 276G > T, which is found in intron 2 of the gene (P < 0.05). The silence SNP G15G ( + 45T > G) in exon 1 and SNP + 349A > G in intron 2 also showed a weak association with type 2 diabetes (P = 0.06 and P = 0.07, respectively), while SNPs - 3971A>G in intron 1 and Y111H, R112C and H241P in exon 3 showed no association (P > 0.05). In conclusion, these findings suggest that Adiponectin gene polymorphisms might be effective on susceptibility for type 2 diabetes development which emerged from the interactions between multiple genes, variants and environmental factors. (C) 2013 Elsevier B.V. All rights reserved.Öğe The Approximation of Interval Bezier Curves by a Fuzzy Rule-Based System(2009) Ülker, Erkan; Arslan, AhmetGenerally, we are interested in knots and control points as variables for finding a well Bezier model from many measuring data points. In this paper, we have selected good knots from given distributed data points on the basis of geometrical information such as curvature and bending of the curve. We have developed interval Bezier curves by estimating control points as an interval according to the knots which are selected by fuzzy rule basis. We have compared these curves and distributed data with the least squares error. We introduced, to the user the as interval Bezier curve, the one who has the minimum error toleration with variations in order to obtain most appropriate widths of Interval control points.Öğe Association Between the T-593A and C6982T Polymorphisms of the Osteopontin Gene and Risk of Developing Nephrolithiasis(Elsevier Science Inc, 2010) Göğebakan, Bülent; İğci, Yusuf Ziya; Arslan, Ahmet; İğci, Mehri; Erturhan, Sakip; Öztuzcu, Serdar; Şen, Haluk; Demiryürek, Seniz; Arıkoğlu, Hilal; Cengiz, Beyhan; Bayraktar, Recep; Yurtseven, Cihanser; Sarıca, Kemal; Demiryürek, Abdullah T.Background and Aims. Increased synthesis of several urinary proteins including osteopontin (OPN) has been shown to be associated with stone formation within the urinary tract. The objective of this study was to analyze the genotype distributions and allele frequencies for OPN gene promoter T-593A and C6982T (in exon 7) polymorphisms among patients with kidney stones. Methods. In this case-control study, the study group consisted of 121 patients with radiologically confirmed nephrolithiasis. Genomic DNA from patients and control cases (n = 100) was analyzed by single-strand conformation polymorphism method and nucleotide sequence analysis. Results. Homozygous carriers of the T-593T genotype were more frequent, but carriers of the A-593A genotype were less frequent in patients than in controls. There was also an increase in -593T allele (88% in patients vs. 79% in controls) and decrease in -593A allele frequencies (21% in control vs. 12% in patients) in the nephrolithiasis groups (p = 0.013). The carriers of C6982C genotype were less frequent, but marked increases in T6982T genotype (25.6% in patients vs. 7% in controls, p = 0.001) and 6982T allele frequency (53.3% in patients vs. 37.5% in controls, p = 0.001) were noted in patients of Turkish ancestry. Conclusions. These results are the first to demonstrate the existence of T-593A promoter polymorphism of the OPN gene and significant association with risk of developing nephrolithiasis. Our results showed marked associations between polymorphisms (C6982T and T-593A) of the OPN gene and the stone-forming phenotypes in the Turkish population.Öğe ASSOCIATION OF SINGLE NUCLEOTIDE POLYMORPHISMS (SNPS)+276 AND+349 IN THE ADIPONECTIN GENE WITH TYPE 2 DIABETES MELLITUS IN TURKISH POPULATION(JOHN WILEY & SONS INC, 2009) Arıkoğlu, Hilal; Erkoç Kaya, Dudu; Özdemir, Hülya; Gotten, Mustafa Sait; Ipckci, Suleyman; Arslan, Ahmet; Hepdoğru Aksoy, Melda[Abstract not Available]Öğe ATTRIBUTE REDUCTION BY PARTITIONING THE MINIMIZED DISCERNIBILITY FUNCTION(ICIC INTERNATIONAL, 2011) Kahramanli, Sirzat; Hacibeyoglu, Mehmet; Arslan, AhmetThe goal of attribute reduction is to reduce the problem size and search space for learning algorithms. The basic solution of this problem is to generate all possible minimal attributes subsets (MASes) and choose one of them, with minimal size. This can be done by constructing a kind of discernibility function (DF) from the dataset and converting it to disjunctive normal form (DNF). Since this conversion is NP-hard, for attribute reduction usually heuristic algorithms are used. But these algorithms generate one or a small number of possible MASes that generally is not sufficient for optimality of dataset processing in such aspects as the simplicity of data representation and description, the speed and classification accuracy of the data mining algorithms and the required amount of memory. In this study, we propose an algorithm that finds all MASes by iteratively partitioning the DF so that the part to be converted to DNF in each of iterations has the space complexity no higher than the square root of the worst-case space complexity of the conversion of the whole DF to DNF. The number of iterations is always fewer than the number of attributes.Öğe Automatic Discovery of the Sequential Accesses From Web Log Data Files via a Genetic Algorithm(Elsevier Science Bv, 2006) Tuğ, Emine; Şakiroğlu, Merve; Arslan, AhmetThis paper is concerned with finding sequential accesses from web log files, using 'Genetic Algorithm' (GA). Web log files are independent from servers, and they are ASCII format. Each transaction, whether completed or not, is recorded in the web log files and these files are unstructured for knowledge discovery in database techniques. Data which is stored in web logs have become important for discovering of user behaviors since the using of internet increased rapidly. Analyzing of these log files is one of the important research area of web mining. Especially, with the advent of CRM (Customer Resource Management) issues in business circle, most of the modem firms operating web sites for several purposes are now adopting web-mining as a strategic way of capturing knowledge about potential needs of target customers, future trends in the market and other management factors. Our work (ALMG-Automatic Log Mining via Genetic) has mined web log files via genetic algorithm. When we search the studies about web mining in literature, it can be seen that, GA is generally used in web content and web structure mining. On the other hand, ALMG is a study about web mining usage. The difference between ALMG and other similar works at literature is this point. As for in another work that we are encountering, GA is used for processing the data between HTML tags which are placed at client PC. But ALMG extracts information from data which is placed at server. It is thought to use log files is an advantage for our purpose. Because, we find the character of requests which is made to the server than detect a single person's behavior. We developed an application with this purpose. Firstly, the application is analyzed web log files, than found sequential accessed page groups automatically.Öğe Automatic Emotional Expression Analysis from Eye Area(SPIE-INT SOC OPTICAL ENGINEERING, 2015) Akkoç, Betül; Arslan, AhmetEyes play an important role in expressing emotions in nonverbal communication. In the present study, emotional expression classification was performed based on the features that were automatically extracted from the eye area. First, the face area and the eye area were automatically extracted from the captured image. Afterwards, the parameters to be used for the analysis through discrete wavelet transformation were obtained from the eye area. Using these parameters, emotional expression analysis was performed through artificial intelligence techniques. As the result of the experimental studies, 6 universal emotions consisting of expressions of happiness, sadness, surprise, disgust, anger and fear were classified at a success rate of 84% using artificial neural networks.Öğe Automatic Emotional Expression Analysis from Lip Shapes Using Artificial Neural Networks(IEEE, 2013) Akkoç, Betül; Ülker, Erkan; Arslan, AhmetIn this study, emotional expressions are automatically analyzed from lip form using artificial neural network. Firstly facial region and then mouth area were found for expressions analysis. After these steps, lip shape was found and the parameters that are used for the analysis were automatically obtained. Artificial neural network trained using these parameters. This network was used for expression analysis. Success rate of 86% is achieved for the seven classes mentioned (happiness, sadness, surprise, disgust, anger, fear and disdain) whereas a success rate of 97% is found when three classes (happiness, sadness and surprised) are used.Öğe Automatic emotional expression analysis from lip shapes using artificial neural networks(2013) Akkoç, Betül; Ülker, Erkan; Arslan, AhmetIn this study, emotional expressions are automatically analyzed from lip form using artificial neural network. Firstly facial region and then mouth area were found for expressions analysis. After these steps, lip shape was found and the parameters that are used for the analysis were automatically obtained. Artificial neural network trained using these parameters. This network was used for expression analysis. Success rate of 86% is achieved for the seven classes mentioned (happiness, sadness, surprise, disgust, anger, fear and disdain) whereas a success rate of 97% is found when three classes (happiness, sadness and surprised) are used. © 2013 IEEE.Öğe Automatic gender determination from 3D digital maxillary tooth plaster models based on the random forest algorithm and discrete cosine transform(ELSEVIER IRELAND LTD, 2017) Akkoç, Betül; Arslan, Ahmet; Kök, HaticeBackground and Objective: One of the first stages in the identification of an individual is gender determination. Through gender determination, the search spectrum can be reduced. In disasters such as accidents or fires, which can render identification somewhat difficult, durable teeth are an important source for identification. This study proposes a smart system that can automatically determine gender using 3D digital maxillary tooth plaster models. Methods: The study group was composed of 40 Turkish individuals (20 female, 20 male) between the ages of 21 and 24. Using the iterative closest point (ICP) algorithm, tooth models were aligned, and after the segmentation process, models were transformed into depth images. The local discrete cosine transform (DCT) was used in the process of feature extraction, and the random forest (RF) algorithm was used for the process of classification. Results: Classification was performed using 30 different seeds for random generator values and 10 fold cross-validation. A value of 85.166% was obtained for average classification accuracy (CA) and a value of 91.75% for the area under the ROC curve (AUC). Conclusions: A multi-disciplinary study is performed here that includes computer sciences, medicine and dentistry. A smart system is proposed for the determination of gender from 3D digital models of maxillary tooth plaster models. This study has the capacity to extend the field of gender determination from teeth. (C) 2017 Elsevier B.V. All rights reserved.Öğe Automatic knot adjustment using an artificial immune system for B-spline curve approximation(ELSEVIER SCIENCE INC, 2009) Uelker, Erkan; Arslan, AhmetReverse engineering transforms real parts into engineering concepts or models. First, sampled points are mapped from the object's surface by using tools such as laser scanners or cameras. Then, the sampled points are fitted to a free-form surface or a standard shape by using one of the geometric modeling techniques. The curves on the surface have to be modeled before surface modeling. In order to obtain a good B-spline curve model from large data, the knots are usually respected as variables. A curve is then modeled as a continuous, nonlinear and multivariate optimization problem with many local optima. For this reason it is very difficult to reach a global optimum. In this paper, we convert the original problem into a discrete combinatorial optimization problem like in Yoshimoto et al. [F. Yoshimoto, M. Moriyama. T. Harada, Automatic knot placement by a genetic algorithm for data fitting with a spline, in: Proceedings of the International Conference on Shape Modeling and Applications, IEEE Computer Society Press, 1999, pp. 162-169] and Sarfraz et al. [M. Sarfraz, S.A. Raza, Capturing outline of fonts using genetic algorithm and splines, in: Fifth International Conference on Information Visualisation (IV'01), 2001, pp. 738-743]. Then, we suggest a new method that solves the converted problem by artificial immune systems. We think the candidates of the locations of knots as antibodies. We define the affinity measure benefit from Akaike's Information Criterion (AIC). The proposed method determines the appropriate location of knots automatically and simultaneously. Furthermore, we do not need any subjective parameter or good population of initial location of knots for a good iterative search. Some examples are also given to demonstrate the efficiency and effectiveness of our method. (c) 2008 Elsevier Inc. All rights reserved.Öğe A Biomedical Decision Support System Using LS-SVM Classifier with an Efficient and New Parameter Regularization Procedure for Diagnosis of Heart Valve Diseases(SPRINGER, 2012) Comak, Emre; Arslan, AhmetClassification success of Support Vector Machine (SVM) depends on the characteristic of given data set and some training parameters (C and sigma). In literature, a few studies have been presented for regularization of these parameters which affects classification performance directly. This study proposes a new approach based on Renyi's entropy and Logistic regression methods for parameter regularization. Our regularization procedure runs at two steps. In the first step, optimal value of kernel parameter interval is found via Renyi's entropy method and optimal C value is found via logistic regression using exponential function in the next step. In addition to, this new decision support system is applied to biomedical research area via an application related to Doppler Heart Sounds (DHS). Experimental results show the efficiency of developed regularization procedure.Öğe A biomedical system based on fuzzy discrete hidden Markov model for the diagnosis of the brain diseases(PERGAMON-ELSEVIER SCIENCE LTD, 2008) Uguz, Harun; Oeztuerk, Ali; Saracoglu, Ridvan; Arslan, AhmetBecause it is a non-invasive, easy to apply and reliable technique, transcranial doppler (TCD) study of the adult intracerebral circulation has increased enormously in the last 10 years. In this study, a biomedical system has been implemented in order to classify the TCD signals recorded from the temporal region of the brain of 82 patients as well as of 24 healthy people. The diseases were investigated cerebral aneurysm, brain hemorrhage, cerebral oedema and brain tumor. The system is composed of feature extraction and classification parts, basically. In the feature extraction stage, the linear predictive coding analysis and cepstral analysis were applied in order to extract the cepstral and delta-cepstral coefficients in frame level as feature vectors. In the classification stage, discrete hidden Markov model (DHMM) based methods were used. In order to avoid loosing information due to vector quantization and to increase the classification performance, a fuzzy approach based similarity was applied to implement the DHMM. The performance of the proposed Fuzzy DHMM (FDHMM) was compared with some methods such as DHMM, artificial neural network (ANN), neuro-fuzzy approaches and obtained better classification performance than these methods. (C) 2007 Elsevier Ltd. All rights reserved.Öğe A biomedical system based on hidden Markov model for diagnosis of the heart valve diseases(ELSEVIER SCIENCE BV, 2007) Uguz, Harun; Arslan, Ahmet; Turkoglu, IbrahimIn this study, a biomedical diagnosis system for pattern recognition with normal and abnormal classes has been developed. First, feature extraction processing was made by using the Doppler Ultrasound. During feature extraction stage, Wavelet transforms and shorttime Fourier transform were used. As next step, wavelet entropy were applied to these features. In the classification stage, hidden Markov model (HMM) was used. To compute the correct classification rate of proposed HMM classifier, it was compared to ANN by using a data set containing 215 samples. In our experiments, specificity rate and sensitivity rates of proposed HMM classifier system with fuzzy C means (FCM)/K-means algorithms were found as 92% and 97.26% respectively. The present study shows that proper selection of the HMMs initial parameter values according to FCM/K-means algorithms improves the recognition rate of the proposed system which was also compared to our previous study named ANN. (c) 2006 Elsevier B.V. All rights reserved.Öğe A Boolean function approach to feature selection in consistent decision information systems(PERGAMON-ELSEVIER SCIENCE LTD, 2011) Kahramanli, Sirzat; Hacibeyoglu, Mehmet; Arslan, AhmetThe goal of feature selection (FS) is to find the minimal subset (MS) R of condition feature set C such that R has the same classification power as C and then reduce the dataset by discarding from it all features not contained in R. Usually one dataset may have a lot of MSs and finding all of them is known as an NP-hard problem. Therefore, when only one MS is required, some heuristic for finding only one or a small number of possible MSs is used. But in this case there is a risk that the best MSs would be overlooked. When the best solution of an FS task is required, the discernibility matrix (DM)-based approach, generating all MSs, is used. There are basically two factors that often cause to overflow the computer's memory due to which the DM-based FS programs fail. One of them is the largeness of sizes of discernibility functions (DFs) for large data sets; the other is the intractable space complexity of the conversion of a DF to disjunctive normal form (DNF). But usually most of the terms of DF and temporary results generated during DF to DNF conversion process are redundant ones. Therefore, usually the minimized DF (DFmin) and the final DNF is to be much simpler than the original DF and temporary results mentioned, respectively. Based on these facts, we developed a logic function-based feature selection method that derives DFmin from the truth table image of a dataset and converts it to DNF with preventing the occurrences of redundant terms. The proposed method requires no more amount of memory than that is required for constructing DFmin and final DNF separately. Due to this property, it can process most of datasets that can not be processed by DM-based programs. (C) 2011 Elsevier Ltd. All rights reserved.Öğe Bulanık birliktelik kurallarının genetik algoritmalarla keşfi(2004) Alataş, Bilal; Arslan, AhmetBirliktelik kurallarının keşfi veri madenciliğinde en çok çalışılan konulardan biridir. Bu çalışmada nitelikleri nicel değerler alabilen veritabanlarında nicel birliktelik kurallarının keşfi için yapay zeka ve yumuşak hesaplama konularından bulanık mantık ve genetik algoritma tabanlı yeni yöntemler geliştirilmiştir. Özellikle genetik algoritmanın ilk aşamasında gelişigüzel üretilen başlangıç populasyonunun dezavantajlarını gideren etkili üç farklı yöntem daha denenmiş ve elde edilen sonuçlar karşılaştırılmıştır. Önerilen yöntemleri test için veritabanı olarak Fırat Üniversitesi Elektrik-Elektronik Mühendisliği lisans öğrencilerinin ders not kayıtlan seçilmiş, kullanışlı ve ilginç kurallar etkili şekilde bulunmuştur.Öğe The calculation of parametric NURBS surface interval values using neural networks(SPRINGER-VERLAG BERLIN, 2006) Ulker, Erkan; Arslan, AhmetThree dimensional coordinate values of parametric NURBS (NonUniform Rational B-Splines) surfaces are obtained from two dimensional parameters u and v. An approach for generating surfaces produces a model by giving a fixed increase to u and v values. However, the ratio of three dimensional parameters increases and fixed increase of u and v values is not always the same. This difference of ratio costs unrequited sized breaks. In this study an artificial neural network method for simulation of a NURBS surface is proposed. Free shaped NURBS surfaces and various three dimensional object simulations with different patches can be produced using a method projected as network training with respect to coordinates which are found from interval scaled parameters. Experimental results show that this method in imaging modeled surface can be used as a simulator.Öğe A cascade learning system for classification of diabetes disease: Generalized discriminant analysis and least square support vector machine(PERGAMON-ELSEVIER SCIENCE LTD, 2008) Polat, Kemal; Guenes, Salih; Arslan, AhmetThe aim of this study is to diagnosis of diabetes disease, which is one of the most important diseases in medical field using Generalized Discriminant Analysis (GDA) and Least Square Support Vector Machine (LS-SVM). Also, we proposed a new cascade learning system based on Generalized Discriminant Analysis and Least Square Support Vector Machine. The proposed system consists of two stages. The first stage, we have used Generalized Discriminant Analysis to discriminant feature variables between healthy and patient (diabetes) data as pre-processing process. The second stage, we have used LS-SVM in order to classification of diabetes dataset. While LS-SVM obtained 78.21% classification accuracy using 10-fold cross validation, the proposed system called GDA-LS-SVM obtained 82.05% classification accuracy using 10-fold cross validation. The robustness of the proposed system is examined using classification accuracy, k-fold cross-validation method and confusion matrix. The obtained classification accuracy is 82.05% and it is very promising compared to the previously reported classification techniques. (c) 2006 Elsevier Ltd. All rights reserved.Öğe Chaotic Systems and Their Recent Implementations on Improving Intelligent Systems(IGI GLOBAL, 2014) Kose, Utku; Arslan, AhmetChaos Theory is a kind of a scientific approach/research effort which is based on examining behaviors of nonlinear dynamical systems which are highly sensitive to their initial conditions. Currently, there are many different scientific studies based on the Chaos Theory and the related solution approaches, methods, or techniques for problems of this theory. Additionally, the theory is used for improving the introduced studies of different fields in order to get more effective, efficient, and accurate results. At this point, this chapter aims to provide a review-based study introducing recent implementations of the Chaos Theory on improving intelligent systems, which can be examined in the context of the Artificial Intelligence field. In this sense, the main research way is directed into the works performed or introduced mostly in years between 2008 and 2013. By providing a review-based study, the readers are enabled to have ideas on Chaos Theory, Artificial Intelligence, and the related works that can be examined within intersection of both fields. At this point, the chapter aims to discuss not only recent works, but also express ideas regarding future directions within the related implementations of chaotic systems to improve intelligent systems. The chapter is generally organized as a reference guide for academics, researchers, and scientists tracking the literature of the related fields: Artificial Intelligence and the Chaos Theory.Öğe Classification of transcranial Doppler signals using their chaotic invariant measures(ELSEVIER IRELAND LTD, 2007) Ozturk, Ali; Arslan, AhmetIn this study, chaos analysis was performed on the transcranial Doppler (TCD) signals recorded from the temporal region of the brain of 82 patients as well as of 24 healthy people. Two chaotic invariant measures, i.e. the maximum Lyapunov exponent and the correlation dimension, were calculated for the TCD signals after applying nonlinearity and stationarity tests to them. The sonograms obtained via Burg autoregressive (AR) method demonstrated that the chaotic invariant measures represented the unpredictability and complexity levels of the TCD signals. According to the multiple linear regression analysis, the chaotic invariant measures were found to be highly significant for the regression equation which fitted to the data. This result suggested that the chaotic invariant measures could be used for automatically differentiating various cerebrovascular conditions via an appropriate classifier. For comparison purposes, we investigated several different classification algorithms. The k-nearest neighbour algorithm outperformed all the other classifiers with a classification accuracy of 94.44% on the test data. We used the receiver operating characteristic (ROC) curves in order to assess the performance of the classifiers. The results suggested that the classification systems which use the chaotic invariant measures as input have potential in detecting the blood flow velocity changes due to various brain diseases. (c) 2007 Elsevier Ireland Ltd. All rights reserved.
