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Öğe Assessment of exercise stress testing with artificial neural network in determining coronary artery disease and predicting lesion localization(PERGAMON-ELSEVIER SCIENCE LTD, 2009) Babaoglu, Ismail; Baykan, Omer Kaan; Aygul, Nazif; Ozdemir, Kurtulus; Bayrak, MehmetThe aim of this study is to show the artificial neural network (ANN) on determination of coronary artery disease existence and localization of lesion based upon exercise stress testing (EST) data. EST and coronary angiography were performed on 330 patients. The data studied acquiring 27 verifying features was normalized employing z-score method. To select training and test data, 10-fold cross-validation methods were involved and multi-layered perceptron neural network was employed for the classification. The interpretation of EST using ANN proved 91%, 73% and 65% diagnostic accuracy for the left main coronary (LMCA), left anterior descending and left circum-flex coronary arteries, respectively. Besides, 69% for the right coronary artery is also predicted. For the LMCA, a 94% negative predictive value (NPV) was obtained. This high percentage of NPV encourages the elimination of LMCA lesions. Some knowledge call also be obtained about lesion localization, besides diagnosing of coronary artery disease by the assessment of EST via ANN. (C) 2007 Elsevier Ltd. All rights reserved.Öğe Bone age determination in young children (newborn to 6 years old) using support vector machines(TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2016) Guraksin, Gur Emre; Uguz, Harun; Baykan, Omer KaanBone age is assessed through a radiological analysis of the left-hand wrist and is then compared to chronological age. A conflict between these two values indicates an abnormality in the development process of the skeleton. This study, conducted on children aged between 0 and 6 years, proposes a computer-based diagnostic system to eliminate the disadvantages of the methods used in bone age determination. For this purpose, primarily an image processing procedure was applied to the X-ray images of the left-hand wrist of children from different ethnic groups aged between 0 and 6 years. A total of 9 features, corresponding to the carpal bones and distal epiphysis of the radius bone with some physiological attributes of the children, were obtained. Then, by using gain ratio, the best 6 features were used for the classification process. Next, the bone age determination process was performed with the obtained features with the help of the support vector machine (SVM), naive Bayes, k-nearest neighborhood, and C4.5 algorithms. Finally, the features used in the determination process and their effects on the accuracies were examined. The results of the designed system showed that SVM method has a better achievement rate than the other methods at a rate of 72.82%. Additionally, in this study, a new feature corresponding to the distance between the centers of gravity of the carpal bones was used for the classification process, and the analysis of the related feature showed that there was a statistically significant difference at P < 0.05 between this feature and bones in children aged between 0 and 6 years.Öğe Classifiers fusion in recognition of wheat varieties(SPRINGER-VERLAG BERLIN, 2007) Raudys, Sarunas; Baykan, Omer Kaan; Babalik, Ahmet; Denisov, Vitalij; Bielskis, Antanas AndriusFive wheat varieties (Bezostaja Cesit1252, Dagdas, Gerek, Kiziltan traded in Konya Exchange of Commerce, Turkey), characterized by nine geometric and three colour descriptive features have been classified by multiple classier system where pair-wise SLP or SV classifiers served as base experts. In addition to standard voting and Hastie and Tibshirani fusion rules, two new ones were suggested that allowed reducing the generalization error up to 5%. In classifying of kernel lots, we may obtain faultless grain recognition.Öğe Detection of Tumor With Otsu-PSO Method On Brain MR Image(IEEE, 2014) Ozic, Muhammet Usame; Ozbay, Yuksel; Baykan, Omer KaanMultiple image thresholding is a popular method used to separate homogeneous subsets of gray level images. To find the optimum threshold in the image in the literature is still a research topic. Many image thresholding method uses the histogram of the image. In this study, the objective function of Otsu method which is a statistical process, Particle Swarm Optimization with an intuitive algorithm (PSO) by maximizing, the optimal threshold values on a medical image were studied to find. The values obtained were tested with a standard test image and brain magnetic resonance (MR) image exposed on the tumor region in segmentation, Otsu-PSO method performance was monitored.Öğe Force Feedback Control of Lower Extremity Exoskeleton Assisting of Load Carrying Human(TRANS TECH PUBLICATIONS LTD, 2014) Sahin, Yusuf; Botsali, Fatih Mehmet; Kalyoncu, Mete; Tinkir, Mustafa; Onen, Umit; Yilmaz, Nihat; Baykan, Omer KaanLower extremity exoskeletons are wearable robot manipulators that integrate human intelligence with the strength of legged robots. Recently, lower extremity exoskeletons have been specifically developed for rehabilitation, military, industrial applications and rescuing, heavy-weight lifting and civil defense applications. This paper presents controller design of a lower-extremity exoskeleton for a load carrying human to provide force feedback control against to external load carried by user during walking, sitting, and standing motions. Proposed exoskeleton system has two legs which are powered and controlled by two servo-hydraulic actuators. Proportional and Integral (PI) controller is designed for force control of system. Six flexible force sensors are placed in exoskeleton shoe and two load cells are mounted between the end of the piston rod and lower leg joint. Force feedback control is realized by comparing ground reaction force and applied force of hydraulic cylinder. This paper discusses control simulations and experimental tests of lower extremity exoskeleton system.Öğe Multilevel image thresholding selection based on grey wolf optimizer(GAZI UNIV, 2018) Koc, Ismail; Baykan, Omer Kaan; Babaoglu, IsmailMultilevel thresholding is an important image process technique for image processing and pattern recognition. Selecting an optimal threshold value is one of the most crucial phase in image thresholding. While bi-level segmentation contains separating the original image into subdivided sections with help of a threshold value, multilevel segmentation involves multi threshold values. Especially in multilevel image tresholding, the computational time of detailed search increases exponentially with the number of preferred thresholds. For compelling problems, swarm intelligence is known as one of the successful and influential optimization methods. In this paper, the grey wolf optimizer (GWO), a recently proposed swarm-based meta-heuristic which imitates the social leadership and hunting behavior of gray wolves in nature is employed for solving the multilevel image thresholding problem. The experimental results on standard benchmark images indicate that the grey wolf optimizer algorithm is comparable with other state of the art algorithms.Öğe A new approach based on particle swarm optimization algorithm for solving data allocation problem(ELSEVIER, 2018) Mahi, Mostafa; Baykan, Omer Kaan; Kodaz, HalifeThe effectiveness distributed database systems highly depends on the state of site that its task is to allocate fragments. This allocation purpose is performed for obtaining the minimum execute time and transaction cost of queries. There are some NP-hard problems that Data Allocation Problem (DAP) is one of them and solving this problem by means of enumeration method can be computationally expensive. Recently heuristic algorithms have been used to achieve desirable solutions. Due to fewer control parameters, robustness, speed convergence characteristics and easy adaptation to the problem, this paper propose a novel method based on Particle Swarm Optimization (PSO) algorithm which is suitable to minimize the total transmission cost for both the each site - fragment dependency and the each inter - fragment dependency. The core of the study is to solve DAP by utilizing and adaptation PSO algorithm, PSO-DAP for short. Allocation of fragments to the site has been done with PSO algorithm and its performance has been evaluated on 20 different test problems and compared with the state-of-art algorithms. Experimental results and comparisons demonstrate that proposed method generates better quality solutions in terms of execution time and total cost than compared state-of-art algorithms. (C) 2017 Elsevier B.V. All rights reserved.Öğe A NEW FEATURE SELECTION METHOD FOR TEXT CATEGORIZATION BASED ON INFORMATION GAIN AND PARTICLE SWARM OPTIMIZATION(IEEE, 2014) Yigit, Ferruh; Baykan, Omer KaanRapid increases of the documents which are created in digital media necessitate analyze and classify of these documents automatically. Feature extraction, feature selection and classifier selection in the analysis of documents and classification affects performance. In text document categorization, it is a fundamental problem that the numbers of extracted features are a lot of. In this study, by using a new feature selection method based on IG (information gain) and PSO (particle swarm optimization) algorithms, text categorization process performed. Reuters 21.578 and Classic3 corpus were used in the experiments. The roots of the words in the texts of corpus were taken as the features. Feature selection and categorization processes performed with k-Nearest Neighbors algorithm (K-NN) and Naive Bayes classifiers by using IG and PSO algorithms. Proposed system performance was evaluated by using CA (Classification Accuracy), Precision, Recall and F-measure criteria.Öğe A new hybrid method based on Particle Swarm Optimization, Ant Colony Optimization and 3-Opt algorithms for Traveling Salesman Problem(ELSEVIER SCIENCE BV, 2015) Mahi, Mostafa; Baykan, Omer Kaan; Kodaz, HalifeThe Traveling Salesman Problem (TSP) is one of the standard test problems used in performance analysis of discrete optimization algorithms. The Ant Colony Optimization (ACO) algorithm appears among heuristic algorithms used for solving discrete optimization problems. In this study, a new hybrid method is proposed to optimize parameters that affect performance of the ACO algorithm using Particle Swarm Optimization (PSO). In addition, 3-Opt heuristic method is added to proposed method in order to improve local solutions. The PSO algorithm is used for detecting optimum values of parameters alpha and beta which are used for city selection operations in the ACO algorithm and determines significance of inter-city pheromone and distances. The 3-Opt algorithm is used for the purpose of improving city selection operations, which could not be improved due to falling in local minimums by the ACO algorithm. The performance of proposed hybrid method is investigated on ten different benchmark problems taken from literature and it is compared to the performance of some well-known algorithms. Experimental results show that the performance of proposed method by using fewer ants than the number of cities for the TSPs is better than the performance of compared methods in most cases in terms of solution quality and robustness. (C) 2015 Elsevier B.V. All rights reserved.Öğe A novel hybrid algorithm based on particle swarm and ant colony optimization for finding the global minimum(ELSEVIER SCIENCE INC, 2012) Kiran, Mustafa Servet; Gunduz, Mesut; Baykan, Omer KaanThis paper presents a novel hybrid algorithm based on particle swarm optimization (PSO) and ant colony optimization (ACO) and called hybrid ant particle optimization algorithm (HAP) to find global minimum. In the proposed method, ACO and PSO work separately at each iteration and produce their solutions. The best solution is selected as the global best of the system and its parameters are used to select the new position of particles and ants at the next iteration. The performance of proposed method is compared with PSO and ACO on the benchmark problems and better quality results are obtained by HAP algorithm. (C) 2012 Elsevier Inc. All rights reserved.Öğe A parallel cooperative hybrid method based on ant colony optimization and 3-Opt algorithm for solving traveling salesman problem(SPRINGER, 2018) Gulcu, Saban; Mahi, Mostafa; Baykan, Omer Kaan; Kodaz, HalifeThis article presented a parallel cooperative hybrid algorithm for solving traveling salesman problem. Although heuristic approaches and hybrid methods obtain good results in solving the TSP, they cannot successfully avoid getting stuck to local optima. Furthermore, their processing duration unluckily takes a long time. To overcome these deficiencies, we propose the parallel cooperative hybrid algorithm (PACO-3Opt) based on ant colony optimization. This method uses the 3-Opt algorithm to avoid local minima. PACO-3Opt has multiple colonies and a master-slave paradigm. Each colony runs ACO to generate the solutions. After a predefined number of iterations, each colony primarily runs 3-Opt to improve the solutions and then shares the best tour with other colonies. This process continues until the termination criterion meets. Thus, it can reach the global optimum. PACO-3Opt was compared with previous algorithms in the literature. The experimental results show that PACO-3Opt is more efficient and reliable than the other algorithms.Öğe Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange(PERGAMON-ELSEVIER SCIENCE LTD, 2011) Kara, Yakup; Boyacioglu, Melek Acar; Baykan, Omer KaanPrediction of stock price index movement is regarded as a challenging task of financial time series prediction. An accurate prediction of stock price movement may yield profits for investors. Due to the complexity of stock market data, development of efficient models for predicting is very difficult. This study attempted to develop two efficient models and compared their performances in predicting the direction of movement in the daily Istanbul Stock Exchange (ISE) National 100 Index. The models are based on two classification techniques, artificial neural networks (ANN) and support vector machines (SVM). Ten technical indicators were selected as inputs of the proposed models. Two comprehensive parameter setting experiments for both models were performed to improve their prediction performances. Experimental results showed that average performance of ANN model (75.74%) was found significantly better than that of SVM model (71.52%). (C) 2010 Elsevier Ltd. All rights reserved.Öğe Shear strength predicting of FRP-strengthened RC beams by using artificial neural networks(WALTER DE GRUYTER GMBH, 2014) Yavuz, Gunnur; Arslan, Musa Hakan; Baykan, Omer KaanIn this study, the efficiency of artificial neural networks (ANN) in predicting the shear strength of reinforced concrete (RC) beams, strengthened by means of externally bonded fiber-reinforced polymers (FRP), is explored. Experimental data of 96 rectangular RC beams from an existing database in the literature were used to develop the ANN model. Eight different input parameters affecting the shear strength were selected for creating the ANN structure. Each parameter was arranged in an input vector and a corresponding output vector that includes the shear strength of the RC beam. For all outputs, the ANN model was trained and tested using a three-layered back-propagation method. The initial performance of back-propagation was evaluated and discussed. In addition, the accuracy of well-known building codes in predicting the shear strength of FRP-strengthened RC beams was also examined, in a comparable way, using same test data. The study shows that the ANN model gives reasonable predictions of the ultimate shear strength of RC beams (R-2 approximate to 0.93). Moreover, the study concludes that the ANN model predicts the shear strength of FRP-strengthened RC beams better than existing building code approaches.