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Öğe Back-propagation algorithm with variable adaptive momentum(ELSEVIER SCIENCE BV, 2016) Hameed, Alaa Ali; Karlik, Bekir; Salman, Mohammad ShukriIn this paper, we propose a novel machine learning classifier by deriving a new adaptive momentum back-propagation (BP) artificial neural networks algorithm. The proposed algorithm is a modified version of the BP algorithm to improve its convergence behavior in both sides, accelerate the convergence process for accessing the optimum steady-state and minimizing the error misadjustment to improve the recognized patterns superiorly. This algorithm is controlled by the learning rate parameter which is dependent on the eigenvalues of the autocorrelation matrix of the input. It provides low error performance for the weights update. To discuss the performance measures of this proposed algorithm and the other supervised learning algorithms such as k-nearest neighbours (k-NN), Naive Bayes (NB), linear discriminant analysis (LDA), support vector machines (SVM), BP, and BP with adaptive momentum (PBPAM) have been compared in term of speed of convergence, Sum of Squared Error (SSE), and accuracy by implementing benchmark problem - XOR and seven datasets from UCI repository. (C) 2016 Elsevier B.V. All rights reserved.Öğe Classification of ECG Arrhythmias using Type-2 Fuzzy Clustering Neural Network(IEEE, 2009) Ceylan, Rahime; Ozbay, Yueksel; Karlik, BekirIn this study, Type-2 Fuzzy Clustering Neural Network (T2FCNN) architecture realized for classification of electrocardiography arrhythmias is presented. Type-2 fuzzy clustering neural network is cascade structure formed by clustering and classification stages. In T2FCNN architecture, clustering stage consisted of select best patterns in all patterns that belongs to same class is executed by type-2 fuzzy c-means clustering (T2FCM). The aim of using T2FCM clustering algorithm is to reduce classification error of neural network by optimization of training pattern set. A new training set consisted of cluster centers obtained by type-2 fuzzy c-means clustering algorithm for each class as separately is formed inputs of neural network. Neural network is trained using backpropagation algorithm. Proposed structure is used classification of five ECG signal class composed normal sinus rhythm, sinus bradycardia, sinus arrhythmia, right bundle branch block and left bundle branch block. Data used in this study is obtained from Physionet database, that belongs to MIT-BIH ECG Arrhythmia Database. In the end of making applications, proposed T2FCNN structure is classified ECG arrhythmias with 99% detection rate.Öğe Comparison Machine Learning Algorithms for Recognition of Epileptic Seizures in EEG(COPICENTRO GRANADA S L, 2014) Karlik, Bekir; Hayta, Sengul BayrakThe aim of this study is to diagnose epileptic seizures by using different machine learning algorithms. For this purpose, the frequency components of the EEG are extracted by using the discrete wavelet transform (DWT) and parametric methods based on autoregressive (AR) model. Both these two feature extraction methods are applied to the input of machine learning classification algorithms such as Artificial Neural Networks (ANN), Naive Bayesian, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM) and k-Means. The results show that k-NN, ANN and SVM were the most efficient method according to test processing of both DWT and AR as feature extraction for recognition of epileptic seizures in EEG.Öğe Computer-Aided Software for Early Diagnosis of Eerythemato-squamous Diseases(IEEE, 2013) Karlik, Bekir; Harman, GunesEarly diagnosis and appropriate treatment remain a necessary challenge. Dermatologic emergencies have insufficient attention by the general population and by physicians from other specialties. The differential diagnosis of erythemato-squamous diseases is a real problem in dermatology. They all share the clinical features of erythema and scaling with very little differences. These diseases are psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, cronic dermatitis, and pityriasis rubra pilaris. Usually a biopsy is necessary for the diagnosis but unfortunately these diseases share many histopathological features as well. In this study, computer-aided software was developed to diagnosis dermatological diseases by using artificial neural networks. The supervised back-propagation algorithm is used to train the networks. Classification of the average value of sensitivity (or recognition percentage) was found as 98% for six erythemato-squamous diseases.Öğe Fruit Juice-Alcohol Mixture Analysis Using Machine Learning and Electronic Nose(WILEY-BLACKWELL, 2016) Ordukaya, Emre; Karlik, BekirThe aim of this study is to analyze the raw data collected from a fruit juice-alcohol mixture (a fruit juice-alcohol mixture and a fruit juice-multiple alcohol mixture) and the Halal authentication of a fruit juice-alcohol mixture with electronic nose. Machine learning techniques such as naive Bayesian classifier, K-nearest neighbors (K-NN), linear discriminant analysis (LDA), decision tree, artificial neural network (ANN), and support vector machine (SVM) were used to classify the feature of these collected raw data. There are three types of classification: the first one is a fruit juice and an alcohol mixture type; the second is a fruit juice and multiple alcohol mixture types, and the third is a Halal authentication of a fruit juice and alcohol mixture. We aimed at making cocktails with more successful results on the first two types of classification in the work. Also, we focused on Halal authentication of fruit juice-alcohol mixture in the third classification. (C) 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.Öğe Fuzzy c-means based support vector machines classifier for perfume recognition(ELSEVIER SCIENCE BV, 2016) Esme, Engin; Karlik, BekirIdentification of more than three perfumes is very difficult for the human nose. It is also a problem to recognize patterns of perfume odor with an electronic nose that has multiple sensors. For this reason, a new hybrid classifier has been presented to identify type of perfume from a closely similar data set of 20 different odors of perfumes. The structure of this hybrid technique is the combination of unsupervised fuzzy clustering c-mean (FCM) and supervised support vector machine (SVM). On the other hand this proposed soft computing technique was compared with the other well-known learning algorithms. The results show that the proposed hybrid algorithm's accuracy is 97.5% better than the others. (C) 2016 Elsevier B.V. All rights reserved.Öğe A Fuzzy Clustering Neural Network Architecture for Classification of ECG Arrhythmias(Pergamon-elsevier Science Ltd, 2006) Özbay, Yüksel; Ceylan, Rahime; Karlik, BekirAccurate and computationally efficient means of classifying electrocardiography (ECG) arrhythmias has been the subject of considerable research effort in recent years. This study presents a comparative study of the classification accuracy of ECG signals using a well-known neural network architecture named multi-layered perceptron (MLP) with backpropagation training algorithm, and a new fuzzy clustering NN architecture (FCNN) for early diagnosis. The ECG signals are taken from MIT-BIH ECG database, which are used to classify 10 different arrhythmias for training. These are normal sinus rhythm, sinus bradycardia, ventricular tachycardia, sinus arrhythmia, atrial premature contraction, paced beat, right bundle branch block, left bundle branch block, atrial fibrillation and atrial flutter. For testing, the proposed structures were trained by backpropagation algorithm. Both of them tested using experimental ECG records of 92 patients (40 male and 52 female, average age is 39.75 +/- 19.06). The test results suggest that a new proposed FCNN architecture can generalize better than ordinary MLP architecture and also learn better and faster. The advantage of proposed structure is a result of decreasing the number of segments by grouping similar segments in training data with fuzzy c-means clustering.Öğe Integration of type-2 fuzzy clustering and wavelet transform in a neural network based ECG classifier(PERGAMON-ELSEVIER SCIENCE LTD, 2011) Ozbay, Yuksel; Ceylan, Rahime; Karlik, BekirThis paper presents a new automated diagnostic system to classification of electrocardiogram (ECG) arrhythmias. The diagnostic system is executed using type-2 fuzzy c-means clustering (T2FCM) algorithm, wavelet transform (WT) and neural network. Method of combining T2FCM and WT is used to improve performance of neural network. We aimed high accuracy rate to classification of ECG beats and constituted the automated diagnostic system to improve of classifier's performance. Ten types of ECG beats selected from MIT-BIH database were used to train the system. Then, this system was tested by the ECG signals of patients. The classification accuracy of the proposed classifier, type-2 fuzzy clustering wavelet neural network (T2FCWNN), is compared with the structures formed by type-1 FCM and WT. Process of T2FCWNN architecture is realized on three stages. First stage is formed the new training set obtained by selection of the best segments for each arrhythmia class using T2FCM. Second stage is feature extraction by WT on the new training set. Third stage is classification of the extracted features using neural network. The research showed that accuracy rate was found as 99% using this system. (C) 2010 Elsevier Ltd. All rights reserved.Öğe Measuring The Optimum Lux Value for More Accurate Measurement of Stereo Vision Systems in Operating Room of Orthognathic Surgery(IEEE, 2014) Comlekciler, Ismail Taha; Gunes, Salih; Irgin, Celal; Karlik, BekirThe successful orthognathic surgery is directly influenced by more accurate measurement of distance or size of the surgery area. There exist various methods of measurements during the orthognathic surgery. One of the newest methods is to make measurements by using stereo vision system with stereo cameras. The result of stereo vision is affected by many factors, such as captured image colour, glossiness, ambient light, geometry, etc. One of the most important influence factors is ambient light, especially on measuring distances with the value of microns (10-6 m). The stereo vision system's cameras are also influenced by ambient light and the objective of this paper is to investigate more accurate result of stereo vision according to the ambient light of the orthognathic surgery operation room.Öğe A New 2-D Convex Combination of Recursive Inverse Algorithms(IEEE, 2014) Hameed, Alaa Ali; Salman, Mohammad Shukri; Karlik, BekirDe-noising magnetic resonance images (MRI) has recently become an interesting topic in medical diagnosis applications. Many algorithms have been proposed for this purpose. However, these algorithms usually suffer from poor performance or time consumption. In this paper, we propose a 2-D version of the recently proposed convex recursive inverse (RI) algorithm that provides fast convergence at the beginning to save time and then provides high performance in terms of noise removal. To test the algorithm, a de-noising experiment has been conducted on MR image that is assumed to be corrupted by an additive white Gaussian noise (AWGN). Simulations show that the proposed algorithm successfully recovers the image.Öğe A New Sparse Convex Combination of ZA-LLMS and RZA-LLMS Algorithms(IEEE, 2015) Salman, Mohammad Shukri; Hameed, Alaa Ali; Turan, Cemil; Karlik, BekirIn 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.Öğe A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network(PERGAMON-ELSEVIER SCIENCE LTD, 2009) Ceylan, Rahime; Ozbay, Yuksel; Karlik, BekirThis paper presents an improved classifier for automated diagnostic systems of electrocardiogram (ECG) arrhythmias. This diagnostic system consists of a combined Fuzzy Clustering Neural Network Algorithm for Classification of ECG Arrhythmias using type-2 fuzzy c-means clustering (T2FCM) algorithm and neural network. Type-2 fuzzy c-means clustering is used to improve performance of neural network. The aim of improving classifier's performance is to constitute the best classification system with high accuracy rate for ECG beats. Ten types of ECG arrhythmias (normal beat, sinus bradycardia, ventricular tachycardia, sinus arrhythmia, atrial premature contraction, paced beat, right bundle branch block, left bundle branch block, atrial fibrillation and atrial flutter) obtained from MIT-BIH database were analyzed. However, the presented structure was tested by experimental ECG records of 92 patients (40 male and 52 female, average age is 39.75 +/- 19.06). The classification accuracy of an improved classifier in training and testing, namely Type-2 Fuzzy Clustering Neural Network (T2FCNN), was compared with neural network (NN) and fuzzy clustering neural network (FCNN). In T2FCNN architecture, decision making has two stages: forming of the new training set obtained by selection of the best arrhythmia for each arrhythmia class using T2FCM and classification using neural network trained on the new training set. The results are demonstrated that the proposed diagnostic systems achieved high (99%) accuracy rate. (C) 2008 Elsevier Ltd. All rights reserved.Öğe Performance Evaluation for Face Recognition Using Wavelet-based Image De-noising(IEEE, 2013) Atamuradov, Vepa; Eleyan, Alaa; Karlik, BekirIn this research we scrutinize the face recognition system performance when the test images are imposed to different levels of noise. We tried to imitate the real world scenarios when the face images are captured from video cameras or scanners and suffer some noise. To investigate the performance of proposed system, we simulate this scenario by adding A WGN (additive white Gaussian noise) to the test images in the face database. For image de-noising, we used two different algorithms namely; Discrete Wavelets Transform (DWT) and Dual-Tree Complex Wavelets Transform (DTCWT). The de-noised images are then fed to a PCA-based face recognition system for better recognition performance.Öğe Quality Control of Olive Oils Using Machine Learning and Electronic Nose(WILEY-HINDAWI, 2017) Ordukaya, Emre; Karlik, BekirThe adulteration of olive oils can be detected with chemical test. This is very expensive and takes very long time. Thus, this study is focused on reducing both time and cost. For this purpose, the raw data has been collected from olive oils by using an e-nose from different regions in Balikesir in Turkey. This study presents two methods to analyze quality control of olive oils. In the first method, 32 inputs are applied to the classifiers directly. In the second, 32-input collected data are reduced to 8 inputs by Principal Component Analysis. These reduced data as 8 inputs are applied to the classifiers. Different machine learning classifiers such as Naive Bayesian,.. - NearestNeighbors (kappa- NN), Linear Discriminate Analysis (LDA), Decision Tree, ArtificialNeuralNetworks (ANN), and Support Vector Machine (SVM) were used. Then performances of these classifiers were compared according to their accuracies.Öğe The Role of Data Reduction for Diagnosis of Pathologies of the Vertebral Column by Using Supervised Learning Algorithms(IEEE, 2015) Bah, Thibaut Judicael; Karlik, BekirToday in data mining research we are daily confronted with large amount of data. Most of the time, these data contain redundant and irrelevant data that it is important to extract before a learning task in order to get good accuracy. The fact that today's computers are more powerful does not solves the problems of this ever-growing data. It is therefore crucial to find techniques which allow handling these large databases often too big to be processed. Data reduction techniques are therefore a very important step to prepare the data before data mining and knowledge discovery. In this paper we present a comparative study on original and reduced data to see the role data reduction in a learning task. For this purpose, we used a medical dataset; especially a vertebral column pathologies database.Öğe Sift-based Iris Recognition Using Sub-Segments(IEEE, 2013) Mesecan, Ibrahim; Eleyan, Alaa; Karlik, BekirIn this paper, we investigate the use of Scale Invariant Feature Transform (SIFT) for iris recognition problem with sub-segments. Instead of using the whole iris, we extracted sub-segments from the iris image for classification. These sub-segments were used separately for classification. Also, feature based fusion is applied using different sub-segments from the same iris. A preprocessing step for cropping the iris area from the images was address in this paper as well to increase performance of the system. The simulation results show high performance on the used database.