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Öğe Artificial 3-D contactless measurement in orthognathic surgery with binocular stereo vision(ELSEVIER SCIENCE BV, 2016) Comlekciler, Ismail Taha; Gunes, Salih; Irgin, CelalStereo vision systems are utilized since it provides contactless measurements of objects in 3-D (threedimensional). Orthognathic surgery is a very sensitive operation that requires very high accuracy in measurements. The reduction of measurement error is an essential problem in orthognathic surgery. Moreover, quality inspection of the process during the course of operation aids the surgeon to avoid or minimize the mitigating circumstances. In this paper, artificial intelligence methods (neural network and neuro-fuzzy system) are used in order to increase the accuracy of positioning of jaws during the real-time practice. The comparison of artificial measurements with the real measurements shows that a statistically acceptable accuracy is achieved in 3-D positioning of teeth. (C) 2016 Elsevier B.V. All rights reserved.Öğe Artificial immune recognition system based classifier ensemble on the different feature subsets for detecting the cardiac disorders from SPECT images(SPRINGER-VERLAG BERLIN, 2007) Polat, Kemal; Sekerci, Ramazan; Gunes, SalihCombining outputs of multiple classifiers is one of most important techniques for improving classification accuracy. In this paper, we present a new classifier ensemble based on artificial immune recognition system (AIRS) classifier and independent component analysis (ICA) for detecting the cardiac disorders from SPECT images. Firstly, the dimension of SPECT (Single Photon Emission Computed Tomography) images dataset, which has 22 binary features, was reduced to 3, 4, and 5 features using FastICA algorithm. Three different feature subsets were obtained in this way. Secondly, the obtained feature subsets were classified by AIRS classifier and then stored the outputs obtained from AIRS classifier into the result matrix. The exact result that denote whether subject has cardiac disorder or not was obtained by averaging the outputs obtained from AIRS classifier into the result matrix. While only AIRS classifier obtained 84.96% classification accuracy with 50-50% train-test split for diagnosing the cardiac disorder from SPECT images, classifier ensemble based on AIRS and ICA fusion obtained 97.74% classification accuracy on the same conditions. The accuracy of AIRS classifier utilizing the reduced feature subsets was higher than those exploiting all the original features. These results show that the proposed ensemble method is very promising in diagnosis of the cardiac disorder from SPECT images.Öğe Attribute weighting via genetic algorithms for attribute weighted artificial immune system (AWAIS) and its application to heart disease and liver disorders problems(PERGAMON-ELSEVIER SCIENCE LTD, 2009) Ozsen, Seral; Gunes, SalihAn increasing number of algorithms and applications have coming into scene in the field of artificial immune systems (AIS) day by day. Whereas this increase is bringing successful studies, still, AIS is not an effective problem solver in some problem fields such as classification, regression, pattern recognition, etc. So far, many of the developed AIS algorithms have used a distance or similarity measure as the case in instance based learning (IBL) algorithms. The efficiency of IBL algorithms lies mainly in the weighting scheme they used. This weighting idea was taken as the objective of our study in that we used genetic algorithms to determine the weights of attributes and then used these weights in our previously developed Artificial Immune System (AWAIS). We evaluated the performance of new configuration (GA-AWAIS) on two medical datasets which were Statlog Heart Disease and BUPA Liver Disorders dataset. We also compared it with AWAIS for those problems. The obtained classification accuracy was very good with respect to both AWAIS and other common classifiers in literature. (C) 2007 Elsevier Ltd. All rights reserved.Öğe Automatic determination of diseases related to lymph system from lymphography data using principles component analysis (PCA), fuzzy weighting pre-processing and ANFIS(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Polat, Kemal; Gunes, SalihIt is evident that usage of machine learning methods in disease diagnosis has been increasing gradually. In this study, diagnosis of lymph diseases, which is a very common and important disease, was conducted with such a machine learning system. In this study, we have detected on lymph diseases using principles component analysis (PCA), fuzzy weighting pre-processing and adaptive neuro-fuzzy inference system (ANFIS). The approach system has three stages. In the first stage, dimension of lymph diseases dataset that has 18 features is reduced to four features using principles component analysis. In the second stage, a new weighting scheme based on fuzzy weighting method was utilized as a pre-processing step before the main classifier. Then, in the third stage, ANFIS was our used classifier. We took the lymph diseases dataset used in our study from the UCI machine learning database. The obtained classification accuracy of our system was 88.83% and it was very promising with regard to the other classification applications in the literature for this problem. (c) 2006 Elsevier Ltd. All rights reserved.Öğe Comparison of different classifier algorithms for diagnosing macular and optic nerve diseases(WILEY, 2009) Polat, Kemal; Kara, Sadik; Guven, Aysegul; Gunes, SalihThe aim of this research was to compare classifier algorithms including the C4.5 decision tree classifier, the least squares support vector machine (LS-SVM) and the artificial immune recognition system (AIRS) for diagnosing macular and optic nerve diseases from pattern electroretinography signals. The pattern electroretinography signals were obtained by electrophysiological testing devices from 106 subjects who were optic nerve and macular disease subjects. In order to show the test performance of the classifier algorithms, the classification accuracy, receiver operating characteristic curves, sensitivity and specificity values, confusion matrix and 10-fold cross-validation have been used. The classification results obtained are 85.9%, 100% and 81.82% for the C4.5 decision tree classifier, the LS-SVM classifier and the AIRS classifier respectively using 10-fold cross-validation. It is shown that the LS-SVM classifier is a robust and effective classifier system for the determination of macular and optic nerve diseases.Öğe Comparison of different classifier algorithms on the automated detection of obstructive sleep apnea syndrome(SPRINGER, 2008) Polat, Kemal; Yosunkaya, Sebnem; Gunes, SalihIn this paper, we have compared the classifier algorithms including C4.5 decision tree, le artificial neural network (ANN), artificial immune recognition system (AIRS), and adaptive neuro-fuzzy inference system (ANFIS) in the diagnosis of obstructive sleep apnea syndrome (OSAS), which is an important disease that affects both the right and the left cardiac ventricle. The goal of this study was to find the best classifier model on the diagnosis of OSAS. The clinical features were obtained from Polysomnography device as a diagnostic tool for obstructive sleep apnea in patients clinically suspected of suffering this disease in this study. The clinical features are arousals index, apnea-hypopnea index (AHI), SaO(2) minimum value in stage of rapid eye movement, and percent sleep time in stage of SaO(2) intervals bigger than 89%. In our experiments, a total of 83 patients (58 with a positive OSAS (AHI > 5) and 25 with a negative OSAS such that normal subjects) were examined. The decision support systems can help to physicians in the diagnosing of any disorder or disease using clues obtained from signal or images taken from subject having any disorder. In order to compare the used classifier algorithms, the mean square error, classification accuracy, area under the receiver operating characteristics curve (AUC), and sensitivity and specificity analysis have been used. The obtained AUC values of C4.5 decision tree, ANN, AIRS, and ANFIS classifiers are 0.971, 0.96, 0.96, and 0.922, respectively. These results have shown that the best classifier system is C4.5 decision tree classifier on the diagnosis of obstructive sleep apnea syndrome.Öğe Computer aided diagnosis of ECG data on the least square support vector machine(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2008) Polat, Kemal; Akdemir, Bayram; Gunes, SalihIn this paper we describe a technique that has successfully classified arrhythmia from an ECG dataset using a least square support vector machine (LSSVM). LSSVM was applied to the ECG dataset to distinguish between healthy persons and diseased persons (arrhythmia). The LSSVM classifier trained with four train-test parts including a training-to-test split of 50-50%, a training-to-test split of 70-30%, and a training-to-test split of 80-20%. We have used the classification accuracy, sensitivity and specificity analysis, and ROC curves to test the performance of LSSVM classifier on the detection of ECG arrhythmia. The classification accuracies obtained are 100% for all the training-to-test splits. These results show that the proposed method is more promising than previously reported classification techniques. The results suggest that the proposed method can be used to enhance the performance of a new intelligent assistance diagnosis system. (C) 2007 Elsevier Inc. All rights reserved.Öğe Diagnosis of atherosclerosis from carotid artery Doppler signals as a real-world medical application of artificial immune systems(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Latifoglu, Fatma; Sahan, Seral; Kara, Sadik; Gunes, SalihIn this study, we have employed the maximum envelope of the carotid artery Doppler sonograms derived from Fast Fourier Transformation-Welch Method and artificial immune systems in order to distinguish between atherosclerosis and healthy subjects. In this classification problem, the used artificial immune system has reached to 99.33% classification accuracy using 10-fold Cross Validation (CV) method with only two system units which reduced classification time considerably. This success shows that whereas artificial immune systems is a new research area, one can utilize from this new field to reach high performance for his problem. (c) 2006 Elsevier Ltd. All rights reserved.Öğe Diagnosis of heart disease using artificial immune recognition system and fuzzy weighted pre-processing (vol 39, pg 2186, 2006)(ELSEVIER SCI LTD, 2011) Polat, Kemal; Gunes, Salih; Tosun, Sulayman[Abstract not Available]Öğe Ensemble adaptive network-based fuzzy inference system with weighted arithmetical mean and application to diagnosis of optic nerve disease from visual-evoked potential signals(ELSEVIER, 2008) Akdemir, Bayram; Kara, Sadik; Polat, Kemal; Guven, Ayegul; Gunes, SalihObjective: This paper presents a new method based on combining principal component analysis (PCA) and adaptive network-based fuzzy inference system (ANFIS) to diagnose the optic nerve disease from visual-evoked potential (VEP) signals. The aim of this study is to improve the classification accuracy of ANFIS classifier on diagnosis of optic nerve disease from VEP signals. With this aim, a new classifier ensemble based on ANFIS and PCA is proposed. Methods and material: The VEP signals dataset include 61 healthy subjects and 68 patients suffered from optic nerve disease. First of all, the dimension of VEP signals dataset with 63 features has been reduced to 4 features using PCA. After applying PCA, ANFIS trained using three different training-testing datasets randomly with 50-50% training-testing partition. Results: The obtained classification results from ANFIS trained separately with three different training-testing datasets are 96.87%, 98.43%, and 98.43%, respectively. And then the results of ANFIS trained with three different training-testing datasets randomly with 50-50% training-testing partition have been combined with three different ways including weighted arithmetical mean that proposed firstly by us, arithmetical mean, and geometrical mean. The classification results of ANFIS combined with three different ways are 98.43%, 100%, and 100%, respectively. Also, ensemble ANFIS has been compared with ANN ensemble. ANN ensemble obtained 98.43%, 100%, and 100% prediction accuracy with three different ways including arithmetical mean, geometrical mean and weighted arithmetical mean. Conclusion: These results have shown that the proposed classifier ensemble approach based on ANFIS trained with different train-test datasets and PCA has produced very promising results in the diagnosis of optic nerve disease from VEP signals. (C) 2008 Published by Elsevier B.V.Öğe Examining the Relevance with Sleep Stages of Time Domain Features of EEG, EOG, and Chin EMG signals(IEEE, 2009) Gunes, Salih; Polat, Kemal; Dursun, Mehmet; Yosunkaya, SebnemSleep staging has an important role in determining sleep disorders such as sleepiness, human fatigue etc. Sleep staging is generally done according to Rechtschaffen and Kales standard (RKS) using EEG signal obtained from PSG signals taken from patient subjects who come with any sleep disorders. Sleep stages are generally divided into three stages including awake, REM and N-REM (stage 1, stage 2, and stage 3). In this study, time domain features of EEG, EOG of right and left eyes, and chin EMG signals belonging to sleep stages were investigated and correlation between these time domain features and sleep stages was calculated. The used time domain features are mean value, standard deviation, peak value, skewness, kurtosis, and shape factor belonging to EEG, EOG of right and left eyes, and chin EMG signals. In experimental studies, PSG recordings of 3 subjects were taken and average recording time of 6.22 h, total recording time was 18.67 h. When investigated correlation coefficients, it is seen that skewness feature in time domain features of EEG signal, standard deviation feature in time domain features of EOG signals belonging to right and left eyes, and mean value feature in time domain features of chin EMG signal were more correlated with sleep stages than other features. Consequently, a feature vector can be constituted combining features determined from time domain features of EEG, EOG belonging to right and left eyes, and chin EMG signals. This obtained feature vector can be easily used in distinguishing sleep stages.Öğe A hybrid approach to medical decision support systems: Combining feature selection, fuzzy weighted pre-processing and AIRS(ELSEVIER IRELAND LTD, 2007) Polat, Kemal; Gunes, SalihThis paper presents a hybrid approach based on feature selection, fuzzy weighted preprocessing and artificial immune recognition system (AIRS) to medical decision support systems. we have used the heart disease and hepatitis disease datasets taken from UCI machine learning database as medical dataset. Artificial immune recognition system has shown an effective performance on several problems such as machine learning benchmark problems and medical classification problems like breast cancer diabetes and liver disorders classification. The proposed approach consists of three stages. In the first stage, the dimensions of heart disease and hepatitis disease datasets are reduced to 9 from 13 and 19 in the feature selection (FS) sub-program by means of C4.5 decision tree algorithm (CBA program), respectively In the second stage, heart disease and hepatitis disease datasets are normalized in the range of [0,1] and are weighted via fuzzy weighted pre-processing. In the third stage, weighted input values obtained from fuzzy weighted pre-processing are classified using AIRS classifier system. The obtained classification accuracies of our system are 9239% and 81.82% using 50-50% training-test split for heart disease and hepatitis disease datasets, respectively with these results, the proposed method can be used in medical decision support systems. (c) 2007 Elsevier Ireland Ltd. All rights reserved.Öğe A hybrid automated detection system based on least square support vector machine classifier and k-NN based weighted pre-processing for diagnosing of macular disease(SPRINGER-VERLAG BERLIN, 2007) Polat, Kemal; Kara, Sadik; Guven, Aysegul; Gunes, SalihIn this paper, we proposed a hybrid automated detection system based least square support vector machine (LSSVM) and k-NN based weighted pre-processing for diagnosing of macular disease from the pattern electroretinography (PERG) signals. k-NN based weighted pre-processing is pre-processing method, which is firstly proposed by us. The proposed system consists of two parts: k-NN based weighted pre-processing used to weight the PERG signals and LSSVM classifier used to distinguish between healthy eye and diseased eye (macula diseases). The performance and efficiency of proposed system was conducted using classification accuracy and 10-fold cross validation. The results confirmed that a hybrid automated detection system based on the LSSVM and k-NN based weighted pre-processing has potential in detecting macular disease. The stated results show that proposed method could point out the ability of design of a new intelligent assistance diagnosis system.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 Medical application of artificial immune recognition system (AIRS): Diagnosis of atherosclerosis from carotid artery Doppler signals(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Latifoglu, Fatma; Kodaz, Halife; Kara, Sadik; Gunes, SalihThis study was conducted to distinguish between atherosclerosis and healthy subjects. Hence, we have employed the maximum envelope of the carotid artery Doppler sonogrants derived from Fast Fourier Transformation-Welch method and Artificial Immune Recognition System (AIRS). The fuzzy appearance of the carotid artery Doppler signals makes physicians suspicious about the existence of diseases and sometimes causes false diagnosis. Our technique gets around this problem using AIRS to decide and assist the physician to make the final judgment in confidence. AIRS has reached 99.29% classification accuracy using 10-fold cross validation. Results show that the proposed method classified Doppler signals successfully. (c) 2006 Elsevier Ltd. All rights reserved.Öğe Medical application of information gain-based artificial immune recognition system (IG-AIRS): Classification of microorganism species(PERGAMON-ELSEVIER SCIENCE LTD, 2009) Kara, Sadik; Aksebzeci, Bekir Hakan; Kodaz, Halife; Gunes, Salih; Kaya, Esma; Ozbilge, HaticeIn this paper, we have made medical application of a new artificial immune system named the information gain-based artificial immune recognition system (IG-AIRS) which is minimized the negative effects of taking into account all attributes in calculating Euclidean distance in shape-space representation which is used in many artificial immune systems. For medical data, microorganism dataset was applied in the performance analysis of our proposed system. Microorganism dataset was obtained using Cyranose 320 electronic nose. Our proposed system reached 92.35% classification accuracy with five-fold cross validation method. This result ensured that IG-AIRS would be helpful in classification of microorganism species based on laboratory tests, and would open the way to various microorganism species determine support by using electronic nose. (C) 2008 Elsevier Ltd. All rights reserved.Öğe Medical decision support system based on artificial immune recognition immune system (AIRS), fuzzy weighted pre-processing and feature selection(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Polat, Kemal; Gunes, SalihIn this study, diagnosis of hepatitis disease, which is a very common and important disease, was conducted with a machine learning system. The proposed machine learning approach has three stages. The first stage, the feature number of hepatitis disease dataset was reduced to 10 from 19 in the feature selection (FS) sub-program by means of C 4.5 decision tree algorithm. Then, hepatitis disease dataset is normalized in the range of [0, 1] and is weighted with fuzzy weighted pre-processing. Then, weighted input values obtained from fuzzy weighted pre-processing is classified by using AIRS classifier system. In this study, fuzzy weighted pre-processing, which can improved by ours, is a new method and firstly, it is applied to hepatitis disease dataset. We took the dataset used in our study from the UCI machine learning database. The obtained classification accuracy of our system was 94.12% and it was very promising with regard to the other classification applications in the literature for this problem. (c) 2006 Elsevier Ltd. All rights reserved.Öğe Microcontroller Compatible Sealed Lead Acid Battery Remaining Energy Prediction Using Adaptive Neural Fuzzy Inference System(IEEE COMPUTER SOC, 2009) Akdemir, Bayram; Gunes, Salih; Comlekciler, Ismail TahaAll over the world, many portable devices need battery to run. Every expert has to use efficient hardware and software documentation to make battery last longer and make a correlation between microcontrollers' duties and the remaining energy of batteries. In order to make battery last longer, battery information must be evaluated continuously. In many devices, fluctuating current is used due to its own load so alternating current makes it hard to compute the remaining battery level. For many devices, there could be battery level indicator as solution. This solution gives clue about the remaining time for user but it does not give any hint for microcontroller about battery situation. For low cost devices, it could be very difficult to estimate the remaining storage energy in the battery. In this study, microcontroller compatible sealed lead acid battery remaining energy predictor based on adaptive neural fuzzy inference system has been designed and proposed. In order to test proposed method, mean absolute error and leave one out have been used to measure proposed system performance. The obtained mean absolute error results for leave one out is 10.55, epoch error is 11.72. Through the study, low adaptive neural fuzzy inference system rules and low microcontroller memory consumption were aimed.Öğe A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Sahan, Seral; Polat, Kemal; Kodaz, Halife; Gunes, SalihThe use of machine learning tools in medical diagnosis is increasing gradually. This is mainly because the effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Such a disease is breast cancer, which is a very common type of cancer among woman. As the incidence of this disease has increased significantly in the recent years, machine learning applications to this problem have also took a great attention as well as medical consideration. This study aims at diagnosing breast cancer with a new hybrid machine learning method. By hybridizing a fuzzy-artificial immune system with k-nearest neighbour algorithm, a method was obtained to solve this diagnosis problem via classifying Wisconsin Breast Cancer Dataset (WBCD). This data set is a very commonly used data set in the literature relating the use of classification systems for breast cancer diagnosis and it was used in this study to compare the classification performance of our proposed method with regard to other studies. We obtained a classification accuracy of 99.14%, which is the highest one reached so far. The classification accuracy was obtained via 10-fold cross validation. This result is for WBCD but it states that this method can be used confidently for other breast cancer diagnosis problems, too. (c) 2006 Elsevier Ltd. All rights reserved.Öğe A new method to forecast of Escherichia coli promoter gene sequences: Integrating feature selection and Fuzzy-AIRS classifier system(PERGAMON-ELSEVIER SCIENCE LTD, 2009) Polat, Kemal; Gunes, SalihWe have investigated the real-world task of recognizing biological concepts in DNA sequences in this work. Recognizing promoters in strings that represent nucleotides (one of A, G, T, or C) has been performed using a novel approach based on feature selection (FS) and Artificial Immune Recognition System (AIRS) with Fuzzy resource allocation mechanism (Fuzzy-AIRS), which is. first proposed by us. The aim of this study is to improve the prediction accuracy of Escherichia coli promoter gene sequences using a novel system based on FS and Fuzzy-AIRS. The E. coli promoter gene sequences dataset has 57 attributes and 106 samples including 53 promoters and 53 non-promoters. The proposed system consists of two parts. Firstly, we have reduced the dimension of E. coli promoter gene sequences dataset from 57 attributes to 4 attributes by means of FS process. Second, Fuzzy-AIRS classifier algorithm has been run to predict the E. coli promoter gene sequences. The robustness of the proposed method is examined using prediction accuracy, sensitivity and specificity analysis, k-fold cross-validation method and confusion matrix. Whilst only Fuzzy-AIRS classifier has obtained 50% prediction accuracy using 10-fold cross-validation, the proposed system has obtained 90% prediction accuracy in the same conditions. These obtained results have indicated that the proposed system obtain the success rate in recognizing promoters in strings that represent nucleotides. (C) 2007 Elsevier Ltd. All rights reserved.
