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Öğe Automated Identification of Diseases Related to Lymph System From Lymphography Data Using Artificial Immune Recognition System With Fuzzy Resource Allocation Mechanism (Fuzzy-Airs)(Elsevier Sci Ltd, 2006) Polat, Kemal; Güneş, SalihArtificial immune recognition system (AIRS) classification algorithm, which has an important place among classification algorithms in the field of artificial immune systems, has showed an effective and intriguing performance oil the problems it was applied. AIRS was previously applied to some medical classification problems including breast cancer, Cleveland heart disease, diabetes and it obtained very satisfactory results. So, AIRS proved to be,in efficient artificial intelligence technique in medical field. In this study, the resource allocation mechanism of AIRS was changed with a new one determined by fuzzy-logic. This system, named as fuzzy-AIRS was used as a classifier in the diagnosis of lymph diseases, which is of great importance in medicine. The classifications of lymph diseases dataset taken from University of California at Irvine (UCI) Machine Learning Repository were done using 10-fold cross-validation method. Reached classification accuracies were evaluated by comparing them with reported classifiers in UCI web site in addition to other systems that are applied to the related problems. Also, the obtained classification performances were compared with AIRS with regard to the classification accuracy, number of resources and classification time. While only AIRS algorithm obtained 83.138% classification accuracy, fuzzy-AIRS classified the lymph diseases dataset with 90.00% accuracy. For lymph diseases dataset, fuzzy-AIRS obtained the highest classification accuracy according to the UCI web site, Beside of this success, fuzzy-AIRS gained an important advantage over the AIRS by means of classification time. By reducing classification time as well as obtaining high classification accuracies in the applied datasets, fuzzy-AIRS classifier proved that it could be used as an effective classifier for medical problems.Öğe Correlation- and covariance-supported normalization method for estimating orthodontic trainer treatment for clenching activity(SAGE PUBLICATIONS LTD, 2009) Akdemir, Bayram; Ökkesim, Şükrü; Kara, Sadık; Güneş, SalihIn this study, electromyography signals sampled from children undergoing orthodontic treatment were used to estimate the effect of an orthodontic trainer on the anterior temporal muscle. A novel data normalization method, called the correlation- and covariance-supported normalization method (CCSNM), based on correlation and covariance between features in a data set, is proposed to provide predictive guidance to the orthodontic technique. The method was tested in two stages: first, data normalization using the CCSNM; second, prediction of normalized values of anterior temporal muscles using an artificial neural network (ANN) with a Levenberg-Marquardt learning algorithm. The data set consists of electromyography signals from right anterior temporal muscles, recorded from 20 children aged 8-13 years with class II malocclusion. The signals were recorded at the start and end of a 6-month treatment. In order to train and test the ANN, two-fold cross-validation was used. The CCSNM was compared with four normalization methods: minimum-maximum normalization, z score, decimal scaling, and line base normalization. In order to demonstrate the performance of the proposed method, prevalent p erformance-measuring methods, and the mean square error and mean absolute error as mathematical methods, the statistical relation factor R-2 and the average deviation have been examined. The results show that the CCSNM was the best normalization method among other normalization methods for estimating the effect of the trainer.Öğe Design and Implementation of Microcontroller Supported Amalgamator(IEEE, Piscataway, NJ, United States, 2000) Güneş, Salih; Yaldız, Ercan; Sayın, M. VehbiIn this study, PIC microcontroller based medical apparatus was constructed for dentistry. The function of the apparatus is to obtain high quality tooth filling material. In dentistry the tooth filling material does not adhere correctly and fall often after some period of time. The reason of this problem is that the mixing process of mercury and amalgam aren't done correctly. With the realized system this mixing problem was solved and achieved successfully.Öğe The Design and Implementation of Microcontroller Supported Amalgamator(IEEE, 2000) Güneş, Salih; Yaldız, E.; Sayın, M. V.In this study, PIC microcontroller based medical apparatus was constructed for dentistry The function of the apparatus is to obtain high quality tooth filling material In dentistry the tooth filling material does not adhere correctly and fall often after some period of time. The reason of this problem is that the mixing process of mercury and amalgam aren't done correctly. With the realized system this mixing problem was solved and achieved successfully.Öğe Diagnosis of Heart Disease Using Artificial Immune Recognition System and Fuzzy Weighted Pre-Processing(Pergamon-Elsevier Science Ltd, 2006) Polat, Kemal; Güneş, Salih; Tosun, SülaymanThis paper presents a novel method for diagnosis of heart disease. The proposed method is based on a hybrid method that uses fuzzy weighted pre-processing and artificial immune recognition system (AIRS). Artificial immune recognition system has showed an effective performance on several problems such as machine learning benchmark problems and medical classification problems like breast cancer, diabetes, liver disorders classification. The robustness of the proposed method is examined using classification accuracy, k-fold cross-validation method and confusion matrix. The obtained classification accuracy is 96.30% and it is very promising compared to the previously reported classification techniques.Öğe EEG, EOG ve Çene EMG Sinyallerinin Zaman Domeni Özelliklerinin Uyku Evreleri ile İlişkisinin İncelenmesi(2009) Güneş, Salih; Polat, Kemal; Dursun, Mehmet; Yosunkaya, ŞebnemSleep 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 Efficient Sleep Stage Recognition System Based on EEG Signal Using K-Means Clustering Based Feature Weighting(Pergamon-Elsevier Science Ltd, 2010) Güneş, Salih; Polat, Kemal; Yosunkaya, ŞebnemSleep scoring is one of the most important methods for diagnosis in psychiatry and neurology. Sleep staging is a time consuming and difficult task conducted by sleep specialists. The purposes of this work are to automatic score the sleep stages and to help to sleep physicians on sleep stage scoring. In this work, a novel data preprocessing method called k-means clustering based feature weighting (KMCFW) has been proposed and combined with k-NN (k-nearest neighbor) and decision tree classifiers to classify the EEG (electroencephalogram) sleep into six sleep stages including awake, N-REM (non-rapid eye movement) stage 1, N-REM stage 2, N-REM stage 3, REM, and non-sleep (movement time). First of all, frequency domain features belonging to sleep EEG signal have been extracted using Welch spectral analysis method and composed 129 features from EEG signal relating each sleep stages. In order to decrease the features, the statistical features comprising minimum value, maximum value, standard deviation, and mean value have been used and then reduced from 129 to 4 features. In the second phase, the sleep stages dataset with four features has been weighted by means of k-means clustering based feature weighting. Finally, the weighted sleep stages have been automatically classified into six sleep stages using k-NN and C4.5 decision tree classifier. In the classification of sleep stages, the k values of 10, 20, 30, 40, 50, and 60 in k-NN classifier have been used and compared with each other. In the experimental results, while sleep stages has been classified with 55.88% success rate using k-NN classifier (for k value of 40), the weighted sleep stages with KMCFW has been recognized with 82.15% success rate k-NN classifier (for k value of 40). And also, we have investigated the relevance between sleep stages and frequency domain features belonging to EEG signal. These results have demonstrated that proposed weighting method have a considerable impact on automatic determining of sleep stages. This system could be used as an online system in the automatic scoring of sleep stages and helps to sleep physicians in the sleep scoring process.Öğe Fault-Tolerant Multicast Routing Algorithm Based on Cube Algebra for Hypercube Multicomputers(IEEE, Piscataway, NJ, United States, 2000) Güneş, Salih; Yılmaz, Nihat; Öztürk, AliIn this study a broadcast routing algorithm has been developed for faulty hypercube parallel processing system using cube algebra. Without any restriction to the number of the faulty nodes, the routing from the source node to the destination node is implemented minimally. The developed routing algorithm has been visually simulated via prepared data routing simulator program. It has been observed that this algorithm can be applied to various routing problems.Öğe A Fault-Tolerant Multicast Routing Algorithm Based on Cube Algebra for Hypercube Multicomputers(IEEE, 2000) Güneş, Salih; Yılmaz, Nihat; Öztürk, AliIn this study a broadcast rooting algorithm has been developed for faulty hypercube parallel processing system using cube algebra. Without any restriction to the number of the faulty nodes, the routing from the source node to the destination node is implemented minimally, The developed routing algorithm has been visually simulated via prepared data renting simulator program. It has been observed that this algorithm can be applied to various routing problems.Öğe A fault-tolerant multicast routing algorithm based on cube algebra for hypercube networks(SPRINGER HEIDELBERG, 2003) Güneş, Salih; Yılmaz, Nihat; Öztürk, AliIn this study a multicast routing algorithm has been developed for faulty hypercube parallel processing system using cube algebra. Without any restriction to the number of the faulty nodes, the routing from the source node to the destination node is implemented minimally. The developed routing algorithm has been visually simulated via prepared data routing simulator program. It has been observed that this algorithm can be applied to various routing problems.Öğe A Hybrid Medical Decision Making System Based on Principles Component Analysis, k-NN Based Weighted Pre-Processing and Adaptive Neuro-Fuzzy Inference System(Academic Press Inc Elsevier Science, 2006) Polat, Kemal; Güneş, SalihProper interpretation of the thyroid gland functional data is an important issue in diagnosis of thyroid disease. The primary role of the thyroid gland is to help regulation of the body's metabolism. Thyroid hormone produced by thyroid gland provides this. Production of too little thyroid hormone (hypo-thyroidism) or production of too much thyroid hormone (hyper-thyroidism) defines the types of thyroid disease. It is evident that usage of machine learning methods in disease diagnosis has been increasing gradually. In this study, diagnosis of thyroid disease, which is a very common and important disease, was conducted with such a machine learning system. In this study, we have detected on thyroid disease using principles component analysis (PCA), k-nearest neighbor (k-NN) based weighted pre-processing and adaptive neuro-fuzzy inference system (ANFIS). The proposed system has three stages. In the first stage, dimension of thyroid disease dataset that has 5 features is reduced to 2 features using principles component analysis. In the second stage, a new weighting scheme based on k-nearest neighbor (k-NN) method was utilized as a pre-processing step before the main classifier. Then, in the third stage, we have used adaptive neuro-fuzzy inference system to diagnosis of thyroid disease. We took the thyroid disease dataset used in our study from the UCI machine learning database. The obtained classification accuracy of our system was 100% and it was very promising with regard to the other classification applications in literature for this problem.Öğe Medical Diagnosis of Rheumatoid Arthritis Disease From Right and Left Hand Ulnar Artery Doppler Signals Using Adaptive Network Based Fuzzy Inference System (ANFIS) and MUSIC Method(ELSEVIER SCI LTD, 2010) Özkan, Ali Osman; Kara, Sadık; Şallı, Ali; Sakarya, Mehmet Emin; Güneş, SalihRheumatoid arthritis (RA) is a multi-systemic autoimmune disease that leads to substantial morbidity and mortality. In this paper, as spectral analysis methods of Multiple Signal Classification (MUSIC) method is used in order to extract the significant features from the right and left hand Ulnar artery Doppler signals for the diagnosis of RA disease. The MUSIC method has been used as subspace method. To extract features from Doppler signals obtained from the right and left hand Ulnar arterial the MUSIC method model degrees of 5, 10, 15, 20, and 25 were used. Then, an adaptive network based fuzzy inference system (ANFIS) was applied to features extracted from the right and left hand Ulnar artery Doppler signals for classifying RA disease. The methods are not new, but the study has a novelty in that the application area of these methods is new. In the hybrid model, the combination of MUSIC and ANFIS yielded classification accuracies of 95% (for a model degree of 20) using the right hand Ulnar artery and classification accuracies of 91.25% (for a model degree of 10) using left hand Ulnar artery Doppler signals in the diagnosis of RA disease. The proposed approach has potential to help with the early diagnosis of RA disease for the specialists who study this subject.Öğe Multi-Class F-Score Feature Selection Approach to Classification of Obstructive Sleep Apnea Syndrome(PERGAMON-ELSEVIER SCIENCE LTD, 2010) Güneş, Salih; Polat, Kemal; Yosunkaya, ŞebnemIn this paper, a new feature selection named as multi-class f-score feature selection is proposed for sleep apnea classification having different disorder degrees (mild OSAS, moderate OSAS, serious OSAS, and non-OSAS). f-Score is used to measure the discriminating power of features in the classification of two-class pattern recognition problems. In order to apply the f-score feature selection to multi-class datasets, we have used the f-score feature selection as pairwise (in the form of two classes) in the diagnosis of obstructive sleep apnea syndrome (OSAS) with four classes. After feature selection process, MLPANN (Multi-layer perceptron artificial neural network) classifier is used to diagnose the OSAS having different disorder degrees. While MLPANN obtained 63.41% classification accuracy on the diagnosis of OSAS, the combination of MLPANN and multi-class f-score feature selection achieved 84.14% classification accuracy using 50-50% training-testing split of OSAS dataset with four classes. These results demonstrate that the proposed multi-class f-score feature selection method is effective and robust in determining the disorder degrees of OSAS.Öğe A Multicast Routing Algorithm Based on Parallel Branching Method for Faulty Hypercubes(Institute of Electrical and Electronics Engineers Inc., 2001) Güneş, Salih; Yılmaz, Nihat; Allahverdi, NovruzIn this study, a multicast routing algorithm based on a parallel branching method has been developed for a faulty hypercube parallel processing system. The routing from the source to the destination nodes is guaranteed in the shortest time with this algorithm. Going through to the destinations from the source is a parallel process at each step. The superiority of the developed algorithm over previous studies is that the routing from the source to the destination is achieved in minimal steps without restriction to the number of faulty nodes. This means that the algorithm is running independently from the number of faulty nodes. The algorithm is simulated with a hypercube routing simulator.Öğe Multifont Ottoman Character Recognition(2000) Öztürk, Ali; Güneş, Salih; Özbay, YükselOttoman characters from three different fonts are used character recognition problem, broadly speaking, is transferring a page that contain symbols to the computer and matching these symbols with previously known or recognized symbols after extraction the features of these symbols via appropriate preprocessing methods. Because of silent features of the characters, implementing an Ottoman character recognition system is a difficult work. Different researchers have done lots of works for years to develop systems that would recognize Latin characters. Although almost one million people use Ottoman characters, great deal of whom has different native languages, the number of studies on this field is insufficient. In this study 28 different machine-printed to train the Artificial Neural Network and a %95 classification accuracy for the characters in these fonts and a %70 classification accuracy for a different font has been found.Öğe A New Method to Medical Diagnosis: Artificial Immune Recognition System (Airs) With Fuzzy Weighted Pre-Processing and Application to Ecg Arrhythmia(Pergamon-elsevier Science Ltd, 2006) Polat, Kemal; Şahan, Seral; Güneş, SalihChanges in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately fatal or cause irreparable damage to the heart sustained over long periods of time. The ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. Artificial immune systems (AISs) is a new but effective branch of artificial intelligence. Among the systems proposed in this field so far, artificial immune recognition system (AIRS), which was proposed by A. Watkins, has showed an effective and intriguing performance on the problems it was applied. Previously, AIRS was applied a range of problems including machine-learning benchmark problems and medical classification problems like breast cancer, diabets, liver disorders classification problems. The conducted medical classification task was performed for ECG arrhythmia data taken from UCI repository of machine-learning. Firsly, ECG 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 be improved by ours, is a new method and firstly, it is applied to ECG dataset. Classifier system consists of three stages: 50-50% of traing-test dataset, 70-30% of traing-test dataset and 80-20% of traing-test dataset, subsequently, the obtained classification accuries: 78.79, 75.00 and 80.77%.Öğe Prediction of Cardiac End-Systolic and End-Diastolic Diameters in M-Mode Values Using Adaptive Neural Fuzzy Inference System(Pergamon-Elsevier Science Ltd, 2010) Akdemir, Bayram; Güneş, Salih; Oran, Bülent; Karaaslan, SevimThe cardiac, end-systolic and end-diastolic diameters values are very important m-mode cardiac parameters for infant, children, and adolescents, due to growing up body. These parameters, belonging to heart, must be known in order to make a decision about the subject. The expert decision occurs after comparing measured value to hard-copied charts. Hard-copied charts were prepared previously as a result of long statistical studies and these charts depend on a certain region. Our proposed method presents a valid virtual chart for the experts. The proposed method comprises of two stages: (i) data normalization based on euclidean distance (ii) normalized cardiac parameters predicting using adaptive neural fuzzy system. In order to present performance of the proposed method, mean absolute error, absolute deviation and two-fold cross-validation were used. In addition to performance criteria, different common normalization methods, z-score, decimal scaling and minimum-maximum normalization methods were used to compare. In this study, the aim is to create a valid virtual chart which helps the expert during making the decision about predicting end-systolic and end-diastolic cardiac m-mode values. The results were compared with real cardiac parameters by expert with 10 years of medical experience.Öğe Usage of Class Dependency Based Feature Selection and Fuzzy Weighted Pre-Processing Methods on Classification of Macular Disease(Pergamon-elsevier Science Ltd, 2009) Polat, Kemal; Kara, Sadık; Güven, Aysegül; Güneş, SalihIn this paper, we propose a new feature selection method called class dependency based feature selection for dimensionality reduction of the macular disease dataset from pattern electroretinography (PERG) signals. in order to diagnosis of macular disease, we have used class dependency based feature selection as feature selection process. fuzzy weighted pre-processing as weighted process and decision tree classifier as decision making. The proposed system consists of three parts. First, we have reduced to 9 features number of features of macular disease dataset that has 63 features using class dependency based feature selection, which is first developed by ours. Second, the macular disease dataset that has 9 features is weighted by using fuzzy weighted pre-processing. And finally, decision tree classifier was applied to PERG signals to distinguish between healthy eye and diseased eye (macula diseases). The employed class dependency based feature selection, fuzzy weighted pre-processing and decision tree classifier have reached to 96.22%, 96.27% and 96.30% classification accuracies using 5-10-15-fold cross-validation, respectively. The results confirmed that the medical decision making system based on the class dependency based feature selection, fuzzy weighted pre-processing and decision tree classifier has potential in detecting the macular disease. The stated results show that the proposed method could point out the ability of design of a new intelligent assistance diagnosis system.Öğe Use of Kernel Functions in Artificial Immune Systems for the Nonlinear Classification Problems(Ieee-inst Electrical Electronics Engineers Inc, 2009) Özşen, Seral; Güneş, Salih; Kara, Sadık; Latifoğlu, FatmaDue to the fact that there exist only a small number of complex systems in artificial immune systems (AISs) that solve nonlinear problems, there is a need to develop nonlinear AIS approaches that would be among the well-known solution methods. In this study, we developed a kernel-based AIS to compensate for this deficiency by providing a nonlinear structure via transformation of distance calculations in the clonal selection models of classical AIS to kernel space. Applications of the developed system were conducted on Statlog heart disease dataset, which was taken from the University of California, Irvine Machine-Learning Repository, and on Doppler sonograms to diagnose atherosclerosis disease. The system obtained a classification accuracy of 85.93% for the Statlog heart disease dataset, while it achieved a 99.09% classification success for the Doppler dataset. With these results, our system seems to be a potential solution method, and it may be considered as a suitable method for hard nonlinear classification problems.
