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Öğe Adaptive neuro-fuzzy inference system for diagnosis of the heart valve diseases using wavelet transform with entropy(SPRINGER LONDON LTD, 2012) Uguz, HarunListening via stethoscope is a preferential method, being used by physicians for distinguishing normal and abnormal cardiac systems. On the other hand, listening with stethoscope has a number of constraints. The interpretation of various heart sounds depends on physician's ability of hearing, experience, and skill. Such limitations may be reduced by developing biomedical-based decision support systems. In this study, a biomedical-based decision support system was developed for the classification of heart sound signals, obtained from 120 subjects with normal, pulmonary, and mitral stenosis heart valve diseases via stethoscope. Developed system comprises of three stages. In the first stage, for feature extraction, obtained heart sound signals were separated to its sub-bands using discrete wavelet transform (DWT). In the second stage, entropy of each sub-band was calculated using Shannon entropy algorithm to reduce the dimensionality of the feature vectors via DWT. In the third stage, the reduced features of three types of heart sound signals were used as input patterns of the adaptive neuro-fuzzy inference system (ANFIS) classifiers. Developed method reached 98.33% classification accuracy, and it was showed that purposed method is effective for detection of heart valve diseases.Öğe Artificial bee colony algorithm with variable search strategy for continuous optimization(ELSEVIER SCIENCE INC, 2015) Kiran, Mustafa Servet; Hakli, Huseyin; Gunduz, Mesut; Uguz, HarunThe artificial bee colony (ABC) algorithm is a swarm-based optimization technique proposed for solving continuous optimization problems. The artificial agents of the ABC algorithm use one solution update rule during the search process. To efficiently solve optimization problems with different characteristics, we propose the integration of multiple solution update rules with ABC in this study. The proposed method uses five search strategies and counters to update the solutions. During initialization, each update rule has a constant counter content. During the search process performed by the artificial agents, these counters are used to determine the rule that is selected by the bees. Because the optimization problems and functions have different characteristics, one or more search strategies are selected and are used during the iterations according to the characteristics of the numeric functions in the proposed approach. By using the search strategies and mechanisms proposed in the present study, the artificial agents learn which update rule is more appropriate based on the characteristics of the problem to find better solutions. The performance and accuracy of the proposed method are examined on 28 numerical benchmark functions, and the obtained results are compared with various classical versions of ABC and other nature-inspired optimization algorithms. The experimental results show that the proposed algorithm, integrated and improved with search strategies, outperforms the basic variants and other variants of the ABC algorithm and other methods in terms of solution quality and robustness for most of the experiments. (C) 2015 Elsevier Inc. All rights reserved.Öğe A Biomedical System Based on Artificial Neural Network and Principal Component Analysis for Diagnosis of the Heart Valve Diseases(SPRINGER, 2012) Uguz, HarunListening via stethoscope is a primary method, being used by physicians for distinguishing normally and abnormal cardiac systems. Listening to the voices, coming from the cardiac valves via stethoscope, upon the flow of the blood running in the heart, physicians examine whether there is any abnormality with regard to the heart. However, listening via stethoscope has got a number of limitations, for interpreting different heart sounds depends on hearing ability, experience, and respective skill of the physician. Such limitations may be reduced by developing biomedical based decision support systems. In this study, a biomedical-based decision support system was developed for the classification of heart sound signals, obtained from 120 subjects with normal, pulmonary and mitral stenosis heart valve diseases via stethoscope. Developed system was mainly comprised of three stages, namely as being feature extraction, dimension reduction, and classification. At feature extraction stage, applying Discrete Fourier Transform (DFT) and Burg autoregressive (AR) spectrum analysis method, features, representing heart sounds in frequency domain, were obtained. Obtained features were reduced in lower dimensions via Principal Component Analysis (PCA), being used as a dimension reduction technique. Heart sounds were classified by having the features applied as input to Artificial Neural Network (ANN). Classification results have shown that, dimension reduction, being conducted via PCA, has got positive effects on the classification of the heart sounds.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 Biomedical system based on the Discrete Hidden Markov Model using the Rocchio-Genetic approach for the classification of internal carotid artery Doppler signals(ELSEVIER IRELAND LTD, 2011) Uguz, Harun; Guraksin, Gur Emre; Ergun, Ucman; Saracoglu, RidvanWhen the maximum likelihood approach (ML) is used during the calculation of the Discrete Hidden Markov Model (DHMM) parameters, DHMM parameters of the each class are only calculated using the training samples (positive training samples) of the same class. The training samples (negative training samples) not belonging to that class are not used in the calculation of DHMM model parameters. With the aim of supplying that deficiency, by involving the training samples of all classes in calculating processes, a Rocchio algorithm based approach is suggested. During the calculation period, in order to determine the most appropriate values of parameters for adjusting the relative effect of the positive and negative training samples, a Genetic algorithm is used as an optimization technique. The purposed method is used to classify the internal carotid artery Doppler signals recorded from 136 patients as well as of 55 healthy people. Our proposed method reached 97.38% classification accuracy with fivefold cross-validation (CV) technique. The classification results showed that the proposed method was effective for the classification of internal carotid artery Doppler signals. (C) 2010 Elsevier Ireland 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 CLASSIFICATION OF HEART SOUNDS BASED ON THE LEAST SQUARES SUPPORT VECTOR MACHINE(ICIC INTERNATIONAL, 2011) Guraksin, Gur Emre; Uguz, HarunThe heart is of crucial significance to human beings. Auscultation with a stethoscope is regarded as one of the pioneer methods used in the diagnosis of heart diseases. However, the fact that auscultation via a stethoscope depends on the skills of the physician's auscultation or his/her experience may lead to some problems in diagnosis. Therefore, the use of an artificial intelligence method in the diagnosis of heart sounds may help the physicians in a clinical environment. In this study, primarily, heart sound signals in numerical format were separated into sub-bands through discrete wavelet transform. Next, the entropy of each sub-band was calculated by using the Shannon entropy algorithm to reduce the dimensionality of the feature vectors with the help of the discrete wavelet transform. The reduced features of three types of heart sound signals were used as input patterns of the least square support vector machines and they were classified by least square support vector machines. In the method used, 96.6% of the classification performance was obtained. The classification performance of the method used was compared with the classification performance of previous studies which were applied to the same data set, and the superiority of the system used was demonstrated.Öğe Classification of internal carotid artery Doppler signals using fuzzy discrete hidden Markov model(PERGAMON-ELSEVIER SCIENCE LTD, 2011) Uguz, Harun; Kodaz, HalifeWe developed a biomedical system based on Discrete Hidden Markov Model (DHMM). The aim of our system is to classify the internal carotid artery Doppler signals. We applied a fuzzy approach to DHMM. Thus we decreased information loss and increased the classification performance. Our system reached 97.38% of classification accuracy with 5 fold cross validation. These results showed that the Fuzzy Discrete Hidden Markov Model (FDHMM) method is effective for classification of internal carotid artery Doppler signals. (C) 2010 Elsevier Ltd. All rights reserved.Öğe Detection of Carotid Artery Disease by Using Learning Vector Quantization Neural Network(SPRINGER, 2012) Uguz, HarunDoppler ultrasound has been usually preferred for investigation of the artery conditions in the last two decades, because it is a non-invasive, easy to apply and reliable technique. In this study, a biomedical system based on Learning Vector Quantization Neural Network (LVQ NN) has been developed in order to classify the internal carotid artery Doppler signals obtained from the 191 subjects, 136 of them had suffered from internal carotid artery stenosis and rest of them had been healthy subject. The system is composed of feature extraction and classification parts, basically. In the feature extraction stage, power spectral density (PSD) estimates of internal carotid artery Doppler signals were obtained by using Burg autoregressive (AR) spectrum analysis technique in order to obtain medical information. In the classification stage, LVQ NN was used classify features from Burg AR method. In experiments, LVQ NN based method reached 97.91% classification accuracy with 5 fold Cross Validation (CV) technique. In addition, the classification performance of the LVQ NN was compared with some methods such as Multi Layer Perceptron (MLP) NN, Naive Bayes (NB), K-Nearest Neighbor (KNN), decision tree and Support Vector Machine (SVM) with sensitivity and specificity statistical parameters. The classification results showed that the LVQ NN method is effective for classification of internal carotid artery Doppler signals.Öğe Detection of heart valve diseases by using fuzzy discrete hidden Markov model(PERGAMON-ELSEVIER SCIENCE LTD, 2008) Uguz, Harun; Arslan, Ahmet; Saracoglu, Ridvan; Turkoglu, IbrahimIn the present study, biomedical based application was developed to classify the data belongs to normal and abnormal samples generated by Doppler ultrasound. This study consists of raw data obtaining and pre-processing, feature extraction and classification steps. In the pre-processing step, a high-pass filter, white de-noising and normalization were used. During the feature extraction step, wavelet entropy was applied by wavelet transform and short time fourier transform. Obtained features were classified by fuzzy discrete hidden Markov model (FDHMM). For this purpose, a FDHMM that consists of Sugeno and Choquet integrals and lambda fuzzy measurement was defined to eliminate statistical dependence assumptions to increase the performance and to have better flexibility. Moreover, Sugeno integral was used together with triangular norms that are mentioned frequently in the literature in order to increase the performance. Experimental results show that recognition rate obtained by Sugeno fuzzy integral with triangular norm is more successful than recognition rates obtained by standard discrete HMM (DHMM) and Choquet integral based FDHMM. In addition to this, it is shown in this study that the performance of the Sugeno integral based method is better than the performances of artificial neural network (ANN) and HMM based classification systems that were used in previous studies of the authors. (c) 2007 Elsevier Ltd. All rights reserved.Öğe Genetic algorithm supported by expert system to solve land redistribution problem(WILEY, 2018) Hakli, Huseyin; Uguz, Harun; Cay, TayfunLand redistribution, a real-world optimization problem, involves the distribution of land parcels in predetermined blocks based on the landowners' preferences. This process, measured in weeks or months, is usually performed manually by a technician with the support of computer software. Although various techniques have been developed in recent years to solve this complex problem, they all require improvement. This study aimed to develop a new technique and produce applicable redistribution plans using a genetic algorithm (GA) in combination with an expert system. Blocks of cadastral parcels were determined by a GA using a new objective function to consider the overflow and residual areas as well as the landowners' preferences. The expert system was employed to close (reduce to zero) the overflow or residual areas occurring after the GA distribution. To investigate the performance of the proposed method, the system was used on a real study area and the results were compared against those obtained for the same cadastral situation undertaken by a technician using a similar method from published literature. The experimental results showed that the method proposed in this study performed better than the other methods because it provided a successful and applicable redistribution plan for the study area in a much shorter time.Öğe A hybrid system based on information gain and principal component analysis for the classification of transcranial Doppler signals(ELSEVIER IRELAND LTD, 2012) Uguz, HarunA transcranial Doppler (TCD) is a non-invasive, easy to apply and reliable technique which is used in the diagnosis of various brain diseases by measuring the blood flow velocities in brain arteries. This study aimed to classify the TCD signals, and feature ranking (information gain - IG) and dimension reduction methods (principal component analysis - PCA) were used as a hybrid to improve the classification efficiency and accuracy. In this context, each feature within the feature space was ranked depending on its importance for the classification using the IG method. Thus, the less important features were ignored and the highly important features were selected. Then, the PCA method was applied to the highly important features for dimension reduction. As a result, a hybrid feature reduction between the selection of the highly important features and the application of the PCA method on the reduced features were achieved. To evaluate the effectiveness of the proposed method, experiments were conducted using a support vector machine (SVM) classifier on the TCD signals recorded from the temporal region of the brain of 82 patients, as well as 24 healthy people. The experimental results showed that using the IG and PCA methods as a hybrid improves the classification efficiency and accuracy compared with individual usage. (C) 2011 Elsevier Ireland Ltd. All rights reserved.Öğe A new algorithm based on artificial bee colony algorithm for energy demand forecasting in Turkey(IEEE, 2015) Uguz, Harun; Hakli, Huseyin; Baykan, Omer K.In this study, an energy demand forecasting algorithm based on the Artificial Bee Colony with Variable Search Strategies (ABCVSS) method was proposed in order to determine Turkey's long-term energy demand. Linear and quadratic equations were used for energy demand forecasting and the coefficients of the equations were determined by means of the ABCVSS method. With the ABCVSS method, an attempt was made to enhance the local and global searching capacity of the ABC algorithm by using five different search strategies. GDP, population, imports and exports data of the period from 1979 to 2005 were chosen as the input parameters for the proposed method. Long-term energy demand was predicted through one scenario and the obtained performance from the proposed method was compared to those obtained from PSO, ACO and HAP algorithms in the literature. It was determined that the proposed method is statistically more successful than the other methods.Öğe A new approach based on a discrete hidden Markov model using the Rocchio algorithm for the diagnosis of heart valve diseases(WILEY-BLACKWELL, 2008) Uguz, Harun; Arslan, AhmetApplication of the Doppler ultrasound technique in the diagnosis of heart diseases has been increasing in the last decade since it is non-invasive, practicable and reliable. In this study, a new approach based on the discrete hidden Markov model (DHMM) is proposed for the diagnosis of heart valve disorders. For the calculation of hidden Markov model (HMM) parameters according to the maximum likelihood approach, HMM parameters belonging to each class are calculated by using training samples that only belong to their own classes. In order to calculate the parameters of DHMMs, not only training samples of the related class but also training samples of other classes are included in the calculation. Therefore HMM parameters that reflect a class's characteristics are more represented than other class parameters. For this aim, the approach was to use a hybrid method by adapting the Rocchio algorithm. The proposed system was used in the classification of the Doppler signals obtained from aortic and mitral heart valves of 215 subjects. The performance of this classification approach was compared with the classification performances in previous studies which used the same data set and the efficiency of the new approach was tested. The total classification accuracy of the proposed approach (95.12%) is higher than the total accuracy rate of standard DHMM (94.31%), continuous HMM (93.5%) and support vector machine (92.67%) classifiers employed in our previous studies and comparable with the performance levels of classifications using artificial neural networks (95.12%) and fuzzy-C-means/CHMM (95.12%).Öğe A new approach for automating land partitioning using binary search and Delaunay triangulation(ELSEVIER SCI LTD, 2016) Hakli, Huseyin; Uguz, Harun; Cay, TayfunOne of the most important, yet time-consuming steps of the land consolidation process, which is related to pooling fragmented lands together, is the production of land partitioning plans. After the land redistribution process is finished, the land partitioning process begins. In that process, the locations of parcels within the blocks are determined. Due to the non-uniform geometric shapes of the blocks, the areas of the parcels cannot be divided directly. The production of an ideal land partitioning plan is not suitable automatically unless a quick, accurate process to divide the lands is secured. In this study, production of a pre-land partitioning plan is realized using both the binary search method and the Delaunay triangulation method, taking into consideration shape, size, value and road access criteria. The result of the experimental study shows that the proposed approach for dividing the parcels makes the process take place more quickly. Thus, a solid base for creating an automatic land partitioning plan-one that is closest to an ideal plan-will be provided with this study. (C) 2016 Elsevier B.V. All rights reserved.Öğe A new hybrid gravitational search-teaching-learning-based optimization method for energy demand estimation of Turkey(SPRINGER LONDON LTD, 2019) Tefek, Mehmet Fatih; Uguz, Harun; Gucyetmez, MehmetIn this study, energy demand estimation (EDE) was implemented by a proposed hybrid gravitational search-teaching-learning-based optimization method with developed linear, quadratic and exponential models. Five indicators: population, gross domestic product as the socio-economic indicators and installed power, gross electric generation and net electric consumption as the electrical indicators, were used in analyses between 1980 and 2014. First, the developed models were trained by the data between 1980 and 2010, and then, accuracy of the models was tested by the data between 2011 and 2014. It is found that the obtained results with the proposed method are coherent with the training data with correlation coefficients in three models as 0.9959, 0.9964 and 0.9971, respectively. Root mean square error values were computed 1.8338, 1.7193 and 1.5497, respectively, and mean absolute percentage errors were obtained as 2.1141, 2.0026 and 1.6792%, respectively, in the three models. These values calculated by the proposed method are better than the results of standard gravitational search algorithm and teaching-learning-based optimization methods and also classical regression analysis. Low, expected and high scenarios were proposed in terms of various changing rates between 0.5 and 1.5% difference in socio-economic and electrical indicators. Those scenarios were used in the EDE study of Turkey between 2015 and 2030 for a comparison with other related studies in the literature. By the proposed method, the strategy in energy importation can be regulated and thus more realistic energy policies can be made.Öğe A new hybrid gravitational search-teaching-learning-based optimization method for the solution of economic dispatch of power systems(TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2019) Tefek, Mehmet Fatih; Uguz, HarunThe economic dispatch problem (EDP) is a complex, constrained, and nonlinear optimization problem. In the EDP, the active power bus should operate between the minimum and maximum bus limits to minimize the fuel cost. In this study, a fast, efficient, and reliable hybrid gravitational search algorithm-teaching learning based optimization (GSA-TLBO) method was proposed for the purpose of solving the EDP in power systems. The proposed method separates the search space into two sections as global and local searching. In the first part, searching was carried out by GSA method effectively to form the second search space. In the second part, the optimum solution was sought in the local search space by the TLBO method. The proposed method was implemented to a constrained benchmark G01 problem. The proposed hybrid method was then applied to the constrained EDP in IEEE 30-bus and IEEE 57-bus test systems and Turkey's 22-bus power system to minimize the fuel cost. Obtained results were compared with other methods. Experimental results show that the proposed method results in shorter, more reliable, and efficient lowest fuel cost solutions. It has been found that the proposed method can be used to solve constrained optimization problems.Öğe A novel approach for automated land partitioning using genetic algorithm(PERGAMON-ELSEVIER SCIENCE LTD, 2017) Hakli, Huseyin; Uguz, HarunLand consolidation is an important tool to prevent land fragmentation and enhance agricultural productivity. Land partitioning is one of the most significant problems within the land consolidation process. This process is related to the subdivision of a block having non-uniform geometric shapes. Land partitioning determines the location of new land parcels and is a complex problem containing many conflicting demands, so conventional programming techniques are not sufficient for this NP optimization problem. Therefore, it is necessary to have an intelligent system with a standard decision-making mechanism capable of processing many criteria simultaneously and evaluating a number of different solutions in a short time. To overcome this problem and accelerate the land partitioning process, we proposed automated land partitioning using a genetic algorithm (ALP-GA). Besides the parcel's size, shape and land value, the proposed method evaluates fixed facilities, and the degree and location of cadastral parcels to generate a land partitioning plan. The proposed method automated the land partitioning process using an intelligent system and was implemented over a real project area, Experimental study shows that the proposed method is more successful and efficient than the designer with respect to the results meeting the objective function. In addition, the land partition process is greatly simplified by the proposed method. (C) 2017 Elsevier Ltd. All rights reserved.Öğe A novel particle swarm optimization algorithm with Levy flight(ELSEVIER SCIENCE BV, 2014) Hakli, Huseyin; Uguz, HarunParticle swarm optimization (PSO) is one of the well-known population-based techniques used in global optimization and many engineering problems. Despite its simplicity and efficiency, the PSO has problems as being trapped in local minima due to premature convergence and weakness of global search capability. To overcome these disadvantages, the PSO is combined with Levy flight in this study. Levy flight is a random walk determining step size using Levy distribution. Being used Levy flight, a more efficient search takes place in the search space thanks to the long jumps to be made by the particles. In the proposed method, a limit value is defined for each particle, and if the particles could not improve self-solutions at the end of current iteration, this limit is increased. If the limit value determined is exceeded by a particle, the particle is redistributed in the search space with Levy flight method. To get rid of local minima and improve global search capability are ensured via this distribution in the basic PSO. The performance and accuracy of the proposed method called as Levy flight particle swarm optimization (LFPSO) are examined on well-known unimodal and multimodal benchmark functions. Experimental results show that the LFPSO is clearly seen to be more successful than one of the state-of-the-art PSO (SPSO) and the other PSO variants in terms of solution quality and robustness. The results are also statistically compared, and a significant difference is observed between the SPSO and the LFPSO methods. Furthermore, the results of proposed method are also compared with the results of well-known and recent population-based optimization methods. (C) 2014 Elsevier B.V. All rights reserved.
