Mühendislik-Mimarlık Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü, Makale Koleksiyonu

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https://hdl.handle.net/20.500.12402/1090

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Enerji nakil hatlarının benzetimine yönelik internet destekli interaktif tabanlı uzaktan eğitim uygulaması
(Kaynak Elektrik Dergisi, 2010-12-29) Gençoğlu, Muhsin Tunay; Kayri, İsmail; Kayri, Murat
Bu çalışmada, ülkemizde elektrik – elektronik eğitimi veren Mühendislik Fakülteleri, Teknik Eğitim Fakülteleri ve Meslek Yüksekokullarının laboratuvar şartlarındaki olumsuzluklardan kaynaklanan, öğrencilerin uygulama yapma yetersizliklerini gidermeye yönelik, enerji nakil hatları ile ilgili benzetimlerin gerçekleştirilmesini açıklayan, internet ve/veya intranet üzerinden uzaktan erişimi mümkün kılan bir web uygulaması tasarlanmış ve gerçekleştirilmiştir. Gerçekleştirilen web sitesinde, salt öğreticiden öğrenene bilgi aktarımından ziyade, öğrenenin, düşüncelerinin ve sorularının dikkate alınabildiği, değerlendirilebildiği ve öğrenenlerle öğreticiler arasında geri beslemelere altyapı sunabilen tam etkileşimli bir yapı kullanılarak, çalışmanın içeriği eğitsel bir ortama dönüştürülmüştür.
Doküman dili tanıma için yeni bir öznitelik çıkarım yaklaşımı: İkili desenler
(Gazi Üniversitesi, 2016-12-14) Kaya, Yılmaz; Ertuğrul, Ömer Faruk
Doğal dil işlemenin önemli alt konularından biri olan dil tanıma (DT), bir dokümanın içeriğine göre yazıldığı dili belirleme işlemidir. Bu çalışmada, karakterlerin UTF-8 değerlerini birbirleri ile karşılaştırmalar sonucu elde edilen ikili desenler kullanarak yeni bir dil tanıma yaklaşımı, bir boyutlu yerel ikili örüntüler (1B-YİÖ) önerilmiştir. Önerilen yöntem farklı sayıda dillerden oluşan metinler içeren dört veri kümesi ile test edilmiştir. 1B-YİÖ ile dokümanlardan elde edilen öznitelikler kullanılarak farklı makine öğrenmesi yöntemleri ile sınıflandırma işlemi gerçekleştirilmiştir. Dört veri kümesi için sınıflandırma başarıları sırası ile %86.20, %92.75, %100 ve %89.77 olarak gözlenmiştir. Elde edilen sonuçlara göre önerilen öznitelik çıkarım yönteminin dil tanıma için önemli örüntüler sağladığı görülmüştür.
Forecasting electricity load by a novel recurrent extreme learning machines approach
(Elsevier, 2016-06) Ertuğrul, Ömer Faruk
Growth in electricity demand also gives a rise to the necessity of cheaper and safer electric supply and forecasting electricity load plays a key role in this goal. In this study recurrent extreme learning machine (RELM) was proposed as a novel approach to forecast electricity load more accurately. In RELM, extreme learning machine (ELM), which is a training method for single hidden layer feed forward neural network, was adapted to train a single hidden layer Jordan recurrent neural network. Electricity Load Diagrams 2011-2014 dataset was employed to evaluate and validate the proposed approach. Obtained results were compared with traditional ELM, linear regression, generalized regression neural network and some other popular machine learning methods. Achieved root mean square errors (RMSE) by RELM were nearly twice less than obtained results by other employed machine learning methods. The results showed that the recurrent type ANNs had extraordinary success in forecasting dynamic systems and also time-ordered datasets with comparison to feed forward ANNs. Also, used time in the training stage is similar to ELM and they are extremely fast than the others. This study showed that the proposed approach can be applied to forecast electricity load and RELM has high potential to be utilized in modeling dynamic systems effectively.
A novel approach for SEMG signal classification with adaptive local binary pattern
(Springer Nature, 2015-12-31) Ertuğrul, Ömer Faruk; Kaya, Yılmaz; Tekin, Ramazan
Feature extraction plays a major role in the pattern recognition process, and this paper presents a novel feature extraction approach, adaptive local binary pattern (aLBP). aLBP is built on the local binary pattern (LBP), which is an image processing method, and one-dimensional local binary pattern (1D-LBP). In LBP, each pixel is compared with its neighbors. Similarly, in 1D-LBP, each data in the raw is judged against its neighbors. 1D-LBP extracts feature based on local changes in the signal. Therefore, it has high a potential to be employed in medical purposes. Since, each action or abnormality, which is recorded in SEMG signals, has its own pattern, and via the 1D-LBP these (hidden) patterns may be detected. But, the positions of the neighbors in 1D-LBP are constant depending on the position of the data in the raw. Also, both LBP and 1D-LBP are very sensitive to noise. Therefore, its capacity in detecting hidden patterns is limited. To overcome these drawbacks, aLBP was proposed. In aLBP, the positions of the neighbors and their values can be assigned adaptively via the down-sampling and the smoothing coefficients. Therefore, the potential to detect (hidden) patterns, which may express an illness or an action, is really increased. To validate the proposed feature extraction approach, two different datasets were employed. Achieved accuracies by the proposed approach were higher than obtained results by employed popular feature extraction approaches and the reported results in the literature. Obtained accuracy results were brought out that the proposed method can be employed to investigate SEMG signals. In summary, this work attempts to develop an adaptive feature extraction scheme that can be utilized for extracting features from local changes in different categories of time-varying signals.
A novel approach for spam email detection based on shifted binary patterns
(Wiley-Blackwell, 2016-01-11) Kaya, Yılmaz; Ertuğrul, Ömer Faruk
Advances in communication allow people flexibility to communicate in various ways. Electronic mail (email) is one of the most used communication methods for personal or business purposes. However, it brings one of the most tackling issues, called spam email, which also raises concerns about data safety. Thus, the requirement of detecting spams is crucial for keeping the users safe and saving them from the waste of time while tackling those issues. In this study, an effective approach based on the probability of the usage of the characters that has similar orders with respect to their UTF-8 value by employing shifted one-dimensional local binary pattern (shifted-1D-LBP) was used to extract quantitative features from emails for spam email detection. Shifted-1D-LBP, which can be described as an ordered set of binary comparisons of the center value with its neighboring values, is a content-based approach to spam detection with low-level information. To validate the performance of the proposed approach, three benchmark corpora, Spamassasian, Ling-Spam, and TREC email corpuses, were used. The average classification accuracies of the proposed approach were 92.34%, 92.57%, and 95.15%, respectively. Analysis and promising experimental results indicated that the proposed approach was a very competitive feature extraction method in spam email filtering.
Detection of Parkinson's disease by Shifted One Dimensional Local Binary Patterns from gait
(Elsevier, 2016-09) Ertuğrul, Ömer Faruk; Kaya, Yılmaz; Tekin, Ramazan; Almalı, Mehmet Nuri
The Parkinson's disease (PD) is one of the most common diseases, especially in elderly people. Although the previous studies showed that the PD can be diagnosed by expert systems through its cardinal symptoms such as the tremor, muscular rigidity, disorders of movements and voice, it was reported that the presented approaches, which utilize simple motor tasks, were limited and lack of standardization. To achieve a standard approach in PD detection, an approach, which is built on shifted one-dimensional local binary patterns (Shifted 1D-LBP) and machine learning methods, was proposed. Shifted 1D-LBP is built on 1D-LBP, which is sensitive to local changes in a signal. In 1D-LBP the positions of neighbors around center data are constant and therefore, the number of patterns that can be exacted by it is limited. This drawback was solved by Shifted 1D-LBP by changeable positions of neighbors. In evaluation and validation stages, the Gait in Parkinson's Disease (gaitpdb) dataset, which consists of three gait datasets that were recorded in different tasks or experiment protocols, were employed. Statistical features were exacted from formed histograms of gait signals transformed by Shifted 1D-LBP. Whole features and selected features were classified by machine learning methods. Obtained results were compared with statistical features exacted from signals in both time and frequency domains and results reported in the literature. Achieved results showed that the proposed approach can be successfully employed in PD detection from gait. This work is not only an attempt to develop a PD detection method, but also a general-purpose approach that is based on detecting local changes in time ordered signals.
A novel feature extraction approach in SMS spam filtering for mobile communication: one-dimensional ternary patterns
(Wiley-Blackwell, 2016-10-19) Kaya, Yılmaz; Ertuğrul, Ömer Faruk
The importance and utilization of mobile communication are increasing day by day, and the short message service (SMS) is one of them. Although SMS is a widely used communication way, it brings together a major problem, which is SMS spam messages. SMS spams do not only use vain in the mobile communication traffic but also disturb users. Based on this fact, blacklisting methods, statistical methods which are built on the frequency of occurrence of words or characters, and machine learning methods have been employed. Because punishments and legal laws are not enough to solve this problem and the Group Special Mobile number of SMS spam can easily be changed, a content-based approach must be proposed. Content-based methods showed high success in spam e-mail filtering, but it is hard in the SMS spam filtering because SMS messages are extremely short and generally contains many abbreviations. In this study, an image processing method, local ternary pattern was improved to extract features from SMS messages in the feature extraction stage. In the proposed one-dimensional ternary patterns, firstly, text message was converted to their UTF-8 values. Later, each character (its UTF-8 value) in the message was compared with its neighbors. Two different feature sets were extracted from the results of these comparisons. Finally, some machine learning methods were employed to classify these features. In order to validate the proposed approach, three different SMS corpora were used. The achieved accuracies and other employee performance measures showed that the proposed approach, one-dimensional ternary patterns, can be effectively employed in SMS spam filtering.
Gender classification from facial images using gray relational analysis with novel local binary pattern descriptors
(Springer Nature, 2016-11-18) Kaya, Yılmaz; Ertuğrul, Ömer Faruk
Gender classification (GC) is one of the major tasks in human identification that increase its accuracy. Local binary pattern (LBP) is a texture method that employed successfully. But LBP suffers a major problem; it cannot capture spatial relationships among local textures. Therefore, in order to increase the accuracy of GC, two LBP descriptors, which are based on (1) spatial relations between neighbors with a distance parameter, and (2) spatial relations between a reference pixel and its neighbor on the same orientation, were employed to extract features from facial images. Additionally, gray relational analysis (GRA) was carried out to identify gender through extracted features. Experiments on the FEI database illustrated the effectiveness of the proposed approaches. Achieved accuracies are 97.14, 93.33, and 92.50% by applying GRA with the nLBPd, dLBPα, and traditional LBP features, respectively. Experimental results indicated that the proposed approaches were very competitive feature extraction methods in GC. Present work also showed that the nLBPd, dLBPα methods were obtained more acceptable results than traditional LBP.
A novel version of k nearest neighbor: Dependent nearest neighbor
(Elsevier, 2017-06) Ertuğrul, Ömer Faruk; Tağluk, Mehmet Emin
k nearest neighbor (kNN) is one of the basic processes behind various machine learning methods In kNN, the relation of a query to a neighboring sample is basically measured by a similarity metric, such as Euclidean distance. This process starts with mapping the training dataset onto a one-dimensional distance space based on the calculated similarities, and then labeling the query in accordance with the most dominant or mean of the labels of the k nearest neighbors, in classification or regression issues, respectively. The number of nearest neighbors (k) is chosen according to the desired limit of success. Nonetheless, two distinct samples may have equal distances to query but, with different angles in the feature space. The similarity of the query to these two samples needs to be weighted in accordance with the angle going between the query and each of the samples to differentiate between the two distances in reference to angular information. This opinion can be analyzed in the context of dependency and can be utilized to increase the precision of classifier. With this point of view, instead of kNN, the query is labeled according to its nearest dependent neighbors that are determined by a joint function, which is built on the similarity and the dependency. This method, therefore, may be called dependent NN (d-NN). To demonstrate d-NN, it is applied to synthetic datasets, which have different statistical distributions, and 4 benchmark datasets, which are Pima Indian, Hepatitis, approximate Sinc and CASP datasets. Results showed the superiority of d-NN in terms of accuracy and computation cost as compared to other employed popular machine learning methods.
Forecasting local mean sea level by generalized behavioral learning method
(Springer Nature, 2017-03-13) Ertuğrul, Ömer Faruk; Tağluk, Mehmet Emin
Determining and forecasting the local mean sea level (MSL), which is a major indicator of global warming, is an essential issue to set public policies to save our future. Owing to its importance, MSL values are measured and shared periodically by many agencies. It is not easy to model or forecast MSL because it depends on many dynamic sources such as global warming, geophysical phenomena, and circulations in the ocean and atmosphere. Several of researchers applied and recommended employing artificial neural network (ANN) in the estimation of MSL. However, ANN does not take into account the order of samples, which may consist essential information. In this study, the generalized behavioral learning method (GBLM), which is based on behavioral learning theories, was employed in order to achieve higher accuracies by using samples in the training dataset and the order of samples. To evaluate and validate GBLM, MSL of seven stations around the world was picked up. These datasets were employed to forecast the local MSL for the future. Obtained results were compared with the ones obtained by ANN that is trained by extreme learning machine and the literature. The GBLM is found to be successful in terms of the achieved high accuracies and the ability to tracking trends and fluctuations of a local MSL.
A novel machine learning method based on generalized behavioral learning theory
(Springer Nature, 2016-04-09) Ertuğrul, Ömer Faruk; Tağluk, Mehmet Emin
Learning is an important talent for understanding the nature and accordingly controlling behavioral characteristics. Behavioral learning theories are one of the popular learning theories which are built on experimental findings. These theories are widely applied in psychotherapy, psychology, neurology as well as in advertisements and robotics. There is an abundant literature associated with understanding learning mechanism, and various models have been proposed for the realization of learning theories. Nevertheless, none of those models are able to satisfactorily simulate the concept of classical conditioning. In this study, popular behavioral learning theories were firstly simplified and the contentious issues with them were clarified by conducting intuitive experiments. The experimental results and information available in the literature were evaluated, and behavioral learning theories were jointly generalized accordingly. The proposed model, to our knowledge, is the first one that possesses not only modeling all features of classical conditioning but also including all features with behavioral theories such as Pavlov, Watson, Guthrie, Thorndike and Skinner. Also, a microcontroller card (Arduino Mega 2560) was used to validate the applicability of the proposed model in robotics. Obtained results showed that this generalized model has a high capacity for modeling human learning. Then, the proposed learning model was further improved to be utilized as a machine learning method that can continuously learn similar to human being. The result obtained from the use of this method, in terms of computational cost and accuracy, showed that the proposed method can be successfully employed in machine learning, especially for time ordered datasets.
A noninvasive time-frequency-based approach to estimate cuffless arterial blood pressure
(TÜBİTAK, 2018-09-28) Ertuğrul, Ömer Faruk; Sezgin, Necmettin
Arterial blood pressure (ABP) is one of the most vital signs in the prophylaxis and treatment of blood pressure-related diseases because raised blood pressure is the most significant cause of death and the second major cause of disability in the world. Higher ABP yields greater strain on arteries and these extra strains turn arteries into thicker, less flexible, and more narrow structures. This increases the possibility of having an artery busting or artery occlusion, which are the primary reasons for heart attacks, kidney disease, or strokes. In addition to its importance in monitoring cardiovascular homeostasis, measurement of ABP is imperative in surgical operations. In this study, a simple and effective approach was proposed to estimate ABP from electrocardiogram (ECG) and photoplethysmograph (PPG) signals by an extreme learning machine (ELM) and statistical properties of the ECG and/or PPG signals in the time-frequency domain. To evaluate and apply the proposed approach, the Cuffless Blood Pressure Estimation Dataset, which was published and shared by UCI, was employed. First, the statistical properties were extracted from ECG and PPG signals that were in the time-frequency domain. Later, extracted features were employed to estimate cuffless ABP for each subject by the ELM and some popular machine learning methods. Achieved results and reported results in the literature showed that the proposed approach can be successfully employed for estimating cuffless blood pressure (BP) from ECGs and/or PPGs. Additionally, with the proposed approach, the systolic BP, mean BP, and diastolic BP can be calculated simultaneously.
A novel type of activation function in artificial neural networks: Trained activation function
(Elsevier, 2018-03) Ertuğrul, Ömer Faruk
Determining optimal activation function in artificial neural networks is an important issue because it is directly linked with obtained success rates. But, unfortunately, there is not any way to determine them analytically, optimal activation function is generally determined by trials or tuning. This paper addresses, a simpler and a more effective approach to determine optimal activation function. In this approach, which can be called as trained activation function, an activation function was trained for each particular neuron by linear regression. This training process was done based on the training dataset, which consists the sums of inputs of each neuron in the hidden layer and desired outputs. By this way, a different activation function was generated for each neuron in the hidden layer. This approach was employed in random weight artificial neural network (RWN) and validated by 50 benchmark datasets. Achieved success rates by RWN that used trained activation functions were higher than obtained success rates by RWN that used traditional activation functions. Obtained results showed that proposed approach is a successful, simple and an effective way to determine optimal activation function instead of trials or tuning in both randomized single and multilayer ANNs.
Two novel versions of randomized feed forward artificial neural networks: Stochastic and pruned stochastic
(Springer Nature, 2017-11-13) Ertuğrul, Ömer Faruk
Although high accuracies were achieved by artificial neural network (ANN), determining the optimal number of neurons in the hidden layer and the activation function is still an open issue. In this paper, the applicability of assigning the number of neurons in the hidden layer and the activation function randomly was investigated. Based on the findings, two novel versions of randomized ANNs, which are stochastic, and pruned stochastic, were proposed to achieve a higher accuracy without any time-consuming optimization stage. The proposed approaches were evaluated and validated by the basic versions of the popular randomized ANNs [1] are the random weight neural network [2], the random vector functional links [3] and the extreme learning machine [4] methods. In the stochastic version of randomized ANNs, not only the weights and biases of the neurons in the hidden layer but also the number of neurons in the hidden layer and each activation function were assigned randomly. In pruned stochastic version of these methods, the winner networks were pruned according to a novel strategy in order to produce a faster response. Proposed approaches were validated via 60 datasets (30 classification and 30 regression datasets). Obtained accuracies and time usages showed that both versions of randomized ANNs can be employed for classification and regression.
Forecasting financial indicators by generalized behavioral learning method
(Springer Nature, 2017-08-09) Ertuğrul, Ömer Faruk; Tağluk, Mehmet Emin
Forecasting financial indicators (indexes/prices) is a complex and a quite difficult issue because they depend on many factors such as political events, financial ratios, and economic variables. Also, the psychological facts or decision-making styles of investors or experts are other major reasons for this difficulty. In this study, a generalized behavioral learning method (GBLM) was employed to forecast financial indicators, which are the indexes/prices of 34 different financial indicators (24 stock indexes, 2 forexes, 3 financial futures, and 5 commodities). The achieved results were compared with the reported results in the literature and the obtained results by artificial neural network, which is widely used and suggested for forecasting financial indicators. These results showed that GBLM can be successfully employed in short-term forecasting financial indicators by detecting hidden market behavior (pattern) from their previous values. Also, the results showed that GBLM has the ability to track the fluctuation and the main trend.
Extreme learning machine model for water network management
(Springer Nature, 2017-04-22) Sattar, Ahmed M.A.; Ertuğrul, Ömer Faruk; Gharabaghi, Bahram; McBean, Edward Arthur; Cao, Jiuwen
A novel failure rate prediction model is developed by the extreme learning machine (ELM) to provide key information needed for optimum ongoing maintenance/rehabilitation of a water network, meaning the estimated times for the next failures of individual pipes within the network. The developed ELM model is trained using more than 9500 instances of pipe failure in the Greater Toronto Area, Canada from 1920 to 2005 with pipe attributes as inputs, including pipe length, diameter, material, and previously recorded failures. The models show recent, extensive usage of pipe coating with cement mortar and cathodic protection has significantly increased their lifespan. The predictive model includes the pipe protection method as pipe attributes and can reflect in its predictions, the effect of different pipe protection methods on the expected time to the next pipe failure. The developed ELM has a superior prediction accuracy relative to other available machine learning algorithms such as feed-forward artificial neural network that is trained by backpropagation, support vector regression, and non-linear regression. The utility of the models provides useful inputs when planning and budgeting for watermain inspection, maintenance, and rehabilitation.
HPA algoritması ile çok makinalı güç sistemi kararlı kılıcısı tasarımı
(Gazi Üniversitesi, 2017-12-08) Ekinci, Serdar; Hekimoğu, Baran
Bu makale, parçacık sürüsü optimizasyonu (PSO) ve yapay arı kolonisine (ABC) dayalı, çok makinalı güç sisteminde güç sistemi kararlı kılıcısının (PSS) optimal tasarımı için iyimser sonuçlar bulmak için güçlü yetilere sahip HPA tekniği adında yeni bir hibrit yaklaşımı tanımlamaktadır. PSS parametrelerinin en uygun ayarlarının elde edilmesi için PSS parametrelerini seçme problemi, özdeğer tabanlı bir amaç fonksiyonu ile basit bir optimizasyon problemine çevrildi ve HPA tekniği kullanılarak çözüldü. Önerilen HPA tabanlı PSS tasarımının etkinliği özdeğer analizi, zaman domeni simülasyonları ve bazı performans indeksleri aracılığıyla farklı arızalar altındaki 3-makinalı 9-baralı güç sistemi üzerinde doğrulandı. Bu çalışmaların sonuçları, HPA algoritmasının PSS parametrelerinin ayarlanması için alternatif ve daha etkin bir iyileştirici olduğunu ve PSO ile ABC’ye oranla güç sisteminin dinamik kararlılığını büyük oranda artırdığını göstermiştir. Ayrıca hesaplama zamanı, yaklaşım hızı ve çözüm kalitesi açısından HPA algoritmasının PSO ve ABC’ye göre potansiyeli ve üstünlüğü kanıtlanmıştır.
Improved kidney-inspired algorithm approach for tuning of PID controller in AVR system
(IEEE, 2019-03-22) Ekinci, Serdar; Hekimoğu, Baran
This paper proposes a novel tuning design of proportional integral derivative (PID) controller via an improved kidney-inspired algorithm (IKA) with a new objective function. The main objective of the proposed approach is to optimize the transient response of the AVR system by minimizing the maximum overshoot, settling time, rise time and peak time values of the terminal voltage, and eliminating the steady state error. After obtaining the optimal values of the three gains of the PID controller (K P , K I , and K D ) with the proposed approach, the transient response analysis was performed and compared with some of the current heuristic algorithms-based approaches in literature to show the superiority of the optimized PID controller. In order to evaluate the stability of the automatic voltage regulator (AVR) system tuned by IKA method, the pole/zero map analysis and Bode analysis are performed. Finally, the robustness analysis of the proposed approach has been carried out with variations in the parameters of the AVR system. The numerical simulation results demonstrated that the proposed IKA tuned PID controller has better control performances compared to the other existing approaches. The essence of the presented study points out that the proposed approach may successfully be applied for the AVR system.
Developing correlations by extreme learning machine for calculating higher heating values of waste frying oils from their physical properties
(Springer Nature, 2017-11-01) Altun, Şehmus; Ertuğrul, Ömer Faruk
In this study, a novel approach was proposed based on extreme learning machine (ELM) for developing correlations in order to calculate higher heating values (HHVs, kj/kg) of waste frying oils from their physical properties such as density (ρ, kg/m 3 ) and kinematic viscosity (v, mm 2 /s) values. These values can easily be determined by using laboratory equipment. For developing the correlations, an experimental dataset from the literature covering 35 samples was collected to be employed in the training and validation steps. The obtained optimum parameters of artificial neural network in the training stage by ELM were employed to develop new correlations. The HHVs calculated by using density-based correlation (HHV = 50823.183 − 12.34095ρ) showed the mean absolute and relative errors of 145.8048 kJ/kg and 0.3695 %, respectively. In the case of the viscosity-based correlation (HHV = 40172.85 − 17.93615v), they were found as 129.04 kJ/kg and 0.327 %, respectively. Additionally, new correlations were performed better than those available in the literature and those obtained by other machine learning methods; therefore, it is highly suggested that the proposed approach can be used for developing new correlations.
A novel approach for extracting ideal exemplars by clustering for massive time-ordered datasets
(TÜBİTAK, 2017-07-30) Ertuğrul, Ömer Faruk
The number and length of massive datasets have increased day by day and this yields more complex machine learning stages due to the high computational costs. To decrease the computational cost many methods were proposed in the literature such as data condensing, feature selection, and filtering. Although clustering methods are generally employed to divide samples into groups, another way of data condensing is by determining ideal exemplars (or prototypes), which can be used instead of the whole dataset. In this study, first the efficiency of traditional data condensing by clustering approach was confirmed according to obtained accuracies and condensing ratios in 9 different synthetic or real batch datasets. This approach was then improved to be employed in time-ordered datasets. In order to validate the proposed approach, 23 different real time-ordered datasets were used in experiments. Achieved mean RMSEs were 0.27 and 0.29 by employing the condensed (mean condensed ratio was 97.17%) and the whole datasets, respectively. Obtained results showed that higher accuracy rates and condensing ratios were achieved by the proposed approach.

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