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Öğe Forecasting financial indicators by generalized behavioral learning method(Springer Nature, 2017-08-09) Ertuğrul, Ömer Faruk; Tağluk, Mehmet EminForecasting 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.Öğe Determining relevant features in estimating short-term power load of a small house via feature selection by extreme learning machine(IEEE, 2017-11-02) Ertuğrul, Ömer Faruk; Sezgin, Necmettin; Öztekin, Abdulkerim; Tağluk, Mehmet EminEstimating short-term power load is a fundamental issue in the power distribution system. Since short-term power load is related to many parameters such as weather conditions, and time. The aim of this study is to determine the relevant parameters in estimating short-term power load not only in order to decrease the computational cost, but also to achieve higher success rates. Furthermore, by using selected features the required memory, equipment and communication costs are also decreased in real time applications. Feature selection by extreme learning machine method was used in determining relevant features. The short-term power loads of two houses (one of them has a power generation capability) were used in tests and achieved results showed lower error rates were obtained by using less number of features.Öğe Grasshopper optimization algorithm for automatic voltage regulator system(IEEE, 2018-06-21) Ekinci, Serdar; Hekimoğu, BaranA novel design method is presented to determine optimum proportional-integral-derivative (PID) controller parameters of an automatic voltage regulator (AVR) system utilizing the grasshopper optimization algorithm (GOA). The proposed approach is a simple and effective algorithm that is able to solve many optimization problems even those with unknown search spaces effectively. The simplicity of algorithm provides high quality tuning of optimal PID controller parameters. The integral of time weighted squared error (ITSE) is used as the performance index to confirm the performance of the proposed GOA-PID controller. When compared to the other PID controllers based on Ziegler- Nichols (ZN), differential evolution (DE), and artificial bee colony (ABC) tuning methods, the proposed method is found highly effective and robust to improve AVR system's transient response.Öğe Laws doku enerji ölçümü tabanli k-NN siniflandirici modeli ile iris tanima sistemi(IEEE, 20013-06-13) Acar, Emrullah; Özerdem, Mehmet SiraçBiyometrik tanıma teknolojisi genellikle çok pahallı ve son derece önemli güvenlik uygulamaları ile ilişkili olmuştur.İris tanıma sistemi, etkili biyometrik tanımasistemlerinden biridir. Bu çalışmada, farklı insanlardan elde edilen gözimgelerininiçerdiği irisdokuözelliklerinegörekişilerin tanınmasıamaçlanmıştır. İmgeler CASIAiris veritabanındanelde edilmiştir. İmge dokusuna duyarlı yeni yöntemlerdenbiri olanLawsDoku Enerji Ölçümü (Laws TEM) kullanılarak, iris dokusunun belirli yerelalanlarındanöznitelik vektörleri elde edilmiştir. kEn Yakın Komşu (k-NN) sınıflandırıcıparametrelerinden komşu sayısı(k) farklı değerlerde alınarak, elde edilen öznitelik vektörleri k-NN sınıflandırıcısı ile ayrıştırılmıştır. Farklı komşu sayılarına göre sisteminperformans değerlerikarşılaştırılmıştır. Sonuç olarak en yüksek ortalama performans,k-NNsınıflandırıcısınınk=1ve 2komşularıyapısında % 80.74olarak gözlemlenmiştir.Öğe EMG sinyallerinin aşırı ögrenme makinesi ile sınıflandırılması(IEEE, 2013-06-13) Ertuğrul, Ömer Faruk; Tağluk, Mehmet Emin; Kaya, Yılmaz; Tekin, Ramazan; Batman Üniversitesi Mühendislik - Mimarlık Fakültesi Bilgisayar Mühendisliği BölümüFrom disease detection to action assessment EMG signals are used variety of field. Miscellaneous studies have been conducted toward analysis of EMG signals. In this study some statistical features of signal were derived, the best evocative features were selected via Linear Discriminant Analysis (LDA) and feature vectors were constructed. This analytic feature vectors were classified through Extreme Learning Machine (ELM). 8 channel EMG signals recorded from 10 normal and 10 aggressive actions were used as an example. By cross-comparison of the obtained results to the ones obtained via various feature identifying methods (AR coefficients, wavelet energy and entropy) and classification methods (NB, SVM, LR, ANN, PART, Jrip, J48 and LMT) the success of the proposed method was determined.Öğe Salp sürüsü algoritması kullanılarak AVR sistemi için PID kontrolör ayarı(IEEE, 2019-01-24) Ekinci, Serdar; Hekimoğu, BaranBu makalede salp sürüsü algoritması (SSA) adında yeni bir yapay zekaya dayalı optimizasyon metodu otomatik gerilim regülatörü (AVR) sisteminin en uygun oransal, integral, türevsel (PID) kontrolör parametrelerinin belirlemesi amacıyla kullanılmıştır. Algoritmanın basitliği, optimal PID kontrolör parametrelerinin yüksek kaliteli ayarını sağlar. Kontrolör parametrelerinin optimize edilmesi için zaman ağırlıklı karesel hatanın integrali (ITSE) amaç fonksiyonu olarak seçildi. Geçici hal cevap analizi, SSA metodunun Ziegler-Nichols (ZN) geleneksel ayarlama yönteminden ve yapay arı kolonisi (ABC) algoritmasından daha iyi bir ayarlama kabiliyetine sahip olduğunu ve bir AVR sisteminin basamak cevabını iyileştirmede daha verimli olduğunu ortaya koymuştur.Öğe Kortikal bir ağ modelinin çıkış verisindeki karmaşıklık ve uyumluluk analizi(IEEE, 2013-06-13) Tekin, Ramazan; Tağluk, Mehmet Emin; Ertuğrul, Ömer Faruk; Sezgin, NecmettinDepending on the complex interconnection of billions of neurons forming cortical network excitation times and the emergence of action potentials or spike trains becomes complex and irregular. The effect of various parameters such as synaptic connections, conductivity and voltage dependent channels on the output of the network has become of research issues. In this study, based on Hodgkin-Huxley neuron model an artificial cortical network that simulates a local region of cortex was designed and the effect of probabilistic values of network parameters used in this model on irregularity and complexity of the spike trains at the neurons' output were investigated. Approximation Entropy, Spectral Entropy and Magnitude Squared Coherence methods were used for irregularity analysis.Öğe Dalgacık dönüşümü tabanlı parmak izi tanıma(IEEE, 2015-06-19) Çalışkan, Abidin; Ertuğrul, Ömer FarukBir biyometrik sistem, bir bireyin sahip olduğu karakteristik veya eşsiz özniteliğe dayalı olarak otomatik tanımlamayı sağlar. Parmak izi, günümüzde birçok alanda geniş bir kullanım alanına sahip bir biyometrik sistemdir. Özellikle insan kimliğinin doğrulanması ve tespit edilmesinde kullanılan parmak izi, erişim için geleneksel olarak kullanılan yöntemlere göre daha güvenilirdir. Bu çalışmada, Gabor dalgacık dönüşümü tabanlı parmak izi tanıma sistemi gerçekleştirilmiştir. Gri seviye parmak izi imgelerinden dalgacık öznitelikleri çıkarılmıştır. Son olarak, parmak izi imgelerinin tanınmasında k en yakın komşuluk sınıflandırıcısı kullanılmıştır. Önerilen algoritma, PolyU yüksek çözünürlüklü parmak izi veri tabanı görüntüleri üzerinde test edilmiştir. Deneysel sonuçlar, önerilen yöntemin mevcut metotların doğruluğunu arttırabildiğini göstermiştir.Öğe Gender classification from facial images using gray relational analysis with novel local binary pattern descriptors(Springer Nature, 2016-11-18) Kaya, Yılmaz; Ertuğrul, Ömer FarukGender 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.Öğe Recognition of daily and sports activities(IEEE, 201-01-24) İnanç, Nihat; Kayri, Murat; Ertuğrul, Ömer FarukSince being physically inactive was reported as one of the major risk factor of mortality, classifying daily and sports activities becomes a critical task that may improve human life quality. In this paper, the daily and sports activities dataset was used in order to evaluate and validate the employed approach. In this approach, the statistical features were extracted from the histograms of the local changes in the wearable sensors logs were obtained by one-dimensional local binary patterns. Later, extracted features were classified by extreme learning machines. Results were showed that the proposed approach is enough to recognize the action type, but in order to recognize the actions, or gender, different feature extraction methods must be employed.
