İleri yönlü yapay sinir ağlarında küçük dünya ağı yaklaşımı ve uygulamaları

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Beyinde öğrenme süreci, biyolojik sinir ağlarının bilgiyi depo etmesi ve bu bilginin ağı oluşturan nöronlar arası iletilmesi aşamalarından meydana gelmektedir. Buradan hareketle biyolojik sinir ağlarının davranışını modelleyebilmek için çeşitli matematiksel yapay sinir ağı topolojileri ortaya konulmuştur. Literatürde en yaygın kullanılan ağ topolojisi farklı katmanlardan meydana gelen ileri yönlü yapay sinir ağı topolojisidir. Model bilginin giriş katmanından çıkış katmanına doğru ileri yönlü yapay nöronlar ile aktarılması ve nöronlar arası sinaptik bağlantı ağırlıklarının değiştirilmesi ilkesine göre çalışmaktadır. Bu çalışmada, Küçük Dünya ağları modellinin ileri yönlü yapay sinir ağlarında uygulanması ve öğrenme performansının araştırılması hedeflenmiştir. Bu bağlamda, Watts-Strogatz, Newman-Watts ve Simard tarafından ortaya konulan bağlantı yenileme yöntemleri ile yeni ileri yönlü ağ topolojileri elde edilmiştir. Bu topolojilerin öğrenme süreci ve modelleme performanslarının testi için farklı alanlardan karmaşık problemler kullanılmıştır. Bu ağlardan elde edilen sonuçlar geleneksel ileri yönlü yapay sinir ağı sonuçları ile karşılaştırılmıştır. Küçük Dünya ağının oluşturulmasında, Global ve Lokal bağlantı uzunluk katsayıları kullanılmış ve Küçük Dünya ağı üretebilmek için gerekli yeni bağlantı sayısı aralığı tespit edilmiştir. Elde edilen bu bağlantı aralığı, denemelerle elde edilen ağın başarılı deneme sayısı dağılımı ile tutarlılık göstermektedir. Watts-Strogatz ve Simard Küçük Dünya ağı modellerinin küçük ölçekli veri seti için daha iyi öğrenme performansı gösterdiği görülmüştür. Ancak veri seti büyüdükçe bu performansın düştüğü gözlemlenmiştir. Ayrıca, bu modellerin yeni bağlantı sayısı aralığının geniş ölçekli olduğu tespit edilmiştir. Newman-Watts Küçük Dünya ağında ise yeni bağlantı sayısı aralığının daha küçük ölçekli olduğu ve bu ağların öğrenme performansının veri seti büyüklüğünden bağımsız olduğu belirlenmiştir. Yapılan çalışmada literatürde ilk defa, Watts-Strogatz Küçük Dünya ağı modeli ile geleneksel ileri yönlü yapay sinir ağı modeli bağımsız örnekli t-testi kullanılarak istatistiksel olarak karşılaştırılmış ve Watts-Strogatz Küçük Dünya ağı modelinin geleneksel ileri yönlü yapay sinir ağı modelinden istatistikî olarak daha anlamlı (p<0.01) bir model olduğu ortaya konulmuştur. Elde edilen sonuçlar ışığında yapay zekâ araştırmalarında ileri yönlü yapay sinir ağı modelinin geleneksel topolojisi yerine Küçük Dünya ağ topolojisinin kullanılabileceği gösterilmiştir.Learning process in the brain occurs two stages which are the storing of information in the biological neural networks and the transmission of the information between neurons consisting of network. Starting from this point, for modeling of behaviors of biological neural networks various mathematical neural network topologies have been proposed. In the literature, the most commonly used network topology is the feed forward artificial neural network topology that composed of different layers. The model has worked according to procedure that is the transmitting of information from input layer to output layer as feed forward manner and adjusting of the synaptic weights between neurons. In this study, investigation of implementation of small-world network model in the feed forward artificial neural networks and learning performances have been aimed. In this context, the new feed forward network topologies are obtained with the rewiring methods proposed by Watts-Strogatz, Newman-Watts and Simard. For testing the learning process and the modeling performances of these topologies, complex problems from different field have been used. Obtained results from these networks have been compared with conventional feed forward artificial neural networks results. In generating of small-world networks the global and the local connectivity length parameters have been used and the new rewiring range required to obtain small-world networks are determined. The obtained rewiring range has showed consistency with the successful test distribution. It was observed that Watts-Strogatz and Simard small-world network models exhibit better performance for small dataset. But it is seen that if the dataset grows this performance decreases. Besides, it is identified that the rewiring range of these models is large scale. In Newman-Watts small-world networks, it is determined that this rewiring range is smaller and learning performances of these networks are independent of the dataset. In the presented study, with the first time in the literature, Watts-Strogatz small-world model is statistically compared with the conventional feed forward artificial neural networks, and it is presented that Watts-Strogatz small-world model is statistically more significant (p<0.01) model than the conventional feed forward artificial neural network model. In the light of obtained results, it was shown that the small-world network topology can be used instead of the conventional topology of feed forward artificial neural network in the researches of artificial intelligent

Modeliranje tlačne čvrstoće paralelno s vlakancima toplinski obrađenog drva škotskog bora (Pinus sylvestris L.) s pomoću umjetne neuronske mreže

In this study, the compressive strength of heat treated Scotch Pine was modeled using artificial neural network. The compressive strength (CS) value parallel to grain was determined after exposing the wood to heat treatment at temperature of 130, 145, 160, 175, 190 and 205ºC for 3, 6, 9, 12 hours. The experimental data was evaluated by using multiple variance analysis. Secondly, the effect of heat treatment on the CS of samples was modeled by using artificial neural network (ANN).Rad prikazuje numeričku proceduru za analizu struktura izrađenih od kompleksnih laminata. U radu se obrađuje modeliranje tlačne čvrstoće toplinski obrađenog drva škotskog bora uz pomoć umjetne neuronske mreže. Vrijednost tlačne čvrstoće (CS) paralelno s vlakancima određena je nakon toplinske obrade pri temperaturi 130, 145, 160, 175, 190 i 205 ºC tijekom 3, 6, 9 i 12 sati. Eksperimentalni podaci analizirani su primjenom višestruke analize varijance. Osim toga, učinak toplinske obrade na tlačnu čvrstoću uzoraka modeliran je uz pomoć umjetne neuronske mreže (ANN)

Flaş ERG Sinyallerinin İşlemesinde Kısa Zamanlı Fourier Dönüşümü ve Sürekli Dalgacık Dönüşümü Tekniklerinin Karşılaştırılması

Flaş Elektroretinogram sinyalleri gözün retina tabakasının flaş bir ışık ile uyarılması sonucu ortaya çıkan elektriksel potansiyellerdir. Bu sinyale ait iki temel bileşeni olan ‘a’ ve ‘b’ dalgaları retina tabakasının değerlendirilmesinde önem arz etmektedir. Bunun için farklı sinyal işleme tekniklerinden yararlanılmaktadır. Yapılan bu çalışmada sağlıklı bireylerden kaydedilen flaş Elektroretinogram sinyallerinin rod, maksimum kombine ve kon yanıtları kullanılarak Kısa Zamanlı Fourier Dönüşümü ve Sürekli Dalgacık Dönüşümü yöntemleriyle sinyallerin ‘a’ ve ‘b’ dalgaları analizi edilmiştir. Bu doğrultuda dalgaların lokasyonlarının tespit edilmesinde hangi yöntemin daha başarılı olduğu irdelenmiştir. Gerçekleştirilen analizler sonucunda her üç yanıtta da dalgaların analizi için Sürekli Dalgacık Dönüşümünün daha başarılı bir yöntem olduğu tespit edilmiştir. Bunun yanı sıra Sürekli Dalgacık Dönüşümünde rod ve kon yanıtları için Coiflet, Gauss, Meksika şapka ve Morlet dalgacıklarının, maksimum kombine yanıtı için ise Morlet dalgacığının kullanılması halinde dalgaların lokasyonlarının daha doğru bir şekilde tespit edebileceği saptanmıştır

Impact of small-world network topology on the conventional artificial neural network for the diagnosis of diabetes

Artificial intelligent systems have been widely used for diagnosis of diseases. Due to their importance, new approaches are attempted consistently to increase the performance of these systems. In this study, we introduce a new approach for diagnosis of diabetes based on the Small-World Feed Forward Artificial Neural Network (SW- FFANN). We construct the small-world network by following the Watts-Strogatz approach, and use this architecture for classifying the diabetes, and compare its performance with that of the regular or the conventional FFANN. We show that the classification performance of the SW-FFANN is better than that of the conventional FFANN. The SW-FFANN approach also results in both the highest output correlation and the best output error parameters. We also perform the accuracy analysis and show that SW-FFANN approach exhibits the highest classifier performance. © 2015 Elsevier Ltd. All rights reserved

Serviks kanseri verilerinin sınıflandırılması ve rastsal altuzaylar algoritmasının sınıflandırma performansına etkisi

Aselsan;et al.;Huawei;IEEE Signal Processing Society;IEEE Turkey Section;Netas26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018 -- -- 137780Computer assisted automatic diagnostic systems are used for the purpose of speeding up diagnosis and treatment and helping to make the right decision. In this study, cervical cancer is identified using four basic classifiers: Naive Bayes (NB), k-Nearest Neighbor (kNN), Multilayer Perceptron (MLP) and Decision Trees (KA-C4.5) algorithms and random subspaces ensemble algorithm. Gain Ratio Attribute Evaluation (GRAE) feature extraction algorithm is applied to contribute to classification performance. The classification results obtained with all datasets and reduced datasets are compared with respect to performance criteria such as accuracy, Root Mean Square Error (RMSE), Sensitivity, Specificity performance criteria. According to the obtained performance analysis, it is seen that the classification performance with the random subspace ensemble algorithm using the kNN basic classifier on the reduced data set is the highest (%95.51). © 2018 IEEE

Detection of Knee Abnormality from Surface EMG Signals by Artificial Neural Networks

25th Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2017 -- Antalya, TURKEYWOS: 000413813100024Using surface EMG signals is a non-invasive measurement method obtained as a result of muscle activity. In this study, surface EMG data have been used for classification, taken from healthy individuals or individuals with knee abnormalities in gait position. For this purpose, first feature extraction was realized by discrete wavelet transform from the data. Then, extracted features were classified by artificial neural network approach that is widely used in the literature. In classification process, artificial neural networks were trained by using simple cross-validation algorithm. During training the optimal network topology was determined. The highest classification performance of proposed model was obtained in rate fiction 80%-20% and 70%-30% of data set. Our results revealed that proposed artificial neural network model is able to detect knee abnormality from surface EMG signals.Turk Telekom, Arcelik A S, Aselsan, ARGENIT, HAVELSAN, NETAS, Adresgezgini, IEEE Turkey Sect, AVCR Informat Technologies, Cisco, i2i Syst, Integrated Syst & Syst Design, ENOVAS, FiGES Engn, MS Spektral, Istanbul Teknik Uni

Impact of small-world topology on the performance of a feed-forward artificial neural network based on 2 different real-life problems

Since feed-forward artificial neural networks (FFANNs) are the most widely used models to solve real-life problems, many studies have focused on improving their learning performances by changing the network architecture and learning algorithms. On the other hand, recently, small-world network topology has been shown to meet the characteristics of real-life problems. Therefore, in this study, instead of focusing on the performance of the conventional FFANNs, we investigated how real-life problems can be solved by a FFANN with small-world topology. Therefore, we considered 2 real-life problems: estimating the thermal performance of solar air collectors and predicting the modulus of rupture values of oriented strand boards. We used the FFANN with small-world topology to solve both problems and compared the results with those of a conventional FFANN with zero rewiring. In addition, we investigated whether there was statistically significant difference between the regular FFANN and small-world FFANN model. Our results show that there exists an optimal rewiring number within the small-world topology that warrants the best performance for both problems. © TUBITAK

Yüzey EMG sinyallerinden diz anormalliğinin yapay sinir ağları ile tespiti

25th Signal Processing and Communications Applications Conference, SIU 2017 -- 15 May 2017 through 18 May 2017 -- -- 128703Using surface EMG signals is a non-invasive measurement method obtained as a result of muscle activity. In this study, surface EMG data have been used for classification, taken from healthy individuals or individuals with knee abnormalities in gait position. For this purpose, first feature extraction was realized by discrete wavelet transform from the data. Then, extracted features were classified by artificial neural network approach that is widely used in the literature. In classification process, artificial neural networks were trained by using simple cross-validation algorithm. During training the optimal network topology was determined. The highest classification performance of proposed model was obtained in rate fiction 80%-20% and 70%-30% of data set. Our results revealed that proposed artificial neural network model is able to detect knee abnormality from surface EMG signals. © 2017 IEEE

Immunohistochemically Dyed Seminiferous Tubules With Feed-Forward Neural Network

25th Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2017 -- Antalya, TURKEYWOS: 000413813100374In this study, the features of the seminiferous tubule sections were extracted and the presence of the cells and cell stain types detected with the help of the feed forward artificial neural network. By looking at the section view with a small window, 78 features were extracted from the pixels seen by the window and used as an input to the artificial neural network. Artificial neural network outputs are decides presence of the cell and the staining of the cell. The results obtained with the artificial neural network were determined by using the connected component labeling method. The results obtained with the help of the user and the results obtained with the artificial neural network were compared. It has been shown that the proposed ANN model performs cell counting process comparable to the literature (%76 accuracy).Turk Telekom, Arcelik A S, Aselsan, ARGENIT, HAVELSAN, NETAS, Adresgezgini, IEEE Turkey Sect, AVCR Informat Technologies, Cisco, i2i Syst, Integrated Syst & Syst Design, ENOVAS, FiGES Engn, MS Spektral, Istanbul Teknik Uni

Optimization of digital holographic setup by a fuzzy logic prediction system

In this study, the optimization of the digital holography setup is achieved by a using fuzzy logic prediction system. In fact, when this optimization process is experimentally performed, some parameters are changed in the setup. These parameters affect directly the obtained image quality after a reconstruction process, which is determined by normalized root mean square. The aim of this study is to achieve the optimization of digital holographic setup by using both experimental and fuzzy logic prediction systems. Furthermore, the required time during the experimental optimization can be lowered by using a numerical method like the fuzzy logic prediction system. Here, the experimental optimization results and the optimization results obtained by the fuzzy logic prediction system are compared. It is offered that the designed experimental system can be optimized by using an artificial intelligent tool. The applied fuzzy logic prediction model is used the first time for optimization of hologram recording setup. As a result, it is reached a conclusion that the optimization of digital holographic setup can be numerically performed by the fuzzy logic prediction system. Moreover, while digital holographic setup is experimentally designed, the required time for optimization is reduced, as well. © 2016 Elsevier Ltd. All rights reserved