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Öneri sistemleri, kullanıcılara kişiselleştirilmiş öneri sunan bilgi filtreleme sistemleridir. Öneri tabanlı uygulamalar e-ticaret, film, makale, restoran ve seyahat gibi birçok alanda kullanılmaktadır. Özellikle metin tabanlı depolamaya sahip sistemler üzerinde geleneksel anahtar kelime tabanlı arama tekniğiyle karşılaştırıldığında, öneri sistemleri büyük veri için daha etkili ve özelleştirilmiş sistemler olarak ön plana çıkmaktadır. Bilimsel çalışma paylaşımının yapıldığı platformlarda içerik havuzunun genişlemesiyle birlikte metinsel veri kullanımında önemli artış görülmektedir. Bu durum, araştırmacıların kendi alanlarıyla ilgili güvenilir ve doğru yayınlara erişimini zorlaştırmaktadır. Araştırmacılar, çalışmalarına katkı sağlayacak en doğru yayınları bulmakta çok fazla zaman harcayabilmektedir. Bilimsel çalışma öneri sistemi, karşılaşılan bu sorunlara çözüm üreterek araştırmacılara ilgi alanlarına uygun yayınları hızlı bir şekilde bulmalarına yardımcı olmaktadır. Sınırlı deneyime sahip kişiler için bilimsel çalışma öneri sistemleri araştırmacıların ufuklarını ve araştırma ilgi alanlarını genişletmeleri doğrultusunda yayınlar sunmaktadır. İçerik tabanlı filtreleme yöntemi, bilimsel çalışma öneri sistemi tasarımında en yaygın kullanılan yöntem olup kullanıcıdan bağımsız modellenir. Bu çalışmada, içerik tabanlı yeni bir bilimsel çalışma öneri sistemi farklı benzerlik yöntemleri üzerinden karşılaştırmalı olarak tavsiye edilmektedir. Yöntemler ve öneri ağırlıkları değişiklik gösterse de aynı veri seti içerisinde aynı yayınların önerildiği görülmektedir. Ölçüm değeri olarak birbirine yakın yöntemler arasında seçim yapmak gerektiğinde ise hesaplama süresini dikkate almak gerektiği sonucuna varılmaktadır., Recommender systems are information filtering systems that offer personalized suggestions to users. Recommender-based applications are used in numerous areas, such as e-commerce, streaming services, textual media, restaurants, and tourism. Compared to traditional keyword-based search techniques, especially on systems with text-based storage, recommender systems stand out as more effective and customized systems for big data. With the expansion of the content pool on platforms where scientific study sharing is made, there is a significant increase in textual data use. This situation makes it difficult for researchers to access reliable and accurate papers in their domains. Researchers may spend a lot of time finding the most accurate publications to contribute to their studies. The scientific study recommender system helps researchers quickly find papers that are suitable for their interests. Scientific study recommender system offers publications to researchers, who have limited experience, to broaden their horizons and research interests. The content-based filtering method is the most widely used procedure in scientific study recommender system designs and is modeled independently from the user. In this study, a new content-based scientific study recommender system is comparatively suggested over different similarity methods. Although the methods and recommender weights vary, it is seen that the same publications are recommended within the same data set. When it is necessary to choose between methods that are close to each other as measurement values, it is concluded that the calculation time should be considered.

Technology monitoring is a natural conjugate to foresight which is the observation and analysis of scientific, technical and technological environment of economic impacts in order to deduce both current and future threats and increase development opportunities. Technology monitoring is a methodology for organizations to systematically analyze technical information in a continuous way in order to gain insight and competitive advantage in a specific technical domain and is based mainly on statistical analysis of published information. This paper aims to review the existing literature and examine the evolution of such a field so that the research trends can be uncovered, including research methods. The results of an effort to identify the performance of energy, renewable energy, power and fuels research related papers published in Turkey during the last decade (2009–2018) are reported by using a bibliometric approach. Bibliometric approaches have been employed in order to assess the field of all energy research. Citation analysis is performed to assess the influence of most productive territories and authors. National and international collaboration was another field investigated in order to be compared with network analysis., Teknoloji izleme, öngörü çalışmalarının doğal eşleniği olmasının yanında mevcutta ve gelecekte olası tehditlerin bertarafı ve gelişme fırsatlarını artırmak için bilimsel, teknik ve teknolojik ortamın gözlenmesi ve analiz edilmesi olarak ifade edilmektedir. Teknoloji izleme aynı zamanda kuruluşların belirli bir alanda içgörü ve rekabet avantajı elde etmek amacıyla teknik bilgilerini sürekli olarak sistematik bir şekilde analiz etmeleri için kullanılan ve temel olarak yayınlanan bilgilerin istatistiksel analizine dayanan bir yöntemdir.Bu çalışma mevcut literatür bilgilerine dayalı enerji alanındaki çalışmaların gözden geçirilmesi ve ilgili yayınlara bağlı olarak araştırma yöntemlerini de içeren teknolojik eğilimin belirlenmesini amaçlamaktadır. Özellikle enerji, yenilenebilir enerji, güç ve yakıt anahtar kelimeleriyle tanımlanan Türkiye adresli son on yılda (2009–2018) yayımlanmış olan makale, patent, tez ve gerçekleştirilen projelerin bibliyometrik yöntem kullanılarak analiz edilmesine odaklanmaktadır. Çalışmada yararlanılan bibliyometrik yaklaşım tüm enerji araştırmalarını değerlendirmek için kullanılmıştır. Gerçekleştirilen çalışmada ulusal ve uluslararası işbirlikleri de incelenmiştir.

Buçalışmada EMG sinyalleri kullanılarak 14 farklı parmak hareketi önerilen yenibir öznitelik çıkarma yöntemiyle sınıflandırılmıştır. EMG sinyallerinden parmakhareketlerinin tespiti / sınıflandırılması, ön işleme, özellik çıkarma vesınıflandırma adımlarını içeren 3 ana adımdan oluşur. EMG sinyallerininsınıflandırılmasında, sınıflandırıcının performansı doğrudan öznitelik çıkarmayöntemine bağlıdır. Bu sebeple öznitelik çıkarma yöntemi uygun bir biçimdeseçilmelidir. Literatürde zaman, histogram ve frekans tabanlı birçok öznitelikçıkarma yöntemi vardır. Ancak bu yöntemler yüksek zaman karmaşıklığı, çok fazlaişleme ihtiyaç duyma, fazla sayıda kontrol parametresi bulundurma, vb. birçokdezavantaja sahiptir. Bu çalışmada EMG sinyallerinden çeşitli parmakhareketlerinin sınıflandırılması amacıyla sayılan dezavantajları ortadankaldırmak için yeni bir öznitelik çıkarma yöntemi sunulmuştur. Bu yöntem ikizaman tabanlı öznitelik çıkarma yönteminin hibritleştirilmesiyle eldeedilmiştir. 10 kat çapraz doğrulamayla elde edilen deneysel sonuçlarına göre, önerilenyöntemin %97,48 doğruluk oranıyla bu çalışmada kullanılan diğer 9 öznitelikçıkarma yöntemlerinden daha iyi olduğu deneysel olarak gösterilmiştir. Deneyselsonuçlar istatistiksel yöntemlerle desteklenmiştir., This studyaims to discuss classification of 14 different finger movements from EMG signalsby using new feature extraction technique. The detection/classification of finger movements consists of 3 mainsteps including, preprocessing, feature extraction and classification steps. Inclassification of EMG signals, the performance of the classifier directly dependson feature extraction methods, including, time, histogram and frequency-basedmethods. However, these feature extraction methods have several drawbacksincluding, high time complexity, high computation demand, user suppliedparameters, etc. In this paper, a new feature extraction method has beenproposed for the classification of finger movements from EMG signals toovercome these problems. The proposed method based on hybridization of 2-timedomain feature extraction techniques. The use of this method resulted in an accuracyof 97.48% after 10-fold-cross-validation. The experimental results supportedwith statistical analysis show that proposed method is better than 9 featureextraction methods investigated in this paper

Üretimplanlaması ve güç sistemlerinin yönetilmesi açısından rüzgar enerjisistemlerinden üretilecek elektrik enerjisinin belirli periyotlar için tahminedilmesi gerekmektedir. Bu kapsamda, asimetrik ve kararsız yapıdaki rüzgar hızı verilerinin hassas birşekilde tahmin edilebilmesi için bir çok farklı rüzgar hızı kestirimyaklaşımları önerilmiştir. Bu çalışma kapsamında, diferansiyel gelişimalgoritması (DE) yaklaşımı ile optimize edilen uyarlamalı sinirsel bulanıkçıkarım sistemi (ANFIS) kullanılarak kısa dönemli (1 saat) rüzgar hızı tahminmodelleri geliştirilmiştir. Çalışma kapsamında ele alınan yöntemde, diferansiyel gelişim sezgisel yaklaşımıkullanarak model parametreleri kısa dönemli rüzgar hızı tahmini içinbelirlenmiş ve karşılaştırmalı olarak analiz edilmiştir. Önerilen yaklaşımıntahmindeki performans kriterlerini doğrulamak için Bilecik ilinde bulunan birgözlem istasyonundaki rüzgar hızı serileri kullanılmıştır. ANFIS modelparametrelerinin optimizasyonunda kullanılan sezgisel algoritma 10 kez tekrarlıbağımsız olarak çalıştırılmış ve eğitim-test performans sonuçları istatistikselolarak sunulmuştur. Ayrıca önerilenhibrit modelin performansı literatürde iyi bilinen Levenberg-Marquardtalgoritması eğitilen ileri beslemeli yapay sinir ağı (ANN) sonuçları ile dekarşılaştırılarak yorumlanmıştır., Theelectrical energy to be produced from wind energy systems should be estimatedfor some periods in order to generation planning and power systems management.In this context, many different wind speed forecasting approaches have beenproposed for accurate estimation of asymmetric and unstable wind speed data. Inthis paper, short-term (1h) wind speed forecasting models have been developedby using Adaptive Neuro Fuzzy Inference System (ANFIS) based on DifferentialEvolution Algorithm (DE). In this paper, the model parameters have beendetermined by differential evolution heuristic approach for short-term windspeed forecasting and analyzed comparatively. To validate the effectiveness ofthe proposed approach, wind speed series collected from a wind observationstation located in Bilecik, Turkey are used in the short-term wind speedforecasting. The meta-heuristic algorithm used in theoptimization of ANFIS model parameters are run 10 times independently and theperformance results are calculated statistically for training and test phasesof ANFIS model. The performances of proposed hybrid models are also comparedwith the well-known feed forward ANN model which is trained by Levenberg-Marquardtin the literature.

In this study, the Internet of things is defined and lightweight protocols that the devices can use to communicate with each other are specified, and message queue telemetry transport (MQTT) protocol, which is designed to be used for restricted devices, is detailed. In the scope of this paper, the studies about the authentication of the objects and the confidentiality of the data have been examined, some suggestions have been made against the identified security vulnerabilities. In the scope of this paper, as the test environment, Mosquitto, the server of the MQTT protocol, and MongoDB in order to store the data were installed. In the prepared test environment, the objects were communicated with each other via the MQTT protocol and identity verification and security vulnerabilities were tried to be detected.

Date of Conference: 16-19 May 2016 Conference Name: IEEE 24th Signal Processing and Communications Applications Conference, SIU 2016 In this paper, we study multi-class classification of tweets, where we introduce highly efficient dimensionality reduction techniques suitable for online processing of high dimensional feature vectors generated from freely-worded text. As for the real life case study, we work on tweets in the Turkish language, however, our methods are generic and can be used for other languages as clearly explained in the paper. Since we work on a real life application and the tweets are freely worded, we introduce text correction, normalization and root finding algorithms. Although text processing and classification are highly important due to many applications such as emotion recognition, advertisement selection, etc., online classification and regression algorithms over text are limited due to need for high dimensional vectors to represent natural text inputs. We overcome such limitations by showing that randomized projections and piecewise linear models can be efficiently leveraged to significantly reduce the computational cost for feature vector extraction from the tweets. Hence, we can perform multi-class tweet classification and regression in real time. We demonstrate our results over tweets collected from a real life case study where the tweets are freely-worded, e.g., with emoticons, shortened words, special characters, etc., and are unstructured. We implement several well-known machine learning algorithms as well as novel regression methods and demonstrate that we can significantly reduce the computational complexity with insignificant change in the classification and regression performance.

In this paper we propose a novel framework using features extracted from Wikipedia for the task of matching named entities. We present how the employed features are extracted from Wikipedia and how its structure is utilized. We describe how a term-concepts table constructed from Wikipedia and the redirect links are integrated in the framework. In addition, the internal links within Wikipedia along with the categories structure are also used to compute the relatedness between concepts. We evaluate the built framework and report its performance in the applications of word sense disambiguation and named entities matching. The system performance is compared against other learning-based state-of-the-art systems and its reported results are found to be competitive. We also present a method in this paper for named entities matching that is context-independent and compare its results with those of other systems.

This paper proposes an optimization method for the optimal placement of phasor measurement units (PMUs) for the complete observability of power systems. The proposed method is based on numerical observability and artificial intelligence. The artificial intelligence algorithm used is the improved tabu search (ITS) algorithm. The ITS is used to find the optimal placement for the PMU to keep the system completely observable. In addition, the paper describes a predictive out-of-step (OOS) algorithm based on the observation of the voltage phase difference between the substations. The proposed optimal placement of the PMUs and the OOS algorithm is tested using the IEEE 6-bus and IEEE 14-bus systems, and the Egyptian 500 kV network. The test systems are simulated using the power system computer aided design software program. The placement algorithm and the OOS prediction algorithm are carried out using MATLAB script programs.

With the advent of electricity markets, the traditional approach to unit maintenance scheduling (UMS) needs to undergo major changes in order to be compatible with competitive environment structures. The transition from a vertical power system to a competitive structure makes many challenges for policymakers and market designers. In this paper, a new approach to UMS in competitive electricity markets is presented. The main part of this study involves both how to treat generating companies (GENCOs) fairly and how to guarantee power system security during the maintenance scheduling. This paper advances the UMS in the electricity market so that one can determine which maintenance plans are to be selected while guaranteeing power system security and ensuring fair competition among GENCOs. The main contribution of this study is the constructing of a new kind of UMS to prevent market power and economic withholding. In order to guarantee power system security, a probabilistic approach of reliability analysis is presented. This probabilistic methodology is designed based on the health levelization and well-being analysis technique. The optimal strategy profile is defined by a genetic algorithm, so it can strike the right balance between profit and security with fair competition. In the end, maintenance scheduling as numerical results for 9 GENCOs of a large-scale IEEE reliability test system is applied to show the applicability of the proposed framework.

İnternet ve bilişim sistemleri üzerinde işlenen suçları tespit etmek amacıyla gerçekleştirilen adli bilişim incelemelerinin önemi, her geçen gün daha iyi anlaşılmaktadır. Bu minvalde suç ve suçlunun tespiti konusunda hukuka uygun dijital delillerin elde edilmesinin önemi çok büyüktür. Adli bilişim incelemelerinde, birçok farklı yöntem ile dijital deliller elde edilebilir. Özellikle bilişim sistemleri üzerinde kullanılan işletim sistemleri buna çok iyi bir örnektir. Küresel ölçekte olduğu gibi ülkemizde de gerek kişisel kullanımlarda gerekse kurumsal şirketlerde Windows işletim sistemi yaygın olarak kullanılmaktadır. Sanal dünya üzerinde işlenen suçların ve mağduriyetlerin çoğunluğu bu sistem üzerinde yaşanmaktadır. Bu nedenle çalışmada, Windows işletim sistemi üzerinde adli bilişim çalışması yapma gereksinimi duyulmuştur. Adli bilişim alanındaki güncel çalışmalar literatüre daha çok kavramsal ve hukuksal düzenleme olarak katkıda bulunmaktadır. Bu çalışmada ise metodolojik ve teknik analizler birlikte kullanılarak Windows PowerShell ve üçüncü parti yazılımlarla adli bilişim çalışması yapılması amaçlanmıştır. Böylece sistem bileşenleri üzerindeki dijital deliller, metedolojik ve teknik yöntemlerle analiz edilip, elde edilen sonuçlar görsel verilerle ortaya konulmuştur. The importance of computer forensic reviews carried out to detect crimes committed on the Internet and information systems is be better understood day by day. In this regard, it is very important to obtain digital evidence in accordance with the law in the detection of crime and criminals. In computer forensic investigation, digital evidence can be obtained by many different methods. Operating systems, especially those used on information systems, are a very good example. It is the most preferred Windows operating system in both personal and corporate structures in our country as it is on a global scale. For this reason, the intensity of the crimes committed and the victimization experienced in the virtual world is on this system. Therefore, this study carries out the forensic work on the Windows operating system. Current studies in the field of forensic informatics contribute to the literature mostly as a conceptual and legal arrangement. However, this paper aims to do forensic work with Windows PowerShell and third party software by using methodological and technical analysis together. Thus, digital evidence on system components has been analyzed using methodological and technical methods, and the results obtained have been presented with visual data.

6th International Conference on Computer Science and Engineering, UBMK 2021 -- 15 September 2021 through 17 September 2021 -- -- 176826, Nowadays, shopping is done more comfortably and without time constraints with the throwing of e-commerce platforms. These platforms allow consumers to examine reviews before purchasing products. Thus, consumers can decide whether to buy a product with positive or negative comments about the products. In this paper, Turkish sentiment analysis was carried out on the product comments at the Hepsiburada platform. For sentiment analysis, firstly, the success of Random Forest, Naive Bayes and Logistic Regression machine learning methods was measured. Then, the effect of BERT, ELECTRA and ALBERT language models on sentiment analysis was analyzed and the success of language models was compared with machine learning methods. While Naive Bayes achieved the highest accuracy with 89.95% among machine learning methods, ELECTRA was the most successful with 92.54% among language models. As a result of the study, it has been shown that the ELECTRA and ALBERT language models are more successful than machine learning methods. © 2021 IEEE

Sentiment analysis is a research field that aims to identify the sentiment orientation (as, positive, negative or neutral) of a particular topic in text documents through machine learning, statistics and natural language processing techniques. Convolutional neural networks are a type of deep learning methods, which process data with a grid-like topology. In this paper, we present empirical results for three deep learning architectures based on convolutional neural network for sentiment analysis on Turkish. The first architecture initially employs word-embedding schemes to represent text documents. Then, a stack of convolution layers (i.e., 1-gram, 2-gram and 3-gram) has been employed to extract 1-gram, 2-gram and 3-gram based features. For each layer, constant number of filters have been employed to construct feature maps. The second examined architecture employs recurrent convolution and maximum pooling schemes on word embedding based representation. The third architecture is a convolution based pyramid configuration. To represent text corpus, word2vec, fastText, GloVe and LDA2vec word embedding schemes have been utilized. The empirical results on Turkish sentiment classification indicate that convolutional deep learning based architectures outperform conventional machine learning methods (such as, k-nearest neighbor algorithm, support vector machines, logistic regression and Naïve Bayes algorithm) and conventional deep learning architectures (such as, recurrent neural networks, long short term memory architecture and gated recurrent unit). We obtained a classification accuracy of 92.53% with convolutional neural network based architecture in conjuncton with word2vec (skip-gram model) based word embedding scheme., Duygu analizi (görüş madenciliği), metin belgeleri içerisinde yer alan nesnelere, ürünlere, servislere ya da organizasyonlara ilişkin görüş, duygu, tutum gibi öznel bilgilerin, makine öğrenmesi, istatistik ve doğal dil işleme gibi alanlardan teknik ve yöntemlerin kullanılması ile çıkarılmasını amaçlayan bir araştırma alanıdır. Duygu analizi, yapısal olmayan bilgiden, yapısal, anlamlı ve kullanışlı bilgiler çıkarılmasını olanaklı hale getirir. Bu bilgi, karar destek sistemleri ve bireysel karar vericiler için önemli bir kaynak olarak işlev görür. Evrişimli sinir ağları, veriyi ızgara benzeri bir topoloji ile işleyen bir tür derin öğrenme yöntemidir. Bu çalışmada, Türkçe metin belgeleri üzerinde duygu analizi için, evrişimli sinir ağı tabanlı üç temel derin öğrenme mimarisinin etkinliği değerlendirilmektedir. Çalışma kapsamında önerilen birinci mimaride, gömme katmanında, metin belgesinde yer alan kelimeler için, kelime gömme yöntemleri tabanlı temsil elde edilmektedir. Ardından, evrişim katmanları yığını (1-gram, 2-gram ve 3-gram) kullanılarak 1-gram, 2-gram ve 3-gram tabanlı özniteliklerin çıkarımı gerçekleştirilmektedir. Her bir katmanda, öznitelik haritalarının oluşturulması için sabit sayıda 80 filtre uygulanmaktadır. İncelenen ikinci evrişimli sinir ağı tabanlı mimaride gömme katmanı sonucu elde edilen metin temsili, yinelenen evrişim ve maksimum havuzlama katmanlarına tabi tutulmaktadır. İncelenen üçüncü mimari ise, evrişim tabanlı piramit mimarisidir. Metin belgelerinin temsilinde, word2vec, fastText, GloVe ve LDA2vec olmak üzere dört temel kelime gömme yöntemi incelenmektedir. İncelenen evrişimli sinir ağı tabanlı mimarilerin, Türkçe duygu analizi için, geleneksel makine öğrenmesi sınıflandırıcılarına (k-en yakın komşu algoritması, destek vektör makineleri, lojistik regresyon ve Naive Bayes algoritması) ve temel derin öğrenme mimarilerine (tekrarlayan sinir ağları, uzun kısa süreli bellek birimleri ve geçitli tekrarlayan birim) kıyasla daha yüksek başarım elde ettiği görülmektedir. Word2vec (Skip-gram modeli) kelime kodlaması yöntemi ile evrişimli sinir ağı tabanlı mimari ile %92.53 doğru sınıflandırma oranı elde edilmiştir.

In this paper, syntactic derivation of the correlative constructions in modern Turkish is examined within the framework of the Generative Grammar for the first time. The following points are emphasized in the study: (i) for simple correlative constructions, a subordinating correlative clause containing a wh-phrase must be merged to the demonstrative in the matrix clause as local as possible in a structural manner, (ii) there are two plausible syntactic accounts for generating Turkish simple correlative constructions, i.e. the first is based on base-generated approach and the second is based on movement approach, (iii) the derivation of simple correlative constructions is diffrent from the one in unconditionals, (iv) regarding multiple correlative constructions, the most local position for a subordinate clausal material which should be merged to the matrix is the minimal IP containing demonstrative pronouns, and (v) the multiple correlative constructions in Turkish can be generated using the non-movement IP adjunction strategy., Bu çalışmada, Türkçedeki bağlılaşık yapıların sözdizimsel türetimi Üretici Dilbilgisi Kuramı çerçevesinde ilk kez incelenecektir. Çalışmada genel olarak şu hususlar vurgulanacaktır: (i) basit bağlılaşık yapılarda ne-öbeğini içeren bağlılaşık yan tümce, ilişkilendirildiği bağlılaşık ana tümce içindeki işaret sözcüğüne yapısal olarak mümkün olan en yerel düzende bağlanmalıdır, (ii) basit bağlılaşık yapıların üretimi için biri taban-üretimli diğeri taşıma içeren iki farklı yaklaşım mümkündür, (iii) basit bağlılaşık yapıların üretilme biçimi şartsız yapılardan farklıdır, (iv) çoklu bağlılaşık yapılarda bağlılaşık yan tümcenin ana tümceye bağlanabileceği en yerel konum işaret sözcüklerini barındıran en küçük çekim öbeğine eklentilemedir ve (v) Türkçedeki çoklu bağlılaşık tümceler taşıma içermeyen çekim öbeği eklentilemesi yapısıyla üretilebilmektedir.

Otomatik görüntü altyazısı, yapay zekânın hem bilgisayarlagörme hem de doğal dil işleme alanlarını kapsayan bir konudur. Makine çevirisialanındaki gelişmelerden ilham alan ve bu alanda başarılı sonuçlar verenkodlayıcı-kod çözücü tekniği, şu anda İngilizce görüntü altyazısı konusunda kullanılanmevcut yöntemlerden biridir. Bu çalışmada, Türkçe dili için otomatik görüntü altyazısıoluşturan bir model sunulmaktadır. Bu çalışma, verilen görüntülerinözelliklerini çıkarmaktan sorumlu olan bir CNN kodlayıcıyı, altyazı oluşturmaktansorumlu olan bir RNN kod çözücüsü ile birleştirilerek, Türkçe MS COCO veritabanını üzerinde Türkçe görüntü altyazısı kodlayıcı-kod çözücü modelini test etmektedir.Üretken modelin performansı yeni oluşturulan veri tabanında hem BLEU, METEOR,ROUGE ve CIDEr gibi en yaygın değerlendirme ölçütleri hem de insan tabanlıyöntemler kullanılarak değerlendirilmiştir. Sonuçlar, önerilen modelinperformansının hem niteliksel hem de niceliksel olarak tatmin edici olduğunugöstermektedir. Çalışma sonunda hazırlanan, herkesin kullanımına açık bir Web uygulamasıuygulaması[1]sayesinde Türkçe dili için MS COCO görüntülerine ait Türkçe girişlerinyapıldığı bir ortam kullanıcıya sunulmuştur. Tüm görüntüler tamamlandığında,Türkçe’ye özgü ve karşılaştırmalı çalışmaların yapıldığı bir veri kümesitamamlanmış olacaktır.[1] http://mscococontributor.herokuapp.com/website/, Automatic image captioning is a challenging issue inartificial intelligence, which covers both the fields of computer vision andnatural language processing. Inspired by the later advances in machinetranslation, a successful encoder-decoder technique is currently thestate-of-the-art in English language captioning. In this study, we proposed animage captioning model for Turkish Language. This paper evaluate theencoder-decoder model on MS COCO database by coupling an encoder CNN -thecomponent that is responsible for extracting the features of the given images-,with a decoder RNN -the component that is responsible for generating captionsusing the given inputs- to generate Turkish captions. We conducted theexperiments using the most common evaluation metrics such as BLEU, METEOR,ROUGE and CIDEr. Results show that the performance of the proposed model issatisfactory in both qualitative and quantitatively evaluations. Finally, thisstudy introduces a Web platform, which is proposed to improve the dataset viacrowd-sourcing and free to use. The Turkish MS COCO database is available forresearch purpose. When all the imagesare completed, a Turkish dataset will be available for comparative studies.

Kablosuz algılayıcı ağlar birçok alanda yaygınolarak kullanılan kablosuz ağ teknolojisidir. Yeraltı, karasal, sualtı veendüstriyel algılayıcı ağlar olmak üzere farklı kullanım alanlarıbulunmaktadır. Kablosuz algılayıcı ağlardaki en önemli sorunlardan birisi, algılayıcıdüğümlerin mekanik nedenlerden dolayı arızaya meyilli olmasıdır. Diğer birsorun ise, kablosuz algılayıcı düğümlerin sınırlı enerjiye sahip olmasıdır. Bubağlamda, hata sezme mekanizmaları bulunan ve enerji verimli olarak çalışanalgılayıcı ağ tasarımları büyük önem taşımaktadır. Bunun yanında, maliyetaçısından algılayıcı düğümlerin yerleştirildiği konumları belirleme tekniğioldukça önemlidir. Düğümlerin yerleştirildiği konumlar, hedef bölgenin birdüğümün algılama alanı içinde olma olasılığını ve bu düğümün baz istasyonu ileolan bağlantısını etkilemektedir. Bu makale çalışmasında, çok sayıda algılayıcıdüğümün geniş bir alana yerleştirildiği farklı bir yaklaşım önerilmektedir. Algılayıcıdüğümler; belirli bir bölgedeki sıcaklık, nem, basınç vb. parametrelerialgılamak için tüm bölgeyi kapsayacak şekilde yerleştirilmektedir. Düğümlertarafından algılanan değerler, merkezde sabit olarak bulunan baz istasyonutarafından toplanmaktadır. Önerilen yaklaşımın benzetim modeli, RiverbedModeler yazılımı kullanılarak gerçekleştirilmiştir. Önerilen yaklaşımsayesinde, belirli bir bölgenin en az sayıda kablosuz algılayıcı düğüm ilesezilmesi sağlanmaktadır., Wireless sensor networks are widelyused wireless network technology in many areas. There are different usage areassuch as underground, terrestrial, underwater and industrial sensor networks. Oneof the most important problems in wireless sensor networks is that the sensornodes are prone to malfunction due to mechanical reasons. Another problem isthat the wireless sensor nodes have limited energy. In this context, sensornetwork designs with error sensing mechanisms and energy efficient operationsare of great importance. In addition, the technique of determining thelocations where the sensor nodes are placed is very important in terms of cost.The locations where the nodes are placed affect the probability that the targetregion is within the detection area of ​​a node and its connection with thebase station. In this paper, a different approach is proposed in which a largenumber of sensor nodes are placed on a large area. Sensor nodes are placed tocover the entire region in a specific area in order to detect the parameterssuch as temperature, humidity, pressure, etc. The values ​​detected by thenodes are collected by the base station which is fixed at the center. Thesimulation model of the proposed approach was performed by using RiverbedModeler software. Thanks to the proposed approach, it is ensured that a certainregion is sensed by the minimum number of wireless sensor nodes.

© 2018 IEEE.Image segmentation is an important image processing method. The segmentation method, which is a process of dividing into different regions, is a meaningful according to certain features of the image. In this paper, histogram based, edge based, region based and active contour methods most widely used in image segmentation methods are investigated. Advantages and disadvantages of these methods are explained and the effects of these methods on the image are compared.

Date of Conference: 2-5 May 2018 In this paper, we study online nonlinear data regression and propose a highly efficient long short term memory (LSTM) network based architecture. Here, we also introduce on-line training algorithms to learn the parameters of the introduced architecture. We first propose an LSTM based architecture for data regression. To diminish the complexity of this architecture, we use an energy efficient operator (ef-operator) instead of the multiplication operation. We then factorize the matrices of the LSTM network to reduce the total number of parameters to be learned. In order to train the parameters of this structure, we introduce online learning methods based on the exponentiated gradient (EG) and stochastic gradient descent (SGD) algorithms. Experimental results demonstrate considerable performance and efficiency improvements provided by the introduced architecture.

Date of Conference: 2-5 May 2018 In this paper, we investigate online parameter learning for Long Short Term Memory (LSTM) architectures in distributed networks. Here, we first introduce an LSTM based structure for regression. Then, we provide the equations of this structure in a state space form for each node in our network. Using this form, we then learn the parameters via our Distributed Particle Filtering based (DPF) training method. Our training method asymptotically converges to the optimal parameter set provided that we satisfy certain trivial requirements. While achieving this performance, our training method only causes a computational load that is similar to the efficient first order gradient based training methods. Through real life experiments, we show substantial performance gains compared to the conventional methods.

Mobile devices begin to spread increasingly recently to offer a lot of services which personal computers offer. This condition has led to increase in the number of security threats in mobile devices and services. In this paper, it has been made a research on mobile malware and malware detection techniques. Within the scope of the study, a a permission based detection system based on the machine learning methods for Android malware was developed. Developed system is analyzed by using Random Forest, Support Vector Machine and Artificial Neural Networks algorithms.

In this paper, an object detection approach suitable for mobile augmented reality is presented. The baseline approach is bused on matching keypoint descriptors and yerin.,ing these matches with geometric constraints. The performance optimizations necessary for speeding up matching are detailed. It is [ifs demonstrated that it is possible to increase the performance of the Locality Sensitive Hashing by exploiting approaches from the information retrieval field.

Today's newly constructed buildings are being turned into intelligent homes, by the addition of embedded systems into them, In order to provide advantages similar to intelligent home users in our country, relatively lower c ost intelligent entry doorsolutions are being offered to middle class consumers, us in other countries, In present work, a new intelligent door accessory is proposed which increases the security and functionality of both new and already installed doors. Our work involves embedded system hardware and sofiwure components. The proposul is supported by a prototype design based on the Arduino platform and a concrete pilotupplicution, The software part c ontuins both u server and a client application, in brief the system provides the house owner with the opportunity to remotely meet the person knocking at the door and the advantages of obtaining in advance information about visitors, by sharing preferred information reciprocally with other social group members, The presented desIcin is not only a concrete example for Internet of Things technology, but it also turns the social media frenzy into un advantage, h this paper, the tests conducted on u system produced according to the proposed design and the test results are also presented, The proposed solution is compared with similar solutions, The comparison results ore discussed and the planned future work is put forward, after the conclusion

Nowadays, along with the development of information technologies, storage and analysis of biomedical datasets are easy in health sector. In this area, Machine Learning methods provide a great contribution for evaluation and interpretation of data. In this paper, in addition to Support Vector Machines, Decision Tree, K-Nearest Neighbors, Naive Bayes and Dictionary Learning methods, Random Feature Subspaces (RDL) and Random Instance Subspaces (BDL) methods which are the ensembles of Dictionary Learning are used in biomedical data classification. In the test results, SVM and Dictionary Learning methods, RDL and BDL, which are generated using random feature/instance subspaces achieve optimum accuracy results.

25th Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2017 -- Antalya, TURKEY WOS: 000413813100575 Large scale dictionaries such as wordnets are one of the important structures used in natural language processing. Wordnet is a comprehensive dictionary containing semantic relations including synonyms, antonyms, etc. In this paper, we try to extract Hypernym-Hyponym relations which are one of the main parts of WordNet. For this aim, we used Wikipedia, Contemporary Dictionary of Turkish and Wiktionary as corpora. We used Finite State Machines for developing several rules for Hypernym-Hyponym extraction. 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 Univ

25th Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2017 -- Antalya, TURKEY WOS: 000413813100519 Along with the widespread use of technology, there have been many studies on human-computer relation recently. Study of the effects of computer systems on human education can be shown as an example of this. In this paper, a reliable and real time tangible user interface system developed for the air drum game which is used both for game and computer based training programs is presented. A user can perform a play action in six different directions using electronic drum sticks. A developed algorithm detects the direction of moving stick by processing sensor data taken from these sticks. Information about the stick which is moved and its direction are sent to the sound system to play the sound file of the relevant drum instrument. In this way the user is presented with a realistic drum experience. The developed system has been tested by different users in real world and its performance has been reported. The results verify the reliability and usability of the electronic drum game. 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 Univ

25th Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2017 -- Antalya, TURKEY WOS: 000413813100251 The Internet and social media provide a major source of information about people's opinions. Due to the rapidly growing number of online documents, it becomes both time-consuming and hard task to obtain and analyze the desired opinionated information. Sentiment analysis is the classification of sentiments expressed in documents. To improve classification perfromance feature selection methods which help to identify the most valuable features are generally applied. In this paper, we compare the performance of four feature selection methods namely Chi-square, Information Gain, Query Expansion Ranking, and Ant Colony Optimization using Maximum Entropi Modeling classification algorithm over Turkish Twitter dataset. Therefore, the effects of feature selection methods over the performance of sentiment analysis of Turkish Twitter data are evaluated. Experimental results show that Query Expansion Ranking and Ant Colony Optimization methods outperform other traditional feature selection methods for sentiment analysis. 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 Univ

Thanks to the new technologies of remote sensors, remote sensor data which increases in size exponentially and transforms to big data with increasing spatial and spectral resolutions causes great difficulties in storage, transportation and processing. It has become a necessity to implement parallel approaches instead of traditional methods, which are inadequate in critical applications when real/near real-time analysis of relevant large data is needed. In this paper, high performance computing approaches such as Multi-core, FPGA, GPU, Cluster and Cloud Computing are investigated for real / near real-time large data analysis in remote sensing applications.

Literatürde, tasarım amaçlarına ulaşmayı sağlayan ve kodla yazılım mimarisi arasındaki uyumsuzlukları bulmaya yarayan bir takım çalışmalar öne sürülmüştür. Mimari bakış açıları ve kod arasındaki uyum nasıl olmalıysa, aynı şekilde her bir bakış açısı da kendi içinde ve diğer bakış açılarıyla uyumlu olmalıdır. Ancak, varolan mimari uygunluğu yöntemleri öncelik olarak kod ile mimari arasındaki uyuma odaklanmış ve bakış açılarının kendi arasındaki uyumsuzluklarını dikkate almamıştır. Bu makalede, yazılım mimarisibakış açılarının kendi aralarındaki tutarsızlığını ele alan sistematik bir yöntem sunmaktayız. Bu amaç doğrultusunda, metamodelleri tanımlanan mimari bakış açılarını uygulamaya sokan ArchViewChecker adında bir araç geliştirdik ve örnek bir çalışma üzerinde aracımızı değerlendirdik. Several approaches have been proposed to detect the inconsistencies between the software architecture and the code to ensure that the original design goals are maintained. Similar to the consistency with the code it is important that an architecture view is consistent within itself and with other related architecture views. Unfortunately, the existing architecture conformance analysis approaches have primarily focused on checking the inconsistencies between the architecture and code, and did not explicitly consider the consistency within and among views. In this paper, we provide a systematic architecture conformance analysis approach that explicitly focuses on conformance analysis within and among architecture views. To this end, we define the meta-models of architecture viewpoints, present the conformance analysis approach, and provide the tool ArchViewChecker with a case study.

Semantic place prediction problem is the process of giving semantic names, such as school, home, office, to locations. Different than the localization problem where the coordinates of a place are predicted, the aim is to semantically characterize the location. While the GPS coordinates of a place can be utilized in solving the problem, phone usage patterns of the users in that location can be used as well. In this paper, the aim is to semantically classify locations of smart phone users utilizing the data collected from wireless interfaces and the motion sensors available on the phones with machine learning algorithms. The efficiency of features extracted from raw data is analysed in terms of metrics such as accuracy, using different classification algorithms. The results show that, while random forest and decision tree algorithms achieve 66% accuracy with only temporal features, adding features from device properties and activity features increases the accuracy up to 99%.

Artificial Bee Colony algorithm, inspired by the foraging behavior of real honey bees, is one of the most important swarm intelligence based optimization algorithms. Like other population based evolutionary computation techniques, Artificial Bee Colony algorithm is suitable for parallelization on distributed architectures. In this paper, we presented a new emigrant creation strategy that is being distributed between subcolonies running simultaneously on the independent compute nodes. The running times and objective function values obtained by the parallelized Artificial Bee Colony algorithm with the proposed model on different number of compute nodes are compared with the sequential counterpart of the algorithm and it is seen that convergence performance of the parallelized Artificial Bee Colony algorithm is significantly improved with the proposed emigrant creation strategy.

In this paper, a new flickermeter, which uses lamp illumination as its input and can compute flicker according to the IEC flickermeter standard for any kind of lamp other than the incandescent lamp, is presented. According to the IEC Standard 61000-4-15, flicker is calculated from input voltage and based on the assumption that the input voltage feeds an incandescent lamp. In this study, a system measuring light has been developed and the illumination of three different types of bulbs, with the same input voltage, has been investigated. Based on the results, IEC flickermeter algorithm, which is designed for the incandescent lamp assumption, has been updated to compute flicker any kind of lamp and a new flickermeter system based on the IEC standard has been developed. The developed flickermeter has been tested with the test signals recommended in the IEC standard and it has been shown that the developed flickermeter can accurately compute flicker.

In this paper, a computationally efficient method is proposed for modeling the indoor visible light communications (VLC) channels using a non-sequential ray tracing technique. We created three dimensional realistic simulation environment to depict indoor scenarios specifying the geometry of the indoor environment, the objects inside, the reflection characteristics of the surface materials as well as the characteristics of the transmitter and receiver. We then compute the received optical power and the delay of direct/indirect rays which are used to obtain the channel impulse response (CIR). Finally, LMS (least mean squares) and RLS (recursive least squares) iterative channel equalization techniques which are wildly employed in traditional electrical wireless communications are tested at the receiver of the VLC system. Their convergence rates as well as impacts on the BER performances are investigated by determining the optimal control parameters of these algorithms. It is concluded that for the equalizer operating with its optimal parameters, the BER performances of the VLC systems are very sensitivie to the field of view (FOV) values.

In this paper, Discrete-wavelet transform (DWT) based algorithm are used for speech signal denoising. Both hard and soft thresholding are used for denoising. Analysis are done on noisy speech signal corrupted by AGWN(Additive gaussian white noise) noise. Simulation and results are performed by using SignalLab application which developed for this research. MSE and SNR is used to compare results. Qt/C++ IDE is used to develop the application. This application developed for not only analyzing speech signal, but also can be used in an embedded system or a real-time applications. According to the obtained results, DWT is a good method for speech denoising when parameters are selected correctly.

24th Signal Processing and Communication Application Conference (SIU) (2016 : Zonguldak, TURKEY), Critical Infrastructure Security had always been an issue. With the technological advancements, there has been a continuous inflow of novel methods for critical infrastructure security. In this paper, design, implementation, and field tests of a novel critical infrastructure security system based on wireless sensor networks is presented.

24th Signal Processing and Communication Application Conference (SIU) -- MAY 16-19, 2016 -- Zonguldak, TURKEY, WOS: 000391250900110, In this paper, 11 different power quality disturbances were automatically detected by using statistical features with wavelet transform and norm entropy techniques. The best of the created features were selected with forward selection algorithm. Performance of classification algorithms, Support Vector Machines (SVM), Multi Layer Perceptron (MLP), k Nearest Neighbor (KNN) and random subspace KNN (Sub-KNN) which is an ensemble method, were examined. Consequently, the best classification accuracy of 99.3% was achieved by using Sub-KNN and it was appeared that compared to other methods, this algorithm was more robust against the noise., IEEE, Bulent Ecevit Univ, Dept Elect & Elect Engn, Bulent Ecevit Univ, Dept Biomed Engn, Bulent Ecevit Univ, Dept Comp Engn

In this paper, a new optical orthogonal frequency division multiplexing (OFDM) method is proposed for multiple-input multiple-output (MIMO) visible light communications (VLC) systems. In the new scheme, non-DC biased optical OFDM (NDC-OFDM) and optical spatial modulation (OSM) methods are combined to obtain a novel optical OFDM scheme which has a better error performance. The new scheme is named as non-DC biased optical spatial modulation OFDM (NDC-OSM-OFDM). It is shown via computer simulations that the proposed scheme achieves better bit error rate (BER) performance than DC-biased optical spatial modulation OFDM (OSM-OFDM-DC), asymmetrically clipped optical spatial modulation OFDM (OSM-OFDM-AC) and NDC-OFDM.

In this paper, an effective classification approach to classify harmonic data has been proposed. In the proposed classifier approach, harmonic data obtained through a 3-phase system have been classified by using $k$-means and least square support vector machine (LS-SVM) models. In order to obtain class details regarding harmonic data, a $k$-means clustering algorithm has been applied to these data first. The training of the LS-SVM model has been realized with the class details obtained through the $k$-means algorithm. To increase the efficiency of the LS-SVM model, the regularization and kernel parameters of this model have been determined with a grid search method and the training phase has been realized. Backpropagation neural network and J48 decision tree classifiers have been applied to the same data and results have been obtained for the purpose of comparing the performance of the LS-SVM model. The real data obtained from the output of distribution system have been used to assess the performance of the proposed classifier system. The obtained results and comparisons suggest that the proposed classifier system approach is quite efficient at classifying harmonic data.

In this paper, a recommendation system based on a mobile and web application is proposed for indoor decoration. The main contribution of this work is to apply two-stage filtering using linear matching and collaborative filtering to make recommendations. In the mobile application part, the image of the medium captured by a mobile phone is analyzed using color quantization methods, and these color analysis results along with other user-defined parameters such as height, width, and type of the product are sent to the web server. In the web application part, a large data set is first filtered via linear matching in which the color content of the medium and user-defined parameters received from the mobile application are matched to those for the products stored in the database. We then apply second-stage filtering, namely collaborative filtering, on the reduced data set. Performance evaluations of various color quantization methods and collaborative filtering methods used in the system are made. Results show the feasibility of using scalar quantization as a color quantization method and the K-nearest neighbor in the collaborative filtering method for our recommendation system. Overall evaluation of the system shows that our recommendation system provides around 90% accuracy.

Wireless monitoring networks can be used for security applications such as the monitoring of narrow passages and operational fields. These networks can be designed based on sensor networks. In sensor networks, each node can hear a message and broadcast the message to its neighbor nodes. Nevertheless, nodes may fail, so that faulty nodes cannot hear or cannot transmit any message, where the locations of the faulty nodes are unknown and their failures are permanent. In this paper, the nodes are situated on a line or a square grid-based topology in a plane for security/monitoring applications. For each topology, 2 nonadaptive and adaptive broadcasting scheduling algorithms are proposed and analyzed. In addition, the scheduling algorithms take the energy consumption of the sensor nodes into account in order to prolong the network's lifetime. The analysis results show that adaptive algorithms need less time than nonadaptive algorithms to inform the whole network domain.

The probabilistic neural network (PNN) is a special type of radial basis neural network used mainly for classification problems. Due to the size of the network after training, this type of network is usually used for problems with a small-sized training dataset. In this paper, a new training algorithm is presented for use with large training databases. Application to the handwritten digit database shows that the reduced PNN performs better than the standard PNN for all of the studied cases with a big gain in size and processing speed. This new type of neural network can be used easily for problems with large training databases like biometrics and data mining applications. An extension of the network is possible for new training samples and/or classes without retraining.

This paper proposes a reliability-based competitive generation resource planning model in electricity markets. The Monte Carlo simulation method is applied to consider random outages of generation units and transmission lines as well as load uncertainty. In order to determine the optimal plan for installation of candidate generating units, the decisions of generation companies (GenCos) and the independent system operators (ISOs) are investigated. The method is based on an iterative process for simulating interactivity among ISOs and GenCos and is repeated until security and reliability constraints assumed by the ISO are fulfilled. In the proposed model, the ISO utilizes a new mechanism for capacity payments such that each GenCo will receive its incentive credits based on its role in improvement of system reliability. In other words, GenCos play indirect roles in system reliability enhancement. Simulation results confirm efficacy of the proposed generation expansion planning model when considering uncertainties in electricity markets.

In this paper, a new throughput-based transmit antenna selection (AS) scheme is evaluated. First, a closed-form solution for the throughput rate of a multiple-input multiple-output (MIMO) system, with a truncated selective repeat automatic repeat request (TSR-ARQ) or selective repeat plus go-back-N automatic repeat request (SR+GBN-ARQ) protocol at the data link layer, is proposed. In addition, a throughput-based AS technique with adaptive modulation is also investigated. For this purpose, the throughput and packet error rates of a MIMO system with a transmit AS and TSR-ARQ protocol at the data link layer are analyzed. This study is conducted in 2 different cases. In the single-transmit AS case, the probability density functions (PDFs) of the throughput and packet error rates at the data link layer are derived. Moreover, in the case with multiple-transmit AS, a lower bound for the throughput rate and an upper bound for the packet error rate at the data link layer are determined. Consequently, the PDFs of these lower and upper bounds are extracted.

23nd Signal Processing and Communications Applications Conference (SIU) -- MAY 16-19, 2015 -- Inonu Univ, Malatya, TURKEY WOS: 000380500900192 Superpixels recently gained in importance in image segmentation and classification problems. In scene labeling the image is initially segmented into visually consistent small regions using a superpixel algorithm; then, superpixels are parsed into different classes. Classification performance heavily depends on the properties and parametric settings of the superpixel algorithm in use. In this paper, a method is proposed to improve scene labeling accuracy by fusing at classifier level the results of multiple superpixel segmentations. First, likelihood ratios are determined for superpixel labels using simple, nonparametric SuperParsing algorithm, which requires no training. Then, final scene segmentation and labeling is performed by pixel-level fusion of the likelihood ratios that are computed for alternative superpixel segmentation scenarios. The proposed method is tested on the SIFT Flow dataset consisting of 2,688 images and 33 labels, and is shown to outperform SuperParsing in terms of classification accuracy. Dept Comp Engn & Elect & Elect Engn, Elect & Elect Engn, Bilkent Univ

Image denoising is an important task in image processing. Various filtering techniques has been developed for image denoising but the algorithms have specific limitations. The main feature of a good image denoising technique is that it will remove noise while preserving structure of image. In this paper, we present a new image denoising method based on averaging. The denoising performance is measured according to PSNR (Peak Signal-to-Nosie Ratio) metric by employing experiments. Experimental results show that the proposed method removes noise significantly and preserve image edges.

Speech processing is working area where the speech signal is digitized and processed. In this paper, it is used LSVT (Lee Silverman Voice Treatment) dataset that belonging to the people living speech disorder because of the Parkinson disease. The dataset contains a large number of features. A large number of features can be occurred negative effect on classification because of noise or less important features. Especially, it is suggested that k nearest neighbor is weighted to reduce this effect distance based classifiers. Therefore, it is benefited from Laplacian score to weight features. Consequently, classification accuracy is increased from 73.61% to 85.83% for k nearest neighbor classifier.

One of the well-known recurrent neural networks is the Elman network. Recently, it has been used in applications of system identification. The network has feedforward and feedback connections. It can be trained essentially as a feedforward network by means of the basic backpropagation algorithm, but its feedback connections have to be kept constant. For training success, it is important to select the correct values for the feedback connections. However, finding these values manually can be a lengthy trial-and-error process. This paper investigates the use of the simulated annealing (SA) algorithm to obtain the weight values of both the feedforward and feedback connections of Elman networks used for dynamic system identification. The SA algorithm is an efficient random search procedure, which can simultaneously obtain the optimal weight values of both the feedforward and feedback connections.

Social networking sites form a special type of virtual community where we share our personal information with people and develop new relationships on the Internet. These sites allow the users to share just about everything, including photos, videos, favorite music, and games, and record all user interactions and retain them for potential use in social data mining. This storing and sharing of large amounts of information causes privacy problems for the users of these websites. In order to prevent these problems, we have to provide strict privacy policies, data protection mechanisms, and trusted and built-in applications that help to protect user privacy by limiting the people who get access to a user's personal information. Thus, the privacy problem has prompted us to provide a solution that offers the users of these social networking websites an opportunity to protect their information. In this paper, a social networking application and its system design, algorithm, and database structure are described. Our application offers a reputation-based trusted architecture to social network users. It creates and monitors social reputations, finds social circles, and helps the users to group their friends easily, meaningfully, and automatically to protect their privacy. This system provides the grouping of users through an automated system into different social circles by analyzing the user's social connections depending on what common information or application they share that should not be accessed by other users.

Due to copyright restrictions, the access to the full text of this article is only available via subscription. The fast changes and developments in the world's economy have substantially increased energy consumption. Consequently, energy planning has become more critical and important. Forecasting is one of the main tools utilized in energy planning. Recently developed computational techniques such as genetic algorithms have led to easily produced and accurate forecasts. In this paper, a natural gas consumption forecasting methodology is developed and implemented with state-of-the-art techniques. We show that our forecasts are quite close to real consumption values. Accurate forecasting of natural gas consumption is extremely critical as the majority of purchasing agreements made are based on predictions. As a result, if the forecasts are not done correctly, either unused natural gas amounts must be paid or there will be shortages of natural gas in the planning periods.

The subject of this paper includes the effect of MAC performance analysis for IEEE 802.15.4 protocol with BCH coding. To analyse the performance, data transmissions are made for different communication environments with different scenarious using BCH coding. Performance results are observed as changing values on MAC parameters. These parameters are composed of average transmission time, transmitted data amount and packet loss.

In present times, automatic computer interpretation of medical images is of great importance for medical diagnosis. Having previously been an obstacle for medical science, early stage diagnoses together with accurately calculating the size and level of disease are very important elements of today's treatment process. The treatment and diagnosis of brain tumors depend on successful processing and evaluation of magnetic resonance (MR) brain images. Image segmentation is the most intractable process, because of the complicated nature of the MR brain images. In processing and analyzing MR images, there is unsuccessful automatic systems or successful semiautomatic system, which is still required large scale experting involvement and assessment. Within this paper the aim is to propose a system that will automatically extract the location, volume and borders of a brain tumor from patient MRI brain images. To achieve this goal, an automated algorithm has been developed, based upon and derived from a successful semi-automatic hybrid algorithm, for automation of the tumor diagnosis process. Patients' MR brain images from varying stages of the diagnosis and treatment were used for testing and validating the automatic algorithm under development. Based upon the achieved results of this algorithm and method, an automatic, sensitive MR brain computer interpretation can be proposed.

22nd IEEE Signal Processing and Communications Applications Conference (SIU) -- APR 23-25, 2014 -- Karadeniz Teknik Univ, Trabzon, TURKEY B. Akar, Gozde/0000-0002-4227-5606 WOS: 000356351400423 In the last two decades the Internet technology has boosted and the connection speeds have been incrased from kilobits to hundred megabits scale. With the rising coverage of the Internet and the usage of mobile devices such as tablets and smart phones, the usage of social media and especially multimedia elements has been increased rapidly. This increment in streaming multimedia created a need for the assessment of the user experience on multimedia and especially video. Even though there are different Video Quality Assessment (VQA) methods for that purpose, most of them are Full-Reference (FR) or Reduced-Reference (RR). In today's world with many mobile devices, the application of these methods are not possible since they need the reference data. The No-Reference (NR) video metrics are much more suitable for the case. In this paper, the main objective is to evaluate a previously proposed NR VQA metric with a new dataset and to compare the results to other high-performance NR metrics such as G.1070 and G.1070E which do not utilize spatial and temporal characteristics of a given video sequence. Evaluation and comparison results show the accuracy and robustness of the proposed metric. IEEE, Karadeniz Tech Univ, Dept Comp Engn & Elect & Elect Engn