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Öğe A Color Image Watermarking Scheme Based on Hybrid Classification Method Particle Swarm Optimization and K-Nearest Neighbor Algorithm(Elsevier Science Bv, 2010) Fındık, Oğuz; Babaoğlu, İsmail; Ülker, ErkanIn this paper a novel robust watermarking technique using particle swarm optimization and k-nearest neighbor algorithm is introduced to protect the intellectual property rights of color images in the spatial domain In the embedding process the color image is separated into non-overlapping blocks and each bit of the binary watermark is embedded into the individual blocks Then in order to extract the embedded watermark features are obtained from watermark embedded blocks using the symmetric cross-shape kernel These features are used to generate two centroids belonging to each binary (1 and 0) value of the watermark implementing particle swarm optimization Subsequently the embedded watermark is extracted by evaluating these centroids utilizing k-nearest neighbor algorithm. According to the test results embedded watermark is extracted successfully even if the watermarked image is exposed to various image processing attacksÖğe A Comparison of Artificial Intelligence Methods on Determining Coronary Artery Disease(Springer-Verlag Berlin, 2010) Babaoğlu, İsmail; Baykan, Ömer Kaan; Aygül, Nazif; Özdemir, Kurtuluş; Bayrak, MehmetThe aim of this study is to show a comparison of multi-layered perceptron neural network (MLPNN) and support vector machine (SVM) on determination of coronary artery disease existence upon exercise stress testing (EST) data. EST and coronary angiography were performed on 480 patients with acquiring 23 verifying features from each. The robustness of the proposed methods is examined using classification accuracy, k-fold cross-validation method and Cohen's kappa coefficient. The obtained classification accuracies are approximately 78% and 79% for MLPNN and SVM respectively. Both MLPNN and SVM methods are rather satisfactory than human-based method looking to Cohen's kappa coefficients. Besides, SVM is slightly better than MLPNN when looking to the diagnostic accuracy, average of sensitivity and specificity, and also Cohen's kappa coefficient.Öğe A Comparison of Feature Selection Models Utilizing Binary Particle Swarm Optimization and Genetic Algorithm in Determining Coronary Artery Disease Using Support Vector Machine(Pergamon-Elsevier Science Ltd, 2010) Babaoğlu, İsmail; Fındık, Oğuz; Ülker, ErkanThe aim of this study is to search the efficiency of binary particle swarm optimization (BPSO) and genetic algorithm (CA) techniques as feature selection models on determination of coronary artery disease (CAD) existence based upon exercise stress testing (EST) data. Also, increasing the classification performance of the classifier is another aim. The dataset having 23 features was obtained from patients who had performed EST and coronary angiography. Support vector machine (SVM) with k-fold cross-validation method is used as the classifier system of CAD existence in both BPSO and CA feature selection techniques. Classification results of feature selection technique using BPSO and CA are compared with each other and also with the results of the whole features using simple SVM model. The results show that feature selection technique using BPSO is more successful than feature selection technique using CA on determining CAD. Also with the new dataset composed by feature selection technique using BPSO, this study reached more accurate values of success on CAD existence research with more little complexity of classifier system and more little classification time compared with whole features used SVM.Öğe A Digital Robust Image Watermarking Against Desynchronization Attacks(Academic Journals, 2010) Fındık, Oğuz; Babaoğlu, İsmail; Ülker, ErkanIn this paper, a robust image watermarking technique using support vector regression (SVR) and particle swarm optimization is introduced to protect intellectual property rights of the gray images in discrete cosine transform domain against a variety of desynchronization attacks. After the division of the original image to 8 x 8 non-overlapping blocks, frequency coefficients of each block are found using discrete cosine transform. Positions of the inputs and output, among the low frequency coefficients which have the significant characteristics of the image, which are used to train SVR are obtained by using particle swarm optimization technique. After SVR is trained using the obtained positions of the inputs and output, watermark embedding and extracting processes are implemented using the trained SVR. Experiments implemented using the optimized coefficients selected among low frequency coefficients show that our watermarking technique has better watermark extracting success after the desynchronization attacks.Öğe Effects of Discretization on Determination of Coronary Artery Disease Using Support Vector Machine(2009) Babaoğlu, İsmail; Fındık, Oğuz; Ülker, ErkanIn this paper, the effect of discretization on determination of coronary artery disease using exercise stress test data by support vector machine classification method is investigated. The study dataset is obtained from cardiology department of Meram faculty of medicine including 480 patients having 23 features. Four classification models are composed. In the first model, the data is classified simply by normalizing it into [-1,1] range. In the second, third and fourth models, the data is classified by employing entropy-MDL, equal width and equal frequency discretization methods on it respectively. Support vector machine is used as the classifier for all classification models. The results show that classification performance of the model implemented by entropy-MDL discretization has the best value.Öğe Effects of Feature Selection Using Binary Particle Swarm Optimization on Wheat Variety Classification(Springer-Verlag Berlin, 2010) Babalık, Ahmet; Baykan, Ömer Kaan; İşcan, Hazim; Babaoğlu, İsmail; Fındık, OğuzIn this article, classification of wheat varieties is aimed with the help of multiclass support vector machines (M-SVM) and binary particle swarm optimization (BPSO) algorithm. For each wheat kernel, 9 geometric and 3 color features are obtained from the digital images which are belong to 5 wheat type. Wheat types are classified using M-SVM. In order to increase the reliability of the classification process, 2 fold cross validation approach is implemented and this process repeated 250 times. Average classification accuracy is obtained as 91.5%. With the aim of increasing the classification accuracy and decreasing the process time, descriptive features are decreased by BPSO algorithm and reduced from 12 to 7. Average of classification accuracy is obtained as 92.02% using 7 features obtained from reduction with BPSO.Öğe Effects of Principle Component Analysis on Assessment of Coronary Artery Diseases Using Support Vector Machine(Pergamon-Elsevier Science Ltd, 2010) Babaoğlu, İsmail; Fındık, Oğuz; Bayrak, MehmetArtificial intelligence techniques are being effectively used in medical diagnostic support tools to increase the diagnostic accuracy and to provide additional knowledge to medical stuff. Effects of principle component analysis on the assessment of exercise stress test with support vector machine in determination of coronary artery disease are studied in this work. Study dataset consist of 480 patients with 23 features for each patient. By reducing study dataset with principle component analysis method, optimum support vector machine model is found for each reduced dimension. According to the obtained results, optimum support vector machine model in which the dataset is reduced to 18 features with principle component analysis is more accurate than optimum support vector machine model which uses the whole 23 featured dataset. Besides, principle component analysis implementation decreases the training error and the sum of the training and test times.Öğe Gri kurt optimizasyon algoritmasına dayanan çok seviyeli imge eşik seçimi(2018) Koç, İsmail; Baykan, Ömer Kaan; Babaoğlu, İsmailÇoklu eşikleme, görüntü işleme ve örüntü tanıma için önemli bir görüntü işleme tekniğidir. Optimal bir eşik değerinin seçimi görüntü eşiklemede en ciddi aşamalardan birisidir. İki seviye segmentasyon eşik değeri yardımıyla orijinal resmi iki alt bölüme ayırmayı içerirken, çoklu segmentasyon çok eşik değerlerini içermektedir. Özellikle çok seviyeli görüntü eşiklemede, detaylı araştırmaya ilişkin hesaplama zamanı tercih edilen eşik sayısıyla birlikte üstel olarak artmaktadır. Zor problemler için, sürü zekâsı başarılı ve etkili optimizasyon metotlarından biri olarak bilinmektedir. Bu çalışmada, doğadaki gri kurtların sosyal liderlik ve avcılık davranışlarını taklit eden son zamanlarda önerilmiş sürü tabanlı meta sezgisel olan gri kurt algoritması (GWO) çok seviyeli görüntü eşikleme probleminin çözümü için kullanılmaktadır. Standart test resimleri üzerinde yapılan deneysel sonuçlar GWO algoritmasının diğer modern algoritmalarla karşılaştırılabilir olduğunu göstermektedir.Öğe Koroner arter hastalığı ve lezyon lokalizasyonu tahminlerinde efor stres testinin yapay zeka yöntemleri ile değerlendirilmesi(Selçuk Üniversitesi Fen Bilimleri Enstitüsü, 2010-07-26) Babaoğlu, İsmail; Ülker, ErkanEgzersiz stres testi (EST), koroner arter hastalığının (KAH) tanısında, yaygınlığının değerlendirilmesinde ve prognozunun (hastalığın seyri ve gelişimi hakkında tahmin) tayininde yaygın olarak kullanılan kolay uygulanabilir, ucuz ve invazif (girişimsel) olmayan bir tanı aracıdır. Bu özellikleri nedeniyle klinisyenler tarafından yaygın olarak kullanılmakla birlikte özellikle düşük duyarlılık (%45-92) ve özgüllüğünün (%17-92) yanında lezyon lokalizasyonu hakkında bilgi vermemesi EST'nin kullanımını sınırlayan en önemli kısıtlılıklarıdır. EST'nin değerlendirilmesinde çok damar hastalığı veya ana koroner arter lezyonları için yüksek risk kriterleri tanımlanmış olmakla birlikte hangi damarda lezyon olduğu söylenememektedir. Bunun tek istisnası EST sırasında ST segment yüksekliği görülmesidir, fakat bu oldukça nadir görülen bir durumdur. Günümüzde kalp hastalıklarının teşhis edilmesinde invazif ve invazif olmayan tekniklerle elde edilen verilerin yapay zeka yöntemleri ile değerlendirildiği çalışmalar yapılmaktadır. Yapay zeka ve makine öğrenmesi algoritmalarının gelişmesi, karma algoritmaların verimliliğinin artması lineer olmayan sistemlerde karar destek imkanını gerçeğe daha çok yaklaştırmaktadır. Bu tez çalışmasında her geçen gün tıp alanında daha geniş kullanım imkanı bulan yapay zeka yöntemlerinin EST verilerine dayanarak KAH varlığı ve lezyon lokalizasyonunu belirlemedeki başarısının ve etkinliğinin araştırılması amaçlanmıştır. Çalışma ile sadece KAH'nın tanısı değil lezyon varsa, mevcut lezyonun lokalizasyonunun da belirlenebilmesi amaçlanmıştır. Gerçekleştirilen çeşitli yapay zeka ve makine öğrenmesi modelleri ve ayrıca bu modellerle birlikte uygulanan çeşitli özellik seçim yöntemleri ile EST verilerine dayanarak KAH tahmininde ve lezyon lokalizasyonunu belirlemede konvansiyonel yöntemlerden daha yüksek duyarlılık, özgüllük ve tanısal doğruluk değerleri elde edilmiştir. Aynı zamanda bu çalışma, EST'nin lezyon lokalizasyonunun belirlenmesinde kullanıldığı sınırlı çalışmalardan biridir.Öğe Thyroid Disease Diagnosis Using Artificial Immune Recognition System (AIRS)(2009) Kodaz, Halife; Babaoğlu, İsmail; İşcan, HazimThe use of artificial intelligence methods in medical diagnosis is increasing gradually. The effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Artificial Immune Systems (AIS) is a new but effective branch of artificial intelligence. This study aims at diagnosing thyroid disease with Artificial Immune Recognition System (AIRS). Thyroid disease diagnosis is an important classification problem. The thyroid data employed in this study is available from UCI Repository site. This data set is a very commonly used data set in the literature relating the use of classification systems for thyroid disease diagnosis and it was used in this study to compare the classification performance of AIRS with regard to other studies. We obtained a classification accuracy of 94.82%, which is one of the highest accuracies reached so far. This result ensured that AIRS would be helpful in diagnosing thyroid function based on laboratory tests, and would open the way to various ill diagnoses support by using the recent clinical examination data, and we are actually in progress.Öğe Watermarking Schema Using an Artificial Immune System in Spatial Domain(2009) Fındık, Oğuz; Babaoğlu, İsmail; Ülker, ErkanIn this paper, we propose a robust watermarking method for image copyright protection in spatial domain based on artificial immune system (AIS). Our method optimizes robustness and imperceptibility which are known to be inversely proportional to each other. The robustness of our watermark method was tested extensively against attacks by lossy JPEG compression. Performance of proposed method was evaluated by comparing with the genetic watermarking method and also the randomly chosen block based watermark method. The experiments indicate that our method is better at robustness over genetic watermarking method. We have shown that our method improves the robustness and imperceptibility comparing with the randomly chosen block based watermarking.
