Karadeniz, Talha
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Dr. Öğr. Üyesi
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talhakaradeniz1@cankaya.edu.tr
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Yazılım Mühendisliği
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Current Staff
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Scholarly Output
8
Articles
8
Citation Count
10
Supervised Theses
2
8 results
Scholarly Output Search Results
Now showing 1 - 8 of 8
Doctoral Thesis Ensemble methods for heart disease prediction(2022) Karadeniz, Talha; Yazılım MühendisliğiBu çalışma otomatik kalp hastalığı tahmini için ensemble metotları içermektedir; bu kritik sağlık işlemi birçok yeni algoritma ile gerçekleştirilmiştir. Birincisi, ikili dizilerin rastgelelik analizine göre bir taban tahmincisi geliştirilmiştir. İkincisi, sıkıştırılmış kovaryans tahmini metotlarına dayalı başka bir sınıflandırıcı tanıtılmıştır. Üçüncüsü, kurtosis ve KS-test önem şemasına göre şekillenen bir sınıflandırıcı geliştirilmiştir. Son olarak, lojistik regresyon, çoğunluk oy uygulamasına ve olasılık yoğunluk tahminine dayalı sınıflandırıcı şemalarımız ile birleştirilmiştir. Bu son sınıflandırıcı, state-of-the-art metotlar ile karşılaştırılmış ve elde edilen isabet oranları raporlanmıştır.Article Citation - WoS: 2Citation - Scopus: 2Improvement of DWT-SVD with Curve Fitting and Robust Regression: An Application to Astronomy Images(Kaunas Univ Technology, 2016) Karadeniz, Talha; Karadeniz, Talha; Elbasi, Ersin; 304886; Yazılım MühendisliğiDWT-SVD is a frequency domain based eigenanalysis watermarking technique. In this work, we improve this method by exploring the relationship between the cover image's DWT singular values and those of the watermark. We show that, via the usage of curve fitting and robust regression, it is possible to achieve accurate results. We also demonstrate that the improved scheme is suitable for the watermarking of astronomy images. In addition to encoding and decoding examples, statistical results on stealth and robustness are deduced from the experiments so that the clear advance can be observed. Quality of the watermark is measured by testing against various attack types.Article Citation - WoS: 0Citation - Scopus: 0A Classifier for Automatic Categorisation of Chronic Venous Insufficiency Images(Kaunas Univ Technology, 2024) Karadeniz, Talha; Tokdemir, Gul; Maras, H. Hakan; Yazılım MühendisliğiChronic venous insufficiency (CVI) is a serious disease characterised by the inability of the veins to effectively return blood from the legs back to the heart. This condition represents a significant public health issue due to its prevalence and impact on quality of life. In this work, we propose a tool to help doctors effectively diagnose CVI. Our research is based on extracting Visual Geometry Group network 16 (VGG-16) features and integrating a new classifier, which exploits mean absolute deviation (MAD) statistics to classify samples. Although simple in its core, it outperforms state-of-the-art method which is known as the CVI-classifier in the literature, and additionally it performs better than the methods such as multi-layer perceptron (MLP), Naive Bayes (NB), and gradient boosting machines (GBM) in the context of VGG-based classification of CVI. We had 0.931 accuracy, 0.888 Kappa score, and 0.916 F1-score on a publicly available CVI dataset which outperforms the state-of-the-art CVI-classifier having 0.909, 0.873, and 0.900 for accuracy, Kappa score, and F1-score, respectively. Additionally, we have shown that our classifier has a generalisation capacity comparable to support vector machines (SVM), by conducting experiments on eight different datasets. In these experiments, it was observed that our classifier took the lead on metrics such as F1-score, Kappa score, and receiver operating characteristic area under the curve (ROC AUC).Article Citation - WoS: 3Citation - Scopus: 5Two Majority Voting Classifiers Applied to Heart Disease Prediction(Mdpi, 2023) Karadeniz, Talha; Maraş, Hadi Hakan; Karadeniz, Talha; Maras, Hadi Hakan; Tokdemir, Gül; Tokdemir, Gul; Ergezer, Halit; Ergezer, Halit; 34410; 293396; Bilgisayar Mühendisliği; Mekatronik Mühendisliği; Yazılım MühendisliğiTwo novel methods for heart disease prediction, which use the kurtosis of the features and the Maxwell-Boltzmann distribution, are presented. A Majority Voting approach is applied, and two base classifiers are derived through statistical weight calculation. First, exploitation of attribute kurtosis and attribute Kolmogorov-Smirnov test (KS test) result is done by plugging the base categorizer into a Bagging Classifier. Second, fitting Maxwell random variables to the components and summating KS statistics are used for weight assignment. We have compared state-of-the-art methods to the proposed classifiers and reported the results. According to the findings, our Gaussian distribution and kurtosis-based Majority Voting Bagging Classifier (GKMVB) and Maxwell Distribution-based Majority Voting Bagging Classifier (MKMVB) outperform SVM, ANN, and Naive Bayes algorithms. In this context, which also indicates, especially when we consider that the KS test and kurtosis hack is intuitive, that the proposed routine is promising. Following the state-of-the-art, the experiments were conducted on two well-known datasets of Heart Disease Prediction, namely Statlog, and Spectf. A comparison of Optimized Precision is made to prove the effectiveness of the methods: the newly proposed methods attained 85.6 and 81.0 for Statlog and Spectf, respectively (while the state of the heart attained 83.5 and 71.6, respectively). We claim that the Majority Voting family of classifiers is still open to new developments through appropriate weight assignment. This claim is obvious, especially when its simple structure is fused with the Ensemble Methods' generalization ability and success.Master Thesis Writer identification based on covariance features(Çankaya Üniversitesi, 2016) Karadeniz, Talha; Yazılım MühendisliğiLocal descriptors have been widely utilized in image analysis for automatic object categorization. In this work, an algorithm based on empirical covariance estimation of region descriptor vectors is formulated and developed. This technique is then specialized in order solve to the task of writer identification via a tricky way of keypoint extraction. Experiment results are reported for ETH-80 and ICFHR 2012 Writer Identification Contest datasets.Article Covariance Features for Trajectory Analysis(Kaunas Univ Technology, 2018) Karadeniz, Talha; Maraş, Hadi Hakan; 304886; 34410; Yazılım Mühendisliği; Bilgisayar MühendisliğiIn this work, it is demonstrated that covariance estimator methods can be used for trajectory classification. It is shown that, features obtained via shrunk covariance estimation are suitable for describing trajectories. Compared to Dynamic Time Warping, application of explained technique is faster and yields more accurate results. An improvement of Dynamic Time Warping based on counting statistical comparison of base distance measures is also achieved. Results on Australian Sign Language and Character Trajectories datasets are reported. Experiment realizations imply feasibility through covariance attributes on time series.Article Citation - WoS: 0Citation - Scopus: 0Covariance Features for Trajectory Analysis(Kaunas Univ Technology, 2018) Karadeniz, Talha; Karadeniz, Talha; Maras, Hakan Hadi; Yazılım MühendisliğiIn this work, it is demonstrated that covariance estimator methods can be used for trajectory classification. It is shown that, features obtained via shrunk covariance estimation are suitable for describing trajectories. Compared to Dynamic Time Warping, application of explained technique is faster and yields more accurate results. An improvement of Dynamic Time Warping based on counting statistical comparison of base distance measures is also achieved. Results on Australian Sign Language and Character Trajectories datasets are reported. Experiment realizations imply feasibility through covariance attributes on time series.Conference Object Citation - WoS: 3Citation - Scopus: 3Improvement of General Inquirer Features with Quantity Analysis(Ieee, 2018) Karadeniz, Talha; Karadeniz, Talha; Dogdu, Erdogan; Doğdu, Erdoğan; Yazılım Mühendisliği; Bilgisayar MühendisliğiGeneral Inquirer is a word-affect association vocabulary having 11896 entries. Ranging from rectitude to expressiveness, it comes with a flavor of categories. Despite the extensive content, a mapping from "To be or not to be." to "How much?" can be beneficial for word representation. In this work, we apply a method of window based analysis to obtain real valued General Inquirer attributes. Sentence Completion task is chosen to calculate the effectiveness of the operation. After whitening post-process, total cosine similarity convention is followed to concentrate on embedding improvement. Results indicate that our quantity focused variant is considerable.
