Karadeniz, Talha

Profile URL
https://hdl.handle.net/20.500.12416/8876
Name Variants
Karadeniz, T.
Job Title
Dr. Öğr. Üyesi
Email Address
talhakaradeniz1@cankaya.edu.tr
Main Affiliation
Yazılım Mühendisliği
Status
Current Staff
Website
ORCID ID
0000-0002-1252-7593
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Files
01791.jpeg (656.9 KB)

Sustainable Development Goals

NO POVERTY1
NO POVERTY
0
Research Products
ZERO HUNGER2
ZERO HUNGER
0
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GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
2
Research Products
QUALITY EDUCATION4
QUALITY EDUCATION
0
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GENDER EQUALITY5
GENDER EQUALITY
0
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CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
0
Research Products
AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
0
Research Products
DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
0
Research Products
REDUCED INEQUALITIES10
REDUCED INEQUALITIES
0
Research Products
SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
0
Research Products
RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
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CLIMATE ACTION13
CLIMATE ACTION
0
Research Products
LIFE BELOW WATER14
LIFE BELOW WATER
0
Research Products
LIFE ON LAND15
LIFE ON LAND
0
Research Products
PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
0
Research Products
This researcher does not have a Scopus ID.
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No records found in other affiliations.
Scholarly Output

13

Articles

6

Views / Downloads

1522/6371

Supervised MSc Theses

1

Supervised PhD Theses

1

WoS Citation Count

8

Scopus Citation Count

15

Patents

0

Projects

0

WoS Citations per Publication

0.62

Scopus Citations per Publication

1.15

Open Access Source

7

Supervised Theses

2

JournalCount
Elektronika ir Elektrotechnika4
-- 24th Signal Processing and Communication Application Conference, SIU 2016 -- Zonguldak -- 1226051
24th Signal Processing and Communication Application Conference (SIU) -- MAY 16-19, 2016 -- Zonguldak, TURKEY1
Applied Sciences1
ICCR 2025 - 3rd International Conference on Cyber Resilience -- 3rd International Conference on Cyber Resilience, ICCR 2025 -- 3 July 2025 through 4 July 2025 -- Dubai -- 2181151
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Scholarly Output Search Results

Now showing 1 - 10 of 13
  • Doctoral Thesis
    Ensemble methods for heart disease prediction
    (2022) Karadeniz, Talha
    Bu 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: 3
    Citation - Scopus: 9
    Two Majority Voting Classifiers Applied To Heart Disease Prediction
    (Mdpi, 2023) Karadeniz, Talha; Maras, Hadi Hakan; Tokdemir, Gul; Ergezer, Halit
    Two 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.
  • Article
    Detection and Classification of Femoral Neck Fractures From Plain Pelvic X-Rays Using Deep Learning and Machine Learning Methods
    (Turkish Assoc Trauma Emergency Surgery, 2025) Sevinc, Huseyin Fatih; Ureten, Kemal; Karadeniz, Talha; Gultekin, Gokhan Koray
    Background: Femoral neck fractures are a serious health concern, particularly among the elderly. The aim of this study is to diagnose and classify femoral neck fractures from plain pelvic X-rays using deep learning and machine learning algorithms, and to compare the performance of these methods. Methods: The study was conducted on a total of 598 plain pelvic X-ray images, including 296 patients with femoral neck fractures and 302 individuals without femoral neck fractures. Initially, transfer learning was applied using pre-trained deep learning models: VGG-16, ResNet-50, and MobileNetv2. Results: The pre-trained VGG-16 network demonstrated slightly better performance than ResNet-50 and MobileNetV2 for detecting and classifying femoral neck fractures. Using the VGG-16 model, the following results were obtained: 95.6% accuracy, 95.5% sensitivity, 93.3% specificity, 95.7% precision, 95.5% F1 Score, a Cohen's kappa of 0.91, and the Receiver Operating Characteristic (ROC) curve of 0.99. Subsequently, features extracted from the convolution layers of VGG-16 were classified using common machine learning algorithms. Among these, the k-nearest neighbor (k-NN) algorithm outperformed the others and exceeded the accuracy of the VGG-16 model by 1%. Conclusion: Successful results were obtained using deep learning and machine learning methods for the detection and classification of femoral neck fractures. The model can be further improved through multi-center studies. The proposed model may be especially useful for physicians working in emergency departments and for those not having sufficient experience in evaluating plain pelvic radiographs.
  • Conference Object
    Citation - WoS: 3
    Citation - Scopus: 4
    Improvement of General Inquirer Features With Quantity Analysis
    (Ieee, 2018) Karadeniz, Talha; Dogdu, Erdogan
    General 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.
  • Conference Object
    Predicting Varicose Vein Recurrence Post-Cyanoacrylate Glue Surgery Using Machine Learning Models
    (Institute of Electrical and Electronics Engineers Inc., 2025) Karadeniz, Talha; Ahmed, Ruaa Saad Ahmed; Enver, Levent; Sungur, Elif Coskun; Tokdemir, Gul
  • Article
    Citation - WoS: 2
    Citation - Scopus: 2
    Improvement of Dwt-Svd With Curve Fitting and Robust Regression: an Application To Astronomy Images
    (Kaunas Univ Technology, 2016) Elbasi, Ersin; Karadeniz, Talha
    DWT-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.
  • Master Thesis
    Writer identification based on covariance features
    (Çankaya Üniversitesi, 2016) Karadeniz, Talha
    Local 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; Maras, Hakan Hadi
    In 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
    Covariance Features for Trajectory Analysis
    (IEEE, 2016) Karadeniz, Talha; Maras, Hadi Hakan
    In this work, we aimed to demonstrate that covariance estimation methods can be used for trajectory classification. We have shown that, features obtained via shrunk covariance estimation are suitable for describing trajectories. We have arrived to the conclusion that, when compared to Dynamic Time Warping, the explained technique is faster and may yield more accurate results.
  • Article
    Covariance Features for Trajectory Analysis
    (Kaunas Univ Technology, 2018) Karadeniz, Talha; Maraş, Hadi Hakan
    In 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.