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Karadeniz, Talha

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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
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Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
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Sustainable Development Goals

11

SUSTAINABLE CITIES AND COMMUNITIES
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3

GOOD HEALTH AND WELL-BEING
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2

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9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
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6

CLEAN WATER AND SANITATION
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LIFE BELOW WATER
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12

RESPONSIBLE CONSUMPTION AND PRODUCTION
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DECENT WORK AND ECONOMIC GROWTH
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NO POVERTY
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QUALITY EDUCATION
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GENDER EQUALITY
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REDUCED INEQUALITIES
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PEACE, JUSTICE AND STRONG INSTITUTIONS
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LIFE ON LAND
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AFFORDABLE AND CLEAN ENERGY
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CLIMATE ACTION
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PARTNERSHIPS FOR THE GOALS
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ZERO HUNGER
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This researcher does not have a Scopus ID.
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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

WoS h-index

2

Scopus h-index

2

Patents

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Projects

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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
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    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.
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    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.
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    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.
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    Conference Object
    Spam Detection With Fasttext Based Features
    (Institute of Electrical and Electronics Engineers Inc., 2024) Karadeniz, T.; Tokdemir, G.; Maraş, H.H.
    Fasttext is a powerful word representation method that creates word representations based on vectors of character n-grams. In this work, we propose a method that utilizes fasttext features for a novel feature engineering model for the spam detection problem. In the feature engineering method, the combination of average, mean of second derivative; mean peak and standard deviation of fasttext features are computed. Finally, tf-idf features are also considered for the modeling process. The success of each feature engineering technique is measured and reported. The combination of the five feature extraction methods, tested on two spam detection datasets, yielded promising results with an accuracy of 0.978 on e-mail spam detection and an accuracy of 0.986 on sms spam classification. © 2024 IEEE.
  • Thumbnail Image
    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.
  • Thumbnail Image
    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
  • Thumbnail Image
    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.
  • Thumbnail Image
    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.
  • Thumbnail Image
    Article
    A Classifier for Automatic Categorisation of Chronic Venous Insufficiency Images
    (Kaunas Univ Technology, 2024) Karadeniz, Talha; Tokdemir, Gul; Maras, H. Hakan; Hakan Maras, H.
    Chronic 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).
  • Thumbnail Image
    Conference Object
    Covariance Features for Trajectory Analysis
    (Institute of Electrical and Electronics Engineers Inc., 2016) Karadeniz, T.; Maras, H.H.
    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. © 2017 Elsevier B.V., All rights reserved.