Tokdemir, Gül
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Tokdemir, Gul
Tokdemir, G.
Tokdemir, G.
Job Title
Doç. Dr.
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gtokdemir@cankaya.edu.tr
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Bilgisayar Mühendisliği
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Current Staff
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Scholarly Output
46
Articles
18
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1854/913
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2
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0
WoS Citation Count
309
Scopus Citation Count
444
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7
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6.72
Scopus Citations per Publication
9.65
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16
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2
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Now showing 1 - 10 of 46
Conference Object Guis With Haptic Interfaces(Springer Verlag, 2015) Ozcelik, E.; Tuner, E.; Sahin, H.; Tokdemir, G.; Arda Aydin, M.; Cagiltay, N.E.While there are many studies regarding utilization of Haptic feedback to enhance desktop GUIs and utilizing Haptic devices as additional interfaces to improve performance in current interaction techniques, there are not many studies that uses Haptic device as a primary input device. In this study, we present an experimentation conducted with 30 students, comparing performance of a Haptic device with mouse to use a GUI elements commonly used with mouse gestures. This study is inspired by a system that utilizes both mouse and a Haptic device, thus also taking task switching into consideration. We conclude that it is possible to achieve an acceptable performance with a Haptic device in a desktop-like GUI but further study and experimentation is necessary. © Springer International Publishing Switzerland 2015.Conference Object An Experimental Study on Decomposition: Process First or Structure First?(2019) Çetinkaya, Anıl; Suloğlu, Selma; Kaya, M. Çağrı; Karamanlıoğlu, Alper; Tokdemir, Gül; Doğru, Ali H.This article explores the answer to the question of considering the process or the structure dimensions earlier, in software development where decomposition is a preferred technique for top-down model construction. In this research, an experimental study was conducted to observe which software modeling practice is more convenient: process or structural modeling, for the beginning. The study was conducted in different courses that include software modeling where students work within groups to model a system with predefined requirements. The students used Business Process Modeling Notation and Component-Oriented Software Engineering Modeling Language modeling tools. Observations based on the results are analyzed and discussed. The results seem to prioritize the process dimension.Conference Object Topic-Aware Multi-Class Classification for Financial Complaints: Comparing BERTopic With Classical Machine Learning Algorithms(Institute of Electrical and Electronics Engineers Inc., 2025) Uguz, Sezer; Kumbasar, Mert; Tokdemir, GulIn today's digital world, customers can utilize a variety of communication channels, such as business emails, consumer forms, feedback platforms, and dedicated complaint websites, to communicate their complaints. This study compares the performance of the supervised Bidirectional Encoder Representations from Transformers for Topic Modeling (BERTopic) with traditional machine learning algorithms, including Random Forest (RF), Support Vector Machines (SVM), Logistic Regression (LR), Naive Bayes (NB), K-nearest Neighbors (KNN), and eXtreme Gradient Boosting (XGBoost), for multi-class classification of financial customer complaints. The dataset consists of 16,715 balanced training data and 3,808 test data across five different categories, with the financial complaint data. Experimental results demonstrate that traditional machine learning models, particularly XGBoost, SVM, and LR, achieved the highest classification performance with accuracy rates close to 88%. BERTopic showed a competitive performance with an accuracy of 82.48%. The results suggest that while BERTopic offers interpretability advantages through topic modeling techniques, traditional algorithms provide higher accuracy. This study highlights the promising potential for future financial text analysis and customer complaint classification using hybrid methods, which could lead to more detailed, topic-aware classification approaches. © 2025 IEEE.Master Thesis Makine Öğrenimi ile Siyanoakrilat Yapıştırıcı Ameliyatı Sonrası Varis Tekrarının Tahminine Yönelik Model Geliştirilmesi(2025) Ahmed, Ruaa Saad Ahmed; Tokdemir, GülVaris hastalığı, yaygın görülen bir vasküler bozukluk olup, sıklıkla siyanoakrilat yapıştırıcı tedavisi gibi minimal invaziv yöntemlerle tedavi edilmektedir. Ancak, nüks önemli bir sorun olmaya devam etmekte ve tedavi sonrası prognozun iyileştirilmesi için öngörücü modellerin geliştirilmesini zorunlu kılmaktadır. Bu çalışma, siyanoakrilat yapıştırıcı tedavisini takiben varis hastalığının nüksünü tahmin etmek amacıyla makine öğrenmesi tabanlı bir öngörü modeli oluşturmayı hedeflemektedir. Bu kapsamda, on yıllık bir dönemi kapsayan ve 430 hastaya ait ultrason raporları, kan test sonuçları ve kronik hastalık göstergelerini içeren bir veri seti bir tıp merkezinden temin edilmiştir. Veri ön işleme sürecinde eksik veriler tamamlanmış, SMOTE ve SMOTEENN yöntemleri kullanılarak dengesiz veri sınıfları dengelenmiştir. Öznitelik seçimi için RFE yöntemi uygulanmış ve karar ağaçları tabanlı önem sıralaması hesaplanmıştır. Çalışmada lojistik regresyon, karar ağaçları, destek vektör makineleri, Random Forest, XGBoost ve CatBoost gibi farklı sınıflandırıcılar eğitilmiş ve test edilmiştir. Eğitim ve test aşamaları için veriler %80 eğitim, %20 test olarak bölünmüş ve 5 katlı çapraz doğrulama yöntemi kullanılmıştır. Model performansı doğruluk (accuracy), kesinlik (precision), duyarlılık (recall), F1-skoru ve ROC-AUC gibi değerlendirme metrikleri ile ölçülmüştür. Elde edilen vii sonuçlar, CatBoost ve XGBoost yöntemlerinin diğer sınıflandırıcılara kıyasla çok daha yüksek performans gösterdiğini ortaya koymaktadır. Venöz ölçümler, kronik hastalık göstergeleri ve belirli kan test parametreleri, klinik karar sürecini iyileştirebilecek en önemli öngörücü değişkenler arasında yer almaktadır. Geliştirilen model, yüksek riskli hastaların belirlenmesine yardımcı olarak erken müdahale stratejilerinin geliştirilmesini sağlayacaktır. Ancak, bu çalışmanın en önemli sınırlamalarından biri, yalnızca tek bir kuruma ait hasta verilerine dayanmasıdır. Gelecekteki çalışmalar, modelin daha geniş ve çeşitli veri kümeleri üzerinde doğrulanmasını sağlamalı ve tahmin doğruluğunu daha da iyileştirmek için derin öğrenme teknikleri ve çok modlu veri kaynaklarının entegrasyonunu araştırmalıdır. Bu araştırma, makine öğrenmesinin vasküler hastalık yönetimindeki potansiyelini vurgulamakta ve klinik uygulamalarda veri odaklı ilerlemelerin önünü açmaktadır.Article Rezes Nesnelerin İnterneti Tabanlı Geri Dönüşüm Uygulama Sistemleri(2021) Uguz, Sezer; Tokdemir, GulNüfus artışı ve plansız sanayileşmenin sonucunda oluşan çevre kirliliği, insanoğlunun neden olduğu en büyük sorunlardan birisidir ve her geçen gün canlı ve cansız varlıklara olan olumsuz etkisi artarak devam etmektedir. Çevre kirliliğinin oldukça büyük bir kısmını oluşturan plastik, cam ve teneke kutu gibi geri dönüştürülmesi mümkün olan katı atıkların doğaya bırakılması sonucunda toprak ve su kirliliği meydana gelmektedir. Bu çalışmada sunulan sistem ile çevre kirliliği problemine yenilikçi bir çözüm getirilerek, geri dönüşümün akıllı bir şekilde yapılması hem ekonomik katma değer sağlayıp hem de çevre kirliliğinin önlenmesi amaçlanmaktadır. REZES (Yenilenebilir Enerji Sıfır Enerji İsrafı) sistemi, Nesnelerin İnterneti, Görüntü İşleme, Büyük Veri Analizi ve Oyunlaştırma gibi en yeni teknoloji ve metotların kullanılmasıyla akıllı bir geri dönüşüm sistemi sunmaktadır. Böylelikle plastik, cam ve teneke kutu gibi katı atıkların geri dönüştürülmesi konusuna yenilikçi bir çözüm getirilmektedir.Conference Object A Case Study on Web-Based Information System Evaluation(Acad Conferences Ltd, 2014) Tokdemir, Gul; Tokdemir, Gül; Bilgen, Semih; Ercil, Yavuz; Bilgisayar MühendisliğiA new framework is proposed to assess web-based information systems (WISs) which is domain-independent, that is, can be applied for profit seeking as well as service oriented or non-profit seeking organizations. Assessment starts from an identification of the critical success factors (CSF) that outline organizational strategies, and proceeds to determine the measures of three categories of relationships: User-WIS, Other systems-WIS, Organization-WIS. These measures and CSF's are evaluated collectively to arrive at an effectiveness measure. A case study illustrating the applicability of the assessment framework in the e-business domain is presented.Article Citation - WoS: 17Citation - Scopus: 24Software Professionals During the Covid-19 Pandemic in Turkey: Factors Affecting Their Mental Well-Being and Work Engagement in the Home-Based Work Setting(Elsevier Science inc, 2022) Tokdemir, GulWith the COVID-19 pandemic, strict measures have been taken to slow down the spread of the virus, and consequently, software professionals have been forced to work from home. However, home based working entails many challenges, as the home environment is shared by the whole family simultaneously under pandemic conditions. The aim of this study is to explore software professionals' mental well-being and work engagement and the relationships of these variables with job strain and resource-related factors in the forced home-based work setting during the COVID-19 pandemic. An online cross-sectional survey based on primarily well-known, validated scales was conducted with software professionals in Turkey. The analysis of the results was performed through hierarchical multivariate regression. The results suggest that despite the negative effect of job strain, the resource related protective factors, namely, sleep quality, decision latitude, work-life balance, exercise predict mental well-being. Additionally, work engagement is predicted by job strain, sleep quality, and decision latitude. The results of the study will provide valuable insights to management of the software companies and professionals about the precautions that can be taken to have a better home-based working experience such as allowing greater autonomy and enhancing the quality of sleep and hence mitigating the negative effects of pandemic emergency situations on software professionals' mental well-being and work engagement. (C)& nbsp;2022 Elsevier Inc. All rights reserved.Article Citation - WoS: 3Citation - Scopus: 7Two Majority Voting Classifiers Applied To Heart Disease Prediction(Mdpi, 2023) Karadeniz, Talha; Maras, Hadi Hakan; Tokdemir, Gul; Ergezer, HalitTwo 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 A Classifier for Automatic Categorisation of Chronic Venous Insufficiency Images(Kaunas Univ Technology, 2024) Karadeniz, Talha; Tokdemir, Gul; Maras, H. HakanChronic 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).Conference Object Multimodal interaction flow representation for ubiquitous environments - MIF: A case study in surgical navigation interface design(2015) Tokdemir, Gül; Altun, Gamze; Çağıltay, Nergiz E.; Maras, H. Hakan; Börcek, Alp ÖzgünWith the advent of technology, new interaction modalities became available which augmented the system interaction. Even though there are vast amount of applications for the ubiquitous devices like mobile agents, smart glasses and wearable technologies, many of them are hardly preferred by users. The success of those systems is highly dependent on the quality of the interaction design. Moreover, domain specific applications developed for these ubiquitous devices involve detailed domain knowledge which normally IT professionals do not have, which may involve a substantial lack of quality in the services provided. Hence, effective and high quality domain specific applications developed for these ubiquitous devices require significant collaboration of domain experts and IT professionals during the development process. Accordingly, tools to provide common communication medium between domain experts and IT professionals would provide necessary medium for communication. In this study, a new modelling tool for interaction design of ubiquitous devices like mobile agents, wearable devices is proposed which includes different interaction modalities. In order to better understand the effectiveness of this newly proposed design tool, an experimental study is conducted with 11 undergraduate students (novices) and 15 graduate students (experienced) of Computer Engineering Department for evaluating defect detection performance for the defects seeded into the interface design of a neuronavigation device. Results show that the defects were realized as more difficult for the novices and their performance was lower compared to experienced ones. Considering the defect types, wrong information and wrong button type of defects were recognized as more difficult. The results of this study aimed to provide insights for the system designers to better represent the interaction design details and to improve the communication level of IT professionals and the domain experts. © Springer International Publishing Switzerland 2015.
