Browsing by Author "Tokdemir, Gul"

Now showing 1 - 20 of 24

Citation - WoS: 0
Citation - Scopus: 0
A case study on web-based information system evaluation
(Acad Conferences Ltd, 2014) Tokdemir, Gul; Bilgen, Semih; Ercil, Yavuz; 17411; Bilgisayar Mühendisliği
A 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.
Citation - WoS: 0
Citation - Scopus: 1
A user task design notation for improved software design
(Peerj inc, 2021) Ozcan, Eda; Topalli, Damla; Tokdemir, Gul; Cagiltay, Nergiz Ercil; 17411; Bilgisayar Mühendisliği
System design is recognized as one of the most critical components of a software system that bridges system requirements and coding. System design also has a significant impact on testing and maintenance activities, and on further improvements during the lifespan of the software system. Software design should reflect all necessary components of the requirements in a clear and understandable manner by all stakeholders of the software system. To distinguish system elements, separation of concerns in software design is suggested. In this respect, identification of the user tasks, i.e., the tasks that need to be performed by the user, is not currently reflected explicitly in system design documents. Our main assumption in this study is that software quality can be improved significantly by clearly identifying the user tasks from those that need to be performed by the computer system itself. Additionally, what we propose has the potential to better reflect the user requirements and main objectives of the system on the software design and thereby to improve software quality. The main aim of this study is to introduce a novel notation for software developers in the frame of UML Activity Diagram (UML-AD) that enables designers to identify the user tasks and define them separately from the system tasks. For this purpose, an extension of UML-AD, named UML-ADE (UML-Activity Diagram Extended) was proposed. Afterwards, it was implemented in a serious game case for which the specification of user tasks is extremely important. Finally, its effectiveness was analyzed and compared to UML-AD experimentally with 72 participants. The defect detection performance of the participants on both diagrams with two real-life serious game scenarios was evaluated. Results show a higher level of understandability for those using UML-ADE, which in turn may indicate a better design and higher software quality. The results encourage researchers to develop specific design representations dedicated to task design to improve system quality and to conduct further evaluations of the impact of these design on each of the above mentioned potential benefits for the software systems.
Citation - WoS: 180
Citation - Scopus: 264
Adoption of e-government services in Turkey
(Pergamon-elsevier Science Ltd, 2017) Kurfali, Murathan; Arifoglu, Ali; Tokdemir, Gul; Pacin, Yudum; 17411; Bilgisayar Mühendisliği
This research aims to investigate underlying factors that play role in citizens' decision to use e-government services in Turkey. UTAUT model which was enriched by introducing Trust of internet and Trust of government factors is used in the study. The model is evaluated through a survey conducted with Turkish citizens who are from different regions of the country. A total of 529 answers collected through purposive sampling and the responses were evaluated with the SEM (Structural Equation Modeling) technique. According to the results, Performance expectancy, Social influence, Facilitating conditions and Trust of Internet were found to have a positive effect on behavioral intention to use e-government services. Additionally, both Trust factors were found to have a positive influence on Performance expectancy of e-government services, a relation which, to our best knowledge, hasn't been tested before in e-government context. Effect of Effort expectancy and Trust of government were found insignificant on behavioral intention. We believe that the findings of this study will guide professionals and policy makers in improving and popularizing e-government services by revealing the citizen's priorities regarding e-government services in Turkey. (C) 2016 Elsevier Ltd. All rights reserved.
Citation - WoS: 2
Citation - Scopus: 1
An Experimental Study On Decomposition: Process First or Structure First?
(Springer international Publishing Ag, 2019) Cetinkaya, Anil; Suloglu, Selma; Kaya, M. Cagri; Karamanlioglu, Alper; Tokdemir, Gul; Dogru, Ali H.; 17411; Bilgisayar Mühendisliği
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.
Citation - WoS: 0
Citation - Scopus: 0
Analysis of Neurooncological Data to Predict Success of operation Through Classification
(Assoc Computing Machinery, 2016) Bagherzadi, Negin; Borcek, Alp Ozgun; Tokdemir, Gul; Cagiltay, Nergiz; Maras, H. Hakan; 17411; Bilgisayar Mühendisliği; Yazılım Mühendisliği
Data mining algorithms have been applied in various fields of medicine to get insights about diagnosis and treatment of certain diseases. This gives rise to more research on personalized medicine as patient data can be utilized to predict outcomes of certain treatment procedures. Accordingly, this study aims to create a model to provide decision support for surgeons in Neurooncology surgery. For this purpose, we have analyzed clinical pathology records of Neurooncology patients through various classification algorithms, namely Support Vector Machine, Multi Perceptron and Naive Bayes methods, and compared their performances with the aim of predicting surgery complication. A large number of factors have been considered to classify and predict percentage of patient's complication in surgery. Some of the factors found to be predictive were age, sex, clinical presentation, previous surgery type etc. For classification models built up using Support Vector Machine, Naive Bayes and Multi Perceptron, Classification trials for Support Vector Machine have shown %77.47 generalization accuracy, which was established by 5-fold cross-validation.
Citation - WoS: 0
Citation - Scopus: 0
A Classifier for Automatic Categorisation of Chronic Venous Insufficiency Images
(Kaunas Univ Technology, 2024) Karadeniz, Talha; Tokdemir, Gul; Maras, H. Hakan; Yazılım Mühendisliği
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).
Citation - WoS: 9
Citation - Scopus: 10
Diagnosis of osteoarthritic changes, loss of cervical lordosis, and disc space narrowing on cervical radiographs with deep learning methods
(Turkish Joint Diseases Foundation, 2022) Maras, Yuksel; Tokdemir, Gul; Ureten, Kemal; Atalar, Ebru; Duran, Semra; Maras, Hakan; 17411; 34410; Bilgisayar Mühendisliği
Objectives: In this study, we aimed to differentiate normal cervical graphs and graphs of diseases that cause mechanical neck pain by using deep convolutional neural networks (DCNN) technology. Materials and methods: In this retrospective study, the convolutional neural networks were used and transfer learning method was applied with the pre-trained VGG-16, VGG-19, Resnet-101, and DenseNet-201 networks. Our data set consisted of 161 normal lateral cervical radiographs and 170 lateral cervical radiographs with osteoarthritis and cervical degenerative disc disease. Results: We compared the performances of the classification models in terms of performance metrics such as accuracy,
Citation - WoS: 2
Citation - Scopus: 2
Gan-Based Novel Approach for Generating Synthetic Medical Tabular Data
(Mdpi, 2024) Nasimov, Rashid; Nasimova, Nigorakhon; Mirzakhalilov, Sanjar; Tokdemir, Gul; Rizwan, Mohammad; Abdusalomov, Akmalbek; Cho, Young-Im
The generation of synthetic medical data has become a focal point for researchers, driven by the increasing demand for privacy-preserving solutions. While existing generative methods heavily rely on real datasets for training, access to such data is often restricted. In contrast, statistical information about these datasets is more readily available, yet current methods struggle to generate tabular data solely from statistical inputs. This study addresses the gaps by introducing a novel approach that converts statistical data into tabular datasets using a modified Generative Adversarial Network (GAN) architecture. A custom loss function was incorporated into the training process to enhance the quality of the generated data. The proposed method is evaluated using fidelity and utility metrics, achieving "Good" similarity and "Excellent" utility scores. While the generated data may not fully replace real databases, it demonstrates satisfactory performance for training machine-learning algorithms. This work provides a promising solution for synthetic data generation when real datasets are inaccessible, with potential applications in medical data privacy and beyond.
Citation - WoS: 1
Citation - Scopus: 1
Graph-based visualization of stochastic dominance in statistical comparisons
(Ieee, 2019) Ertek, Gurdal; Tokdemir, Gul; Hammoudi, Mohamad Mustafa; 17411; Bilgisayar Mühendisliği
In this paper, a graph visualization scheme and methodology is proposed for representing, understanding, and interpreting the statistical comparison of means and the resulting stochastic dominance. The practicality and applicability of the visualization scheme and the methodology is illustrated through a case study, with data coming from higher education institutes in the United States of America (U.S.A.). The objective of the research is to make statistical results more accessible and readable, enabling the visual derivation of actionable insights.
Home-Based Working During Covid-19 Pandemic: Experience of Turkish Software Professionals
(2021) Tokdemir, Gul; 17411; Bilgisayar Mühendisliği
Bu çalışma, Covid-19 salgını sırasında yazılım profesyonellerinin evden çalışma deneyimlerini araştırmaktadır. Bir anket aracılığıyla, bu tür çalışma ortamlarının özellikleriyle ilişkili olarak evden çalışmanın zorlukları incelenmiştir. Ayrıca, iki değişkenli analiz yoluyla, ev tabanlı çalışma özellikleri ile üretkenlik arasındaki ilişki araştırılmıştır. Bu çalışmanın sonuçları, yazılım profesyonellerinin pandemi döneminde daha uzun saatler çalıştıklarını ve evden çalışma ortamına adapte olmanın çoğunlukla kolay olduğunu göstermektedir. Evden çalışma ortamlarında ev işleri ve çocukların en önemli kesinti nedeni olduğu bildirilmiştir. Ayrıca yazılım profesyonelleri için öğleden sonraları ve sabahların en verimli çalışma aralıkları olduğu belirtilmiştir.
Citation - WoS: 1
Citation - Scopus: 1
Influence of Gamification on Skill-Based Training of Surgical Residents
(Serious Games Soc, 2025) Topalli, Damla; Tokdemir, Gul; Cagiltay, Nergiz Ercil
Potentially games increase motivation and thus support the learning process. Gamification effect on different skill levels of surgical residents was limitedly studied. This study aims to better understand the effect of motivation gained through gamification on simulation-based surgical training environments for novice and intermediate surgical residents' performances. An educational scenario with a haptic interface is designed in two versions: gamified and nongamified. The tasks are performed twice, with the dominant and non-dominant hands resemble the task difficulty. 26 novice and intermediate surgical residents were randomly assigned to one of the groups (gamified or nongamified). Gamification positively improved novice surgical residents' performances under both hand conditions. However, surprisingly, in some situations, results indicated lower performance by the intermediates compared to the novices. A flow model for this specific scenario is proposed. To benefit the gamification effect, learners' skill levels and content should be carefully assessed and balanced on simulation-based surgical skill training materials.
Citation - WoS: 2
Citation - Scopus: 3
Investigating the Relationship Between SLOC and Logical Database Measures to Improve the Early Estimation of Software Cost
(World Scientific Publ Co Pte Ltd, 2019) Tokdemir, Gul; Cagiltay, Nergiz Ercil; 17411; Bilgisayar Mühendisliği
Project planning is a critical activity in the software development life cycle. At the early stages of a project, the managers need to estimate required time, effort and cost to plan, track and then to deliver the project successfully. Many studies have attempted to provide methods for precise software cost estimation. The current software cost estimation methods are mainly based on software size estimation and functional system requirements. The main assumption of this study is that, as the primary source of complexity in today's software is the interaction between the database and the user, database measures may provide inputs allowing current software estimation methods to achieve more accurate results. Accordingly, this study attempts to gain insights from objective measures, collected through the logical database model of software systems, for better prediction of the software's effort and hence cost through software lines of code (SLOC) measure. For this purpose, more than 2.5 million lines of code developed by four different companies, for 79 different software packages with their related database design measures, are analyzed. The results of this study show that there is a close correlation between the software size and database design measure, namely, the number of tables which can be collected at the logical database design stage. By adapting this result, the current estimation models could be improved significantly.
Citation - WoS: 1
Citation - Scopus: 0
Neuronavigation Systems and Passive Usage Problem
(Ieee, 2015) Cagiltay, Nergiz; Topalli, Damla; Borcek, Alp Ozgun; Tokdemir, Gul; Maras, Hakan; Tonbul, Gokcen; Aydin, Elif; 17411; Yazılım Mühendisliği; Bilgisayar Mühendisliği; Elektrik-Elektronik Mühendisliği
Nowadays, neuronavigation systems are used in brain surgery procedures, known as a technology to help the surgeon during the operational period. However, the surgeons have faced several problems with the existing systems. Some of these problems are related to the systems software and user interfaces. In this study, such problems are examined and the "Passive Usage" term is added to the literature by establishing a connection between the problems of endoscopic surgical procedures and similar issues occurred in other domains. The passive usage problem is generalized on different domains for the first time with this study. The results of the study expected to gather up the similar passive usage problems experienced in different domains. Accordingly, the methodologies and studies that are conducted in different research areas may lead to eliminate the Passive Usage problems efficiently.
Citation - WoS: 13
Citation - Scopus: 19
New knowledge in strategic management through visually mining semantic networks
(Springer, 2017) Ertek, Gurdal; Tokdemir, Gul; Sevinc, Mete; Tunc, Murat Mustafa; 17411; Bilgisayar Mühendisliği
Today's highly competitive business world requires that managers be able to make fast and accurate strategic decisions, as well as learn to adapt to new strategic challenges. This necessity calls for a deep experience and a dynamic understanding of strategic management. The trait of dynamic understanding is mainly the skill of generating additional knowledge and innovative solutions under the new environmental conditions. Building on the concepts of information processing, this paper aims to support managers in constructing new strategic management knowledge, through representing and mining existing knowledge through graph visualization. To this end, a three-stage framework is proposed and described. The framework can enable managers to develop a deeper understanding of the strategic management domain, and expand on existing knowledge through visual analysis. The model further supports a case study that involves unstructured knowledge of profit patterns and the related strategies to succeed using these patterns. The applicability of the framework is shown in the case study, where the unstructured knowledge in a strategic management book is first represented as a semantic network, and then visually mined for revealing new knowledge.
Citation - WoS: 1
Citation - Scopus: 4
Patient Safety & Clinical Decision Support Systems (CDSS): A Case Study in Turkey
(Ieee, 2015) Menekse, Gonca Gokce; Cagiltay, Nergiz Ercil; Tokdemir, Gul; 17411; Bilgisayar Mühendisliği
Decision making process is crucial in several stages of clinical procedures. On the other hand, there are not many studies showing the implications of decision support systems in clinical environments. Hence, adaptation of Decision Support Systems to clinical environment is getting more important as we can collect more data through sensors and yet cannot use it effectively in decision making process. This study aims to understand the effects, benefits and obstacles utilizing CDSS in healthcare. For this purpose, 60 CDSS studies were analyzed to better understand their purpose, implementation domain, and success degrees in the world. Also, a case study was made for analyzing the situation in Turkey. The results show that in the field of neurosurgery, the level of analysis of neurosurgical data in Turkey is very low. The results show an urgent need for collaboration of IT experts and medical authorities to better record and analyze clinical data in the field of neurosurgery.
Citation - WoS: 20
Citation - Scopus: 36
Performing and analyzing non-formal inspections of entity relationship diagram (ERD)
(Elsevier Science inc, 2013) Cagiltay, Nergiz Ercil; Tokdemir, Gul; Kilic, Ozkan; Topalli, Damla; 17411; Bilgisayar Mühendisliği
Designing and understanding of diagrammatic representations is a critical issue for the success of software projects because diagrams in this field provide a collection of related information with various perceptual signs and they help software engineers to understand operational systems at different levels of information system development process. Entity relationship diagram (ERD) is one of the main diagrammatic representations of a conceptual data model that reflects users' data requirements in a database system. In today's business environment, the business model is in a constant change which creates highly dynamic data requirements which also requires additional processes like modifications of ERD. However, in the literature there are not many measures to better understand the behaviors of software engineers during designing and understanding these representations. Hence, the main motivation of this study is to develop measures to better understand performance of software engineers during their understanding process of ERD. Accordingly, this study proposes two measures for ERD defect detection process. The defect detection difficulty level (DF) measures how difficult a defect to be detected according to the other defects for a group of software engineers. Defect detection performance (PP) measure is also proposed to understand the performance of a software engineer during the defect detection process. The results of this study are validated through the eye tracker data collected during the defect detection process of participants. Additionally, a relationship between the defect detection performance (PP) of a software engineer and his/her search patterns within an ERD is analyzed. Second experiment with five participants is also conducted to show the correlation between the proposed metric results and eye tracker data. The results of experiment-2 also found to be similar for DF and PP values. The results of this study are expected to provide insights to the researchers, software companies, and to the educators to improve ERD reasoning process. Through these measures several design guidelines can be developed for better graphical representations and modeling of the information which would improve quality of these diagrams. Moreover, some reviewing instructions can be developed for the software engineers to improve their reviewing process in ERD. These guidelines in turn will provide some tools for the educators to improve design and review skills of future software engineers. (c) 2013 Elsevier Inc. All rights reserved.
REZES Nesnelerin İnterneti Tabanlı Geri Dönüşüm Uygulama Sistemleri
(2021) Uguz, Sezer; Tokdemir, Gul; 17411; Mekatronik Mühendisliği; Bilgisayar Mühendisliği
Nüfus 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.
Citation - WoS: 15
Citation - Scopus: 23
Software 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, Gul; 17411; Bilgisayar Mühendisliği
With 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.
Citation - WoS: 8
Citation - Scopus: 11
The Diagnosis of Developmental Dysplasia of the Hip From Hip Ultrasonography Images With Deep Learning Methods
(Lippincott Williams & Wilkins, 2023) Atalar, Hakan; Ureten, Kemal; Tokdemir, Gul; Tolunay, Tolga; Ciceklidag, Murat; Atik, Osman Sahap; 17411; Bilgisayar Mühendisliği
Background:Hip ultrasonography is very important in the early diagnosis of developmental dysplasia of the hip. The application of deep learning-based medical image analysis to computer-aided diagnosis has the potential to provide decision-making support to clinicians and improve the accuracy and efficiency of various diagnostic and treatment processes. This has encouraged new research and development efforts in computer-aided diagnosis. The aim of this study was to evaluate hip sonograms using computer-assisted deep-learning methods. Methods:The study included 376 sonograms evaluated as normal according to the Graf method, 541 images with dysplasia and 365 images with incorrect probe position. To classify the developmental hip dysplasia ultrasound images, transfer learning was applied with pretrained VGG-16, ResNet-101, MobileNetV2 and GoogLeNet networks. The performances of the networks were evaluated with the performance parameters of accuracy, sensitivity, specificity, precision, F1 score, and AUC (area under the ROC curve). Results:The accuracy, sensitivity, specificity, precision, F1 score, and AUC results obtained by testing the VGG-16, ResNet-101, MobileNetV2, and GoogLeNet models showed performance >80%. With the pretrained VGG-19 model, 93%, 93.5%, 96.7%, 92.3%, 92.6%, and 0.99 accuracy, sensitivity, specificity, precision, F1 score, and AUC results were obtained, respectively. Conclusion:In this study, in addition to the ultrasonography images of dysplastic and healthy hips, images were also included of probe malpositioning, and these images were able to be successfully evaluated with deep learning methods. On the sonograms, which provided criteria appropriate for evaluation, successful differentiation could be made of healthy hips and dysplastic hips.
Citation - WoS: 1
Citation - Scopus: 0
The effect of population and tourism factors on Covid-19 cases in Italy: Visual data analysis and forecasting approach
(Wiley, 2022) Uguz, Sezer; Yaganoglu, Mete; Ozyer, Baris; Ozyer, Gulsah Tumuklu; Tokdemir, Gul; 17411; Mekatronik Mühendisliği; Bilgisayar Mühendisliği
At the beginning of 2020, the new coronavirus disease (Covid-19), a deadly viral illness, is declared as a public health emergency situation by WHO. Consequently, it is accepted as pandemic that affected millions of people worldwide. Italy is one of the most affected countries by Covid-19 disease among the world. In this article, our main goal is to investigate the effect of intensity of Covid-19 cases based on the population size and tourism factors in certain regions of Italy by visual data analysis. The regions of Lombardia, Veneto, Campania, Emilia-Romagna, Piemonte are the top five regions covering 58.50% of the total Covid-19 cases diagnosed in Italy. It has been shown by visual data analysis that population and tourism factors play an important role in the spread of Covid-19 cases in these five regions. In addition, a prediction model was created using Bi-LSTM and ARIMA algorithms to forecast the number of Covid-19 cases occurring in these five regions in order to take early action. We can conclude that these northern regions have been affected mostly by Covid-19 and the distribution of the resident population and tourist flow factors affected the number of Covid-19 cases in Italy.