Bilgisayar Mühendisliği Bölümü Yayın Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/253
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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: 1Using Text Mining for Research Trends in Empirical Software Engineering(Gazi Univ, 2021) Tokdemir, GulThis paper intends to examine the research trends in Empirical Software Engineering domain within the last two decades using text mining. It studies published articles in the relevant literature with an emphasis on abstracts of 10658 articles published in the literature on Experimental Software Engineering domain. Using a probabilistic topic modelling technique (Latent Dirichlet Allocation), it brings forward the main topics of research within this domain. By further analysis, the paper evaluates the changes of focus in published works in the last two decades and depicts the recent trends in research content wise. Through a timely comparison, it portrays the alteration of interest within empirical software engineering research and proposes a future research agenda to develop an advanced field, beneficial both for academics and practitioners.Article Citation - WoS: 19Citation - Scopus: 27Software 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: 2The Effect of Population and Tourism Factors on Covid-19 Cases in Italy: Visual Data Analysis and Forecasting Approach(Wiley, 2022) Ozyer, Baris; Ozyer, Gulsah Tumuklu; Tokdemir, Gul; Uguz, Sezer; Yaganoglu, MeteAt 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.Article Covid-19 Salgını Sırasında Evden Çalışma: Türk Yazılım Profesyonellerinin Deneyimleri(2021) Tokdemir, GulBu 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.Article Citation - WoS: 11Citation - Scopus: 12Diagnosis of Osteoarthritic Changes, Loss of Cervical Lordosis, and Disc Space Narrowing on Cervical Radiographs With Deep Learning Methods(Turkish Joint Diseases Foundation, 2022) Tokdemir, Gul; Ureten, Kemal; Atalar, Ebru; Duran, Semra; Maras, Hakan; Maras, YukselObjectives: 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,Article Citation - WoS: 3Citation - Scopus: 9Two 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 Citation - WoS: 13Citation - Scopus: 15The Diagnosis of Developmental Dysplasia of the Hip From Hip Ultrasonography Images With Deep Learning Methods(Lippincott Williams & Wilkins, 2023) Ureten, Kemal; Tokdemir, Gul; Tolunay, Tolga; Ciceklidag, Murat; Atik, Osman Sahap; Atalar, HakanBackground: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.Article Citation - WoS: 22Citation - Scopus: 37Performing and Analyzing Non-Formal Inspections of Entity Relationship Diagram (Erd)(Elsevier Science inc, 2013) Tokdemir, Gul; Kilic, Ozkan; Topalli, Damla; Cagiltay, Nergiz ErcilDesigning 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.Conference Object Citation - WoS: 2Citation - Scopus: 1An Experimental Study on Decomposition: Process First or Structure First(Springer international Publishing Ag, 2019) Suloglu, Selma; Kaya, M. Cagri; Karamanlioglu, Alper; Tokdemir, Gul; Dogru, Ali H.; Cetinkaya, Anil; Cagri Kaya, M.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.