Bilgisayar Mühendisliği Bölümü Yayın Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/253
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Book Part Büyük Veri Mahremiyeti ve Güvenliği(Grafiker Yayınları, 2017) Saran, Ayşe NurdanBilim geliştikçe teknolojiler gelişmekte ve yeni teknolojilerde bilimin daha da gelişmesine ve bilinmezleri daha iyi anlamamıza, yeni çalışmalar yapmamıza en önemlisi çevremizi ve dünyamızı daha iyi anlamamızı kolaylaştırmaktadır. Son yıllarda “büyük veri”, “veri bilimi”, “açık veri” “büyük veri analitiği”, “bilgi ekonomisi” gibi başlıklar ülkemizde de pek çok etkinlikte tartışılmakta, çözümler geliştirilmeye çalışılmakta ve iyi örnekler oluşturulmaya çalışılmaktadır. Bu kitabın ülkemizde açık veri ve büyük veri analitiği, güvenliği ve mahremiyetinin gelişmesine katkılar sağlaması beklenmektedir. Verilerin günümüzün altın rezervleri olduğunun bilinciyle çalışmalar yapılmalıdır.Book Part Citation - Scopus: 1Text-Based Fake News Detection Via Machine Learning(Springer Science and Business Media Deutschland GmbH, 2021) Genç, B.; Sever, H.; Mertoğlu, U.The nature of information literacy is changing as people incline more towards using digital media to consume content. Consequently, this easier way of consuming information has sparked off a challenge called “Fake News”. One of the risky effects of this notorious term is to influence people’s views of the world as in the recent example of coronavirus misinformation that is flooding the internet. Nowadays, it seems the world needs “information hygiene” more than anything. Yet real-world solutions in practice are not qualified to determine verifiability of the information circulating. Presenting an automated solution, our work provides an adaptable solution to detect fake news in practice. Our approach proposes a set of carefully selected features combined with word-embeddings to predict fake or valid texts. We evaluated our proposed model in terms of efficacy through intensive experimentation. Additionally, we present an analysis linked with linguistic features for detecting fake and valid news content. An overview of text-based fake news detection guidance derived from experiments including promising results of our work is also presented in this work. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.Book Part Model analytics for defect prediction based on design-level metrics and sampling techniques(Academic Press, 2020) Kaya, Aydın; Keçeli, Ali Seydi; Çatal, Çağatay; Tekinerdoğan, BedirPredicting software defects in the early stages of the software development life cycle, such as the design and requirement analysis phase, provides significant economic advantages for software companies. Model analytics for defect prediction lets quality assurance groups build prediction models earlier and predict the defect-prone components before the testing phase for in-depth testing. In this study, we demonstrate that Machine Learning-based defect prediction models using design-level metrics in conjunction with data sampling techniques are effective in finding software defects. We show that design-level attributes have a strong correlation with the probability of defects and the SMOTE data sampling approach improves the performance of prediction models. When design-level metrics are applied, the Adaboost ensemble method provides the best performance to detect the minority class samples.Book Part Variability Incorporated Simultaneous Decomposition of Models under Structural and Procedural Views(CRC Press, 2019) Kaya, M. Çağrı; Tokdemir, Gül; Suloğlu, Selma; Tekinerdoğan, MetinThis chapter presents hierarchical variability as an important development notion especially when considered together with a systems specification through decomposition. A matured domain-specific environment is the precondition for variability-centric engineering for compositional approaches as targeted in this study: Most of the requirements have been already modeled, and most of the problem domain elements have corresponding reusable solutions. Also, a mature domain enjoys a wide community of developers who are familiar with those problems and solution-space elements and an effective set of specific tools. Decomposition is a fundamental mechanism in many approaches for the specification of various dimensions of modeling. Decomposition of especially structure modeling for software is not new. Here, variability guidance is incorporated into both structure and process decomposition. This chapter combines such notions in the demonstration of variabilitycentric development suggesting a structural and procedural decomposition of the system. The predecessors, component-oriented approaches rely on the structural decomposition whereas service-oriented development is being supported by process decomposition. A vending machine case study is presented in this chapter for demonstrating the propagation of variability specification along with the enhancements of the componentoriented model and the process model.Book Part Enterprise Architecture for Personalization of E-Government Services: Reflections From Turkey(Igi Global, 2012) Medeni, Ihsan Tolga; Medeni, Tunc D.; Erdem, AlpayAs there has not yet been enough work on enterprise architectures for fully integrated knowledge-based, highly-sophisticated (citizen-oriented) personalized services, this chapter aims to articulate a perspective to design architectures for the development and provision of sophisticated, personalized services. Doing so, the authors benefit from their knowledge and experience in the Turkish e-Government Gateway (eGG) and general e-Government services development and provision. First providing an introduction and background information, the chapter discusses the development of eGG services in Turkey, and then provides a visionary suggestion for knowledge-based personalized, citizen-centric e-Government. Among the suggested perspectives, an E-Citizen Decision Support System, and Entity-Utility and Information Flow Model could be useful for eGG development in Turkey and elsewhere.Book Part Parallelization of sparsity-driven change detection method(IEEE, 2017) Özgür, Atilla; Saran, Ayşe Nurdan; Nar, FatihIn this study, Sparsity-driven Change Detection (SDCD) method, which has been proposed for detecting changes in multitemporal synthetic aperture radar (SAR) images, is parallelized to reduce the execution time. Parallelization of the SDCD is realized using OpenMP on CPU and CUDA on GPU. Execution speed of the parallelized SDCD is shown on real-world SAR images. Our experimental results show that the computation time of the parallel implementation brings significant speed-ups.Book Part Predicting flight delays with artificial neural networks: case study of an airport(IEEE, 2017) Demir, Engin; Demir, Vahap BurhanAir transportation has an important place among transportation systems and it is indispensable for the flights to perform their voyages in scheduled time in order to ensure the comfort of passengers and controllability of operational costs. There are several reasons for flight delays like weather conditions, excessive intensity in air traffic, accidents or closed airfields, conditions that will lead to an increase in distances between planes and operational delays in ground services. In this study, using the data collected from the sensors located in the airport and the information about the flight, the goal is develop a machine learning model to estimate departure delays of flights using artificial neural networks.Book Part Topic distribution constant diameter overlay design algorithm (TD-CD-ODA)(IEEE, 2017) Öztoprak, Kasım; Layazali, Sina; Doğdu, ErdoğanPublish/subscribe communication systems, where nodes subscribe to many different topics of interest, are becoming increasingly more common in application domains such as social networks, Internet of Things, etc. Designing overlay networks that connect the nodes subscribed to each distinct topic is hence a fundamental problem in these systems. For scalability and efficiency, it is important to keep the maximum node degree of the overlay in the publish/subscribe system low. Ideally one would like to be able not only to keep the maximum node degree of the overlay low, but also to ensure that the network has low diameter. We address this problem by presenting Topic Distribution Constant Diameter Overlay Design Algorithm (TD-CD-ODA) that achieves a minimal maximum node degree in a low-diameter setting. We have shown experimentally that the algorithm performs well in both targets in comparison to the other overlay design algorithms.