Browsing by Author "Kilinç D."

Now showing 1 - 6 of 6
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    Yazilim hata kestiriminde kolektif siniflandirma modellerinin etkisi
    (CEUR-WS, 2015) Kilinç D.; Borandag E.; Yücalar F.; Özçift A.; Bozyigit F.
    [No abstract available]
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    Metin madenciligi kullanarak yazilim kullanimina dair bulgularin elde edilmesi
    (CEUR-WS, 2015) Kilinç D.; Bozyigit F.; Özçift A.; Yücalar F.; Borandag E.
    [No abstract available]
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    The average scattering number of graphs
    (EDP Sciences, 2016) Aslan E.; Kilinç D.; Yücalar F.; Borandaǧ E.
    The scattering number of a graph is a measure of the vulnerability of a graph. In this paper we investigate a refinement that involves the average of a local version of the parameter. If v is a vertex in a connected graph G, then scv(G) = max {ω(G - Sv) - | Sv |}, where the maximum is taken over all disconnecting sets Sv of G that contain v. The average scattering number of G denoted by scav(G), is defined as scav(G) = Σv ϵ V(G) scv(G) / n, where n will denote the number of vertices in graph G. Like the scattering number itself, this is a measure of the vulnerability of a graph, but it is more sensitive. Next, the relations between average scattering number and other parameters are determined. The average scattering number of some graph classes are obtained. Moreover, some results about the average scattering number of graphs obtained by graph operations are given. © EDP Sciences 2016.
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    A hybrid sentiment analysis method for Turkish
    (Turkiye Klinikleri, 2019) Erşahin B.; Aktaş Ö.; Kilinç D.; Erşahin M.
    This paper presents a hybrid methodology for Turkish sentiment analysis, which combines the lexicon-based and machine learning (ML)-based approaches. On the lexicon-based side, we use a sentiment dictionary that is extended with a synonyms lexicon. Besides this, we tackle the classification problem with three supervised classifiers, naive Bayes, support vector machines, and J48, on the ML side. Our hybrid methodology combines these two approaches by generating a new lexicon-based value according to our feature generation algorithm and feeds it as one of the features to machine learning classifiers. Despite the linguistic challenges caused by the morphological structure of Turkish, the experimental results show that it improves the accuracy by 7% on average. © TÜBİTAK
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    Automatic concept identification of software requirements in Turkish
    (Turkiye Klinikleri Journal of Medical Sciences, 2019) Bozyigit F.; Aktaş Ö.; Kilinç D.
    Software requirements include description of the features for the target system and express the expectations of users. In the analysis phase, requirements are transformed into easy-to-understand conceptual models that facilitate communication between stakeholders. Although creating conceptual models using requirements is mostly implemented manually by analysts, the number of models that automate this process has increased recently. Most of the models and tools are developed to analyze requirements in English, and there is no study for agglutinative languages such as Turkish or Finnish. In this study, we propose an automatic concept identification model that transforms Turkish requirements into Unified Modeling Language class diagrams to ease the work of individuals on the software team and reduce the cost of software projects. The proposed work is based on natural language processing techniques and a new rule-set containing twenty-six rules is created to find object-oriented design elements from requirements. Since there is no publicly available dataset on the online repositories, we have created a well-defined dataset containing twenty software requirements in Turkish and have made it publicly available on GitHub to be used by other researchers. We also propose a novel evaluation model based on an analytical hierarchy process that considers the experts’ views and calculate the performance of the overall system as 89%. We can state that this result is promising for future works in this domain. © TÜBİTAK.
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    Supervised Learning Approaches to Flight Delay Prediction
    (Sakarya University, 2020) Atlioğlu M.C.; Bolat M.; Şahin M.; Tunali V.; Kilinç D.
    Delays in flights and other airline operations have significant consequences in quality of service, operational costs, and customer satisfaction. Therefore, it is important to predict the occurrence of delays and take necessary actions accordingly. In this study, we addressed the flight delay prediction problem from a supervised machine learning perspective. Using a real-world airline operations dataset provided by a leading airline company, we identified optimum dataset features for optimum prediction accuracy. In addition, we trained and tested 11 machine learning models on the datasets that we created from the original dataset via feature selection and transformation. CART and KNN showed consistently good performance in almost all cases achieving 0.816 and 0.807 F-Scores respectively. Similarly, GBM, XGB, and LGBM showed very good performance in most of the cases, achieving F-Scores around 0.810. © 2020, Sakarya University. All rights reserved.