Yazar "Biroğul, S." seçeneğine göre listele

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    Analysing Content Ratings of Google Apps with Ensemble Learning
    (TUBITAK, 2022) Atagün, Ercan; Timuçin, Tunahan; Biroğul, S.
    Google Play was launched under the name of Android Market and made its reputation known all over the world. The mobile application market, which is a package manager developed by Google for Android users, contains applications that appeal to many areas and age ranges. Applications are spread over a wide range of uses. Thus, the amount and size of the data increased, and this situation began to attract the attention of researchers. The excessive increase in the number of applications makes it difficult for parents to follow up on the content. To provide the content rating of applications on Google Play, it is needed to be classified by machine learning methods. In this study, content rating classification was made by analyzing “Category, Rating, Reviews, Size, Installs, Type, Genres, Last Updated, Current Version, Android Version” features of 10757 applications on Google Play, Ensemble Learning methods (Adaboost, Bagging, Random Forest, Stacking), Logistic Regression, Artificial Neural Network, K-Nearest Neighbors algorithms. © 2022, TUBITAK. All rights reserved.
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    Effectiveness of Genetic Algorithm in the Solution of Multidisciplinary Conference Scheduling Problem
    (Springer Science and Business Media Deutschland GmbH, 2021) Atagün, E.; Biroğul, S.
    Multidisciplinary conferences are the types of conferences that allow the presentation of studies in different disciplines such as naturel science, social sciences, health and arts sciences etc. In these conferences, determining the days, halls and sessions and making presentations according to the related main-scope and sub-scopes, are an important limited optimization problem. The fact that there are presentations from different disciplines in same session during the conference is a big problem for the conference participants. In this study, the solution of the scheduling problem of multidisciplinary conferences with Genetic Algorithm approach is discussed. The basic concepts of Genetic Algorithm are given and conference scheduling schemes and the elements to be considered in scheduling are indicated. In this study, two different multidisciplinary conference datasets have been used. An application has been developed with Genetic Algorithm in C# language under some constraints of the different days, different sessions and different rooms. As a result of the study, it is seen that the solutions obtained with Genetic Algorithm are generally close to optimum solutions. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.