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    Conference Object
    Citation - Scopus: 1
    Biletini Devret: a Secure Mobile App for Ticket Sales
    (Ieee, 2021) Ak, Firat; Ozkan, Veli Batuhan; Gonder, Gokhan; Sumeroglu, Ersun; Eryilmaz, Meltem
    It has been known that smartphones are the first thing that comes to mind when technology is mentioned. Almost every person has a smartphone, and they are used for social media, shopping, trade, and more. In the past, phones were just used for calculating something, or text messaging each other. However, nowadays, as mentioned above, they are used for complicated applications or works. Therefore, users need security for their private information. The Biletini Devret application in this study keeps users' private information secure with the help of Google Cloud Platforms and this application has two-factor verification to be more secure and to prevent unauthorized users. In particular, the Biletini Devret application has a Face Recognition System which has the most reliable authentication system all in the world.
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    Article
    Citation - WoS: 6
    Deep Learning-Based Defect Prediction for Mobile Applications
    (Mdpi, 2022) Jorayeva, Manzura; Akbulut, Akhan; Catal, Cagatay; Mishra, Alok
    Smartphones have enabled the widespread use of mobile applications. However, there are unrecognized defects of mobile applications that can affect businesses due to a negative user experience. To avoid this, the defects of applications should be detected and removed before release. This study aims to develop a defect prediction model for mobile applications. We performed cross-project and within-project experiments and also used deep learning algorithms, such as convolutional neural networks (CNN) and long short term memory (LSTM) to develop a defect prediction model for Android-based applications. Based on our within-project experimental results, the CNN-based model provides the best performance for mobile application defect prediction with a 0.933 average area under ROC curve (AUC) value. For cross-project mobile application defect prediction, there is still room for improvement when deep learning algorithms are preferred.