Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

Browse

Filters

2023 - 20242

Settings

Language: search.filters.language.enSubject: search.filters.subject.Predictive models

Search Results

Now showing 1 - 2 of 2
  • Article
    Regression Via Classification for Fingerprint Orientation Estimation
    (Ieee-inst Electrical Electronics Engineers inc, 2024) Erdogmus, Nesli
    Estimating the direction in which the ridges and valleys of the fingerprint pattern are aligned often serves as a pivotal first step in fingerprint recognition systems. The ridge orientation map is a fundamental reference for subsequent processing stages, such as image enhancement, feature extraction, and matching. Therefore, its accuracy is essential to achieve high recognition rates. Ridge orientation estimation entails a regression problem since the task is to estimate an angle between 0 degrees and 180 degrees for each sub-region in the fingerprint image. However, the majority of the approaches in the literature pivot towards framing this regression task as a classification problem. This paper systematically analyzes the regression via classification methodology for fingerprint orientation estimation, exploring various discretization and encoding strategies. Specifically, we examine single and multiple discretization schemes designed to ensure that resulting bins maintain uniform length or uniform probability or are allocated randomly, paired with one-hot, ordinal, and cyclic encoding techniques. Our experiments are conducted on the FOE-TEST database from FVC-onGoing, the sole publicly available fingerprint orientation dataset. The findings highlight the efficacy of cyclic encoding over the one-hot encoding prevalent in prior research, while equal-length and equal-probability discretization strategies yield comparable results.
  • Article
    Link Prediction for Completing Graphical Software Models Using Neural Networks
    (IEEE, 2023) Leblebici, Onur; Tuğlular, Tuğkan; Belli, Fevzi
    Deficiencies and inconsistencies introduced during the modeling of software systems may result in high costs and negatively impact the quality of all developments performed using these models. Therefore, developing more accurate models will aid software architects in developing software systems that match and exceed expectations. This paper proposes a graph neural network (GNN) method for predicting missing connections, or links, in graphical models, which are widely employed in modeling software systems. The proposed method utilizes graphs as allegedly incomplete, primitive graphical models of the system under consideration (SUC) as input and proposes links between its elements through the following steps: (i) transform the models into graph-structured data and extract features from the nodes, (ii) train the GNN model, and (iii) evaluate the performance of the trained model. Two GNN models based on SEAL and DeepLinker are evaluated using three performance metrics, namely cross-entropy loss, area under curve, and accuracy. Event sequence graphs (ESGs) are used as an example of applying the approach to an event-based behavioral modeling technique. Examining the results of experiments conducted on various datasets and variations of GNN reveals that missing connections between events in an ESG can be predicted even with relatively small datasets generated from ESG models. Author