Master Degree / Yüksek Lisans Tezleri

Permanent URI for this collectionhttps://hdl.handle.net/11147/3008

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1997 - 199922020 - 20222

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Department: search.filters.department.Thesis (Master)--İzmir Institute of Technology, Computer EngineeringSubject: search.filters.subject.Neural networks (Computer science)

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Now showing 1 - 4 of 4
  • Master Thesis
    Analysis of fingerprint matching performance with deep neural networks
    (01. Izmir Institute of Technology, 2022) Göçen, Alper; Erdoğmuş, Nesli
    Fingerprints are unique biometric properties for each person. In the literature and industry, they are widely used for identification purposes. Collecting biometric datasets is a tedious work since it is not possible without the owners’ consent, and existing fingerprint datasets are either not sufficient to use in deep learning tasks by means of size or most of them are kept private to the collectors’ use. This increases the need of synthetic fingerprint images and their use in a variety of tasks especially for training deep learning models. In this study, the performance of a CNN architecture named Finger ConvNet[1] is compared to well-known networks and the question of whether a mixed dataset consisting of synthetically generated and real fingerprint images can reach a performance close or equal to ones having only real images is discussed. As a result of experiments, it is shown that the number of real images in the dataset is an important factor and that the performance of the mixed dataset was less than the one having only real images proposed in the referred study.
  • Master Thesis
    Application of Graph Neural Networks on Software Modeling
    (01. Izmir Institute of Technology, 2020) Leblebici, Onur Yusuf; Tuğlular, Tuğkan; Belli, Fevzi
    Deficiencies and inconsistencies introduced during the modeling of software systems can cause undesirable consequences that may result in high costs and negatively affect the quality of all developments made using these models. Therefore, creating better models will help the software engineers to build better software systems that meet expectations. One of the software modelling methods used for analysis of graphical user interfaces is Event Sequence Graphs (ESG). The goal of this thesis is to propose a method that predicts missing or forgotten links between events defined in an ESG via Graph Neural Networks (GNN). A five-step process consisting of the following steps is proposed: (i) data collection from ESG model, (ii) dataset transformation, (iii) GNN model training, (iv) validation of trained model and (v) testing the model on unseen data. Three performance metrics, namely cross entropy loss, area under curve and accuracy, were used to measure the performance of the GNN models. Examining the results of the experiments performed on different datasets and different variations of GNN, shows that even with relatively small datasets prepared from ESG models, predicts missing or forgotten links between events defined in an ESG can be achieved.
  • Master Thesis
    Trajectory Prediction of Moving Objects by Means of Neural Networks
    (Izmir Institute of Technology, 1997) Barışık, Hakan; Aytaç, İsmail Sıtkı; Aytaç, İsmail Sıtkı
    Estimating the three-dimensional motion of an object from a sequence of object positions and orientation is of significant importance in variety of applications in control and robotics. For instance, autonomous navigation, manipulation, servo, tracking, planning and surveillance needs prediction of motion parameters. Although "motion estimation" is an old problem (the formulations date back to the beginning of the century), only recently scientists have provided with the tools from nonlinear system estimation theory to solve this problem eural Networks are the ones which have recently been used in many nonlinear dynamic system parameter estimation context. The approximating ability of the neural network is used to identifY the relation between system variables and parameters of a dynamic system. The position, velocity and acceleration of the object are estimated by several neural networks using the II most recent measurements of the object coordinates as input to the system Several neural network topologies with different configurations are introduced and utilized in the solution of the problem. Training schemes for each configuration are given in detail. Simulation results for prediction of motion having different characteristics via different architectures with alternative configurations are presented comparatively.
  • Master Thesis
    Image Classifcation by Means of Pattern Recognition Techniques
    (Izmir Institute of Technology, 1997) Güzel, Cumhur; Püskülcü, Halis
    Image classification plays an important role in many computer vision tasks such as surface inspection, shape determination etc. Various 2-D image classification techniques are investigated, assessed and a computational method to classifY the 2-D X-ray images is developed and evaluated in this study. Various pattern recognition techniques are devised for the solution of the image classification. Those techniques may be divided into mainly two groups. First one, is mathematical and statistical model based, second one, is the artificial neural network based techniques. We have concentrated on artificial neural network techniques. In the experiments, both techniques were applied for the classification of the VUR (vesico ureteral reflux) images, in this study. However, according to the experiments performed on VUR case study, neural network technique was more successful than others, in terms of classifier. A hybrid method is proposed in this study, rather than pure artificial neural network solution. Representation of images is performed via transformation invariant mathematical structure called Fourier Descriptors and these structures are used as input to train the neural network for the classification part.The application is performed as follows: Feature extraction is performed first, then extracted features are used as pattern vectors for training the neural network. Representation of the shapes in X-ray images is performed by using Fourier Descriptors. Usage of Fourier descriptors as a method of representation of the shapes, provides the transformation invariant' (translation, rotation, scaling invariant structure) representation of X-ray images. These new vector representation is fed to the neural network. Backpropagation is used as a training algorithm. After training is finished, system is readyfor questioning. The minimum-mean-distance and nearest neighbor rules are also applied for the pattern vectors generated for the experiments. But the multilayer perceptron trained by backpropagation outperforms both of these statistical classifiers.