Browsing by Subject "Visibility graphs"

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    Cracked Wall Image Classification Based on Deep Neural Network Using Visibility Graph Features
    (Institute of Electrical and Electronics Engineers Inc., 2021) Altundogan T.G.; Karakose M.
    Visibility graphs are graphs created by making use of the relations of objects with each other depending on their visibility features. Today, visibility graphs are used quite frequently in signal processing applications. In this study, cracked and non-cracked wall images taken from a dataset were classified by a deep neural network depending on the visibility graph properties. In the proposed method, firstly, histograms of the images are obtained. The resulting histogram is then expressed by visibility graphs. A feature vector of each image is created with the maximum clique and maximum degree features of the obtained visibility graphs. Then, deep neural network training is performed with the feature vectors created. The classification success of the proposed method on images separated for testing is 99%. © 2021 IEEE.
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    EEG Signal Classification with Deep Neural Networks using Visibility Graphs
    (Institute of Electrical and Electronics Engineers Inc., 2022) Altundogan T.G.; Karakose M.
    EEG signals are data presented by collecting electrical activities in the brain at a certain frequency. Today, applications using the EEG signal are implemented in many fields such as medicine, computer science, robotic. Visibility Graphs, on the other hand, are graphs where certain points are associated according to their visibility features in order to perform mapping and operations in areas such as robotics. Visibility Graphs are also used today to express signals. In this study, the EEG signals are expressed with visibility graphs after certain pre-processing. Then, the classification of the obtained graph depending on the clique and degree features was carried out by using deep artificial neural networks. EEG signals have a very noisy nature, and complex pre-processing and feature extractions are used in applications using EEG signals. In the proposed method, EEG signals are subjected to very simple pre-processing and classified with a 95% success rate. © 2022 IEEE.