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Scopus Q: Q1Subject: classification

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    Citation - WoS: 2
    Citation - Scopus: 2
    Classification of Different Recycled Rubber-Epoxy Composite Based on Their Hardness Using Laser-Induced Breakdown Spectroscopy (libs) With Comparison Machine Learning Algorithms
    (Mdpi, 2023) Yilmaz, Vadi Su; Yılmaz, Vadi Su; Eseller, Kemal Efe; Aslan, Ozgur; Aslan, Özgür; Bayraktar, Emin; Eseller, Kemal Efe; Yılmaz, Vadi Su; Aslan, Özgür; Eseller, Kemal Efe; Electrical-Electronics Engineering; Department of Electrical & Electronics Engineering; Mechanical Engineering; Electrical-Electronics Engineering; Mechanical Engineering; Department of Electrical & Electronics Engineering
    This paper aims toward the successful detection of harmful materials in a substance by integrating machine learning (ML) into laser-induced breakdown spectroscopy (LIBS). LIBS is used to distinguish five different synthetic polymers where eight different heavy material contents are also detected by LIBS. Each material intensity-wavelength graph is obtained and the dataset is constructed for classification by a machine learning (ML) algorithm. Seven popular machine learning algorithms are applied to the dataset which include eight different substances with their wavelength-intensity value. Machine learning algorithms are used to train the dataset, results are discussed and which classification algorithm is appropriate for this dataset is determined.
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    Citation - WoS: 55
    Citation - Scopus: 70
    Assessment of Features and Classifiers for Bluetooth Rf Fingerprinting
    (Ieee-inst Electrical Electronics Engineers inc, 2019) Ali, Aysha M.; Uzundurukan, Emre; Kara, Ali
    Recently, network security has become a major challenge in communication networks. Most wireless networks are exposed to some penetrative attacks such as signal interception, spoofing, and stray. Radio frequency (RF) fingerprinting is considered to be a promising solution for network security problems and has been applied with various improvements. In this paper, extensive data from Bluetooth (BT) devices are utilized in RF fingerprinting implementation. Hilbert-Huang transform (HHT) has been used, for the first time, for RF fingerprinting of Bluetooth (BT) device identification. In this way, time-frequency-energy distributions (TFED) are utilized. By means of the signals' energy envelopes, the transient signals are detected with some improvements. Thirteen features are extracted from the signals' transients along with their TFEDs. The extracted features are pre-processed to evaluate their usability. The implementation of three different classifiers to the extracted features is provided for the first time in this paper. A comparative analysis based on the receiver operating characteristics (ROC) curves, the associated areas under curves (AUC), and confusion matrix are obtained to visualize the performance of the applied classifiers. In doing this, different levels of signal to noise ratio (SNR) levels are used to evaluate the robustness of the extracted features and the classifier performances. The classification performance demonstrates the feasibility of the method. The results of this paper may help readers assess the usability of RF fingerprinting for BT signals at the physical layer security of wireless networks.