Перегляд за автором "Ahmad M. Ayaz"

Зараз показано 1 - 3 з 3
  • Зображення мініатюри
    Публікація
    Parametric Model of Gyroscopes
    (Scholars Academic and Scientific Publisher, 2018) Sotnik, S.; Ahmad M. Ayaz; Belova, N.; Lyashenko, V.
    The classification of gyroscopes based on the parametric model is considered. It is shown that the proposed parametric model can be used as a basis for developing a mathematical model in the process of creating a module for the automated vibration gyroscopes design, mechanical or optical type. Investigations of the failure probability and the failure-free operation probability for a period of 50 hours to 150 hours and for a period of 200 hours to 400 hours were conducted. Parameters and graphical representation of computational models are given.
  • Зображення мініатюри
    Публікація
    Recognition of Voice Commands Based on Neural Network
    (TEM Journal, 2021) Lyashenko, V.; Laariedh, Farah; Sotnik, S.; Ahmad M. Ayaz
    The paper considers features of voice commands pronunciation models, namely, dependence: system accuracy on number of states for phonemes; system accuracy on learning rate; accuracy of system on value of training set. A speech recognition system based on neural networks is proposed. A speech recognition system is not easy to implement and requires an understanding of speech recognition basics. The developed system is compared with Speech Recognition from Google and Pocket Sphinx. The proposed system can recognize voice commands with an accuracy of 84.4 %.
  • Зображення мініатюри
    Публікація
    The Research of Image Classification Methods Based on the Introducing Cluster Representation Parameters for the Structural Description
    (Seventh Sense Research Group, 2021) Ahmad M. Ayaz; Gorokhovatskyi, V.; Tvoroshenko, I.; Vlasenko, N.; Mustafa Syed Khalid
    The results of the development of high-speed methods for classifying images in computer vision systems using the description as a set of keypoints descriptors are presented. Classification methods based on the system of cluster centers parameters, which are independently constructed for etalon descriptors set, are researched. The competitive voting of the descriptors of object being recognized on a set of etalon centers is proposed. An optimal way of comparing the sets of cluster centers for an object and etalons is applied. Experimental estimation of the efficiency for the two presented classification methods in terms of computation time and classification accuracy based on the results of applied dataset processing are shown. Based on the research, a conclusion about the effectiveness of classification technologies using cluster centers for structural descriptions of images to ensure decision-making in real-time is made.