Yazar "Abdulazeez, Adnan Mohsin" seçeneğine göre listele

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    Gait-Based Human Gender Classification Using Lifting 5/3 Wavelet and Principal Component Analysis
    (Institute of Electrical and Electronics Engineers Inc., 2018) Hassan, Omer Mohammed Salih; Abdulazeez, Adnan Mohsin; Tiryaki, Volkan Möjdat
    This study describes a representation of gait appearance for the purpose of person identification and classification. The gait representation is based on wavelet 5/3 lifting scheme simple features such as features extracted from video silhouettes of human walking motion. Regardless of its effortlessness, this may lead us to say that, the resulting feature vector contains enough information to perform well on human identification and gender classification tasks. We found out the recognition behaviors of different methods to total features over time functions under different recognition tasks. In addition to that, we provide results of gender classification based on our gait appearance features using a (C4.5 algorithm). So, the result of classification rate for CASIA-B gait databases is 97.98% and the result of recognition rate for OU-ISIR gait Database Large Population Dataset is 97.5%, these results have been obtained from gender classification data. Gait database demonstrates that the proposed method achieves better recognition performance than the most existing methods in the literature, and particularly under certain walking variations. © 2018 IEEE.
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    Öğe
    Management of wireless communication systems using artificial intelligence-based software defined radio
    (International Association of Online Engineering, 2020) Bargarai, Faiq A. Mohammed; Abdulazeez, Adnan Mohsin; Tiryaki, Volkan Müjdat; Zeebaree, Diyar Qader
    The wireless communication system was investigated by novel methods, which produce an optimized data link, especially the software-based methods. Software-Defined Radio (SDR) is a common method for developing and implementing wireless communication protocols. In this paper, SDR and artificial intelligence (AI) are used to design a self-management communication system with variable node locations. Three affected parameters for the wireless signal are considered: channel frequency, bandwidth, and modulation type. On one hand, SDR collects and analyzes the signal components while on the other hand, AI processes the situation in real-time sequence after detecting unwanted data during the monitoring stage. The decision was integrated into the system by AI with respect to the instantaneous data read then passed to the communication nodes to take its correct location. The connectivity ratio and coverage area are optimized nearly double by the proposed method, which means the variable node location, according to the peak time, increases the attached subscriber by a while ratio. © 2020 International Association of Online Engineering.