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    Wyświetlanie 1-2 z 2
    Tytuł:
    People tracking in video surveillance systems based on artificial intelligence
    Autorzy:
    Nasry, Abir
    Ezzahout, Abderrahmane
    Omary, Fouzia
    Powiązania:
    https://bibliotekanauki.pl/articles/27314232.pdf
    Data publikacji:
    2023
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    person tracking
    person detection
    person identification
    video surveillance
    artificial intelligence
    Opis:
    As security is one of the basic human needs, we need security systems that can prevent crimes from happen‐ ing. In general, surveillance videos are used to observe the environment and human behavior in a given location. However, surveillance videos can only be used to record images or videos, without additional information. There‐ fore, more advanced cameras are needed to obtain other additional information such as the position and move‐ ment of people. This research extracted this information from surveillance video footage using a person tracking, detection, and identification algorithm. The framework for these is based on deep learning algorithms, a popu‐ lar branch of artificial intelligence. In the field of video surveillance, person tracking is considered a challenging task. Many computer vision, machine learning, and deep learning techniques have been developed in recent years. The majority of these techniques are based on frontal view images or video sequences. In this work, we will compare some previous work related to the same topic.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2023, 17, 1; 59--68
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Resource optimisation in cloud computing: comparative study of algorithms applied to recommendations in a big data analysis architecture
    Autorzy:
    Ndayikengurukiye, Aristide
    Ez-Zahout, Abderrahmane
    Aboubakr, Akou
    Charkaoui, Youssef
    Fouzia, Omary
    Powiązania:
    https://bibliotekanauki.pl/articles/2141815.pdf
    Data publikacji:
    2021
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    cloud computing
    Big Data
    IoT
    recommender system
    KNN algorithm
    Opis:
    Recommender systems (RS) have emerged as a means of providing relevant content to users, whether in social networking, health, education, or elections. Furthermore, with the rapid development of cloud computing, Big Data, and the Internet of Things (IoT), the component of all this is that elections are controlled by open and accountable, neutral, and autonomous election management bodies. The use of technology in voting procedures can make them faster, more efficient, and less susceptible to security breaches. Technology can ensure the security of every vote, better and faster automatic counting and tallying, and much greater accuracy. The election data were combined by different websites and applications. In addition, it was interpreted using many recommendation algorithms such as Machine Learning Algorithms, Vector Representation Algorithms, Latent Factor Model Algorithms, and Neighbourhood Methods and shared with the election management bodies to provide appropriate recommendations. In this paper, we conduct a comparative study of the algorithms applied in the recommendations of Big Data architectures. The results show us that the K-NN model works best with an accuracy of 96%. In addition, we provided the best recommendation system is the hybrid recommendation combined by content-based filtering and collaborative filtering uses similarities between users and items.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2021, 15, 4; 65-75
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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