Bonci, A.;                     Kermenov, R.;                     Longarini, L.;                     Longhi, S.;                     Pompei, G.;                     Prist, M.;                     Verdini, C.    
        An Echo State Network-Based Light Framework for Online Anomaly Detection: An Approach to Using AI at the Edge. Machines 2024, 12, 743.
    https://doi.org/10.3390/machines12100743
    AMA Style
    
                                Bonci A,                                 Kermenov R,                                 Longarini L,                                 Longhi S,                                 Pompei G,                                 Prist M,                                 Verdini C.        
                An Echo State Network-Based Light Framework for Online Anomaly Detection: An Approach to Using AI at the Edge. Machines. 2024; 12(10):743.
        https://doi.org/10.3390/machines12100743
    
    Chicago/Turabian Style
    
                                Bonci, Andrea,                                 Renat Kermenov,                                 Lorenzo Longarini,                                 Sauro Longhi,                                 Geremia Pompei,                                 Mariorosario Prist,                                 and Carlo Verdini.        
                2024. "An Echo State Network-Based Light Framework for Online Anomaly Detection: An Approach to Using AI at the Edge" Machines 12, no. 10: 743.
        https://doi.org/10.3390/machines12100743
    
    APA Style
    
                                Bonci, A.,                                 Kermenov, R.,                                 Longarini, L.,                                 Longhi, S.,                                 Pompei, G.,                                 Prist, M.,                                 & Verdini, C.        
        
        (2024). An Echo State Network-Based Light Framework for Online Anomaly Detection: An Approach to Using AI at the Edge. Machines, 12(10), 743.
        https://doi.org/10.3390/machines12100743