Kamiyama, T.;                     Hirano, K.;                     Sato, H.;                     Ono, K.;                     Suzuki, Y.;                     Ito, D.;                     Saito, Y.    
        Application of Machine Learning Methods to Neutron Transmission Spectroscopic Imaging for Solid–Liquid Phase Fraction Analysis. Appl. Sci. 2021, 11, 5988.
    https://doi.org/10.3390/app11135988
    AMA Style
    
                                Kamiyama T,                                 Hirano K,                                 Sato H,                                 Ono K,                                 Suzuki Y,                                 Ito D,                                 Saito Y.        
                Application of Machine Learning Methods to Neutron Transmission Spectroscopic Imaging for Solid–Liquid Phase Fraction Analysis. Applied Sciences. 2021; 11(13):5988.
        https://doi.org/10.3390/app11135988
    
    Chicago/Turabian Style
    
                                Kamiyama, Takashi,                                 Kazuma Hirano,                                 Hirotaka Sato,                                 Kanta Ono,                                 Yuta Suzuki,                                 Daisuke Ito,                                 and Yasushi Saito.        
                2021. "Application of Machine Learning Methods to Neutron Transmission Spectroscopic Imaging for Solid–Liquid Phase Fraction Analysis" Applied Sciences 11, no. 13: 5988.
        https://doi.org/10.3390/app11135988
    
    APA Style
    
                                Kamiyama, T.,                                 Hirano, K.,                                 Sato, H.,                                 Ono, K.,                                 Suzuki, Y.,                                 Ito, D.,                                 & Saito, Y.        
        
        (2021). Application of Machine Learning Methods to Neutron Transmission Spectroscopic Imaging for Solid–Liquid Phase Fraction Analysis. Applied Sciences, 11(13), 5988.
        https://doi.org/10.3390/app11135988