Oyewola, D.O.;                     Dada, E.G.;                     Omotehinwa, T.O.;                     Emebo, O.;                     Oluwagbemi, O.O.    
        Application of Deep Learning Techniques and Bayesian Optimization with Tree Parzen Estimator in the Classification of Supply Chain Pricing Datasets of Health Medications. Appl. Sci. 2022, 12, 10166.
    https://doi.org/10.3390/app121910166
    AMA Style
    
                                Oyewola DO,                                 Dada EG,                                 Omotehinwa TO,                                 Emebo O,                                 Oluwagbemi OO.        
                Application of Deep Learning Techniques and Bayesian Optimization with Tree Parzen Estimator in the Classification of Supply Chain Pricing Datasets of Health Medications. Applied Sciences. 2022; 12(19):10166.
        https://doi.org/10.3390/app121910166
    
    Chicago/Turabian Style
    
                                Oyewola, David Opeoluwa,                                 Emmanuel Gbenga Dada,                                 Temidayo Oluwatosin Omotehinwa,                                 Onyeka Emebo,                                 and Olugbenga Oluseun Oluwagbemi.        
                2022. "Application of Deep Learning Techniques and Bayesian Optimization with Tree Parzen Estimator in the Classification of Supply Chain Pricing Datasets of Health Medications" Applied Sciences 12, no. 19: 10166.
        https://doi.org/10.3390/app121910166
    
    APA Style
    
                                Oyewola, D. O.,                                 Dada, E. G.,                                 Omotehinwa, T. O.,                                 Emebo, O.,                                 & Oluwagbemi, O. O.        
        
        (2022). Application of Deep Learning Techniques and Bayesian Optimization with Tree Parzen Estimator in the Classification of Supply Chain Pricing Datasets of Health Medications. Applied Sciences, 12(19), 10166.
        https://doi.org/10.3390/app121910166