Cite
Deep learning and hyperparameter optimization for assessing one's eligibility for a subcutaneous implantable cardioverter-defibrillator.
MLA
Dunn, Anthony J., et al. “Deep Learning and Hyperparameter Optimization for Assessing One’s Eligibility for a Subcutaneous Implantable Cardioverter-Defibrillator.” Annals of Operations Research, vol. 328, no. 1, Sept. 2023, pp. 309–35. EBSCOhost, https://doi.org/10.1007/s10479-023-05326-1.
APA
Dunn, A. J., Coniglio, S., ElRefai, M., Roberts, P. R., Wiles, B. M., & Zemkoho, A. B. (2023). Deep learning and hyperparameter optimization for assessing one’s eligibility for a subcutaneous implantable cardioverter-defibrillator. Annals of Operations Research, 328(1), 309–335. https://doi.org/10.1007/s10479-023-05326-1
Chicago
Dunn, Anthony J., Stefano Coniglio, Mohamed ElRefai, Paul R. Roberts, Benedict M. Wiles, and Alain B. Zemkoho. 2023. “Deep Learning and Hyperparameter Optimization for Assessing One’s Eligibility for a Subcutaneous Implantable Cardioverter-Defibrillator.” Annals of Operations Research 328 (1): 309–35. doi:10.1007/s10479-023-05326-1.