Research Publications for Michael J Mayo

Welcome to the University of Waikato research publications search page. This database includes all research publications produced by the University from 1998.

See Also: Research Links | Student Research Theses | Research Commons

Author's Publications

Publications ByMAYO, Michael J

  Use our Online Phonebook to contact our current staff members.

  • Yogarajan, V., Pfahringer, B., & Mayo, M. (2020). A review of automatic end-to-end de-Identification: Is high accuracy the only metric?. Applied Artificial Intelligence, 34(3), 251-269. doi:10.1080/08839514.2020.1718343

  • Mayo, M., & Frank, E. (2020). Improving naive Bayes for regression with optimised artificial surrogate data. Applied Artificial Intelligence, 34(6), 484--514. doi:10.1080/08839514.2020.1726615

  • Wang, H., Chepulis, L., Paul, R. G., & Mayo, M. (2020). Metaheuristics for discovering favourable continuous intravenous insulin rate protocols from historical patient data. In P. Sitek, M. Pietranik, M. Krótkiewicz, & C. Srinilta (Eds.), Proc 12th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2020) LNCS 12033 (pp. 157-169). Phuket, Thiland: Springer. doi:10.1007/978-3-030-41964-6_14

  • Hébert-Losier, K., Hanzlíková, I., Zheng, C., Streeter, L., & Mayo, M. (2020). The 'DEEP' landing error scoring system. Applied Sciences (Switzerland), 10(3). doi:10.3390/app10030892

  • Yogarajan, V., Gouk, H., Smith, T., Mayo, M., & Pfahringer, B. (2020). Comparing high dimensional word embeddings trained on medical text to bag-of-words for predicting medical codes. In P. Sitek, M. Petranik, M. Krótkiewicz, & C. Srinilta (Eds.), Proc 12th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2020) LNCS 12033 (pp. 97-108). Phuket, Thailand: Springer. doi:10.1007/978-3-030-41964-6_9

  • Hebert-Losier, K., Hanzlíková, I., Zheng, C., Streeter, L., & Mayo, M. (2019). The Deep Landing Error Scoring System calculation method can make an important difference!. In XXVII Congress of the International Society of Biomechanics. Calgary, Canada.

  • Mayo, M., & Yogarajan, V. (2019). A nearest neighbour-based analysis to identify patients from continuous glucose monitor data. In N. T. Nguyen, F. L. Gaol, T. P. Hong, & B. Trawinski (Eds.), Intelligent Information and Database Systems. ACIIDS 2019. Lecture Notes in Computer Science Vol. 11432 (pp. 349-360). Cham: Springer. doi:10.1007/978-3-030-14802-7_30

  • Mayo, M. (2019). Improving the robustness of the glycemic variability percentage metric to sensor dropouts in continuous glucose monitor data. In N. T. Nguyen, F. L. Gaol, T. P. Hong, & B. Trawinski (Eds.), Intelligent Information and Database Systems. ACIIDS 2019 LNCS 11432 (pp. 373-384). Cham: Springer. doi:10.1007/978-3-030-14802-7_32

  • Daoud, M., Mayo, M., & Cunningham, S. J. (2019). RBFA: Radial Basis Function Autoencoders. In 2019 IEEE Congress on Evolutionary Computation (IEEE CEC 2019) (pp. 2966-2973). Wellington, NZ. doi:10.1109/CEC.2019.8790041

  • Mayo, M., Chepulis, L., & Paul, R. G. (2019). Glycemic-aware metrics and oversampling techniques for predicting blood glucose levels using machine learning. PLOS ONE, 14(12), e0225613. doi:10.1371/journal.pone.0225613

This page has been reformatted for printing.