Research Publications for Michael J Mayo
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Podolskiy, V., Mayo, M., Koay, A., Gerndt, M., & Patros, P. (2019). Maintaining SLOs of cloud-native applications via self-adaptive resource sharing. In Proc 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2019) (pp. 72-81). Umeå, Sweden: IEEE. doi:10.1109/SASO.2019.00018
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. Lecture Notes in Computer Science Vol. 11432 (pp. 373-384). Cham: Springer. doi:10.1007/978-3-030-14802-7_32
Other publications by:
MAYO, Michael JDaoud, M., & Mayo, M. (2019). A survey of neural network-based cancer prediction models from microarray data. Artificial Intelligence in Medicine, 97, 204-214. doi:10.1016/j.artmed.2019.01.006
Yogarajan, V., Mayo, M., & Pfahringer, B. (2018). Privacy protection for health information research in New Zealand district health boards. New Zealand Medical Journal, 131(1485), 19-26.
Open Access version at:
https://hdl.handle.net/10289/12161
Other publications by:
YOGARAJAN, Vithya :: MAYO, Michael J :: PFAHRINGER, Bernhard MMayo, M., Wakes, S., & Anderson, C. (2018). Neural networks for predicting the output of wind flow simulations over complex topographies. In X. Wu, O. Y. Soon, C. Aggarwal, & H. Chen (Eds.), Proc 2018 IEEE International Conference on Big Knowledge (ICBK) (pp. 184-191). Conference held Singapore: IEEE. doi:10.1109/ICBK.2018.00032
Daoud, M., & Mayo, M. (2018). A novel synthetic over-sampling technique for imbalanced classification of gene expressions using autoencoders and swarm optimization. In T. Mitrovic, B. Xue, & X. Li (Eds.), Proc 31st Australasian Joint Conference on Advances in Artificial Intelligence (AI 2018) Vol. LNAI 11320 (pp. 603-615). Conference held Wellington, NZ: Springer. doi:10.1007/978-3-030-03991-2_55
Other publications by:
MAYO, Michael JHirsz, M., Hunt, L., Chepulis, L., & Mayo, M. (2018). Associations between symptoms and colorectal cancer outcome in GP/hospital e-referrals. In Australasian Applied Statistics Conference. Conference held Rotorua, NZ.
Mayo, M., & Frank, E. (2018). Improving Naive Bayes for Regression with Optimised Artificial Surrogate Data. arXiv. Retrieved from http://arxiv.org/pdf/1707.04943v3
Goltz, N., & Mayo, M. (2017). Enhancing regulatory compliance by using artificial intelligence text mining to identify penalty clauses in legislation. In MIREL 2017 - Workshop on 'Mining and REasoning with Legal texts', held in conjunction with the 16th International Conference on Artificial Intelligence and Law. Conference held at King’s College, London, UK.
Open Access version at:
https://hdl.handle.net/10289/11940
Other publications by:
GOLTZ, Nachshon (Sean) :: MAYO, Michael J