Research Publications for Michael J Mayo

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Author's Publications

Publications ByMAYO, Michael J

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  • Daoud, M., & Mayo, M. (2019). A survey of neural network-based cancer prediction models from microarray data. Artificial Intelligence in Medicine, in press. doi:10.1016/j.artmed.2019.01.006

  • 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

  • Hirsz, 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.

  • 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.

  • Mayo, 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

  • Mayo, M., & Frank, E. (2018). Improving Naive Bayes for Regression with Optimised Artificial Surrogate Data. arXiv. Retrieved from http://arxiv.org/pdf/1707.04943v3

  • 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

  • Doaud, M., & Mayo, M. (2017). Using swarm optimization to enhance autoencoder’s images. In V. Torra, Y. Narukawa, A. Honda, & S. Inoue (Eds.), USB Proc 14th International Conference on Modeling Decisions for Artificial Intelligence (MDAI 2017) (pp. 118-131). Conference held Kitakyushu, Japan.

  • Wilson, B., Wakes, S., & Mayo, M. (2017). Surrogate modeling a computational fluid dynamics-based wind turbine wake simulation using machine learning. In Proc 2017 IEEE Symposium Series on Computational Intelligence (SSCI 2017) (pp. 1-8). Conference held Honolulu, Hawaii: IEEE. doi:10.1109/SSCI.2017.8280844

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