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. (2018). A novel synthetic over-sampling technique for imbalanced classification of gene expressions using autoencoders and swarm optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11320 LNAI (pp. 603-615). doi:10.1007/978-3-030-03991-2_55

  • 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., & Goltz, N. (2017). Constructing document vectors using kernel density estimates. In V. Torra, Y. Narukawa, A. Honda, & S. Inoue (Eds.), Modeling Decisions for Artificial Intelligence. MDAI 2017 (pp. 183-194). Cham: Springer. doi:10.1007/978-3-319-67422-3_16

  • 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). 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). Honolulu, Hawaii: IEEE. doi:10.1109/SSCI.2017.8280844

  • 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.

  • Mayo, M., & Daoud, M. (2017). Aesthetic local search of wind farm layouts. Information, 8(2), 39. doi:10.3390/info8020039

  • Mayo, M., & Daoud, M. (2016). Informed mutation of wind farm layouts to maximise energy harvest. Renewable Energy, 89, 437-448. doi:10.1016/j.renene.2015.12.006

  • Mayo, M., & Bifet, A. (2016). Deferral classification of evolving temporal dependent data streams. In Proc 31st Annual ACM Symposium on Applied Computing (pp. 952-954). Pisa, Italy: ACM. doi:10.1145/2851613.2851890

  • Mayo, M. J., & Omranian, S. (2016). Towards a new evolutionary subsampling technique for heuristic optimisation of load disaggregators. In H. Cao, J. Li, & R. Wang (Eds.), Trends and Applications in Knowledge Discovery and Data Mining, PAKDD 2016 Workshops, Revised Selected Papers Vol. LNCS 9794 (pp. 3-14). Conference held at Auckland, NZ: Springer. doi:10.1007/978-3-319-42996-0_1

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