Research Publications for Michael J Mayo

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Author's Publications

Publications ByMAYO, Michael J

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  • 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., & 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

  • 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

  • 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.

  • 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

  • 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. J., & Zheng, C. (2016). BlockCopy-based operators for evolving efficient wind farm layouts. In Proc 2016 IEEE Congress on Evolutionary Computation (pp. 1085-1092). Vancouver, Canada: IEEE. doi:10.1109/CEC.2016.7743909

  • Mayo, M. J., Daoud, M., & Zheng, C. (2016). Randomising block sizes for BlockCopy-based wind farm layout optimisation. In G. Leu, H. K. Singh, & S. Elsayed (Eds.), Proc 20th Asia Pacific Symposium on Intelligent and Evolutionary Systems Vol. Proceedings in Adaptation, Learning and Optimization 8 (pp. 277-289). Canberra, Australia: Springer. doi:10.1007/978-3-319-49049-6_20

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