Research Publications for Michael J Mayo
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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.
Open Access version at:
https://hdl.handle.net/10289/11940
Other publications by:
GOLTZ, Nachshon (Sean) :: MAYO, Michael JMayo, 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.
Other publications by:
MAYO, Michael JMayo, 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