By Richard Khoury, Christopher Drummond
This ebook constitutes the refereed complaints of the twenty ninth Canadian convention on synthetic Intelligence, Canadian AI 2016, held in Victoria, BC, Canada, in May/June 2016. The 12 complete papers and 27 brief papers provided have been rigorously reviewed and chosen from ninety seven submissions. the focal point of the convention was once at the following matters: activities and behaviours, audio and visible reputation, usual language processing, reasoning and studying, streams and dispensed computing.
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Additional resources for Advances in Artificial Intelligence: 29th Canadian Conference on Artificial Intelligence, Canadian AI 2016, Victoria, BC, Canada, May 31 - June 3, 2016. Proceedings
IEEE (2012) 7. : Visual perception of biological motion and a model for its analysis. Percept. Psychophy. 14(2), 201–211 (1973) 8. : Derivative dynamic time warping. SIAM (2001) 9. : On space-time interest points. IJCV 64(2–3), 107–123 (2005) 10. : Learning realistic human actions from movies. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2008) 11. : Action recognition based on a bag of 3D points. In: CVPR Workshop (CVPRW), pp. 9–14. IEEE (2010) 12. : Recognizing human actions by attributes.
The results provided in Table 2 along with the confusion matrix depicted in Figs. 3 and 4 demonstrate the superiority of the proposed methodology. A. Bagheri et al. Table 2. Comparing classiﬁcation accuracy of our method with the state-of-the-art methods on the MSRAction3D dataset. 85 Vieira et al. 09 By only considering the skeleton data, our results achieved the accuracies of many works based on depth data Considering the fact that we have only employed the skeleton data, not depth sequences, the results are promising.
Sci. World J. 2013, 24 (2013). 1155/2013/408280. Article ID: 408280 12. : Novelty detection approach for keystroke dynamics identity veriﬁcation. , Yin, H. ) Intelligent Data Engineering and Automated Learning. LNCS, vol. 2690, pp. 1016–1023. Springer, Heidelberg (2003) Fishing Activity Detection from AIS Data Using Autoencoders Xiang Jiang1 , Daniel L. Silver2 , Baifan Hu1 , Erico N. ca 1 Introduction Marine life has signiﬁcant impact on our planet, providing food, oxygen and biodiversity. But, 90 percent of the large ﬁsh are gone primarily because of overﬁshing, according to the 2010 Census of Marine Life.