Download PDF by Richard Khoury, Christopher Drummond: Advances in Artificial Intelligence: 29th Canadian

By Richard Khoury, Christopher Drummond

ISBN-10: 3319341103

ISBN-13: 9783319341101

ISBN-10: 3319341111

ISBN-13: 9783319341118

This booklet constitutes the refereed complaints of the twenty ninth Canadian convention on man made Intelligence, Canadian AI 2016, held in Victoria, BC, Canada, in May/June 2016. The 12 complete papers and 27 brief papers offered have been rigorously reviewed and chosen from ninety seven submissions. the point of interest of the convention used to be at the following matters: activities and behaviours, audio and visible acceptance, ordinary language processing, reasoning and studying, streams and allotted computing.

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Extra info for Advances in Artificial Intelligence: 29th Canadian Conference on Artificial Intelligence, Canadian AI 2016, Victoria, BC, Canada, May 31 - June 3, 2016. Proceedings

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In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3337–3344. IEEE (2011) 13. : Generalized support vector machines. In: Advances in Neural Information Processing Systems, pp. 135–146 (1999) 14. : Capturing global and local dynamics for human action recognition. In: 2014 22nd International Conference on Pattern Recognition (ICPR), pp. 1946– 1951. IEEE (2014) 15. : HON4D:: histogram of oriented 4D normals for activity recognition from depth sequences. In: IEEE Conference on Computer Vision and Pattern Recognition (2013) 16.

AIS equipment is required to be fitted aboard international voyaging ships that are 300 tons or above, and all passenger ships. This makes AIS data an ideal source to detect ship trajectories and fishing activities. This work follows in the footsteps of [5], which was the first use of Machine Learning approaches for fishing activity detection. In this paper, we present a new approach to detect fishing activities using autoencoders with AIS data. This work is the first attempt to use deep learning approaches to automatically find features in AIS data to detect fishing activities.

We propose that the RBM autoencoder will discover features in the sliding window matrices that characterize the differences between fishing and non-fishing activities. 36 4 X. Jiang et al. Experiments The objective is to create a supervised classifier that can predict if a window of AIS data is fishing or not.

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Advances in Artificial Intelligence: 29th Canadian Conference on Artificial Intelligence, Canadian AI 2016, Victoria, BC, Canada, May 31 - June 3, 2016. Proceedings by Richard Khoury, Christopher Drummond


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