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- W3011787441 abstract "The Melbourne eResearch Group (www.eresearch.unimelb.edu.au) are involved in amultitude of projects, many of which are focused on big data and data analytics. Manyresearcher challenges have much to benefit from artificial intelligence and especially fromtheapplication of deep learning and convolutional neural networks (CNNs). This talk will providean overview of a portfolio of projects that have benefited from recent advances in the deeplearning domain. These include case studies related to:• pedestrian/crowd counting for the City of Melbourne;• (early) fruit counting on trees (for fruit growers to estimate yield);• tree volume canopy estimation (for fruit growers to estimate the amount of sprayingneeded);• truck and trailer classification for VicRoads;• feral cat classification for ecology researchers working in rural Victoria;• plant and flower classification for commercial agricultural companies, and• encroachment of vegetation on powerlines for a range of utility companiesThe talk will cover a brief background to deep learning and CNNs and focus on the resultsthat are now possible, with specific focus on projects requiring image detection andclassification.ABOUT THE AUTHORProfessor Richard O. Sinnott is the Director of eResearch at the University of Melbourneand Chair of Applied Computing Systems. In these roles he is responsible for all aspects ofeResearch (research-oriented IT development) at the University. He has been lead softwareengineer/architect on an extensive portfolio of national and international projects, withspecific focus on those research domains requiring finer-grained access control (security).He has over 400 peer reviewed publications across a range of applied computing researchareas" @default.
- W3011787441 created "2020-03-23" @default.
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- W3011787441 date "2020-03-11" @default.
- W3011787441 modified "2023-09-23" @default.
- W3011787441 title "Applied Deep Learning for Diverse Research Communities" @default.
- W3011787441 doi "https://doi.org/10.6084/m9.figshare.11965548.v1" @default.
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