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      BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Machine learning and data mining advance predictive big data analysis in precision animal agriculture 1

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          Abstract

          Precision animal agriculture is poised to rise to prominence in the livestock enterprise in the domains of management, production, welfare, sustainability, health surveillance, and environmental footprint. Considerable progress has been made in the use of tools to routinely monitor and collect information from animals and farms in a less laborious manner than before. These efforts have enabled the animal sciences to embark on information technology-driven discoveries to improve animal agriculture. However, the growing amount and complexity of data generated by fully automated, high-throughput data recording or phenotyping platforms, including digital images, sensor and sound data, unmanned systems, and information obtained from real-time noninvasive computer vision, pose challenges to the successful implementation of precision animal agriculture. The emerging fields of machine learning and data mining are expected to be instrumental in helping meet the daunting challenges facing global agriculture. Yet, their impact and potential in “big data” analysis have not been adequately appreciated in the animal science community, where this recognition has remained only fragmentary. To address such knowledge gaps, this article outlines a framework for machine learning and data mining and offers a glimpse into how they can be applied to solve pressing problems in animal sciences.

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          The Adaptive Lasso and Its Oracle Properties

          Hui Zou (2006)
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            Deep Learning in Neural Networks: An Overview

            (2014)
            In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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              Applications of machine learning in animal behaviour studies

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                Author and article information

                Journal
                J Anim Sci
                J. Anim. Sci
                ansci
                Journal of Animal Science
                Oxford University Press (US )
                0021-8812
                1525-3163
                April 2018
                16 March 2018
                16 March 2018
                : 96
                : 4
                : 1540-1550
                Affiliations
                [1 ]Department of Animal Science, University of Nebraska, Lincoln, NE
                [2 ]Beef Improvement Opportunities, Elora, Ontario, Canada
                [3 ]Department of Animal Nutrition and Production, School of Veterinary Medicine and Animal Science, University of São Paulo, Pirassununga, São Paulo, Brazil
                [4 ]Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Brazil
                [5 ]Department of Mathematical Sciences, Ritsumeikan University, Shiga, Japan
                Author notes
                Corresponding author: morota@ 123456unl.edu
                Article
                sky014
                10.1093/jas/sky014
                6140937
                29385611
                e2673c83-54b1-4f91-8608-9503e4fba7ab
                © The Author(s) 2018. Published by Oxford University Press on behalf of American Society of Animal Science.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                Page count
                Pages: 11
                Funding
                Funded by: USDA – Agriculture and Food Research Initiative (AFRI)
                Award ID: 2012-68002-19823
                Categories
                Symposia

                big data,data mining,machine learning,precision agriculture,prediction

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