In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning
Clicks: 367
ID: 110077
2015
Pattern classification of ingestive behavior in grazing animals has extreme importance in studies related to animal nutrition, growth and health. In this paper, a system to classify chewing patterns of ruminants in in vivo experiments is developed. The proposal is based on data collected by optical …
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v2015sensorsin
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Authors | Pegorini V;Karam LZ;Pitta CS;Cardoso R;da Silva JC;Kalinowski HJ;Ribeiro R;Bertotti FL;Assmann TS;; |
Journal | Sensors (Basel, Switzerland) |
Year | 2015 |
DOI | DOI not found |
URL | |
Keywords |
National Center for Biotechnology Information
NCBI
NLM
MEDLINE
animals
pubmed abstract
nih
national institutes of health
national library of medicine
research support
non-u.s. gov't
algorithms
pmid:26569250
pmc4701289
doi:10.3390/s151128456
vinicius pegorini
leandro zen karam
tangriani simioni assmann
biomechanical phenomena
equipment design
feeding behavior / classification*
feeding behavior / physiology*
fiber optic technology / instrumentation*
fiber optic technology / methods
machine learning*
mastication / physiology*
ruminants
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