AIMC Topic: Eating

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Machine Learning Algorithms to Classify and Quantify Multiple Behaviours in Dairy Calves Using a Sensor: Moving beyond Classification in Precision Livestock.

Sensors (Basel, Switzerland) Dec 25, 2020
Previous research has shown that sensors monitoring lying behaviours and feeding can detect early signs of ill health in calves. There is evidence to suggest that monitoring change in a single behaviour might not be enough for disease prediction. In ...
Livestock Cattle Eating Behavior, Animal Machine Learning Algorithms Animals
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Statistical and Machine-Learning Analyses in Nutritional Genomics Studies.

Nutrients Oct 14, 2020
Nutritional compounds may have an influence on different OMICs levels, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and metagenomics. The integration of OMICs data is challenging but may provide new knowledge to explain...
Eating Data Mining Nutrigenomics Nutritional Physiological Phenomena Genome-Wide Association Study Cluster Analysis Data Interpretation, Statistical Nutrition Disorders Humans Machine Learning Algorithms
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Current Developments in Digital Quantitative Volume Estimation for the Optimisation of Dietary Assessment.

Nutrients Apr 22, 2020
Obesity is a global health problem with wide-reaching economic and social implications. Nutrition surveillance systems are essential to understanding and addressing poor dietary practices. However, diets are incredibly diverse across populations and ...
Obesity Nutrition Assessment Humans Recommended Dietary Allowances Eating Diet Digital Technology Public Health Deep Learning
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Using animal-mounted sensor technology and machine learning to predict time-to-calving in beef and dairy cows.

Animal : an international journal of animal bioscience Jan 13, 2020
Worldwide, there is a trend towards increased herd sizes, and the animal-to-stockman ratio is increasing within the beef and dairy sectors; thus, the time available to monitoring individual animals is reducing. The behaviour of cows is known to chang...
Sensitivity and Specificity Animals Cattle Monitoring, Physiologic Machine Learning Phenotype Pregnancy Female Eating Parturition
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Validation of a Deep Learning System for the Full Automation of Bite and Meal Duration Analysis of Experimental Meal Videos.

Nutrients Jan 13, 2020
Eating behavior can have an important effect on, and be correlated with, obesity and eating disorders. Eating behavior is usually estimated through self-reporting measures, despite their limitations in reliability, based on ease of collection and ana...
Video Recording Automation Feeding Behavior Meals Deep Learning Image Processing, Computer-Assisted Eating Humans Algorithms Female Reproducibility of Results Young Adult Adult Male
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Machine Learning Based Prediction of Insufficient Herbage Allowance with Automated Feeding Behaviour and Activity Data.

Sensors (Basel, Switzerland) Oct 16, 2019
Sensor technologies that measure grazing and ruminating behaviour as well as physical activities of individual cows are intended to be included in precision pasture management. One of the advantages of sensor data is they can be analysed to support f...
Animal Feed Machine Learning Lactation Female Milk Decision Making Animals Feeding Behavior Eating Cattle Body Weight
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Learning Deep Representations for Video-Based Intake Gesture Detection.

IEEE journal of biomedical and health informatics Sep 30, 2019
Automatic detection of individual intake gestures during eating occasions has the potential to improve dietary monitoring and support dietary recommendations. Existing studies typically make use of on-body solutions such as inertial and audio sensors...
Image Processing, Computer-Assisted Eating Video Recording Pattern Recognition, Automated Humans Deep Learning Gestures
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Using person-specific neural networks to characterize heterogeneity in eating disorders: Illustrative links between emotional eating and ovarian hormones.

The International journal of eating disorders Aug 21, 2018
OBJECTIVE: Emotional eating has been linked to ovarian hormone functioning, but no studies to-date have considered the role of brain function. This knowledge gap may stem from methodological challenges: Data are heterogeneous, violating assumptions o...
Progesterone Magnetic Resonance Imaging Emotions Models, Statistical Brain Mapping Eating Saliva Female Menstrual Cycle Humans Feeding Behavior Adult Neural Networks, Computer Feeding and Eating Disorders Surveys and Questionnaires Brain Twins Estradiol
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Application of kernel principal component analysis and computational machine learning to exploration of metabolites strongly associated with diet.

Scientific reports Feb 21, 2018
Computer-based technological innovation provides advancements in sophisticated and diverse analytical instruments, enabling massive amounts of data collection with relative ease. This is accompanied by a fast-growing demand for technological progress...
Humans Principal Component Analysis Eating Data Mining Machine Learning Hippurates Diet Metabolomics Metabolome
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Genetic Variation in CD36 Is Associated with Decreased Fat and Sugar Intake in Obese Children and Adolescents.

Journal of nutrigenetics and nutrigenomics Feb 25, 2017
BACKGROUND/AIMS: Taste is recognized as an important predictor of food choices. Thus, polymorphisms in genes encoding taste receptors may explain the variability in food preference and intake. Here, we aimed to determine whether genetic variation in ...
Taste Perception Dietary Fats Dietary Sugars Food Preferences Child Humans Genotype Cross-Sectional Studies Feeding Behavior Genetic Predisposition to Disease Female Genetic Association Studies CD36 Antigens Polymorphism, Single Nucleotide Male Risk Factors Pediatric Obesity Brazil Eating Adolescent
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