AIMC Topic: Feeding Behavior
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Providing concentrate feed outside of the milking robot increases feed intake in dairy cows without reducing motivation to visit the robot.
Animal : an international journal of animal bioscience
Feb 10, 2025
Appropriate and adequate feeding is essential to maintaining good health, productivity and welfare of dairy cows. Within automatic milking systems, concentrate feed is offered inside the milking robot, and is thought to play a key role in motivating ...
Applying machine learning to ecological momentary assessment data to identify predictors of loss-of-control eating and overeating severity in adolescents: A preliminary investigation.
Appetite
Feb 6, 2025
OBJECTIVE: Several factors (e.g., interpersonal stress, affect) predict loss-of-control (LOC) eating and overeating in adolescents, but most past research has tested predictors separately. We applied machine learning to simultaneously evaluate multip...
Predicting dyslipidemia in Chinese elderly adults using dietary behaviours and machine learning algorithms.
Public health
Dec 19, 2024
OBJECTIVES: We aimed to predict dyslipidemia risk in elderly Chinese adults using machine learning and dietary analysis for public health.
An integrated three-stream network model for discriminating fish feeding intensity using multi-feature analysis and deep learning.
PloS one
Oct 21, 2024
Feed costs constitute a significant part of the expenses in the aquaculture industry. However, feeding practices in fish farming often rely on the breeder's experience, leading to feed wastage and environmental pollution. To achieve precision in feed...
Gene-diet interactions in carbonated sugar-sweetened beverage consumption and metabolic syndrome risk: A machine learning analysis in a large hospital-based cohort.
Clinical nutrition ESPEN
Oct 11, 2024
BACKGROUND AND AIM: Carbonated sugar-sweetened beverages (CSSB) intake has been increasingly linked to metabolic diseases. To investigate the association between CSSB intake and metabolic syndrome (MetS) risk, and the interaction between genetic pred...
Controlled and Real-Life Investigation of Optical Tracking Sensors in Smart Glasses for Monitoring Eating Behavior Using Deep Learning: Cross-Sectional Study.
JMIR mHealth and uHealth
Sep 26, 2024
BACKGROUND: The increasing prevalence of obesity necessitates innovative approaches to better understand this health crisis, particularly given its strong connection to chronic diseases such as diabetes, cancer, and cardiovascular conditions. Monitor...
An artificial intelligence tool to assess the risk of severe mental distress among college students in terms of demographics, eating habits, lifestyles, and sport habits: an externally validated study using machine learning.
BMC psychiatry
Aug 27, 2024
BACKGROUND: Precisely estimating the probability of mental health challenges among college students is pivotal for facilitating timely intervention and preventative measures. However, to date, no specific artificial intelligence (AI) models have been...
Combining spectrum and machine learning algorithms to predict the weathering time of empty puparia of Sarcophaga peregrine (Diptera: Sarcophagidae).
Forensic science international
Jul 14, 2024
The weathering time of empty puparia could be important in predicting the minimum postmortem interval (PMImin). As corpse decomposition progresses to the skeletal stage, empty puparia often remain the sole evidence of fly activity at the scene. In th...
Effectiveness of an Artificial Intelligence-Assisted App for Improving Eating Behaviors: Mixed Methods Evaluation.
Journal of medical Internet research
May 7, 2024
BACKGROUND: A plethora of weight management apps are available, but many individuals, especially those living with overweight and obesity, still struggle to achieve adequate weight loss. An emerging area in weight management is the support for one's ...
Dietary patterns associated with the incidence of hypertension among adult Japanese males: application of machine learning to a cohort study.
European journal of nutrition
Feb 25, 2024
PURPOSE: The previous studies that examined the effectiveness of unsupervised machine learning methods versus traditional methods in assessing dietary patterns and their association with incident hypertension showed contradictory results. Consequentl...
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