AIMC Topic: Feeding Behavior

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Combining spectrum and machine learning algorithms to predict the weathering time of empty puparia of Sarcophaga peregrine (Diptera: Sarcophagidae).

Forensic science international
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
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
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...

Will biomimetic robots be able to change a hivemind to guide honeybees' ecosystem services?

Bioinspiration & biomimetics
We study whether or not a group of biomimetic waggle dancing robots is able to significantly influence the swarm-intelligent decision making of a honeybee colony, e.g. to avoid foraging at dangerous food patches using a mathematical model. Our model ...

Development and Analysis of a CNN- and Transfer-Learning-Based Classification Model for Automated Dairy Cow Feeding Behavior Recognition from Accelerometer Data.

Sensors (Basel, Switzerland)
Due to technological developments, wearable sensors for monitoring the behavior of farm animals have become cheaper, have a longer lifespan and are more accessible for small farms and researchers. In addition, advancements in deep machine learning me...

Evaluation of mouse behavioral responses to nutritive versus nonnutritive sugar using a deep learning-based 3D real-time pose estimation system.

Journal of neurogenetics
Animals are able to detect the nutritional content of sugar independently of taste. When given a choice between nutritive sugar and nonnutritive sugar, animals develop a preference for nutritive sugar over nonnutritive sugar during a period of food d...

Monitoring the respiratory behavior of multiple cows based on computer vision and deep learning.

Journal of dairy science
Automatic respiration monitoring of dairy cows in modern farming not only helps to reduce manual labor but also increases the automation of health assessment. It is common for cows to congregate on farms, which poses a challenge for manual observatio...

Design of Residents' Sports Nutrition Data Monitoring System Based on Genetic Algorithm.

Computational intelligence and neuroscience
With the development of modern Internet technology, the health assessment model based on computer technology has gradually become a research hotspot. In the process of studying the health level of residents, exercise status and diet nutrition are imp...

Thought on Food: A Systematic Review of Current Approaches and Challenges for Food Intake Detection.

Sensors (Basel, Switzerland)
Nowadays, individuals have very stressful lifestyles, affecting their nutritional habits. In the early stages of life, teenagers begin to exhibit bad habits and inadequate nutrition. Likewise, other people with dementia, Alzheimer's disease, or other...

Intake monitoring in free-living conditions: Overview and lessons we have learned.

Appetite
The progress in artificial intelligence and machine learning algorithms over the past decade has enabled the development of new methods for the objective measurement of eating, including both the measurement of eating episodes as well as the measurem...