AIMC Topic: Nutrients
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Artificial Intelligence Applications to Measure Food and Nutrient Intakes: Scoping Review.
Journal of medical Internet research
Nov 28, 2024
BACKGROUND: Accurate measurement of food and nutrient intake is crucial for nutrition research, dietary surveillance, and disease management, but traditional methods such as 24-hour dietary recalls, food diaries, and food frequency questionnaires are...
Machine learning models for prediction of nutrient concentrations in surface water in an agricultural watershed.
Journal of environmental management
Nov 19, 2024
Prediction and quantification of nutrient concentrations in surface water has gained substantial attention during recent decades because excess nutrients released from agricultural and urban watersheds can significantly deteriorate surface water qual...
Validation of artificial intelligence-based application to estimate nutrients in daily meals.
Journal of cardiology
Oct 30, 2024
BACKGROUND: Diet modification is a mainstay for the successful management of metabolic syndrome and potentially may reduce the risk of cardiovascular disease. Accurate estimation of essential nutrients in daily meals is currently challenging to quant...
Combining the probabilistic finite element model and artificial neural network to study nutrient levels in the human intervertebral discs.
Clinical biomechanics (Bristol, Avon)
Sep 27, 2024
BACKGROUND: Diffusion distance and diffusivity are known to affect nutrient transport rates, but the probabilistic analysis of these two factors remains vacant. There is a lack of effective tools to evaluate disc nutrient levels.
Using VIS-NIR hyperspectral imaging and deep learning for non-destructive high-throughput quantification and visualization of nutrients in wheat grains.
Food chemistry
Jul 29, 2024
High-throughput and low-cost quantification of the nutrient content in crop grains is crucial for food processing and nutritional research. However, traditional methods are time-consuming and destructive. A high-throughput and low-cost method of quan...
Quantifying massively parallel microbial growth with spatially mediated interactions.
PLoS computational biology
Jul 22, 2024
Quantitative understanding of microbial growth is an essential prerequisite for successful control of pathogens as well as various biotechnology applications. Even though the growth of cell populations has been extensively studied, microbial growth r...
Can the AI tools ChatGPT and Bard generate energy, macro- and micro-nutrient sufficient meal plans for different dietary patterns?
Nutrition research (New York, N.Y.)
Jul 14, 2024
Artificial intelligence chatbots based on large language models have recently emerged as an alternative to traditional online searches and are also entering the nutrition space. In this study, we wanted to investigate whether the artificial intellige...
A critical systematic review on spectral-based soil nutrient prediction using machine learning.
Environmental monitoring and assessment
Jul 4, 2024
The United Nations (UN) emphasizes the pivotal role of sustainable agriculture in addressing persistent starvation and working towards zero hunger by 2030 through global development. Intensive agricultural practices have adversely impacted soil quali...
Machine learning in soil nutrient dynamics of alpine grasslands.
The Science of the total environment
Jun 25, 2024
As a terrestrial ecosystem, alpine grasslands feature diverse vegetation types and play key roles in regulating water resources and carbon storage, thus shaping global climate. The dynamics of soil nutrients in this ecosystem, responding to regional ...
Machine Learning Identification of Nutrient Intake Variations across Age Groups in Metabolic Syndrome and Healthy Populations.
Nutrients
May 28, 2024
This study undertakes a comprehensive examination of the intricate link between diet nutrition, age, and metabolic syndrome (MetS), utilizing advanced artificial intelligence methodologies. Data from the National Health and Nutrition Examination Surv...
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