Results 31 to 40 of about 2,433,447 (120)
Public Health Nutrition, 2020 Objective: To identify and monitor food industry use of political practices during the adoption of nutrition warning labels (WL) in Colombia. Design: Document analysis of publicly available information triangulated with interviews.
M. Mialon +5 more
semanticscholar +1 more source
A Nutritional Label for Rankings [PDF]
, 2018 Algorithmic decisions often result in scoring and ranking individuals to determine credit worthiness, qualifications for college admissions and employment, and compatibility as dating partners. While automatic and seemingly objective, ranking algorithms can discriminate against individuals and protected groups, and exhibit low diversity.
arxiv +1 more source
NutritionVerse-Real: An Open Access Manually Collected 2D Food Scene Dataset for Dietary Intake Estimation [PDF]
arXiv, 2023 Dietary intake estimation plays a crucial role in understanding the nutritional habits of individuals and populations, aiding in the prevention and management of diet-related health issues. Accurate estimation requires comprehensive datasets of food scenes, including images, segmentation masks, and accompanying dietary intake metadata.
arxiv
Scale-Score: Investigation of a Meta yet Multi-level Label to Support Nutritious and Sustainable Food Choices When Online Grocery Shopping [PDF]
arXiv, 2023 Food consumption is one of the biggest contributors to climate change. However, online grocery shoppers often lack the time, motivation, or knowledge to contemplate a food's environmental impact. At the same time, they are concerned with their own well-being. To empower grocery shoppers in making nutritionally and environmentally informed decisions, we
arxiv
Research and lobbying conflicting on the issue of a front-of-pack nutrition labelling in France
Archives of Public Health, 2016 Front-of-pack nutrition labelling has been highlighted as a promising strategy to help consumers making healthier food choices at the point of purchase.
C. Julia, S. Hercberg
semanticscholar +1 more source
DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion [PDF]
arXiv, 2023 A reasonable and balanced diet is essential for maintaining good health. With the advancements in deep learning, automated nutrition estimation method based on food images offers a promising solution for monitoring daily nutritional intake and promoting dietary health.
arxiv
Malaysian journal of nutrition, 2019 Introduction: The consumption of processed food is increasing in developing countries. Nutrition labels on food packaging are important for the education of consumers, particularly adolescents, to help them make healthier food choices.
Norsakira Jefrydin, N. M. Nor, R. Talib
semanticscholar +1 more source
The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards [PDF]
arXiv, 2018 Artificial intelligence (AI) systems built on incomplete or biased data will often exhibit problematic outcomes. Current methods of data analysis, particularly before model development, are costly and not standardized. The Dataset Nutrition Label (the Label) is a diagnostic framework that lowers the barrier to standardized data analysis by providing a ...
arxiv
Eating Smart: Advancing Health Informatics with the Grounding DINO based Dietary Assistant App [PDF]
Eating Smart: Advancing Health Informatics with the Grounding
DINO-based Dietary Assistant App, International Journal of Scientific and
Innovative Studies, June 2024, Volume 3, Number 3, Pages 26-34, Available
online at IJSRISThe Smart Dietary Assistant utilizes Machine Learning to provide personalized dietary advice, focusing on users with conditions like diabetes. This app leverages the Grounding DINO model, which combines a text encoder and image backbone to enhance food item detection without requiring a labeled dataset. With an AP score of 52.5 on the COCO dataset, the
arxiv +1 more source
Matcha: An IDE Plugin for Creating Accurate Privacy Nutrition Labels [PDF]
Apple and Google introduced their versions of privacy nutrition labels to the mobile app stores to better inform users of the apps' data practices. However, these labels are self-reported by developers and have been found to contain many inaccuracies due to misunderstandings of the label taxonomy.
arxiv +1 more source