Results 21 to 30 of about 383,029 (253)

Brazilian Food Guide attacked. Now, overwhelming support for the Guide in Brazil and worldwide

open access: yesWorld Nutrition, 2020
The present commentary summarizes the attacks to the Dietary Guidelines for the Brazilian Population from ultra-processed food manufacturers and the Brazilian Ministry of Agriculture, Livestock and Supply (MAPA).
Carlos Augusto Monteiro   +1 more
doaj   +1 more source

A Study on Food Value Estimation From Images: Taxonomies, Datasets, and Techniques

open access: yesIEEE Access, 2023
Monitoring nutritional values in food can help an individual in planning a healthy diet. In addition, regular dietary assessment can improve and maintain the physical and mental health of individuals.
Jamalia Sultana   +5 more
doaj   +1 more source

Food Classification and Meal Intake Amount Estimation through Deep Learning

open access: yesApplied Sciences, 2023
This paper proposes a method to classify food types and to estimate meal intake amounts in pre- and post-meal images through a deep learning object detection network. The food types and the food regions are detected through Mask R-CNN.
Ji-hwan Kim   +2 more
doaj   +1 more source

Grupos de alimentos para investigação de risco para diabetes tipo 2 e doenças associadas Food Groups for the investigation of risk of type 2 diabetes and associated diseases

open access: yesRevista Brasileira de Epidemiologia, 2011
INTRODUÇÃO: Os grupos de alimentos convencionalmente empregados em atividades de orientação nutricional foram estabelecidos de acordo com o seu teor de macronutrientes.
Renata Yumi Nishimura   +4 more
doaj   +1 more source

Sentiment Classification of Food Reviews

open access: yesCoRR, 2016
Sentiment analysis of reviews is a popular task in natural language processing. In this work, the goal is to predict the score of food reviews on a scale of 1 to 5 with two recurrent neural networks that are carefully tuned. As for baseline, we train a simple RNN for classification. Then we extend the baseline to GRU.
Hua Feng, Ruixi Lin
openaire   +2 more sources

Food hypersensitivity - classification, pathogenesis, diagnosis. What are food allergies?

open access: yesJournal of Education, Health and Sport, 2022
Introduction: Food hypersensitivity is a pathological, increased and inadequate reaction of the body to a particular substance, the consumption of which can cause various types of body symptoms.
Jan Dąbrowski   +4 more
doaj   +1 more source

New records and an annotated checklist of the thick-headed flies from Algeria (Conopidae, Brachycera, Diptera)

open access: yesEgyptian Journal of Biological Pest Control, 2022
Background The Conopidae are an interesting family of small- to large-sized endoparasitic flies, commonly known as thick-headed flies. These flies have been proposed as potential biological control agents of invasive social wasps (subfamilies: Polistinae
Magdi S. A. El-Hawagry   +4 more
doaj   +1 more source

Bio-Inspired Spotted Hyena Optimizer with Deep Convolutional Neural Network-Based Automated Food Image Classification

open access: yesBiomimetics, 2023
Food image classification, an interesting subdomain of Computer Vision (CV) technology, focuses on the automatic classification of food items represented through images.
Hany Mahgoub   +5 more
doaj   +1 more source

Muti-Stage Hierarchical Food Classification

open access: yesProceedings of the 8th International Workshop on Multimedia Assisted Dietary Management, 2023
Food image classification serves as a fundamental and critical step in image-based dietary assessment, facilitating nutrient intake analysis from captured food images. However, existing works in food classification predominantly focuses on predicting 'food types', which do not contain direct nutritional composition information.
Xinyue Pan   +2 more
openaire   +2 more sources

Characterization and Classification of Foods by Texture of Food

open access: yesNIPPON SHOKUHIN KAGAKU KOGAKU KAISHI, 2003
食物テクスチャーからみた咀嚼指導の一つの指標を得るため,日常よく摂食されていると考えられる112種の食物について,テクスチャーからの食物の分類を行った.(1) 112種の食物中,咀嚼筋活動量と関係する咀嚼性が高い(1.00以上)食物は30%以下であって,噛みごたえのある食物は少なかった.(2) 主成分分析によって,112種の食物中111種の食物を,かたさおよび弾力性がともに低い「基本食物グループ」,かたさが高い「グループI-1型,グループI-2型」,かたさおよび弾力性がともに高い「グループII-1型,グループII-2型,グループII-3型」,弾力性が高い「グループIII-1型,グループIII-2型,グループIII-3型」の9つに分類できた.これらグループの中で,咀嚼性が高い食物はグループI ...
Koga, Takako   +3 more
openaire   +2 more sources

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