Please use this identifier to cite or link to this item: http://10.9.150.37:8080/dspace//handle/atmiyauni/2095
Title: Calorie Measurement and Food Recognition Using Machine Learning
Authors: Zalavadia, Jayesh N.
Ramani, Jaydeep
Atkotiya, Nisarg K.
Keywords: Convolutional Neural Network (CNN)
Health application
K Nearest Neighbour Model
VGG16 Model
Food Recognition
Issue Date: 2024
Publisher: Journal of Computer Technology & Applications
Citation: Zalavadia, Jayesh N.; Ramani, Jaydeep; Atkotiya, Nisarg K.(2024) Calorie Measurement and Food Recognition Using Machine Learning, Journal of Computer Technology & Applications,15(1), 1-10, 2347-7229
Abstract: Now a days whole over the world most of the people suffering from different types of diseases or obesity. Because of bad food habits or eating food without knowing the calorie and other sources from the foods. Precise techniques for gauging food and energy consumption play a vital role in addressing obesity. Offering users or patients accessible and smart solutions to assess their food intake and gather dietary information constitutes valuable insights for long-term prevention and effective treatment programs. So Innovative health applications that encourage informed food choices and customized nutrition tracking have been developed resulting from the increased prevalence of obesity and diseases linked to lifestyle. A successful method for offering predictions of healthy foods is the health application that uses cutting-edge image recognition technology, “Convolutional Neural Networks (CNN)”, to deliver realtime nutrition information based on food photographs. The application's CNN system provides accurate food recognition, doing away with the need for laborious database searches or human data entry. Users receive thorough nutritional information, including information on the macronutrient breakdown, micronutrient content, and probable allergies, enabling them to make quick, health-conscious selections.
URI: http://10.9.150.37:8080/dspace//handle/atmiyauni/2095
ISSN: 2347-7229
Appears in Collections:01. Journal Articles

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