Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
A problem that humans face on a daily basis is how to make a conscious decision regarding our daily food consumption that is nutritious and healthy. By having a tool that helps facilitate the decision-making process of what type of food to eat by showing useful nutritional information to us immediately would greatly improve our lives. By critically analyzing prominent research papers that relate to deep learning techniques to classify food and their nutrients composition, we decided upon the suitable Deep Learning algorithm to classify food nutrients composition as well as the appropriate image dataset to be used. Therefore in this paper we propose the classification of food nutrients composition utilizing deep learning techniques. The proposed framework uses convolution neural networks (CNN) as a basis of recognizing images of food and classifying the food into their corresponding nutrients composition such as fats, carbohydrates, proteins and more the chosen dataset shall be used to train the model where patterns and characteristics of the food images are distinguished over multiple passes of the neural network.
"NUTRITION WEB APPLICATION USING ARTIFICIAL INTELLIGENCE", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 5, page no.477 - 483, May-2023, Available :http://www.ijrti.org/papers/IJRTI2305074.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator