Machine Learning based Nutritional assistant using Convolution-al Neural Network
Ranjeesh R
, Ravikumar P , Vijayaprabu R , Vishnu V , Jaipriya S
Nutrional Assistant, Diet Planning, Machine Learning, Neural Network
The nutrition assistant system proposed in the study is a technology-based solution to help individuals make healthier dietary choices. The system uses advanced machine learning techniques, specifically a convolution-al neural network (CNN), to accurately identify food items from images taken by the user's smartphone camera. The CNN model was trained on a large dataset of food images to ensure accurate identification of food items. The diet plan application integrated with the CNN model allows users to set their health goals and dietary preferences, such as vegetarian or gluten-free diets. Based on this information, the system provides personalized dietary recommendations and meal plans that meet the user's nutritional needs. The system not only identifies food items but also estimates portion sizes of the foods in the image, which is used to provide more accurate nutrient content information and track the user's daily caloric intake. The user study conducted to evaluate the system found that the system helped users make healthier food choices and understand their nutritional needs. The system's ability to provide personalized dietary recommendations was particularly valued by participants, who reported that it helped them better understand their nutritional needs and make more informed food choices. Overall, the nutrition assistant system has the potential to prevent diet-related health issues by providing tailored dietary recommendations that are specific to each individual's health goals and dietary preferences.
"Machine Learning based Nutritional assistant using Convolution-al Neural Network", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 3, page no.b485-b489, March-2023, Available :https://ijnrd.org/papers/IJNRD2303158.pdf
Volume 8
Issue 3,
March-2023
Pages : b485-b489
Paper Reg. ID: IJNRD_188645
Published Paper Id: IJNRD2303158
Downloads: 000118854
Research Area: Information Technology
Country: Kanyakumari, Tamil Nadu, India
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJNRD (IJ Publication) Janvi Wave
