International Research Journal of Engineering and Technology (IRJET) Volume: 09 Issue: 12 | Dec 2022
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e-ISSN: 2395-0056 p-ISSN: 2395-0072
Survey paper on AI chatbot on intelligent nutritionist Vinayak Pati1, Dr. Brijendra Gupta2 Department of Information technology, Siddhant College Of Engineering, Pune ---------------------------------------------------------------------***--------------------------------------------------------------------other pertinent domain knowledge. (2) The most notable Abstract - People each around the world is getting
distinction from the user's perspective is that dietary recommendations are highly relevant to users' health. As a result, the ideal meal recommendation system should selfadaptively create a trade-off between individual dietary preferences or interests and nutritional needs.
increasingly concerned with their health and way of life in moment's ultramodern terrain of the moment. Still, simply avoiding junk food and exercising isn't enough, not sufficient; we need a well-balanced diet. We can live a healthy life with a balanced diet grounded on our height, weight, and age. Your diet can help you achieve and maintain a healthy weight, lower your chance of developing chronic conditions( including cancer and heart complaints), and ameliorate your general health when combined with physical exertion. For this, there is a need for a smart AI chatbot that can be a personal chatbot for suggesting diet and exercise and calculating BMI.
Integration of context and knowledge:The ability to filter out unrelated recommendations can be aided by basic context information (like time and location). Compared Food recommendations involve more complex, varied, and even dynamic factors than other types of recommendations do. Rich user context and external environmental context information provide crucial information for an exact match between user requirements and food items of interest by describing users' actual physical conditions and their surroundings. Numerous wearable electronic devices and ambient sensors have been developed over the past ten years. By connecting users to nearby machines, they can instantly monitor changes in the environment and conditions of people's bodies everywhere.
Key Words: Chatbot, Smart nutritionist, BMI Calculator, Bot, Machine Learning.
1. INTRODUCTION The thing of food recommendation is to give consumers a list of ranked food products that will satisfy their unique salutary requirements. Then, the term '' food" has a broader meaning and refers to all food- related products, including reflections, fashions, coffee shops, and dining establishments. Exploration on nutrition, food wisdom, psychology, biology, anthropology, sociology, and other natural and social disciplines is frequently multidisciplinary in nature.
2. Related Work Many medical Chatbot prototypes have been released in recent years with the intention of guiding the user with medical advice after extracting the illness details from user messages. This research describes a system and approach for virtual discussion that can help adolescents deal with their psychological stress. With the help of this chatbot, users will be able to ask inquiries like they would to a real person. Natural Language Processing ("NLP") is the technology at the heart of the proposed chatbot. [1]
There are primarily three factors that make food recommendations different from other feathers of recommendations. Food recommendations bear a variety of environment and subject- matter moxie. Rich stoner environment( similar as heart rate and number of way taken) and external environmental environment( similar as physical exertion-applicable and health-applicable environment) collected from colorful detectors describe druggies' factual physical conditions and their surroundings, and as a result, give useful information for precise matching between stoner demand and food particulars.
This essay offers an analysis of the types of many recommender systems recommendations that focus mostly on divided into three groups: cooperative contentbased filtering, filtering, and hybrid filtering. This essay also covers benefits. and drawbacks of recommendation techniques. Each technique has advantages and disadvantages. that are relevant to the field.
For instance, a food recommendation after exercise that uses sensors is likely to suggest to one person foods high in protein and water. Additionally, eating advice is crucial for good health. Therefore, for constraint optimization and computing, the food recommender system should also include medical information, nutritional knowledge, and
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This article suggests a method for developing a chat application with knowledge that forbids users from sending improper or unsuitable messages to other users by implementing natural language processing at the lowest level possible (NLP). [3]
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