AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This paper presents the development of a diet plan generator system that uses user-specific parameters such as age, weight, gender, activity level, dietary goals, and food preferences to recommend a structured meal plan.
This research proposes an AI-driven personalized diet recommendation system that generates customized diet plans based on user health information and demonstrates the ability to generate safe and personalized diet recommendations.
The findings support the technical feasibility of the MAI-DIET framework and its preliminary acceptability among healthcare professionals, and further evaluation is required before conclusions can be drawn regarding its practical effectiveness.
The increased rate of nutrition and lifestyle-related diseases has caused an urgent need for intelligent and personalised nutrition management systems. The traditional approaches to dietary recommendations are primarily based on universal meal planning guidelines without addressing specific health needs, requirements,...
Experimental results demonstrate that the proposed PLAN'EAT Nutrition Advisor can efficiently generate personalized, nutritionally compliant, and diverse weekly meal plans while maintaining transparent expert-rule-driven optimization.
BACKGROUND Personalized meal planning by registered dietitian nutritionists (RDNs) is time-intensive. Large language models (LLMs) may automate drafting meal plans, but their nutritional accuracy in clinical practice is uncertain. METHODS In this proof-of-concept study, five outpatient RDNs and four LLMs (Gemini, CoP...
An AI-driven calorie burn prediction system that leverages machine learning to provide an accessible, low-cost alternative to conventional methods, with potential applications in personalized fitness analytics and digital health platforms.
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