The HIVENUS project is a full-stack web application that leverages the power of Spring Boot, React, and MongoDB to create a health tracking system. Here’s a high-level overview of the project:
Backend: Spring Boot Spring Boot is used to create the backend of the application. It provides a way to create stand-alone, production-grade Spring-based applications that can be “just run”. It simplifies the setup of Spring applications. It uses Spring Web MVC for REST APIs and Spring Data MongoDB for interaction with the MongoDB database.
Frontend: React React is used for the frontend of the application. It is a JavaScript library for building user interfaces, especially single-page applications. It allows developers to create large web applications that can change data, without reloading the page.
Database: MongoDB MongoDB, a source-available cross-platform document-oriented database program, is used as the database for the project. Classified as a NoSQL database program, MongoDB uses JSON-like documents with optional schemas.
Health Tracking with Machine Learning Algorithms The application is designed to track health metrics. While specific details about the ML algorithms used are not provided, it’s common in such applications to use machine learning for predictive analytics, anomaly detection, and personalization. For example, ML algorithms could be used to predict future health metrics based on past data, detect anomalies in health data that could indicate a problem, or personalize health recommendations for each user.
Please note that this is a general overview based on the technologies you mentioned. The actual implementation details of the VENUS project could vary. If you have more specific questions about the project, feel free to ask!
