ML Application · HTML
Safe-Home
Concrete Health Guard AI: A hybrid structural health monitoring system combining Snowflake data warehousing, Random Forest sensor analysis, and MobileNetV2 computer vision to detect concrete cancer (spalling) and predict internal stress risks in real-time. Built with Flask & Tailwind CSS.
What it is
SafeHome is a Flask-based web application that integrates pre-trained machine learning models (Random Forest for sensor data and MobileNetV2/Florence-2 for image analysis) to assess concrete health by predicting corrosion risk and detecting defects from images.
Merit describes the code at this commit. Later pushes count once the owner rescans. How merit is scored
Where it counts
- Machine Learning90%
- Computer Vision80%
- NLP & LLMs30%
- Data Science10%
- MLOps10%
How much of the project is about each field, as scoring counts it. It ranks in the field it is most about.
Around it on the Machine Learning board
Whole board- 73, shared rankVISTAArchitecture Modification / Research · Jupyter Notebook48.5ImplementationSuceveanu Mircea Dan SanduMachine Learning
- 75automatos-voiceML Application · Python48.2ImplementationAutomatos AIMachine Learning
- 76Safe-HomeML Application · HTML47.5ImplementationIrine MiltonMachine Learning
- 77advanced-clusteringEducational Implementation · Jupyter Notebook46.5ImplementationPop AlexandruMachine Learning
- 78AI-Driven-Metamaterial-FoundryML Application · Python46.0ImplementationLuiz Cesar SpiesMachine Learning
