RTACC is an advanced AI-powered crisis detection and emergency response system that leverages NVIDIA GPU acceleration to monitor multiple data streams in real-time and predict emergency situations before they escalate.
RTACC analyzes weather conditions, traffic patterns, social media sentiment, and news reports using machine learning to:
- 🔍 Detect emerging crises 30+ minutes before escalation
- 📊 Predict crisis evolution with confidence scoring
- 🚀 Recommend optimal resource deployment for emergency response
- 🌍 Monitor any global location with dynamic map visualization
GPU-accelerated real-time crisis detection with climate overlays and multi-city monitoring
┌─────────────────┐ ┌────────────────────┐ ┌─────────────────┐
│ Data Sources │───▶│ CUDA Processor │───▶│ Dashboard │
├─────────────────┤ ├────────────────────┤ ├─────────────────┤
│ • Weather APIs │ │ • PyTorch NNs │ │ • Streamlit UI │
│ • Traffic Data │ │ • Scikit-learn │ │ • Plotly Maps │
│ • Reddit API │ │ • GPU Acceleration │ │ • Real-time │
│ • News Feeds │ │ • Anomaly Detection│ │ • Multi-location│
└─────────────────┘ └────────────────────┘ └─────────────────┘
- 🔥 NVIDIA CUDA: PyTorch neural networks running on GPU
- ⚡ Tensor Operations: Real-time multi-source data fusion
- 🧠 GPU Memory: Optimized for continuous data processing
- 📊 Mixed Precision: Faster inference with maintained accuracy
- Python 3.8+
- NVIDIA GPU with CUDA support (recommended)
- 8GB+ RAM (16GB+ recommended)
- Internet connection for API access
Project Maintainer: Nodshley Marcelin
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