Smart NET AI
Inefficient network planning and unmanaged traffic congestion hinder public connectivity, especially in underserved areas. Existing methods fail to strategically place infrastructure or classify network traffic for better bandwidth allocation. Our project, "AI Unified Network Optimizer for Public Connectivity," predicts the optimal placement of network towers and infrastructure using geospatial and demographic data. Machine learning models classify network traffic by type, enabling smarter prioritization and paving the way for AI-driven bandwidth optimization in the future. This hybrid solution combines strategic planning and real-time insights, providing a scalable and cost-effective approach to enhance public connectivity.
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