Malik Danial Ahmed@danial

This project leverages machine learning to optimize TVWS base station locations using datasets on schools, towers, demographics, elevation, and spectrum. The dashboard provides key insights, including predicted interference, failure risks, and AI-driven recommendations to enhance network performance. A dedicated Potential Location page simulates TVWS availability on an interactive map, identifying optimal base station placements based on factors like population density and elevation. Additionally, a Procurement page, currently in progress, will track infrastructure and equipment acquisition for future deployments. By integrating historical data and predictive analytics, this system enables proactive decision-making to improve TVWS coverage and reliability.

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🔍Create AI-driven, open-source solutions to optimize public sector network management in critical areas like predictive maintenance, total cost optimization (TOC), and energy efficiency—enhancing public sector networks' scalability, efficiency, and sustainability in underserved regions. 📅 Join us February 28–March 2, 2025: Participate online or in person in vibrant Barcelona, on the sidelines of MWC 2025, to collaborate with peers worldwide and shape the future of public sector connectivity. 👨‍🏫 Work alongside experienced industry mentors who will provide guidance and insights to elevate your solutions. 💰 $5,000 prize pool for the winners! 🏆 Compete for a chance to join the lablab NEXT incubator program! 🧑🏻‍💻 Don’t wait! Sign up now to secure your spot before the Kick-Off!

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