Juri Krivoruchko@jkrivoru985

Access to high-speed internet is essential for modern education, yet many schools, especially in remote areas, lack proper connectivity. Traditional approaches, such as building new 5G towers, are expensive and inefficient. Our project leverages Artificial Intelligence and mathematical modeling algorithms to classify schools based on their connectivity needs and generate the most cost-effective scalable expansion strategy. Using AI-powered clustering techniques, we analyze school locations, existing network coverage, and infrastructure constraints to categorize schools into four main types: 1 - Fully Connected Clusters: Single or groups of schools where all have internet connection. 2 - Partially Connected Clusters: Groups of schools where at least one already has internet access. 3 - Isolated Clusters: Multiple schools with no existing internet infrastructure. 4 - Remote Standalone Schools: Single schools lacking any connection. Our mathematical optimization model then determines the optimal network expansion plan, recommending the most efficient infrastructure improvements. By intelligently leveraging existing connectivity and optimizing the placement of additional access points, we provide universal school connectivity with minimal cost and maximum efficiency. Key AI Features and Benefits - AI-Driven Classification & Clustering: Algorithms analyze geographical, infrastructure, and network data to classify schools and group them strategically. - Network Optimization: Mathematical models forecast the best expansion strategy, ensuring connectivity with the least infrastructure investment. - Cost-Efficient & Scalable Solution: Data-based decision-making enables rapid scalability and adaptation to different regions. This project is designed for governments, NGOs, and telecom providers seeking AI-driven, data-backed solutions to bridge the digital divide in education efficiently and affordably.