Use of AI and open source to optimize networks

The project is focusing on track 3. We are optimizing resource allocation by integration open-source especially python and MySQL with AI technologies from IBM Cloud and Huawei Cloud. The problem is that schools in the public sector may not be conversant with monitoring telecommunication infrastructure such as determining signal coverage or even locating possible WI-FI faults. Additionally, their is lack of an optimization framework in underserved regions for how to use the internet. Our solution relates to creation of AI intelligence assistants together with data platforms, the goals for providing insights of factors such as predictive maintenance or performing tasks autonomously. We have created an intelligent assistant that is able to answer questions such as describing relation of signal strength, internet speed, bandwidth and reliability with having high downtime and latency. The Intelligent assistant also provides recommendations such as how to improve performance of basic telecommunication infrastructure. We are integrating open source platforms by using AI to generate new responses then taking the code used to generate the responses then placing it in open source platforms such as python or MySQL. Currently, the language being is used for the code is python, the code is obtained from Watsonx.ai which is from IBM. For asset utilization analytics i am using Cognos analytics so as to identify dormant or underused infrastructure with AI-driven tools. The AI driven tools provide narrative insights based on data uploaded in the form of excel sheets. Performance of the telecommunication infrastructure needs to be summarized into an excel document The project for last mile enhancement is designed by an application from Huawei which is called Appcube, Appcube has various features such an IoT (Internet of Things) platform that is used to simulate performance of telecommunication devices. The demo link is a prompt notebook from Watsonx.ai from IBM.