Malik Muhammad Zubair@MalikZubair069

Smart Meeting Summarizer Smart Meeting Summarizer revolutionizes how professionals manage meetings by transforming lengthy transcripts into actionable insights. It leverages cutting-edge AI technology, including NLP models, Gradio for a user-friendly interface, and Hugging Face Spaces for deployment, to deliver a seamless, interactive experience. The tool supports diverse input formats such as text, PDFs, and live transcriptions, making it versatile for various use cases. By summarizing key points, decisions, and action items, it helps users save time and stay focused on critical tasks. Additional features include keyword highlighting and sentiment analysis to provide context and enhance understanding. The backend, developed in Python, uses advanced libraries like Hugging Face's Transformers, spaCy, and NLTK to ensure high-quality processing. Gradio powers the intuitive frontend, allowing users to upload data, view summaries, and download insights effortlessly. Key benefits include: Time-saving: Quickly extract critical information from lengthy discussions. User-friendly: Clean interface for easy navigation. Action-oriented: Focus on decisions and next steps. Customizable: Supports multiple formats for diverse needs. Deployed on Hugging Face Spaces, Smart Meeting Summarizer ensures scalability, accessibility, and robust integration. Future plans include real-time summarization, multilingual support, and integrations with platforms like Slack and Zoom. Smart Meeting Summarizer is a powerful tool for professionals seeking efficiency and clarity in their meetings, turning conversations into actionable outcomes.

Forecasting algorithms for network prediction via simulated data, to integrate modern solutions (Satelitte ISPs) - for most people in the world. The solution is in python + using some google colabs. Github repository is available in : https://github.com/Synthapse/Network and Google Colab in https://colab.research.google.com/drive/1MGkMvDWOphm4Iyn8Kwq-_I2LyH07Khv9?usp=sharing#scrollTo=pSjoPXIHn2P7. There are a lot of potential cases for improving solutions - and feed it with real data. Cause the real network may contains more nodes- the solution contains only 217 nodes, that the separating via different kafka brokers and kubernetes will be supportive

We focused on London for PoC - cause it’s the most developed smart city with the network infrastracture. And from that point for development is great for One Replication Strategy The London Smart City Optimization project enhances urban infrastructure using AI, IoT, and cybersecurity. The focus areas include This initiative ensures London remains a sustainable, secure, and efficient smart city. Network.AI - 3 LLM Agents (which communicate to each other for gathering spec and automate documentation process (cyb1, net1, ene1) Network.Data - Algorithms for data science analysis, mostly free databases + kaggle (London, 2014y, 2021y) Network.Frontend - Promotional react app, which integrate agents + provide basic auth (firebase) Network.Backend - Analyse of different protocols, and communicating with frontend due to rapport generating