Adeeba Rafi@Adeeba12

Tax departments often face challenges like long waiting times, overwhelmed staff, & inconsistent responses to citizen inquiries. Our solution, "AI Tax Assistant" addresses these issues by leveraging Large Language Model (LLM) technology to automate citizen interactions in tax departments.This AI chatbot is designed to act as a virtual staff member, capable of: Answering a wide range of citizen tax-related questions using a comprehensive knowledge base and also deal inquiries through an intuitive interface. Providing accurate, real-time information on policies, procedures, and deadlines. Targeted at citizens and tax officials, the solution minimizes workload, reduces operational inefficiencies, & enhances citizen satisfaction. Unique features include a constantly updated knowledge base, 24/7 availability, multilingual support, & seamless integration with existing office systems. By embracing this innovation, tax departments can significantly improve service delivery & operational efficiency.

Our idea focuses on optimizing IT infrastructure and data center management by predicting maintenance needs. The project uses metrics like CPU load, storage utilization, and temperature to assess risk levels and determine when maintenance is required. By analyzing these metrics, the system predicts the next service date, ensuring high availability of critical systems, reducing downtime, and preventing server crashes. The interactive tool, built using Python and Gradio, provides a user-friendly interface for IT managers to make informed decisions and streamline operations. Our AI-powered tool predicts maintenance needs based on key metrics. This prevents downtime, improves performance, and saves costs by enabling proactive action.

The Smart Space Habitat Manager is a cutting-edge AI-driven solution designed to autonomously manage life-support systems in space habitats like the International Space Station (ISS), lunar bases, and Mars colonies. This system integrates real-time astronaut health monitoring, machine learning-based predictive maintenance, and statistical anomaly detection to prevent failures before they occur. By leveraging Random Forest regression models for failure prediction and statistical analysis for anomaly detection, it ensures a safe, self-sustaining environment without constant human intervention. Live sensor data tracking oxygen levels, temperature, CO₂ concentration, humidity, and power usage provides instant alerts for potential risks, enabling proactive decision-making. With its automated monitoring, predictive AI models, and real-time anomaly detection, this project represents a crucial step toward the future of sustainable space exploration, ensuring that astronauts remain safe while humanity expands beyond Earth