Asma Zubair@Asma_Zubair

WorkUp is a project management tool designed to streamline and automate the process of project setup and management. Using AI-powered algorithms, the tool efficiently distributes tasks to team members based on their expertise, ensuring optimal productivity. WorkUp allows users to upload project descriptions and team expertise in various formats (PDF, DOCX, TXT) or manually input the data. The AI system analyzes this information to assign tasks, generate detailed workflows, and create visual flowcharts for a clearer understanding of the project’s progression. Additionally, WorkUp suggests creative project names and generates a starter code structure to kickstart development. Built on a user-friendly Streamlit interface, the tool allows for seamless interaction and feedback collection, enabling continuous improvement. WorkUp saves time, enhances collaboration, and scales easily to accommodate projects of different sizes and complexities. Future enhancements include advanced customization options, integration with existing project management tools, and mobile support.

DocuSummarize is an innovative AI-powered chatbot designed to simplify document analysis by providing instant summaries of PDF and PowerPoint files. Built using the Llama model, it enables users to interact with the chatbot, asking follow-up questions to dive deeper into the content. This tool is perfect for professionals, students, and researchers who frequently manage large documents. It supports seamless text extraction and summarization from PDFs and PowerPoint slides, providing concise insights in real time. Looking ahead, DocuSummarize plans to expand its capabilities by incorporating image summarization through OCR, making it a more versatile tool for comprehensive content analysis.

Network Anomaly Detection & Congestion Prediction System This tool is designed to help you analyze and visualize your network traffic data to identify unusual patterns (anomalies) and predict potential network congestion. Whether you’re a network admin, a data scientist, or simply curious about traffic analysis, this app has you covered. Key Features: Anomaly Detection: Identify abnormal network traffic based on various metrics like packet length, protocol type, and time. Congestion Prediction: Predict potential network congestion by analyzing traffic patterns and detecting anomalies. Interactive Visualizations: Stunning charts and graphs for a more insightful analysis of the data, anomalies, and congestion. Technologies Used: Streamlit: The backbone of this web app, providing an interactive and easy-to-use interface. Pandas: For data manipulation and analysis of the network traffic data. NumPy: For numerical operations and data transformations. Plotly: Interactive and dynamic visualizations like histograms, scatter plots, and pie charts. Scikit-learn: Machine learning models like Isolation Forest for anomaly detection and congestion prediction. Python: The main programming language behind the app’s backend logic. Steps to Use: Step 1: Upload your network traffic data (CSV format). Step 2: Run Anomaly Detection to detect abnormal traffic patterns. Step 3: Predict Network Congestion based on detected anomalies. Visual Enhancements: Anomaly Distribution: A histogram to visualize how many records are normal and how many are anomalous. Anomalies Over Time: Scatter plot shows anomalies in relation to the time of day. Anomaly vs Normal: A pie chart to compare the number of normal vs anomalous records.

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

Our project, DeepSeek Web Assistant, is a powerful AI-driven browser extension designed to make web browsing more interactive and insightful. With this extension, users can: --Summarize web pages instantly using DeepSeek’s AI models. --Ask questions about web content and receive relevant answers. --Use the "Highlight to Ask" feature, allowing them to highlight text and ask questions related to the selected content. --Interact with research papers, articles, and general web pages to extract key insights efficiently. --Seamlessly integrate AI-powered search and summarization into their browsing experience. This tool is particularly useful for students, researchers, and professionals who need quick, precise information while browsing the web.