AAQIB ALI@aaqib_ali96

Dr. AIML revolutionizes healthcare by integrating cutting-edge AI with precision, adaptability, and empathy. It leverages advanced APIs and technologies to deliver real-time medical consultations, personalized diagnostics, and ethical care anytime, anywhere. Dr. AIML interacts seamlessly with OpenAI, Llama3 Groq, and Together.ai APIs, enabling access to powerful language models and medical insights. Using python-dotenv, it securely manages API keys and environment variables for streamlined operations. The platform’s user interface is powered by NiceGUI, a FastAPI-based framework that ensures a responsive and user-friendly experience. Backend operations utilize aiohttp for high-performance asynchronous HTTP handling and asyncio for efficient concurrent processing. For data handling, Dr. AIML employs ujson for fast JSON processing and psutil for system-level process management, ensuring optimal performance. Additionally, Streamlit is used to create an interactive and engaging front-end experience for users. With its mission to provide accessible, accurate, and ethical healthcare, Dr. AIML combines state-of-the-art APIs, secure integrations, and advanced machine learning to redefine the future of medical consultation. It’s more than just AI; it’s a trusted partner in safeguarding human health.

Attach Spin Astronauts: A Spin-Launch Payload System Attach Spin Astronauts is a spin-launch payload system for manned orbital missions, using CrewAI and a browser-controlled agent to enhance efficiency and reduce carbon emissions. Five-Stage Mission Framework Spin Acceleration: A ground-based launcher propels the payload to high velocity, minimizing onboard propulsion needs. Orbital Insertion: The payload is guided into orbit with small thruster corrections. On-Orbit Stabilization: Attitude correction ensures readiness for docking. Rendezvous: A separately launched manned vehicle docks with the payload. Mission Execution: The payload is integrated into mission objectives. Supporting Programs Browser-Based Control: Real-time mission oversight and adjustments. Spin Launcher Optimization: AI-driven launch efficiency improvements. Orbital Navigation AI: Ensures stability and collision avoidance. Autonomous Docking: CrewAI agents enable seamless payload integration. Payload Utilization System: Manages deployment and mission execution. Development Process The project was designed using ChatGPT, followed by two CrewAI programs: one for Design Review and another for a Launch Press Release. Sora and Suno generated a video demo, and a Python Orbit Simulation was developed based on CrewAI’s redesign. Finally, Browser Use was assigned as the Launch Control Engineer, overseeing all programs. Advantages Cost-Efficient: Reduces reliance on chemical rockets. Eco-Friendly: Minimizes emissions for sustainable space exploration. Scalable: Adaptable for research and logistics missions. This innovative approach provides a lower-cost alternative to traditional launches, optimizing efficiency while supporting manned missions in space.

1. Problem Statement Worldwide, millions of communities and schools in remote or economically disadvantaged areas suffer from unreliable or absent internet connectivity. Traditional infrastructure is often too expensive or fails under challenging conditions such as floods, mountainous terrain, or rural isolation. This connectivity gap restricts educational opportunities, emergency response, and overall socioeconomic development. 2. Our Solution The Giga Node system introduces a Level-4 autonomous connectivity network that self-manages and optimizes data routes with minimal human oversight. Its key innovations include: Autonomous Decision-Making: By harnessing AI models (such as GPT-4 and LSTM-based predictors), the system can dynamically plan and re-route traffic, ensuring optimal connectivity even in the face of environmental challenges. Terrain Intelligence: Using advanced GIS tools (ArcGIS and WhiteboxTools), the system analyzes terrain features, predicts flood or drainage risks, and strategically places network nodes to avoid potential disruptions. Decentralized Security and Transparency: Each decision is securely logged on an Ethereum-based blockchain. This not only ensures tamper-proof records but also builds trust in the system by verifying network changes in real time. Real-Time IoT Monitoring: Integrated IoT sensors continuously track the health and performance of each Giga Node, enabling proactive maintenance and automatic failover during outages. 3. Technical Architecture The system is organized into several interlocking layers that together form a robust, scalable network: Data Acquisition Layer: GIS Terrain Data: Satellite imagery, Digital Elevation Models (DEM), and weather data. IoT Sensors: Deployed on Giga Nodes to collect real-time operational data. AI Engine: