WAQAS ALI@WAQAS

Disease identify and mapping are artificial intelligence use camera to capture and give report output from disease symptoms it's like our doctor watching our health as private healthcare, and give us good response to our disease. It's taking much time to our doctor for take information from patients, it's more useful make this AI trainable for our doctor and the fast respon and massif data collecting make our doctor job's less busy and they can take attention to our health development time by time Starts from now. But in this prototype I'm use a small case with Small data but not in real time condition.

We're Team TerraSentinels part of MindsDB hackathon , on a mission to revolutionize land use monitoring with AI and geospatial data! Our project: Intelligent Land Use Monitoring Agent, aims to detect unauthorized land use, deforestation, and urban expansion in real-time by analyzing satellite images and large-scale geospatial datasets. We plan to use tech like MindsDB, Google Earth Engine, and TensorFlow, all wrapped in a sleek web interface with Flask. If you're passionate about AI, geospatial data, or just love tackling big challenges with a positive, can-do attitude, we’d love to have you on board!

Granite Gurus Many Eyes is an extension of three previous Hackathons: Build Your AI Startup, Codestral, and Edge Runners. This Hackathon introduced two powerful principles: (1) Uniqueness and (2) Many Eyes, both of which were crucial in advancing the exploration of Multidimensional (MD) space. These principles were effectively demonstrated through a custom search engine built on top of IBM Granite, highlighting their transformative potential. The Principle of Uniqueness asserts that spatial points can be arranged in any configuration as long as the number of points remains constant and each point is unique. This principle underpins the 2D Infinite Plane, which aids in visualizing interactions in higher dimensions and emphasizes representing numeric values as symbols rather than numbers to reduce computational costs. The Principle of Many Eyes posits that knowing one aspect of a system allows for understanding the entire system, thanks to the symmetries in MD-space. Unlike traditional programming, where functions have a single input and output, MD-systems can yield multiple outputs from a single input by leveraging these symmetries. This was illustrated in our use of IBM Granite for pattern search, which returned multiple results due to MD-space symmetries, and then predicted the next pattern, showcasing the system's adaptability. Integrating IBM Granite with the 2D Infinite Mathematical solution creates a powerful combination that enhances search engine performance by reducing computational overhead, improving accuracy, and accelerating processing times. This approach not only offers a superior user experience but also aligns with sustainability and cost-saving goals, positioning the search engine as a leader in the AI-driven market. By optimizing resources and delivering faster, more accurate results, this project demonstrates the potential to revolutionize search technology, making it a valuable asset for businesses aiming to lead in the AI space.

Our philosophy is to fix and add, not cut. We approach DOGE with abundance, leveraging AI as a one-to-many solution that addresses problems with innovative, unexpected solutions. Reimagining Learning with AI By incorporating agentics—autonomous AI agents—learning evolves into interactive and immersive experiences: Enhanced Learning: Fictitious companies come to life, engaging learners in dynamic discussions. Software Integration: Agentics assist in creating functional software, enabling learners to produce real-world products. Addressing Education’s Challenges Education is broken: costly, impractical, and uncreative. AI-driven agentic scrum teams offer solutions by emphasizing collaboration, practical skills, and real-world impact. Project Workflow The project workflow consisted of three main steps: Building a JavaScript Course Player: The foundation for an interactive course experience. Creating the Grok Agentic System: A configuration file informed Grok where agentics were located within the course. This included a Grok Agentic Dialog Team, represented by the agentic agents: Zoe Kim, Software Engineer Alex Patel, DevOps Engineer Jack Dawson, Cloud Architect Designing the Grok Query System: This system accompanies the learner throughout the course, providing rich, interactive conversations and coding examples. Transformative Approach Agentic teams foster hands-on learning where students actively solve challenges, such as designing efficient housing. They empower learners to leave the classroom with deployable skills and creative solutions. Acknowledgments Special thanks to the team: Jaweria, Amanullah, ALI, Abeera, and Davy.

Problem: Public sector networks in underserved regions face crippling energy costs (40-60% of budgets), frequent outages (15-20% downtime), and reactive maintenance models that drain resources. Solution: GridGuardians leverages AI to: • Predict equipment failures 72hrs in advance using Prophet time-series forecasting • Reduce energy waste by 22% via anomaly detection (Isolation Forest) • Prioritize repairs using cost-benefit simulations (Random Forest regression) Target Audience: Governments, NGOs, and institutions managing connectivity for: • Rural schools (Giga partnership focus) • Healthcare facilities (HealthSites integration) • Low-income urban communities Unique Features: • Offline-first design for low-bandwidth areas • Open-source core with federated learning • Carbon credit reporting for sustainability grants

The DeepSeek Business Creator demonstrates how to leverage DeepSeek's latent reasoning space to emulate three-agent interaction for multi-business creation on the web. This system integrates a simple Markov chain optimization process with browser automation, allowing businesses to emerge dynamically in an optimized sequence. The approach enables AI-driven enterprises to launch and scale efficiently, simulating real-world economic expansion with minimal human intervention. From an initial dataset of 200 business ideas, 20 were selected based on predefined success factors. Each of these businesses was assigned three AI agents, each specializing in different aspects of business creation: market research, operational strategy, and adaptive growth. The sequence of business creation was optimized using a Markov chain model, ensuring that dependencies between business types and market readiness were accounted for. This optimization increased the likelihood of success by structuring the order in which AI-driven businesses launched and scaled. AI agents interacted within DeepSeek’s latent space to generate business plans dynamically. These plans emerged from the interplay of three AI agents, refining concepts based on strategic reasoning. Once validated, browser automation was used to execute the launch of these businesses, coordinating their deployment across different online markets. As AI-driven businesses launched, they began to emerge in various markets worldwide. The system's ability to simulate economic scaling in a decentralized manner demonstrated the potential of AI agents to drive real-world business success autonomously. As businesses evolved, AI agents adapted and absorbed the most successful strategies. The agent absorption rule ensured that underperforming agents were phased out, while the most effective decision-making patterns were integrated into the next generation of business iterations.