Muhammad Danish@danishbaloch

Here I trained an anomaly detector for the prediction of the quake timestamp on the lunar dataset provided by NASA with 99% accuracy.

The AI-Powered Medical Query Assistant is designed to provide users with quick, accurate answers about medicines, their uses, and potential side effects. By leveraging a vast dataset from PubChem, the app uses advanced AI techniques like sentence embeddings and FAISS indexing to process user queries efficiently. This tool benefits patients, caregivers, medical professionals, and researchers by offering easy access to critical drug-related information. It helps users make informed decisions about medications, avoid harmful side effects, and discover alternative treatments. With its user-friendly interface, this app ensures medical information is readily available for anyone seeking reliable health insights.

TestFastTrack is an innovative app designed to enhance IELTS test preparation by providing immediate, intuitive feedback. Traditional learning methods often leave users struggling to understand their mistakes; TestFastTrack addresses this by utilizing real IELTS test passages and images to create a clear feedback loop. Users receive instant, detailed explanations for their errors, facilitating actionable learning rather than rote memorization. The app’s visual approach and progress tracking empower learners to engage with the material meaningfully and monitor their improvement over time. With over 3.5 million IELTS test-takers annually, TestFastTrack meets the growing demand for efficient, personalized learning tools.

In this project, we confront the linguistic barriers faced by Yoruba speakers due to limited language resources. Image generation models primarily excel with English prompts, posing a challenge for non-English speakers. To address this, we embarked on a dual-track approach: data collection and model development. Firstly, recognizing the scarcity of Yoruba datasets, particularly in image generation prompts, we meticulously curated our own dataset. English sentences were carefully selected to serve as image generation prompts and then translated into Yoruba using a dictionary-based approach. Next, we developed a custom translator model trained specifically to translate Yoruba into English. This intermediary step ensures seamless integration with image generation models, allowing for smoother operation and accurate results. Through rigorous testing, we achieved an impressive 85% accuracy on the test set, affirming the efficacy of our approach. The core strength of our project lies in its ability to empower users to generate images in their native language without encountering language barriers. By collecting our own data and training custom models, we circumvent the limitations imposed by the scarcity of Yoruba resources. Leveraging the SDXL API for image generation further enhances the user experience, ensuring high-quality outputs. Looking ahead, we envision extending our efforts to include additional languages such as Fon and Dendi, expanding our dataset and catering to a broader audience. Furthermore, our ultimate goal is to develop a model capable of directly generating images from Yoruba, Fon, and Dendi without the need for translation into English. In summary, our project not only addresses a pressing need within the Yoruba-speaking community but also lays the groundwork for future advancements in multi-lingual image generation. Through our innovative approach, we pave the way for inclusive, barrier-free communication and creative expression.

🔍Create AI-driven solutions using open-source technologies to optimize public sector network management—planning, procurement, and maintenance—for scalable, sustainable school connectivity in underserved regions. 📅 Join us January 24-26: Participate online or in person in Geneva to develop innovative solutions with peers globally. 👨‍🏫 Work alongside industry mentors for insights and guidance on your projects. 🏆 Compete for a chance to join the Lablab NEXT incubator program! 🧑🏻‍💻 Sign up before the Kick-Off Stream to secure your spot!

⌚ Take on a 48-hour challenge packed with creativity. 🚀 Build an app powered by Llama 3.2’s capabilities. 🎓 Get advice and support from industry experts along every step of the way. 🏆 Win an opportunity to participate in the Lablab NEXT acceleration program 💰 Stand a chance to win $5,000 in prizes. 🌟 Win the opportunity to be invited, with transport and accommodation covered, to our upcoming Giga Hackathons in Barcelona or Geneva in partnership with UNICEF. 🧑🏻‍💻 Sign up before the Kick-Off Stream to lock in your spot!

🚀 Unleash 10x Dev Speed with Replit & Cursor: Welcome to the Future of Coding. ⚡ Turbocharge your builds, prototypes, and launches with cutting-edge AI integration. 🤖 Master the power of Claude Sonnet’s LLM to streamline every step of your workflow. 🌌 Team up with visionary developers and create something extraordinary together. 📅 Secure your spot now and unlock the key to unstoppable efficiency!