8
2
Pakistan
1 year of experience
Moavia Hassan I am a Computer Science student in my 7th semester at UET Taxila, on an exciting journey of exploration and innovation. My academic voyage has been a thrilling dive into the tech realm, where I've honed my programming, problem-solving, and software development skills. Currently, I'm deeply engaged in a groundbreaking project, "Bone Crack Detection," combining my tech enthusiasm with a passion for societal betterment. Alongside my dedicated team, we're pioneering a solution that could revolutionize medical diagnostics, enhancing patient care through AI-driven bone fracture detection. Beyond academics, I thrive in the dynamic world of coding competitions, hackathons, and collaborative ventures. My unwavering commitment to growth drives me to push the boundaries within Computer Science. As I approach the end of my undergraduate journey, I'm exhilarated by the endless possibilities that lie ahead. My vision is to leverage my tech prowess to create innovative solutions, shaping a brighter future. With unwavering determination, I stand ready to embrace challenges and seize opportunities, all in pursuit of a world where technology empowers positive change. Join me on this thrilling journey, where the future knows no bounds, and innovation reigns supreme.
TranspareX is an advanced platform that leverages AI and blockchain technology to redefine fund management by making it transparent, efficient, and secure. With Ethereum-powered smart contracts, fund releases are automated, reducing delays and minimizing human errors. A user-friendly, real-time dashboard offers stakeholders full visibility into transactions, fostering trust and accountability in financial workflows. Built using React.js for the frontend, Flask for the backend, and integrated with Python (AI/ML) and SQL databases, TranspareX prioritizes secure, tamper-proof fund tracking. Designed for organizations, NGOs, and businesses, this platform bridges technology and trust, setting a new benchmark for financial transparency.
26 Jan 2025
Patient Input: Patients can enter their symptoms, medical history, or concerns into the app. This could range from describing a specific condition, reporting a set of symptoms, or even sharing their daily lifestyle and medical background. LLM-Driven Analysis: The app utilizes advanced language models that analyze the input provided by the patient. These models are trained on a vast dataset of medical knowledge, helping to identify potential conditions or concerns based on the data provided. Preliminary Medical Report: Based on the patientโs input, the app generates a preliminary medical report. This report offers insights into possible diagnoses, recommended next steps, and any potential lifestyle adjustments. It serves as an informative starting point for further medical consultation. Improved Doctor-Patient Interaction: The app enables more effective communication between the patient and the healthcare provider by delivering well-organized, structured reports. This helps doctors understand the patient's concerns more quickly, making their consultations more efficient. The report is not a definitive diagnosis but a tool to guide the doctorโs examination.
16 Feb 2025