Muhammad Uzair Wajeeh

PROFESSIONAL PROFILE Final-year Electrical Engineering student at NUST SEECS with a growing interest in computer and communication networks. Completed an internship at CSN Lab, NUST, and currently undertaking an engineering internship at Transworld, gaining initial exposure to optical fiber communication networks and network planning concepts. Possess foundational understanding of networking principles and system-level engineering workflows. Currently pursuing a Final Year Project related to secure AI-driven healthcare systems and motivated to further develop practical networking and communication engineering skills. EDUCATION Bachelors in Electrical Engineering (BEE) School of Electrical Engineering and Computer Science(SEECS) , Islamabad , 2.25 (2022-2026) INTERNSHIP EXPERIENCE Summer Intern at CSN lab(NUST) 25-Jun-2025 - 25-Aug-2025 Learned and applied LaTeXand Overleaf for professionalacademic witing, collaborative editing, and paper formatting. Gained hands- on skills in cross-platform appdevelopment using React Native for building mobile applications. Learned to use Postman for testingand validating RESTful APls Transworld Home 08-Jan-2026 - 09-Feb-2026 Engineering Intern - OFC Planning and Development FINAL YEAR PROJECT Secure AI-Driven Healthcare System for Disease Diagnosis and Patient Management Artificial intelligence (AI) and machine learning (ML) are transforming healthcare by enhancing disease diagnosis, hospital workflow, and patient management. Traditional diagnostic methods for diseases like malaria, pneumonia, and other infections often suffer from human error, delays, and accessibility issues, particularly in remote and resource-limited regions. There is a growing need for an AI- powered healthcare management and disease diagnosis platform that can support automated medical analysis and patient-doctor connectivity. However, AI-driven healthcare systems also introduce security and privacy concerns, as unauthorized access, data tampering, and lack of accountability can result in serious privacy breaches, misdiagnoses, and regulatory violations. Without strict access control, any staff member, whether a doctor, nurse, or technician, could view or alter sensitive medical data, increasing the risks of errors, fraudulent activity, and loss of patient trust. Given the sensitive nature of medical records and personal health information, it is critical to implement advanced security measures to protect patient data from cyber threats and unauthorized access. This FYDP aims to develop a Secure AI-Driven healthcare Management and Disease Diagnosis System that integrates AI- powered disease detection, patient-doctor connectivity, and hospital workflow automation while ensuring robust security mechanisms to safeguard sensitive medical data. The system will enhance early disease detection and medical decision-making by utilizing deep learning models for medical imaging analysis and Large Language Models (LLMs) for real-time patient guidance. Beyond diagnosis, the system will support secure patient data management, appointment scheduling, resource tracking, and emergency alerts. In critical cases, it will automatically connect patients to the nearest available doctor and facilitate appointment booking. To address security concerns, the system will incorporate Role-Based Access Control (RBAC) to restrict access based on user roles, ensuring that only authorized personnel can view or modify specific patient records. RBAC enhances data security by preventing unauthorized access, improving accountability, and maintaining a detailed audit trail of all system activities. Additionally, Multi-Factor Authentication (MFA) will strengthen login security, End-to-End Encryption will secure data transmission, and Blockchain Audit Logs will provide an

Final-year Electrical Engineering student at NUST SEECS with a growing interest in computer and communication networks. Completed an internship at CSN Lab, NUST, and currently undertaking an engineering internship at Transworld, gaining initial exposure to optical fiber communication networks and network planning concepts. Possess foundational understanding of networking principles and system-level engineering workflows. Currently pursuing a Final Year Project related to secure AI-driven healthcare systems and motivated to further develop practical networking and communication engineering skills.