Projects

Managing a university timetable, especially when it’s stored in a shared Google Sheet, can be a hassle for students and faculty. At FAST University (Islamabad Campus), many students, faculty, and MS/Bachelor’s candidates struggle with accessing their class schedules efficiently. That’s where the NU Timetable app comes in a beautiful, user-friendly solution designed to streamline the entire process and offer powerful features, both online and offline.

Your ultimate fitness companion! Track daily steps, set goals, view history, and calculate calories and distance effortlessly. Enjoy ad-free, secure usage with offline support and background operation. Created by Ahmad Development Studio to keep you active and healthy. Stay motivated, stay fit!

Developed an Cafe Management System using C# and Microsoft SQL Server. The system streamlines restaurant operations by managing orders, menus, inventory, and customer data. It features a user-friendly interface for efficient order processing, real-time database integration, and robust reporting tools to enhance operational efficiency and decision-making.

Simplify your entrance exam aggregate calculation with this user-friendly mobile app. No internet or personal information required just install and calculate effortlessly, even offline!

The Society Management System, built with Microsoft SQL, Microsoft .NET, and C#, is a transformative platform that streamlines society management in universities. It enables societies to register, update information, manage members and resources, and receive feedback to drive continuous improvement and foster engagement

Android app built with Kotlin and Firebase, provides a seamless platform for users to log in, manage accounts, and engage in adopting or selling pets. With real-time data synchronization and secure authentication, it ensures an efficient and user-friendly experience

This project implements and compares Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and a simplified Residual Network (ResNet) using PyTorch to classify hand gestures (rock, paper, scissors) from CGI-generated images. The workflow includes data loading, preprocessing, augmentation, model training, evaluation, and visualization using TensorBoard.