ADRIAN RAMDHANY

Anticipating a massive turnout of Gen Z and local communities at Lokananta Bloc, I took charge as the Tech Lead to engineer an independent, high-speed digital ticketing platform—making ticket sales seamless and completely eliminating the need for third-party services. To ensure attendee security and prevent gate bottlenecks, I built a custom gate entry system with real-time QR validation, maintaining zero server downtime during peak loads.

Developing purely in Java and compiling into an Android APK, I engineered the robot's autonomous execution sequence. The core algorithmic challenge involved computing spatial navigation to capture target QR codes, analyzing the data, and calculating the exact 3D trajectory to fire a laser at designated coordinates. This required highly optimized image processing and movement logic to maximize the score while preventing resource exhaustion on the Astrobee's hardware before the time limit expired.

An artificial intelligence-based forex trading system engineered to analyze market trends and predict real-time price movements. The architecture deploys machine learning algorithms to process historical data and technical indicators. This computational approach systematically optimizes automated trade execution strategies.

Oxygenix provides early detection for Chronic Obstructive Pulmonary Disease (COPD) risks. The system utilizes machine learning algorithms trained on pulmonary health parameters. It executes fast and accurate risk analyses. This computational tool directly assists medical professionals and users in preventive health monitoring.

The short-form content phenomenon moves at lightning speed, but complex editing workflows hold creators back. AWSM AI Clipper is an innovative desktop application that turns raw video links into viral, ready-to-use and ready to post and giving full control back to the user without the monthly subscriptions.

Dermascan engineers an automated diagnostic tool for skin disease classification. The system implements Convolutional Neural Networks (CNN) to process and analyze dermatological images. The model extracts structural features from clinical photographs to predict specific skin conditions accurately. This computational framework accelerates preliminary diagnoses and supports clinical decision-making.