DevOps Engineer (AWS) and Backend Developer with hands-on experience building scalable infrastructure and production-ready backend services. I work with Infrastructure as Code (Terraform, AWS CDK), Docker-based deployments, CI/CD automation, and AWS monitoring/troubleshooting.
I enjoy designing clean, modular backend architecture in NestJS and integrating reliable deployment workflows to improve release stability and environment lifecycle management. I’m also focused on security, observability, and delivering dependable systems end-to-end.
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An advanced machine learning-powered web application for detecting AI-generated and manipulated images using ensemble learning techniques. It uses ensemble ML models trained on 140,000 images (70k real + 70k AI-generated) to distinguish authentic from forged media at 94.5% accuracy, combining feature extraction methods like HOG, LBP, and FFT spectrum analysis.
Key Features:
4 Ensemble ML Models: XGBoost (94.5% accuracy, primary), Random Forest (91.2%), SVM (88.7%), Logistic Regression (86.4%)
Advanced Feature Extraction:
HOG (Histogram of Oriented Gradients) — structural anomaly detection
LBP (Local Binary Patterns) — texture micro-pattern/GAN artifact detection
FFT (Fast Fourier Transform) — frequency domain irregularities, checkerboard artifacts
Flask backend integration powered by Google Gemini AI for enhanced analysis
Interactive analytics dashboard with real-time model performance visualization
GitHub URl :https://www.twine.net/signin
Live URl: https://www.twine.net/signin
Developed a full-stack health tracking app using MongoDB, Express.js, React, Node.js, and Tailwind CSS. Designed and
integrated RESTful APIs for user authentication and workout tracking (cardio and resistance). Ensured a responsive and
user-friendly interface with secure JWT-based login.
GitHub Repo:https://www.twine.net/signin
Live URl :https://www.twine.net/signin
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