Syed Abdul Wahab LNU

• Developed a simple, responsive UI using React.js, HTML5, CSS3, and JavaScript (ES6+), ensuring cross-device compatibility and a mobile-friendly design using Bootstrap.
• Implemented the fetch API and RESTful APIs to retrieve and display user fitness data dynamically.
• Built RESTful APIs using Spring Boot, following an MVC (Model-View-Controller) pattern for managing user authentication, workouts, and progress tracking.
• Used Hibernate/JPA for efficient database interactions with MySQL, ensuring data consistency, and optimized query performance.
• Implemented JWT-based authentication for secure user login and session management.
• Created features for user registration, authentication with secure password storage, and a dashboard to track workout progress and fitness statistics.
• Enabled CRUD operations for workout plans (Create, Read, Update, Delete).
• Used Git and GitHub for version control and collaboration, deploying the application on Heroku/AWS Free Tier for accessibility.
• Implemented basic CI/CD pipelines using GitHub Actions for smooth deployment.
• Performed unit testing using JUnit for backend logic verification, and conducted manual testing for UI functionality and user flows.
• Used Postman to test API endpoints and ensure proper request handling.
• Documented RESTful APIs using Swagger for future maintenance, and integrated third-party fitness APIs for additional workout recommendations.

Full Stack Development

• Developed a responsive UI using React.js with Material-UI/Bootstrap for a modern user experience.
• Built RESTful APIs using Spring Boot following an MVC architecture for image processing and user interactions.
• Implemented JWT-based authentication for secure access.
• Used Hibernate/JPA with PostgreSQL/MySQL for efficient data storage and retrieval.
• Integrated real-time WebSocket communication for instant traffic sign updates.
• Deployed the frontend (Vercel/Netlify), backend (AWS EC2), and ML service (Google Cloud/AWS Lambda).
• Implemented CI/CD pipelines using GitHub Actions for automated testing and deployment.

Machine Learning Integration:

• Developed a CNN model for traffic sign detection and an SVM for classification.
• Used OpenCV for image preprocessing (contrast enhancement, noise reduction).
• Integrated DBSCAN clustering for real-time traffic congestion analysis.
• Hosted the trained ML model as a Flask/FastAPI microservice for seamless backend integration.
• Implemented text-to-speech (TTS) for real-time audio alerts.