An application to automate book issue process where users can search for books and issue them within an organization. Restful APIs have been implemented using Spring Boot and the frontend is built using React.js.
Spring Boot | ReactJS | MySQL
Web Server for RFID based Attendance & Door Lock System
A web server that manages attendance and door lock system using RFID cards. The system uses an ESP8266 microcontroller to read RFID cards and send the data to the Java Backend. The server stores the attendance data and controls the door lock system.
The system is built using Spring Boot.
Spring Boot | REST API | MySQL
Expense Tracker Application
A web application that allows users to track their expenses. The system is built using React.js and Tailwind CSS.
The system allows users to add, update, delete, and view expenses. The data is stored in Firebase.
ReactJS | Tailwind | Firebase
Student Database Management System
A web application that manages student data using a MySQL database. The system is built using Node.js, Express.js, and MySQL.
The system allows users to add, update, delete, and view student data. API endpoints have been implemented according to REST architecture.
NodeJS | ExpressJS | MySQL
Task Management Application
A web application that allows users to manage tasks. The system is built using React.js, Node.js, Express.js, and MySQL.
The system allows users to add, update, delete, and view tasks. The frontend is built using React.js and the backend is built using Node.js and Express.js.
ReactJS | NodeJS | ExpressJS | MySQL
Driver Drowsiness Detection System
A system that detects driver drowsiness using a camera and alerts the driver.
The system uses a Convolutional Neural Network (CNN) to detect the driver's eyes and determine if they are closed or open. If the eyes are closed for a certain period of time, the system alerts the driver by sounding an alarm.
The system is built using Python and the Keras library, and is deployed using Flask.
Deep Learning | Neural Networks | Flask
Predictive Model for Industrial Device Fault Detection
A system that detects faults in industrial devices using machine learning.
The system uses data collected from industrial devices to train a machine learning model. The model is then used to predict faults in the devices.
The system is built using Python and the Scikit-learn library, and is deployed using Flask.
Machine Learning | Data Analysis | Flask
Portfolio Website
My personal portfolio website built using React.js, Next.js, and Tailwind CSS.
The website is deployed on Vercel.