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This project is a digital food redistribution platform called Food Bridge, developed to reduce food waste and improve food distribution by connecting food suppliers with needy institutions such as orphanages and old age homes. The system includes four main users: Admin, Food Supplier, Volunteer, and Consumer. Food suppliers can register and add surplus food details, consumers can browse available food and place orders, volunteers help with pickup and delivery, and the admin manages approvals, monitoring, and overall system control. The platform supports both free and paid food options, along with payment integration, OTP verification, notifications, and automatic volunteer assignment. It is developed using Next.js for the frontend and Node.js for the backend, with a database to manage users, food listings, orders, payments, and activity history. Overall, the system provides a structured, secure, and socially beneficial solution for reducing edible food waste and helping communities in need.

This project is a smart accident detection and emergency alert system designed to improve road safety by detecting accidents and fire incidents in real time through live road camera feeds. The system uses the YOLOv8 object detection model, trained on a custom dataset, to analyze video streams and identify emergency events automatically. It includes a web-based dashboard for admin monitoring and station management, a Flask-based backend for system processing, Firebase for real-time data storage, and a hardware communication module using ESP32 and SIM800L GSM for automatic phone calls and SMS alerts. The system also sends email notifications with event details and video evidence, and helps notify nearby emergency services such as hospitals, police stations, and fire brigades. Overall, it provides an integrated AI-based solution for fast accident reporting, automatic alert generation, and improved emergency response.

This project is a smart and integrated Fleet Management System developed to improve vehicle monitoring, transport safety, and operational efficiency. The system is designed for different types of vehicles such as buses, ambulances, and cargo trucks, and provides a single platform for real-time tracking, geofencing, driver fatigue detection, ticket checking, goods tracking, fuel monitoring, and door security. It uses a driver mobile application, an admin web dashboard, a Python-based backend, and Firebase for real-time data storage and synchronization. The project also integrates IoT hardware components such as ESP32, an ultrasonic sensor for fuel level monitoring, and an EMR relay for door status detection. Overall, the system offers a connected and intelligent transport solution that enhances safety, monitoring, and management in modern fleet operations.

Glucotwin is an AI-driven insulin dosage prediction system designed to improve pediatric Type 1 diabetes management through proactive monitoring and personalized recommendations. The system uses a Pediatric Digital Twin model to analyze real-time blood glucose data, carbohydrate intake, and patient-specific health factors in order to predict insulin dosage needs more accurately. It is built as a cross-platform mobile application with cloud-based backend support, allowing guardians and clinicians to monitor patient status, receive predictive alerts for hypoglycemia and hyperglycemia, and make safer treatment decisions. The system also includes safety guardrail logic, secure health data handling, and dedicated dashboards for both guardians and doctors, making it a smart, clinically focused healthcare solution.

This project is a Smart Attendance System Mobile Application designed to improve traditional attendance methods in educational institutions. The system uses Flutter for mobile app development and Firebase as a real-time database to manage data efficiently. It introduces advanced technologies such as face recognition and geofencing to ensure secure and accurate attendance marking. The system includes three main users: Admin, Teacher, and Student, each with their own dashboard and functionalities. Attendance is marked only when the student is physically present within a defined location and successfully verified through face recognition using a Raspberry Pi-based system. This dual verification helps prevent proxy attendance and reduces human errors. The application also provides real-time updates, notifications, and easy management of classes, subjects, and attendance records. Overall, the system offers a reliable, automated, and user-friendly solution for modern attendance management.

This project presents a Sign Language Recognition and Translation System designed to bridge the communication gap between deaf and hearing individuals. The system enables two-way communication by converting sign language into text and speech, and vice versa. It uses machine learning and computer vision techniques, including YOLOv8 for hand sign detection and MediaPipe for real-time hand tracking and gesture recognition. The backend is developed in Python with a Flask-based web application, while the frontend uses HTML, CSS, and JavaScript. The system also integrates text-to-speech functionality and supports real-time interaction through a user-friendly interface, making it suitable for practical communication and learning purposes.

NeuroLearn is an intelligent AI-powered e-learning platform designed to provide personalized learning experiences for students. Unlike traditional systems, this platform adapts to individual student needs using a generative AI engine that analyzes user behavior, progress, and learning patterns. The system is built on a role-based architecture, including Admin, Teacher, and Student modules. Admin ensures quality by approving teachers, teachers create and manage courses, while students can enroll, track progress, and receive AI-based learning recommendations. Developed using Python Flask backend, the platform efficiently handles authentication, course management, and AI integration. NeuroLearn enhances engagement by offering adaptive learning paths, progress tracking, and intelligent tutoring support, making it a modern solution for digital education. 👉 This project demonstrates the integration of Artificial Intelligence + Web Development + Educational Technology in a single smart platform.