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Latest Project Ideas
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Food Bridge: Smart Surplus Food Redistribution Platform
Food Bridge is a smart food redistribution platform developed to reduce food waste by connecting food suppliers with needy institutions such as orphanages and old age homes. The system provides a structured digital solution where suppliers can list surplus food, consumers can browse and request available items, volunteers can support delivery and handover, and the admin can manage approvals and monitor the complete workflow. The platform supports both free and paid food options, secure payment integration, OTP verification, notifications, and role-based access. Food Bridge helps improve food distribution efficiency, reduces unnecessary waste, and creates a practical social impact through technology.
Smart Road Accident and Fire Detection & Emergency Alert System
This project presents an AI-based smart road accident and fire detection system that monitors live camera feeds and detects critical incidents in real time using the YOLOv8 model. The system is developed with a web-based dashboard using Flask, HTML, CSS, and JavaScript, while Firebase is used for real-time data storage and station management. When an accident or fire is detected, the system automatically identifies the relevant station and sends alerts through SMS, phone call, and email using ESP32 and SIM800L. The proposed solution helps reduce emergency response time, improves road safety, and provides an intelligent automated monitoring system for modern traffic environments.
Fleet Management System
The Fleet Management System is a smart and integrated solution designed to improve vehicle monitoring, transport safety, and operational efficiency. The system provides a single platform for managing buses, ambulances, and cargo vehicles in real time. It combines GPS-based live tracking, geofencing, driver fatigue detection, digital ticket checking, goods tracking, fuel level monitoring, and door status alerts. The project includes a driver mobile application, an admin web dashboard, a Python-based backend, and Firebase for real-time data storage and synchronization. Hardware components such as ESP32, ultrasonic sensor, and EMR relay are used to collect and transmit live vehicle data. This system helps organizations improve route monitoring, driver safety, cargo security, and overall fleet control.
Glucotwin - AI-driven Insulin Dosage Prediction System
GLUCOTWIN is an AI-driven healthcare application developed to support diabetes management in children aged 3 to 12 years. The system helps guardians by predicting insulin dosage based on important health factors such as blood pressure, body temperature, BMI, blood sugar condition, meal timing, and carbohydrate intake. The application is built using Flutter and Firebase, while a trained machine learning model processes health inputs and returns insulin predictions in real time. To improve safety and reliability, doctors can review the predicted results and provide medical recommendations. The system offers a structured, user-friendly, and intelligent platform for better child diabetes care
Smart Attendance System
This project presents a Smart Attendance System Mobile Application designed to improve traditional attendance methods in educational institutions. The system uses Flutter for the mobile app and Firebase for real-time data management. It ensures secure and accurate attendance by combining geofencing and face recognition using Raspberry Pi. The system verifies both the student’s location and identity before marking attendance, which helps prevent proxy attendance and reduces manual errors. It provides separate dashboards for Admin, Teacher, and Student, making the system efficient, user-friendly, and suitable for real-time academic management.
Sign Language Recognition and Translation System Using Machine Learning and Computer Vision
This project presents a smart Sign Language Recognition and Translation System designed to reduce the communication gap between sign language users and non-sign language users. The system supports two-way communication by converting text and speech into sign language and translating sign language into text and speech. It uses machine learning and computer vision techniques to recognize hand signs and gestures in real time. YOLOv8 is used for sign alphabet detection, while MediaPipe is used for hand landmark tracking and gesture analysis. The system also stores gesture patterns and hand angle data in JSON format for sentence-level recognition. A Flask-based web interface is used to provide an interactive and user-friendly experience. This project offers a practical solution for real-time communication, accessibility, and learning support.
NeuroLearn – AI Powered E-Learning Platform
NeuroLearn – AI Powered E-Learning Platform
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