A Computer Vision based system to detect fire at an early stage in real-time and alert the user through a mobile application.
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Updated
May 20, 2024 - Dart
A Computer Vision based system to detect fire at an early stage in real-time and alert the user through a mobile application.
FRONTEND - An AI Flutter application developed for self-checkout for stationery items at KMUTT Bookstore as part of coursework for CSC340 (Artificial Intelligence)
It is an innovative app that integrates a YOLOv5-based machine learning model that automatically detects problems in images in real time and generates automated reports to local authorities. #FlutterApp #AI/ML #SmartCity #YOLOv5
The Autism Behavior Analysis Platform is designed to assist caregivers, therapists, and researchers in analyzing and assessing behavioral patterns of children with autism. The system provides features for predicting autism behavior levels, analyzing emotional states, and supporting personalized behavioral interventions.
Real-time traffic congestion analysis and route optimization app for Singaporean drivers.
A mobile travel companion app tailored for you. Organizes trips into customizable containers, using YOLOv8 for visual item detection and smart packing suggestions based on your destination and preferences.
menggunakan model Yolov8 dan framework Sklearn
Ledgerly is an offline-first Flutter app for small retailers to manage customer dues, billing, and inventory, with optional AI-powered Smart Billing Scan using a YOLO backend.
Flutter Animal Rescue Service Application integrated with Firebase and Machine Learning
This is Dun Diary Application for note a BP from monitor by yourself or using Object Detection model
Flutter-based mobile IP camera that streams Android camera frames to a Python YOLOv8 server for real-time human detection, with live bounding-box overlay on both phone and web.
CrashLens is a graduation project that applies computer vision and deep learning techniques to vehicle damage assessment. The system analyzes accident images to detect damage, classify severity levels, and estimate repair costs, supporting faster and more consistent preliminary evaluations. The project focuses on practical dataset experimentation,
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