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Project Library

AI Nusantara research projects are driven by our research centres, industry collaborations, and students as they endeavour to create meaningful impact and advance AI applications.

Explore some of our projects

Project Gallery

Eat-Dentify

The "Eat-dentify" project utilises machine learning algorithms to provide personalised restaurant and meal recommendations. Users input their dining preferences, and the app instantly generates tailored suggestions, simplifying decision-making and enhancing the dining experience. This project demonstrates the practical application of technology in culinary recommendations, showcasing its real-world potential.

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PersonaTour

The "PersonaTour" project integrates multiple travel planning functions into a single app. Using machine learning algorithms and the Google Places API, it offers personalised travel recommendations, optimised itineraries, and real-time route updates. Users can input their preferences, and the app provides tailored suggestions, helping to streamline trip planning and enhance the travel experience. This project demonstrates the effective use of technology to simplify travel planning and ensure a more enjoyable journey.

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Pothole Detection

The "Pothole Detection" project employs the YOLO v2 object detection algorithm to identify potholes on roads. By utilising datasets from sources such as Kaggle and Roboflow, the project involves setting up the necessary machine learning environment, including dependencies like OpenCV and CUDA. Through dataset manipulation, including augmentation and splitting, the model is trained to accurately detect potholes. This project highlights the practical application of machine learning in improving road safety by providing a reliable method for early pothole detection and maintenance.

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Driver Drowsiness Detection

The “Driver Drowsiness Detection” project uses the YOLOv2 algorithm to detect signs of driver fatigue. The project involves preparing a comprehensive dataset from Kaggle, labelling data with LabelImg, and training the model using the DarkNet framework. The system identifies drowsy drivers by analysing images and provides real-time alerts. This project demonstrates the application of machine learning in enhancing road safety by addressing the critical issue of driver drowsiness.

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IntelliCare

The “IntelliCare” project is a comprehensive childcare assistant application designed to support parents, guardians, and teachers. It offers personalised advice, meal scanning for allergen detection, and medication management. IntelliCare helps users keep track of important details about their children, providing tailored guidance and 24/7 availability. This project demonstrates how technology can enhance childcare by consolidating essential functions into a single, easy-to-use app.

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