Honours and Minor Thesis projects

Word sense disambiguation (WSD), the process of computationally identifying the appropriate meaning of a word within its context, is a fundamental task in Natural Language Processing (NLP). Effective WSD is crucial for building accurate machine translation systems, information retrieval tools, and sentiment analysis applications, especially when dealing with diverse languages and linguistic variations.

The Javanese language, spoken by a population of over 98 million people, faces notable challenges in digital and technological applications, especially when compared to globally recognized languages. This disparity is highlighted in several studies that discuss the lack of deep learning research benefits due to data scarcity for Javanese. Additionally, other studies have pointed out the inaccessibility of data resources and benchmarks for Javanese, contrasting with languages like English and Mandarin Chinese.

Political polarization is a phenomenon that permeates societies worldwide, manifesting in divergent ideologies, entrenched viewpoints, and societal fragmentation. In the context of Indonesia, a diverse and populous nation with a complex socio-political landscape, understanding political polarization is crucial for fostering social cohesion and effective governance.

In the evolving landscape of data reporting, traditional text-based and quantitative methods are increasingly being supplemented by rich, community-generated qualitative data, including audio and video content. This shift presents unique challenges and opportunities in how non-profits, government bodies, and community organizations present and utilize this data.

Recent advancements in wearable technology have enabled continuous health monitoring, significantly expanding the capabilities of devices like the Apple Watch, Fitbit, and Samsung Watch in medical diagnostics. Among these, Electrocardiography (ECG) interpretation is a critical function, traditionally requiring expert analysis. This project proposes the use of deep learning (DL) algorithms to automate ECG interpretation on these devices, enhancing diagnostic accuracy and accessibility.

Description:

Acute patient length of stay is the leading contributor to hospital costs in Australia. The Victorian Auditor General has reported that ≈145,000 extra bed-days could be made available if all hospitals managed length of stay more efficiently. Such optimisation of length of stay could translate into $125 million in annual savings for Victorian taxpayers.

Become part of the Monash Immersive Analytics Lab, and explore exciting new ways to visualise, interact, and analyse all types of data with VR and AR! We are looking for enthusiastic students to work on immersive visualisation using latest technology, such as head-mounted displays with integrated eye-trackers (Microsoft HoloLens and others), gesture recognition devices, and large wall displays.

The world’s energy markets are transforming, and more renewable energy is integrated into the electric energy market. The intermittent renewable supply leads to unexpected demand-supply mismatches and results in highly fluctuating energy prices. Energy arbitrage aims to strategically operate energy devices to leverage the temporal price spread to smooth out the price differences in the market, which also generates some revenue.

This project focuses on modeling heart rate data captured via FitBit Sense devices worn by team members in collaborative situations such as supervision meetings, group teaching, or nursing simulation scenarios. The primary goal is to identify stressful situations or similar events by analysing heart rate variations.

This project focuses on the automated classification of teachers' activities and co-teaching behaviors using positioning data captured via sensors and microphone data. The main task involves developing and applying machine learning techniques to analyse multimodal datasets, combining positioning and speech data to identify and categorize various teaching activities. By leveraging large language models (LLMs), Generative AI (GenAI), and Natural Language Processing (NLP), the project aims to extract features that enhance the accuracy and effectiveness of these classification tasks.