Chatbots for mental health are shown to be helpful for preventing mental health issues and improving the wellbeing of individuals, and to ease the burden on health, community and school systems. However, the current chatbots in this area cannot interact naturally with humans and the types of interactions are limited to short text, predefined buttons etc. In contrast, psychologists in real-world interact with patients with multiple modalities, including accustic and visual information.
Honours and Minor Thesis projects
Displaying 41 - 50 of 199 honours projects.
Large Language Models (LLMs) have revolutionized natural language processing (NLP). These models have shown an unprecedented level of knowledge and reasoning, pushing the boundaries of what is achievable in NLP. However, the use of LLMs in the real world still presents numerous difficult challenges and application of LLMs beyond simple API/Prompt calls is very under-explored.
Machine learning (ML) training and evaluation usually involve large-scale datasets and complicated computation. To process data efficiently, a promising solution is to outsource the processes to cloud platforms. However, traditional approaches of collecting users' data at cloud platforms are vulnerable to data breaches.
This project is based on the paper "Academic Journals, Incentives, and the Quality of Peer Review: A Model", in which we analyse strategic interactions between scientists and science journals. Our results shed light on how different objectives for journals shape the strategies that scientists adopt when aiming to publish their work.
The tennis tour is a series of tennis tournaments played globally over a calendar year, where professional tennis players compete for prize money and ranking points. The structure of the tennis tour is organised into different tiers for both men and women, including grand slam tournaments and ATP/WTA tour events. In this project we use stochastic processes to model and simulate the tour under different experimental rules.
Description:
Magnetic Resonance Imaging (MRI) stands as a cornerstone in medical imaging, providing non-invasive, high-resolution images of the human body's internal structures. Brain tumor segmentation from MRI scans is essential for precise diagnosis and treatment planning. MRI provides detailed views of brain structures and abnormalities, but challenges like image noise, contrast imperfections and tumor variations can make segmentation difficult.
Description:
The early detection of neurological abnormalities through Magnetic Resonance Imaging (MRI) is crucial in the medical field, potentially leading to timely interventions and better patient outcomes. However, the traditional diagnostic process is often time-consuming and subject to human error. This project seeks to improve this aspect by employing deep learning for anomaly detection in MRI scans, exclusively using images from healthy participants for model training [1].
This is one of our CSIRO Next Generation AI Graduates projects:
https://www.monash.edu/it/ssc/raise/projects
Note: *** Must be Domestic Student i.e. Australian or New Zealand Citizen or Australian Permanent Resident *** for RAISE programme
Project Description
We have an existing product that focus on mid-market service or product delivery companies. The product is offering a community solution that offers support around a specific product or service. We want to increase its capabilities to offer smart content moderation and smart responses.
Feedback is crucial to learning success; yet, higher education continues to struggle with effective feedback processes. It is important to recognise that feedback as a process requires both teachers and students to take active roles and work as partners. However, one challenge to facilitate a two-way process of feedback is the difficulty to track feedback impact on learning, particularly how students interact with feedback.