Honours and Minor Thesis projects
Displaying 11 - 20 of 220 honours projects.
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.
Autonomous electric vehicle (EV) fleets are emerging as an effective way to solve environmental problems and reduce commute costs in smart cities. Due to the complex spatiotemporal behaviors of passengers and their trips, the unmanaged electric charging demand from EV fleets will significantly impact the existing transportation and electric power infrastructure. Reliable charging networks and charging strategies for EV fleets are the prerequisites to the successful adoption of autonomous EV fleets.
We aim to take the first step to
Thanks to the widespread deployment of smart meters, high volumes of residential load data have been collected and made available to both consumers and utility companies. Smart meter data open up tremendous opportunities, and various analytical techniques have been developed to analyse smart meter data using machine learning. This project will provide a new angle toward energy data analytics and aims to discover the consumption patterns, lifestyle, and behavioural changes of consumers.
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.
Problem Statement: The forensic identification of human remains is a critical legal process, culminating in the issuance of a death certificate by the appropriate authority. It is a multifaceted procedure that integrates scientific evidence—from antemortem records to advanced DNA analysis, forensic odontology, and anthropology—to match unidentified remains with missing persons.
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This project will apply feature selection techniques for identifying features that can effectively predict the Logistics Performance Index (LPI), building upon our previously published work [1].
This project aims to analyse the comments of Twitter on non-communicable diseases. Students are expected to carry out Aspects Detection to identify the specific aspects discussed in the tweets e.g., causes, transmission and symptoms. Subsequently, students are expected to conduct sentiment analysis utilizing tools like TextBlob or VADER, while also taking into account the importance of considering emojis to enhance classification accuracy.
The increasing integration of Large Language Models (LLMs) into various sectors has recently brought to light the pressing need to align these models with human preferences and implement safeguards against the generation of inappropriate content. This challenge stems from both ethical considerations and practical demands for responsible AI usage. Ethically, there is a growing recognition that the outputs of LLMs must align with laws, societal values, and norms.