Honours and Masters project

Deepfakes detection deals with machine learning methods, which detect if an image/video/audio sample is manipulated with a generative AI software. In recent years, deepfakes have been increasingly used for malicious purposes, including financial fraud, misinformation campaigns, identity theft, and cyber harassment. The ability to generate highly realistic synthetic content poses a serious threat to digital security, privacy, and trust in media. This project will develop methods for detecting deepfakes.

Project Description: The project is focused on developing game-based learning environments where the users’ trace or interaction data could be collected. The game-based environment needs to be designed to allow the users to navigate and explore at their own pace. Using the environment, the participants can practice their technical/professional skills from various options.

Project description

This research project aims to explore how researchers are investigating various epistemic emotions that arise within educational contexts. Through a combination of literature review, citation and bibliometric analysis, this project will uncover trends in the field and may guide future directions. The outcome of this research is expected to be a peer-reviewed publication that may provide a valuable addition to your CV.

Mental health challenges disproportionately affect vulnerable populations, often due to limited access to traditional healthcare services. The rise of Generative AI offers a groundbreaking opportunity to bridge this gap by providing personalized, scalable, and accessible mental health support. This project, led out of Action Lab, aims to harness the potential of Generative AI to develop innovative technologies tailored for mental health interventions.

Our groundbreaking research explores the intricate relationship between natural language processing (NLP) and electroencephalography (EEG) brain signals [1]. By leveraging advanced machine learning techniques, we aim to decode the neural patterns associated with language comprehension and production, ultimately enabling seamless communication between humans and machines. Our innovative approach has the potential to revolutionize brain-computer interfaces,speech recognition technologies, and assistive devices for individuals with communication impairments.

Collecting and analysing social media content (e.g., Reddit), along with using Google Trends, presents a great opportunity to develop social media epidemic intelligence. This approach can enhance the understanding of chronic conditions such as arthritis, back pain, and knee pain, as well as track associated areas such as treatments and risk factors, including obesity, diet, physical activity, and exercise. This approach can be also used to understand the community attitudes about these conditions, and see if there are changes over time as there as new public campaigns.

Current studies on diabetes recommender systems and apps mainly focus on the performance and personalisation of AI models and techniques, including machine learning and deep learning models that are trained on user data. These works often use a one-size-fits-all approach for presenting information to users. Yet, research shows that humans process information in different ways, and their attitudes towards an action depend on their attitude-function styles.

Current studies on diabetes recommender systems and apps mainly focus on the performance and personalisation of AI models and techniques, including machine learning and deep learning models that are trained on user data. These works often use a one-size-fits-all approach for presenting information to users. Yet, research shows that humans process information in different ways, and their attitudes towards an action depend on their attitude-function styles.

This project aims to develop effective machine learning algorithms for detecting deepfake videos, which have become a significant concern for disinformation and cybersecurity. The objectives include pre-processing the data for feature extraction, and training machine learning models to accurately classify videos as either real or manipulated. The methodology involves using advanced techniques such as convolutional neural networks, recurrent neural networks or video vision transformer models to analyse visual and temporal patterns in the videos.

This project aims to develop foundation models for detecting anomalies in time series data. Anomalies, such as unusual patterns or unexpected events, can signal critical issues in systems like healthcare, finance, or cybersecurity. Current methods are often limited by the fact that they reuire long training before one can test the model on a new time series due to complexity and variability of real-world time series data. By leveraging advanced machine learning techniques, this project seeks to create robust and adaptable models that can generalize across diverse time series scenarios.