Thanh Thi Nguyen

• Doctor of Philosophy
• Associate Professor at Monash University (Australia)

Recommender systems have become an integral part of online services due to their ability to help users locate specific information in a sea of data. However, existing studies show that some recommender systems are vulnerable to poisoning attacks particularly those that involve learning schemes. A poisoning attack is where an adversary injects caref...

In recent years, visual facial forgery has reached a level of sophistication that humans cannot identify fraud, which poses a significant threat to information security. A wide range of malicious applications have emerged, such as deepfake, fake news, defamation or blackmailing of celebrities, impersonation of politicians in political warfare, and...

In today’s intricate information technology landscape, the escalating complexity of computer networks is accompanied by a myriad of malicious threats seeking to compromise network components. To address these security challenges, we propose an approach that synergizes reinforcement learning and deep neural networks. Our method involves training aut...

Deep neural networks (DNNs) have received a great deal of interest in solving everyday tasks in recent years. However, their computational and energy costs limit their use on mobile and edge devices. The neuromorphic computing approach called spiking neural networks (SNNs) represents a potential solution for bridging the gap between performance and...

The emergence of SARS-CoV-2 has unleashed a global health crisis, demanding advanced research into its genomic mutations and their consequences. Our study combines computational models and empirical validation to predict the effects of these mutations, aiming to understand their impact on the virus's behaviour, including its transmissibility and im...

The emergence of viruses and their variants has made virus taxonomy more important than ever before in controlling the spread of diseases. The creation of efficient treatments and cures that target particular virus properties can be aided by understanding virus taxonomy. Alignment-based methods are commonly used for this task, but are computational...

Maximal frequent subgraph mining (MFSM) is the task of mining only maximal frequent subgraphs, i.e. subgraphs that are not a part of other frequent subgraphs. Although many intelligent systems require MFSM, MFSM is challenging compared to frequent subgraph mining (FSM), as maximal frequent subgraphs lie in the middle of graph lattice, and FSM algor...

Accurate and efficient predictions concerning stock prices are an intriguing and sought-after task in the field of computational financial analysis. This paper aims to leverage and validate novel deep learning pipelines for predicting NSE stock prices of Adani Ports, Reliance, and Tata Steel using recurrent neural architectures. The scope of this p...

Maximal frequent subgraph mining (MFSM) is the task of mining only maximal frequent subgraphs, i.e. subgraphs that are not a part of other frequent subgraphs. Although many intelligent systems require MFSM, MFSM is challenging compared to frequent subgraph mining (FSM), as maximal frequent subgraphs lie in the middle of graph lattice, and FSM algor...

Computer vision has found many applications in automatic wildlife data analytics and biodiversity monitoring. Automating tasks like animal recognition or animal detection usually require machine learning models (e.g., deep neural networks) trained on annotated datasets. However, image datasets built for general purposes fail to capture realistic co...

The emergence of viruses and their variants has made virus taxonomy more important than ever before in controlling the spread of diseases. The creation of efficient treatments and cures that target particular virus properties can be aided by understanding virus taxonomy. Alignment-based methods are commonly used for this task, but are computational...

Deep reinforcement learning (RL) has demonstrated great capabilities in dealing with sequential decision-making problems, but its performance is often bounded by suboptimal solutions in many complex applications. This paper proposes the use of human expertise to increase the performance of deep RL methods. Human domain knowledge is characterized by...

Patients in hospitals frequently exhibit psychological issues such as sadness, pessimism, eccentricity, and anxiety. However, hospitals normally lack tools and facilities to continuously monitor the psychological health of patients. It is desirable to identify depression in patients so that it can be managed by instantly providing better therapy. T...

Taking inspiration from the brain, spiking neural networks (SNNs) have been proposed to understand and diminish the gap between machine learning and neuromorphic computing. Supervised learning is the most commonly used learning algorithm in traditional ANNs. However, directly training SNNs with backpropagation-based supervised learning methods is c...

Estimation of crowd size for large gatherings is an indispensable metric for event planners, local authorities, and emergency management. Currently, most crowd counting relies on dated methods such as people counters, entrance sensors, and ticket sales. Over the past decade, there has been rapid development in crowd counting techniques and related...

Data redundancy has been one of the most important problems in data-intensive applications such as data mining and machine learning. Removing data redundancy brings many benefits in efficient data updating, effective data storage, and error-free query processing. While it has been studied for four decades, existing works on data redundancy mostly f...

Data redundancy has been one of the most important problems in data-intensive applications such as data mining and machine learning. Removing data redundancy brings many benefits in efficient data updating, effective data storage, and error-free query processing. While it has been studied for four decades, existing works on data redundancy mostly f...

Deep learning has been widely adopted in automatic emotion recognition and has lead to significant progress in the field. However, due to insufficient training data, pre-trained models are limited in their generalisation ability, leading to poor performance on novel test sets. To mitigate this challenge, transfer learning performed by fine-tuning p...

Along with the massive growth of the Internet from the 1990s until now, various innovative technologies have been created to bring users breathtaking experiences with more virtual interactions in cyberspace. Many virtual environments have been developed with immersive experience and digital transformation, but most are incoherent instead of being i...

Fraud detection is one of the most important tasks in Web platforms such as e-commerce, social media, network security, and financial systems. To prevent fraudulent actions from misleading customers or causing significant losses for businesses, various fraud detection methods have been proposed in recent years. However, research on fraud definition...

COVID-19 is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This deadly virus has spread worldwide, leading to a global pandemic since March 2020. A recent variant of SARS-CoV-2 named Delta is intractably contagious and responsible for more than four million deaths globally. Therefore, developing an...

Origin of the COVID-19 virus has been intensely debated in the scientific community since the first infected cases were detected in December 2019. The disease has caused a global pandemic, leading to deaths of thousands of people across the world and thus finding origin of this novel coronavirus is important in responding and controlling the pandem...

Deep learning has been successfully applied to solve various complex problems ranging from big data analytics to computer vision and human-level control. Deep learning advances however have also been employed to create software that can cause threats to privacy, democracy and national security. One of those deep learning-powered applications recent...

Today’s social networks continuously generate massive streams of data, which provide a valuable starting point for the detection of rumours as soon as they start to propagate. However, rumour detection faces tight latency bounds, which cannot be met by contemporary algorithms, given the sheer volume of high-velocity streaming data emitted by social...

Artificial neural networks (ANNs) have experienced a rapid advancement for their success in various application domains, including autonomous driving and drone vision. Researchers have been improving the performance efficiency and computational requirement of ANNs inspired by the mechanisms of the biological brain. Spiking neural networks (SNNs) pr...

Reinforcement learning (RL) has emerged as an effective approach for building an intelligent system, which involves multiple self-operated agents to collectively accomplish a designated task. More importantly, there has been a renewed focus on RL since the introduction of deep learning that essentially makes RL feasible to operate in high-dimension...

Skin cancer is a deadly disease that is increasing with each passing year. It becomes important to create a faster automation system that can easily detect and classify skin cancer with less human intervention , so that patients can take early treatment to increase their survival chances. In this work, four different convolutional neural network-ba...

Today's social networks continuously generate massive streams of data, which provide a valuable starting point for the detection of rumours as soon as they start to propagate. However, rumour detection faces tight latency bounds, which cannot be met by contemporary algorithms, given the sheer volume of high-velocity streaming data emitted by social...

Social networks continuously generate massive streams of data very fast. Such high-velocity streams exceed any reasonable limit for any rumour detection algorithms in terms of latency. Indeed, the input bu er of a rumour detector is signi cantly small compared to the whole social network and its the detection latency is extremely important: rumours...

Origin of the COVID-19 virus (SARS-CoV-2) has been intensely debated in the scientific community since the first infected cases were detected in December 2019. The disease has caused a global pandemic, leading to deaths of thousands of people across the world and thus finding origin of this novel coronavirus is important in responding and controlli...

With the rapid emergence of advanced technologies for wireless communications, automatic modulation classification (AMC) has been deployed in the physical layer to blindly identify the modulation fashion of an incoming signal at the receiver and consequently improve the efficiency of spectrum utilization and management. Although recent works on AMC...

Along with the massive growth of the Internet from the 1990s until now, various innovative technologies have been created to bring users breathtaking experiences with more virtual interactions in cyberspace. Many virtual environments with thousands of services and applications, from social networks to virtual gaming worlds, have been developed with...

Recent advances in Artificial Intelligence (AI) and Internet of Things (IoT) have facilitated continuous improvement in smart city based applications such as smart healthcare, transportation, and environmental management. Digital Twin (DT) is an AI-based virtual replica of the real-world physical entity. DTs have been successfully adopted in manufa...

Epilepsy is a group of neurological disorders that affect normal brain activities and human behavior. Electroencephalogram based automatic epileptic seizure detection has significant applications in epilepsy treatment and medical diagnosis. In this study, a novel epileptic seizure detection method is proposed with a combination of empirical mode de...

Cancer begins when healthy cells change and grow out of control, forming a mass called a tumor. Head and Neck (H&N) cancers usually develop in or around the head and neck, including the mouth (oral cavity), nose and sinuses, throat (pharynx), and voice box (larynx). 4% of all cancers are H&N cancers with a very low survival rate (a five-year surviv...

Epilepsy is a group of neurological disorders that affect normal brain activities and human behavior. Electroencephalogram based automatic epileptic seizure detection has significant applications in epilepsy treatment and medical diagnosis. In this study, a novel epileptic seizure detection method is proposed with a combination of empirical mode de...

In recent years, visual forgery has reached a level of sophistication that humans cannot identify fraud, which poses a significant threat to information security. A wide range of malicious applications have emerged, such as fake news, defamation or blackmailing of celebrities, impersonation of politicians in political warfare, and the spreading of...

The scale of Internet-connected systems has increased considerably, and these systems are being exposed to cyber attacks more than ever. The complexity and dynamics of cyber attacks require protecting mechanisms to be responsive, adaptive, and large-scale. Machine learning, or more specifically deep reinforcement learning (DRL), methods have been p...

Automatic modulation classification (AMC), which aims to blindly identify the modulation type of an incoming signal at the receiver in wireless communication systems, is a fundamental signal processing technique in the physical layer to improve the spectrum utilization efficiency. Motivated by deep learning (DL) high-impact success in many informat...

An increasing number of complex problems have naturally posed significant challenges in decision-making theory and reinforcement learning practices. These problems often involve multiple conflicting reward signals that inherently cause agents’ poor exploration in seeking a specific goal. In extreme cases, the agent gets stuck in a sub-optimal solut...

Indexed literature (from 2015 to 2020) on artificial intelligence (AI) technologies and machine learning algorithms (ML) pertaining to disasters and public health emergencies were reviewed. Search strategies were developed and conducted for PubMed and Compendex. Articles that met inclusion criteria were filtered iteratively by title followed by abs...

Web platforms, especially social media, are facing a new and ever-evolving cyber threat operating at the information level. Their open nature allows a high velocity flow of rumours that emerge unexpectedly and spread quickly. While rumour detection has attracted many theoretical and practice studies, the timing of the detection is often neglected o...

Multimodal dimensional emotion recognition has drawn a great attention from the affective computing community and numerous schemes have been extensively investigated, making a significant progress in this area. However, several questions still remain unanswered for most of existing approaches including: (i) how to simultaneously learn compact yet r...

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly pathogenic virus that has caused the global COVID-19 pandemic. Tracing the evolution and transmission of the virus is crucial to respond to and control the pandemic through appropriate intervention strategies. This paper reports and analyses genomic mutations in the coding reg...

In classification problems, normally there exists a large number of features, but not all of them contributing to the improvement of classification performance. These redundant features make the classification problem time consuming and often result in poor performance. Feature selection methods have been proposed to reduce the number of features,...

Credit card frauds are at an ever-increasing rate and have become a major problem in the financial sector. Because of these frauds, card users are hesitant in making purchases and both the merchants and financial institutions bear heavy losses. Some major challenges in credit card frauds involve the availability of public data, high class imbalance...

Web platforms, especially social media, are facing a new and ever-evolving cyber threat operating at the information level. Their open nature allows a high velocity flow of rumours that emerge unexpectedly and spread quickly. While rumour detection has attracted many theoretical and practice studies, the timing of the detection is often neglected o...

This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL) framework based on deep Q-networks. We develop a high-performance MODRL framework that supports both single-policy and multi-policy strategies, as well as both linear and non-linear approaches to action selection. The experimental results on two benchmark probl...

Wearable technology is gaining enormous attention among researchers due to their low cost and ease to transfer from laboratory environment to real world applications. In this paper we evaluate the detection of cognitive load using an off the shelf low cost electroencephalography (EEG) device, namely the EMOTIV EPOC+, by utilising four classifiers i...

Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This paper presents a survey of AI methods being used in various applications in the fight agai...

Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This paper presents a survey of AI methods being used in various applications in the fight agai...

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly pathogenic virus that has caused the global COVID-19 pandemic. Tracing the evolution and transmission of the virus is crucial to respond to and control the pandemic through appropriate intervention strategies. This paper reports and analyses genomic mutations in the coding reg...

Deep learning has been widely adopted in automatic emotion recognition and has lead to significant progress in the field. However, due to insufficient annotated emotion datasets, pre-trained models are limited in their generalization capability and thus lead to poor performance on novel test sets. To mitigate this challenge, transfer learning perfo...

Multimodal dimensional emotion recognition has drawn a great attention from the affective computing community and numerous schemes have been extensively investigated, mak- ing a significant progress in this area. However, several questions still remain unanswered for most of existing approaches includ- ing: (i) how to simultaneously learn compact y...

Multimodal dimensional emotion recognition has drawn a great attention from the affective computing community and numerous schemes have been extensively investigated, making a significant progress in this area. However, several questions still remain unanswered for most of existing approaches including: (i) how to simultaneously learn compact yet r...

Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This paper presents a survey of AI methods being used in various applications in the fight agai...

Reinforcement learning (RL) algorithms have been around for decades and employed to solve various sequential decision-making problems. These algorithms, however, have faced great challenges when dealing with high-dimensional environments. The recent development of deep learning has enabled RL methods to drive optimal policies for sophisticated and...

The integration of deep learning to reinforcement learning (RL) has enabled RL to perform efficiently in high-dimensional environments. Deep RL methods have been applied to solve many complex real-world problems in recent years. However, development of a deep RL-based system is challenging because of various issues such as the selection of a suitab...

Deep reinforcement learning has been applied successfully to solve various real-world problems and the number of its applications in the multi-agent settings has been increasing. Multi-agent learning distinctly poses significant challenges in the effort to allocate a concealed communication medium. Agents receive thorough knowledge from the medium...

Deep learning has been successfully applied to solve various complex problems ranging from big data analytics to computer vision and human-level control. Deep learning advances however have also been employed to create software that can cause threats to privacy, democracy and national security. One of those deep learning-powered applications recent...

Deep learning has been successfully applied to solve various complex problems ranging from big data analytics to computer vision and human-level control. Deep learning advances however have also been employed to create software that can cause threats to privacy, democracy and national security. One of those deep learning-powered applications recent...

The scale of Internet-connected systems has increased considerably, and these systems are being exposed to cyber attacks more than ever. The complexity and dynamics of cyber attacks require protecting mechanisms to be responsive, adaptive, and large-scale. Machine learning, or more specifically deep reinforcement learning (DRL), methods have been p...

Situation awareness (SA) is an important constituent in human information processing and essential in pilots' decision-making processes. Acquiring and maintaining appropriate levels of SA is critical in aviation environments as it affects all decisions and actions taking place in flights and air traffic control. This paper provides an overview of r...

Single nucleotide polymorphisms (SNPs) are one type of genetic variations and each SNP represents a difference in a single DNA building block, namely a nucleotide. Previous research demonstrated that SNPs can be used to identify the correct source population of an individual. In addition, variations in the DNA sequences have an influence on human d...

Deep reinforcement learning (DRL) has emerged as the dominant approach to achieving successive advancements in the creation of human-wise agents. By leveraging neural networks as decision-making controllers, DRL supplements traditional reinforcement methods to address the curse of dimensionality in complicated tasks. However, agents in complicated...

In robotic surgery, pattern cutting through a deformable material is a challenging research field. The cutting procedure requires a robot to concurrently manipulate a scissor and a gripper to cut through a predefined contour trajectory on the deformable sheet. The gripper ensures the cutting accuracy by nailing a point on the sheet and continuously...

Deep learning has enabled traditional reinforcement learning methods to deal with high-dimensional problems. However, one of the disadvantages of deep reinforcement learning methods is the limited exploration capacity of learning agents. In this paper, we introduce an approach that integrates human strategies to increase the exploration capacity of...

Surgeons normally need surgical scissors and tissue grippers to cut through a deformable surgical tissue. The cutting accuracy depends on the skills to manipulate these two tools. Such skills are part of basic surgical skills training as in the Fundamentals of Laparoscopic Surgery. The gripper is used to pinch a point on the surgical sheet and pull...

Surgeons normally need surgical scissors and tissue grippers to cut through a deformable surgical tissue. The cutting accuracy depends on the skills to manipulate these two tools. Such skills are part of basic surgical skills training as in the Fundamentals of Laparoscopic Surgery. The gripper is used to pinch a point on the surgical sheet and pull...

Reinforcement learning (RL) algorithms have been around for decades and been employed to solve various sequential decision-making problems. These algorithms however have faced great challenges when dealing with high-dimensional environments. The recent development of deep learning has enabled RL methods to drive optimal policies for sophisticated a...

The difference in data distributions among related, but different domains is a long standing problem for knowledge adaptation. A new method to transform the source domain knowledge to fit the target domain is proposed in this work. The proposed method uses deep learning method and limited number of samples from target domain to transform the source...

Improving classification accuracy of motor imagery-based brain computer interface (MI-BCI) systems has been discussed widely in the BCI research community. Analyses of multi-class MI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to binary-class data. This paper int...

Background: Artificial neural networks (ANN) is one of the widely used classifiers in the brain computer interface (BCI) systems-based on noninvasive electroencephalography (EEG) signals. Among the different ANN architectures, the most commonly applied for BCI classifiers is the multilayer perceptron (MLP). When appropriately designed with optimal...

In 2015, Google's Deepmind announced an advancement in creating an autonomous agent based on deep reinforcement learning (DRL) that could beat a professional player in a series of 49 Atari games. However, the current manifestation of DRL is still immature, and has significant drawbacks. One of DRL's imperfections is its lack of "exploration" during...