Tony Jan

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Verified

Tony verified their affiliation via an institutional email.

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• PhD (Computing Science), BE (Engineering)
• Professor at Torrens University Australia

With the number of users of social media and web platforms increasing day-by-day in recent years, cyberbullying has become a ubiquitous problem on the internet. Controlling and moderating these social media platforms manually for online abuse and cyberbullying has become a very challenging task. This paper proposes a Recurrent Neural Network (RNN)...

A decentralized service placement policy plays a key role in distributed systems, such as fog computing, where sharing workloads fairly among active computing nodes is critical. A decentralized policy is an inherent feature of the service placement process that may improve load balancing among computers and can reduce the latency in many real‐time...

The extreme workloads on the fog layer caused a misalignment in some fog nodes that affect its efficiency and degenerate fog technology’s primary goal. Therefore, creating a balanced computing environment via the offloading process is the key. However, there are many obstacles to balance computing nodes in the fog environment, such as offloading st...

Detecting anomalies, intrusions, and security threats in the network (including Internet of Things) traffic necessitates the processing of large volumes of sensitive data, which raises concerns about privacy and security. Federated learning, a distributed machine learning approach, enables multiple parties to collaboratively train a shared model wh...

Anomaly detection is crucial in high-performance computing (HPC) systems for maintaining effective, efficient, and secure operations. This survey focuses on the current status of the application of machine learning and deep learning in HPC systems for detecting various types of anomalies, including performance anomalies, operational anomalies, and...

This chapter offers an overview of climate change education strategies across four distinct educational stages in Africa, focusing on the proposed frameworks tailored for each level. Rather than analysing the current state of climate change teaching, it introduces innovative approaches designed to enhance educational practices and outcomes. These f...

The rapid proliferation of Large Language Models (LLMs) across industries such as healthcare, finance, and legal services has revolutionized modern applications. However, their increasing adoption exposes critical vulnerabilities, particularly through adversarial prompt attacks that compromise LLM security. These prompt-based attacks exploit weakne...

Ensuring the security and integrity of Federated Learning (FL) models against adversarial attacks is critical. Among these threats, targeted data poisoning attacks, particularly label flipping, pose a significant challenge by undermining model accuracy and reliability. This paper investigates targeted data poisoning attacks in FL systems, where a s...

The escalating sophistication of cyber threats poses significant risks to individuals, organizations, and nations. Cybercrime, encompassing activities like hacking and data breaches, has severe economic and societal consequences. In today’s interconnected world, robust cybersecurity measures are paramount to mitigate these risks and protect sensiti...

Machine learning (ML) has become a key technology for addressing water quality issues. In this study, we present the application of machine learning for real-time water quality management in a remote village located in Sindh, Pakistan. We conducted two experiments using IoT infrastructure. The first experiment utilized traditional ML models for dat...

The transition from industry 4.0 to industry 5.0 represents a profound paradigm shift in the manufacturing domain, emphasizing the convergence of advanced digital technologies with human-centric collaboration, sustainability, and operational resilience. This paper rigorously investigates the transformative potential of softwarization and servitizat...

Mobile dependence amplifies privacy risks associated with advanced malware threats such as ransomware, Trojan horses, botnet and spyware. Ransomware, in particular, encrypts those portable devices, demanding payment for access, presenting a critical challenge for user privacy, economic stability, and corporate trust. This paper proposes an effectiv...

The global fashion industry is undergoing rapid digitisation in which new Industry 4.0 technologies are changing the way fashion is designed and produced. The Australian fashion industry is considered to be slow in adopting these new technologies. The purpose of this research study is to investigate the current acceptability of Industry 4.0 technol...

The topic of public health is indispensable to talk about. It is essential to discuss new inventions, new and improved treatments, and their efficiencies with different combinations, but one thing that is important to remember is whether these inventions are available for those in need. Availability concerns are linked with affordability, as the af...

Intrusion Detection Systems (IDS) are essential for securing computer networks by identifying and mitigating potential threats. However, traditional IDS systems face challenges related to scalability, privacy, and computational demands as network data complexity increases. Federated Learning (FL) has emerged as a promising solution, enabling collab...

In the rapidly evolving landscape of drone technology, securing unmanned aerial vehicles (UAVs) presents critical challenges and demands unique solutions. This paper offers a thorough examination of the security requirements, threat models, and solutions pertinent to UAVs, emphasizing the importance of cybersecurity and drone forensics. This resear...

Smart shoes have ushered in a new era of personalised health monitoring and assistive technologies. Smart shoes leverage technologies such as Bluetooth for data collection and wireless transmission, and incorporate features such as GPS tracking, obstacle detection, and fitness tracking. As the 2010s unfolded, the smart shoe landscape diversified an...

Deep learning utilizing convolutional neural networks (CNNs) stands out among the state-of-the-art procedures in PC-supported medical findings. The method proposed in this paper consists of two key stages. In the first stage, the proposed deep sequential CNN model preprocesses images to isolate regions of interest from skin lesions and extracts fea...

Load balancing is crucial in distributed systems like fog computing, where efficiency is paramount. Offloading with different approaches is the key to balancing the load in distributed environments. Static offloading (SoA) falls short in heterogeneous networks, necessitating dynamic offloading to reduce latency in time-sensitive tasks. However, pre...

Recognizing fraudulent activity in the banking system is essential due to the significant risks involved. When fraudulent transactions are vastly outnumbered by non-fraudulent ones, dealing with imbalanced datasets can be difficult. This study aims to determine the best model for detecting fraud by comparing four commonly used machine learning algo...

Lung diseases are the third-leading cause of mortality in the world. Due to compromised lung function, respiratory difficulties, and physiological complications, lung disease brought on by toxic substances, pollution, infections, or smoking results in millions of deaths every year. Chest X-ray images pose a challenge for classification due to their...

As we enter the era of Industry 4.0, the fusion of advanced technologies with industrial processes has unlocked unprecedented opportunities for growth and innovation. The seamless integration of cyber-physical systems, internet of things (IoT) devices, and artificial intelligence has elevated manufacturing efficiency and productivity to new heights...

Digital multimedia elements such as text, image, audio, and video can be easily manipulated because of the rapid rise of multimedia technology, making data protection a prime concern. Hence, copyright protection, content authentication, and integrity verification are today’s new challenging issues. To address these issues, digital image watermarkin...

The Internet of Medical Things (IoMT) is the subset of the Internet of Things (IoT) that connects multiple medical devices, collect information/data from devices, and transmits and process data in real-time. IoMT is crucial for increasing electronic device accuracy, reliability, and productivity in the healthcare industry. IoMT has emerged as a nex...

In recent years, there has been a marked increase in the use of drones, with a global surge in the demand for diverse applications. This widespread adoption is largely due to drones’ ability to meet various user needs, offering an aerial perspective that can be utilized almost anywhere and at any time. However, the rise in drone usage has also resu...

The ubiquitous proliferation of small cells (SCs) is for boosting system capacity, coverage, and quality of services (QoS) for smart applications. However, interference from the adjacent SCs is inevitable due to their dense deployments — satisfying the low signal-to-interference-plus-noise ratio (SINR) regime. In this paper, we quantify the minimum...

Recent advances in Generative Artificial Intelligence (AI) have increased the possibility of generating hyper-realistic DeepFake videos or images to cause serious harm to vulnerable children, individuals, and society at large with misinformation. To overcome this serious problem, many researchers have attempted to detect DeepFakes using advanced ma...

Load balancing is crucial in distributed systems like fog computing, where efficiency is paramount. Offloading with different approaches is the key to balancing the load in distributed environments. Static offloading (SOS) falls short in heterogeneous networks, necessitating dynamic offloading to reduce latency in time-sensitive tasks. However, pre...

SocialMedia Marketing (SMM) has become a mainstream promotional scheme. Almost every business promotes itself through social media, and an educational institution is no different. The users’ responses to social media posts are crucial to a successful promotional campaign. An adverse reaction leaves a long-term negative impact on the audience, and t...

This paper delves into the complex task of evaluating a website user interface (UI) and user experience (UX), a process complicated by gaps in research. To bridge this, we introduced an innovative human–computer interaction (HCI) framework that synergizes expert cognitive walkthroughs and user surveys for a comprehensive view. We transformed user r...

The term “soft computing” refers to a system that can work with varying degrees of uncertainty and approximations in real-life complex problems using various techniques such as Fuzzy Logic, Artificial Neural Networks (ANN), Machine Learning (ML), and Genetic Algorithms (GA). Owing to the low-cost and high-performance digital processors today, the u...

Internet of Medical Things (IoMT) is an ecosystem composed of connected electronic items such as small sensors/actuators and other cyber-physical devices (CPDs) in medical services. When these devices are linked together, they can support patients through medical monitoring, analysis, and reporting in more autonomous and intelligent ways. The IoMT...

Alzheimer’s disease is a chronic neurodegenerative disease that causes brain cells to degenerate, resulting in decreased physical and mental abilities and, in severe cases, permanent memory loss. It is considered as the most common and fatal form of dementia. Although mild cognitive impairment (MCI) precedes Alzheimer’s disease (AD), it does not ne...

Supply chain management can significantly benefit from contemporary technologies. Among these technologies, blockchain is considered suitable for anti-counterfeiting and traceability applications due to its openness, decentralization, anonymity, and other characteristics. This article introduces different types of blockchains and standard algorithm...

This article studies the impact of social media posts specific to a disaster incident – the Australian bushfires of 2019–2020. We analyse the social media content posted by the Australian Red Cross Organization’s Facebook page, and the user generated comments on their posts. We identify user sentiments in response to the natural disaster and toward...

Landslide hazards give rise to considerable demolition and losses to lives in hilly areas. To reduce the destruction in these endangered regions, the prediction of landslide incidents with good accuracy remains a key challenge. Over the years, machine learning models have been used to increase the accuracy and precision of landslide predictions. Th...

Agile product development cycles and re-configurable Industrial Internet of Things (IIoT) allow more flexible and resilient industrial production systems that can handle a broader range of challenges and improve their productivity. Reinforcement Learning (RL) was shown to be able to support industrial production systems to be flexible and resilient...

This SI is dedicated to publishing novel contributions from researchers on the realization of real-time and reliable WBANs that address major challenges including energy harvesting, communications reliability, human and animal body communications, wireless routing protocols, data security, data privacy, ICT ethical issues, wearable health monitorin...

The analysis and classification of Alcohol Use Disorder (AUD) using non-invasive measurements, such as EEG records from the brain scalp, are of significant importance in neuroscience. Analysis and diagnosis of brain diseases associated with alcoholic subjects using EEG records remain challenging. This study proposes a graph theory-based approach fo...

Large-scale clinical information sharing (CIS) provides significant advantages for medical treatments, including enhanced service standards and accelerated scheduling of health services. The current CIS suffers many challenges such as data privacy, data integrity, and data availability across multiple healthcare institutions. This study introduces...

In this paper, we propose a novel trust computation framework (TCF) for cloud services. Trust is computed by taking into consideration multi-dimensional quality of service (QoS) evidence and user feedback. Feedback provides ample evidence regarding the quality of experience (QoE) of cloud service users. However, in some cases, users may behave mali...

This study presents an analytic model to support the general public in evaluating digital currency exchange platforms. Advances in technologies have offered profitable opportunities, but the general public has difficulty accessing appropriate information on digital currency exchange platforms to facilitate their investments and trading. This study...

In today’s environment, an enormous amount of unstructured data is generated in an exponential manner. Understanding such complex unstructured data is imperative in the applications including analysis of social media data, image and video data, sensor data, medical data, and customer review data. Generally, clustering is a well-accepted model in cl...

In remote sensing imagery, segmentation techniques fail to encounter multiple regions of interest due to challenges such as dense features, low illumination, uncertainties, and noise. Consequently, exploiting vast and redundant information makes segmentation a difficult task. Existing multilevel thresholding techniques achieve low segmentation accu...

This article describes an empirical study to evaluate how the flipped learning (FL) approach has impacted a learner’s perception in attaining the graduate attributes (GAs) of five capstone project units offered at Melbourne Institute of Technology in Australia, where the authors are affiliated. The subjects include one undergraduate and one postgra...

The World is moving toward Smart traffic management and monitoring technologies. Vehicle detection and classification are the two important features of intelligent transportation system. Several algorithms for detection of vehicles such as Sobel, Prewitt, and Robert etc. but due to their less accuracy and sensitivity to noise they could not detect...

Fog computing has emerged as an essential alternative to the cloud. Fog computing is the future as it is nearer to the edge where actually the IOT devices and sensors are located. A Fog Server or Fog Node is located near to the IOT devices, connecting directly (wired or wireless) to them. The Fog Server has a functionality of fast accessibility to...

Traffic control is one of the most challenging issues in metropolitan cities with growing populations and increased travel demands. Poor traffic control can result in traffic congestion and air pollution that can lead to health issues such as respiratory problems, asthma, allergies, anxiety, and stress. The traffic congestion can also result in tra...

Drones have become prevalent for the delivery of goods by many retail companies such as Amazon and Dominos. Amazon has an issued patent that describes how drones scan and collect data on their flyovers while dropping off packages [1]. In this context, we propose a path optimization algorithm for a drone multi-hop communications network that can car...

Electricity Price forecast is a major task in smart grid operation. There is a massive amount of data flowing in the power system including the data collection by control systems, sensors, etc. In addition, there are many data points which are not captured and processed by the energy market operators and electricity network operators including gros...

This paper proposes an intelligent and compact Probabilistic Neural Network which integrates locally enhanced semi-parametric base classifiers with AdaBoosting for intrusion detection system in IoT environment. The proposed model is to provide an improved intrusion detection at an affordable computational complexity. The proposed model is applied t...

This paper proposes an intelligent and compact machine learning model for IoT intrusion detection using an ensemble of semi-parametric models with Ada boost. The proposed model provides an adequate real-time intrusion detection at an affordable computational complexity suitable for the IoT edge networks. The proposed model is evaluated against othe...

Most of the currently available network security techniques are not able to cope with the dynamic and increasingly complex nature of cyber attacks on distributed computer systems. Therefore, an automated and adaptive defensive tool is imperative for computer networks. Alongside the existing prevention techniques such as encryption and firewalls, In...

When dealing with real-world problems, there is considerable amount of prior domain knowledge that can provide insights on various aspect of the problem. On the other hand, many machine learning methods rely solely on the data sets for their learning phase and do not take into account any explicitly expressed domain knowledge. This paper proposes a...

6.1. Conclusion remarks Motivated by the low detection rates on rare and complicated attacks in the KDD-99 benchmark, we develop the Multi-Expert Classification Framework (MECF) in which Vector Quantized Generalized Regression Neural Network (VQ-GRNN) and Adaptive Boosting (AdaBoost) are deployed. It is shown that some learning algorithms that use...

Most of the currently available network security techniques are not able to cope with the dynamic and increasingly complex nature of cyber attacks on distributed computer systems. Therefore, an automated and adaptive defensive tool is imperative for computer networks. Alongside the existing prevention techniques such as encryption and firewalls, In...

Among many biometric characteristics, the facial biometric is considered to be the least intrusive technology that can be deployed in the real-world visual surveillance environment. However, in facial biometric, little research attention has been paid to facial expression changes. In fact, facial expression changes have often been treated as noise...

This paper introduces a framework that employs the Fisher linear discriminant model (FLDM) and classifier (FLDC) on integrated facial appearance and facial expression features. The principal component analysis (PCA) is firstly applied for dimensionality reduction. The normalized fusion method is then applied to the reduced lower dimensional subspac...

Email is a commonly used tool for communication which allows rapid and asynchronous communication. The growing popularity and low cost of e-mails have made spamming an extremely serious problem today. Several anti-spam filtering techniques have been developed but most of them suffer from low accuracy and high false alarm rate due to complexity and...

Physiological and/or behavioural characteristics of humans such as face, gait and/or voice have been used in biometric recognition technology. Apart from these characteristics (which have been reported in the literature), the hypothesis of this research was to investigate if facial behaviour could be used for human identification. We analysed and p...

The purpose of this research is three fold: (1) to demonstrate that the within-class variation under facial expression changes will increase the uncertain regions for classification; hence, degrades the classification performance, (2) the low-dimensional subspace with enhanced discriminatory power could provide better feature space for classificati...

Network security is a critical component for any sized organization. While static defence technologies such as firewalls and anti-virus provide basic protection for computer networks, an intrusion detection system (IDS) can improve overall security by identifying and responding to novel malicious activities. The current existing IDS methods suffer...

Physiological and/or behavioral characteristics of humans such as face, gait and/or voice have been used in biometric recognition technology. Apart from those characteristics reported in the literature, the hypothesis of this research was to initially investigate if human facial behaviors could also be used as another behavioral traits for human id...

In face recognition, if the extracted input data contains misleading information (uncertainty), the classifiers may produce degraded classification performance. In this paper, we employed kernel-based discriminant analysis method for the non-separable problems in face recognition under facial expression changes. The effect of the transformations on...

First, a hierarchical modelling method, VQSVM, is introduced, and some remarks are discussed. Secondly the proposed VQSVM is applied to a nonstandard learning environment, imbalanced data sets. In cases of extremely imbalanced dataset with high dimensions, standard machine learning techniques tend to be overwhelmed by the large classes. The hierarc...

The paper reviews the recent developments of incorporating prior domain knowledge into inductive machine learning, and proposes a guideline that incorporates prior domain knowledge in three key issues of inductive machine learning algorithms: consistency, gen-eralization and convergence. With respect to each issue, this paper gives some approaches...

In cases of extremely imbalanced dataset with high dimensions, standard machine learning techniques tend to be overwhelmed by the large classes. This paper rebalances skewed datasets by compressing the majority class. This approach combines Vector Quantization and Support Vector Machine and constructs a new approach, VQ-SVM, to rebalance datasets w...

The prediction of stock market has been an important issue in the field of finance, mathematics and engineering due to its great potential financial gain. In addition, uncertainty in the prediction of the financial time series has attracted interest from many researchers. In this study, we present recent developments in stock market prediction mode...

Most of the currently available network security techniques are not able to cope with the dynamic and increasingly complex nature of the attacks on distributed computer systems. An automated and adaptive defensive tool is imperative for computer networks. One of the emerging solutions for Network Security is the Intrusion Detection System (IDS). Ho...

The paper discusses an approach of using traditional time series analysis, as domain knowledge, to help the data-preparation of support vector machine for classifying documents. Classifying unexpected news impacts to the stock prices is selected as a case study. As a result, we present a novel approach for providing approximate answers to classifyi...

This paper surveys machine learning techniques for stock market prediction. The prediction of stock markets is regarded as a challenging task of financial time series prediction. In this paper, we present recent developments in stock market prediction models, and discuss their advantages and disadvantages. In addition, we investigate various global...

Face recognition has been recognized as most simple and non-intrusive technology that can be applied in many places. However, there are still many unsolved face recognition problems such as facial deformations, pose or illumination variations. Nonetheless, little research has been done on facial deformation problems. The hypothesis of this research...

Face recognition has been recognized as most simple and non-intrusive technology that can be applied in many places without possible hazardous problems. However, there are still many unsolved face recognition problems (especially when real-time identification is required) due to different facial expressions (deformations), poses, illumination or oc...

This paper is devoted to theoretic algorithms development and experimental research of automatic target detection of acoustic signals, especially for boats generated signals. In this paper, an observation space is created by sampling and dividing input analog acoustic signal into multiple frames and each frame is transformed into frequency domain....

We present a novel approach for providing approximate answers to classifying news events into simple three categories. The approach is based on the authors' previous research: incorporating domain knowledge into machine learning [1], and initially explore the results of its implementation for this particular field. In this paper, the process of con...