Tony Jan

Torrens University Australia · Design and Technology Vertical (Faculty)

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...

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...

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...

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...

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)...

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...

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...

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...

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...

In this paper, a model is proposed which combines multiple local linear models with a novel modified probabilistic neural network (MPNN). The proposed model is developed to approximate multiple nonlinear model with reduced computational requirement. The proposed model shows to provide both low bias and variance with reduced computations by utilizin...

Background modelling is widely used in computer vision for the detection of foreground objects in a frame sequence. The more accurate the background model, the more correct is the detection of the foreground objects. In this paper, we present an approach to background modelling based on a mean-shift procedure. The mean shift vector convergence prop...

Recent medical studies show that there exist aesthetic ideal features for facial beauty based on facial proportions. Automated tools that can provide information about the prediction of how the surgery will improve the patients' perceived beauty or 'peer-esteem' will find applications in various areas. In our previous work, we introduced an automat...

In multimedia applications such as MPEG-4, an efficient model is required to encode and classify video objects such as human, car and building. Recently, support vector machine (SVM) has been shown to be a good classifier; however, its large computational requirement prohibited its use in real time video processing applications. In this paper, a mo...

In automated visual surveillance systems (AVSS), reliable detection of suspicious human behavior is of great practical importance. Many conventional classifiers have shown to perform inadequately because of unpredictable nature of human behavior. Flexible models such as artificial neural network (ANN) models can perform better; however, computation...

In this paper, a model is proposed which combines multiple local linear models with a novel modified probabilistic neural network (MPNN). The proposed model is shown to provide improved regularization with reduced computation utilizing semiparametric model approach and efficient vector quantization of data space. In this paper, the proposed model i...

Humans use their faces, hands and body as an integral part of their communication with others. For the computer to interact intelligently with human users, computers should be able to recognize emotions, by analyzing the human’s affective state, physiology and behavior. Multimodal interfaces allow humans to interact with machines through multiple m...

In this paper, a hybrid classifier is introduced which combines a linear discriminant classifier and a nonlinear non-parametric neural network based classifier such as the radial basis function neural networks. This hybrid model provides a linear parametric coding of the coarse-level information about the underlying image, and then uses the neural...

In this paper, an ensemble of models is introduced which combines a linear parametric model and a nonlinear non-parametric model such as artificial neural network (ANN). This model aims to embody the desirable characteristics of linear parametric model such as stable generalization capability while retaining the data-based learning and prediction c...

The fact that perception of facial beauty may be a universal concept has long been debated amongst psychologists and anthropologists. In this paper, we performed experiments to evaluate the extent of beauty universality by asking a number of diverse human referees to grade a same collection of female facial images. Results obtained show that the di...

Multimodal systems allow humans to interact with machines through multiple modalities such as speech, facial expression, gesture, and gaze. This paper presents a bimodal model of facial and upper-body gesture for affective HCI suitable for use in a vision-based multimodal system. What distinguishes the present study from its predecessors is that, t...

In automated visual surveillance applications, detection of suspicious human behaviors is of great practical importance. However due to random nature of human movements, reliable classification of suspicious human movements can be very difficult. Artificial neural network (ANN) classifiers can perform well however their computational requirements c...

In many business applications, accurate short term prediction is vital for survival. Many different techniques have been applied to model business data in order to produce accurate prediction. Artificial neural network (ANN) have shown excellent potential however it requires better extrapolation capacity in order to provide reliable prediction. In...

In business applications, robust short term prediction is important for survival. Artificial neural network (ANN) have shown excellent potential however it needs better extrapolation capacity in order to provide reliable short term prediction. In this paper, a combination of linear regression model in parallel with general regression neural network...

The advances in computer vision, which is a branch of artificial intelligence that focuses on providing computers with the functions typical of human vision is discussed. Computer vision has produced important application in fields such as robotics, biomedicine, industrial automation and satellite observation of Earth. The basic idea behind the use...

The fact that facial beauty might be a universal concept and can be measured by using mathematical ratios of facial features has long been debated amongst psychologists and anthropologists. Accordingly, in this paper we present results of experiments to evaluate the extent of universal beauty. The experiments were performed by asking a number of di...

Assessment of abnormal and suspicious behaviors can to some extent be performed by an automated visual surveillance system. In this paper, we present an approach to visual surveillance of a car park against potential offenders by use of neural network classifiers. First, trajectories of people within the car parks are extracted. Then velocity patte...

For the computer to interact intelligently with human users, computers should be able to recognize emotions, by analyzing the human's affective state, physiology and behavior. In this paper, we present a survey of research conducted on face and body gesture and recognition. In order to make human-computer interfaces truly natural, we need to develo...

For separation of signals with overlapping spectra. Classical linear filters fail to perform effectively. Nonlinear filters such as Volterra filters or artificial neural networks (ANNs) can perform better but their implementations are often impractical due to their computational complexity. In this paper an ANN based hyperspace signal modeling is u...

A basic limitation of all data-driven approximation methods is their inability to extrapolate accurately once the input is outside of the training data range. This paper examines the effectiveness and utility of combining a linear regression model with general regression neural network or modified probabilistic neural network for better linear extr...

In this paper, an Adaptive Median Constant False Alarm Rate (AMCFAR) and multi-frame post detection integration algorithm is proposed for effective real time automatic target detection of boat-generated acoustic signals, in which, an observation space is cre-ated by sampling and dividing input analog acoustic signal into multiple frames and each fr...