ORCID Profile
0000-0002-7886-3653
Current Organisation
The University of Newcastle
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Architecture And Urban Environment Not Elsewhere Classified | Architecture | Architecture | Applied Mathematics not elsewhere classified | Artificial Intelligence and Image Processing | Mechanical Engineering not elsewhere classified | Wireless Communications | Neural, Evolutionary and Fuzzy Computation | Coding and Information Theory | Communications Technologies | Mineral Processing/Beneficiation | Interdisciplinary Engineering Not Elsewhere Classified | Mechanical Engineering | Numerical Modelling and Mechanical Characterisation | Robotics And Mechatronics | Interdisciplinary Engineering | Intelligent Robotics |
ANZSRC Socio-Economic Objective (SEO)
Other | Recreation | First Stage Treatment of Ores and Minerals not elsewhere classified | Housing | Mobile Data Networks and Services | Mining Machinery and Equipment | Expanding Knowledge in the Information and Computing Sciences | Other
Related Links
Publications
Affective visual perception using machine pareidolia of facial expressions
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2014
Optimisation of 2D U-Net Model Components for Automatic Prostate Segmentation on MRI
Publisher: MDPI AG
Date: 09-04-2020
DOI: 10.3390/APP10072601
Abstract: In this paper, we develop an optimised state-of-the-art 2D U-Net model by studying the effects of the in idual deep learning model components in performing prostate segmentation. We found that for ups ling, the combination of interpolation and convolution is better than the use of transposed convolution. For combining feature maps in each convolution block, it is only beneficial if a skip connection with concatenation is used. With respect to pooling, average pooling is better than strided-convolution, max, RMS or L2 pooling. Introducing a batch normalisation layer before the activation layer gives further performance improvement. The optimisation is based on a private dataset as it has a fixed 2D resolution and voxel size for every image which mitigates the need of a resizing operation in the data preparation process. Non-enhancing data preprocessing was applied and five-fold cross-validation was used to evaluate the fully automatic segmentation approach. We show it outperforms the traditional methods that were previously applied on the private dataset, as well as outperforming other comparable state-of-the-art 2D models on the public dataset PROMISE12.
Software Development in the City Evolutions Project
Publisher: ACM
Date: 02-12-2014
Towards staged evolution of an artificial player for hex by enlarging the boardsize during training
Publisher: IEEE
Date: 2003
A Novel U-Shaped Transfer Function for Binary Particle Swarm Optimisation
Publisher: Springer Singapore
Date: 2020
A study on hill climbing algorithms for neural network training
Publisher: IEEE
Date: 1999
GDTW-P-SVMs: Variable-length time series analysis using support vector machines
Publisher: Elsevier BV
Date: 2013
A Liver Segmentation Algorithm Based on Wavelets and Machine Learning
Publisher: IEEE
Date: 06-2009
A multi-agent cooperative reinforcement learning model using a hierarchy of consultants, tutors and workers
Publisher: Springer Science and Business Media LLC
Date: 05-07-2015
Impact of tactical variations in the RoboCup four-legged league
Publisher: ACM Press
Date: 2006
Architectural evaluation of simulated pedestrian spatial behaviour
Publisher: Informa UK Limited
Date: 05-2011
Artificial intelligence (AI ) and machine learning (ML ) based decision support systems in mental health: An integrative review
Publisher: Wiley
Date: 06-02-2023
DOI: 10.1111/INM.13114
Abstract: An integrative review investigating the incorporation of artificial intelligence (AI) and machine learning (ML) based decision support systems in mental health care settings was undertaken of published literature between 2016 and 2021 across six databases. Four studies met the research question and the inclusion criteria. The primary theme identified was trust and confidence . To date, there is limited research regarding the use of AI‐based decision support systems in mental health. Our review found that significant barriers exist regarding its incorporation into practice primarily arising from uncertainty related to clinician's trust and confidence, end‐user acceptance and system transparency. More research is needed to understand the role of AI in assisting treatment and identifying missed care. Researchers and developers must focus on establishing trust and confidence with clinical staff before true clinical impact can be determined. Finally, further research is required to understand the attitudes and beliefs surrounding the use of AI and related impacts for the wellbeing of the end‐users of care. This review highlights the necessity of involving clinicians in all stages of research, development and implementation of artificial intelligence in care delivery. Earning the trust and confidence of clinicians should be foremost in consideration in implementation of any AI‐based decision support system. Clinicians should be motivated to actively embrace the opportunity to contribute to the development and implementation of new health technologies and digital tools that assist all health care professionals to identify missed care, before it occurs as a matter of importance for public safety and ethical implementation. AI‐basesd decision support tools in mental health settings show most promise as trust and confidence of clinicians is achieved.
A decentralised multi-agent system for rail freight traffic management
Publisher: Springer Science and Business Media LLC
Date: 06-07-2021
Semi-Supervised Manifold Alignment Using Parallel Deep Autoencoders
Publisher: MDPI AG
Date: 06-09-2019
DOI: 10.3390/A12090186
Abstract: The aim of manifold learning is to extract low-dimensional manifolds from high-dimensional data. Manifold alignment is a variant of manifold learning that uses two or more datasets that are assumed to represent different high-dimensional representations of the same underlying manifold. Manifold alignment can be successful in detecting latent manifolds in cases where one version of the data alone is not sufficient to extract and establish a stable low-dimensional representation. The present study proposes a parallel deep autoencoder neural network architecture for manifold alignment and conducts a series of experiments using a protein-folding benchmark dataset and a suite of new datasets generated by simulating double-pendulum dynamics with underlying manifolds of dimensions 2, 3 and 4. The dimensionality and topological complexity of these latent manifolds are above those occurring in most previous studies. Our experimental results demonstrate that the parallel deep autoencoder performs in most cases better than the tested traditional methods of semi-supervised manifold alignment. We also show that the parallel deep autoencoder can process datasets of different input domains by aligning the manifolds extracted from kinematics parameters with those obtained from corresponding image data.
Towards Robot Soccer Team Behaviours Through Approximate Simulation
Publisher: IEEE
Date: 2005
Analysis of pedestrian spatial behaviour using GDTW-P-SVMs
Publisher: IEEE
Date: 06-2012
Fast Automatic Optimisation of CNN Architectures for Image Classification Using Genetic Algorithm
Publisher: IEEE
Date: 06-2019
Machine Learning With AIBO Robots in the Four-Legged League of RoboCup
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2007
Affective analysis of visual scenes using face pareidolia and scene-context
Publisher: Elsevier BV
Date: 05-2021
Optimization of Robot Movements Using Genetic Algorithms and Simulation
Publisher: Springer International Publishing
Date: 2019
Robust Multi-Objective optimization using Conditional Pareto Optimal Dominance
Publisher: IEEE
Date: 07-2020
A component based approach for classifying the seven universal facial expressions of emotion
Publisher: IEEE
Date: 04-2013
Variations of the two-spiral task
Publisher: Informa UK Limited
Date: 06-2007
The machine intelligence Hex project
Publisher: Informa UK Limited
Date: 12-2005
Motivated Reinforcement Learning for Improved Head Actuation of Humanoid Robots
Publisher: Springer Berlin Heidelberg
Date: 2014
Support vector clustering through proximity graph modelling
Publisher: Nanyang Technol. Univ
Date: 2002
Aligning Manifolds of Double Pendulum Dynamics Under the Influence of Noise
Publisher: Springer International Publishing
Date: 2018
Uncertainty Estimation in the Neural Model for Aeromagnetic Compensation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2018
Fast Evolution of CNN Architecture for Image Classification
Publisher: Springer Singapore
Date: 2020
A small spiking neural network with LQR control applied to the acrobot
Publisher: Springer Science and Business Media LLC
Date: 10-05-2008
Voxel‐based supervised machine learning of peripheral zone prostate cancer using noncontrast multiparametric MRI
Publisher: Wiley
Date: 08-08-2020
DOI: 10.1002/ACM2.12992
Estimating Betti Numbers Using Deep Learning
Publisher: IEEE
Date: 07-2019
DQR: Deep Q-Routing in Software Defined Networks
Publisher: IEEE
Date: 07-2020
Sliding window bag-of-visual-words for low computational power robotics scene matching
Publisher: IEEE
Date: 04-2018
A Fast Method for Adapting Lookup Tables Applied to Changes in Lighting Colour
Publisher: Springer International Publishing
Date: 2015
A face-house paradigm for architectural scene analysis
Publisher: ACM Press
Date: 2008
Incremental training of first order recurrent neural networks to predict a context-sensitive language
Publisher: Elsevier BV
Date: 09-2003
Adjacent network for semantic segmentation of liver CT scans
Publisher: IEEE
Date: 10-2018
Interpretations of Innovation: The Role of Technology in Explanation Seeking Related to Psychosis
Publisher: Hindawi Limited
Date: 08-02-2023
DOI: 10.1155/2023/4464934
Abstract: Background. The prominence of technology in modern life cannot be understated. However, for some people, these innovations or their related plausible advancements can be associated with perceptual misinterpretation and/or incorporation into delusional concepts. Objective. This paper aims to explore the intersection of technological advancement and experiencing psychosis. We present a discussion about the explanation seeking that incorporates the concept, that for some people, of technological innovation becoming intertwined with delusional symptoms over the past 100 years. Methods. A longitudinal review of the literature was conducted to synthesize and draw these concepts together, mapping them to a timeline that aligns computing science and healthcare expertise and presents the significant technological changes of the modern era charted against mental health milestones and reports of technology-related delusions. Results. It is possible for technology to be incorporated into the content of delusions with evidence supporting a link between the rate of technological change, the content of delusions, and the use of technology as a way of seeking an explanation. Moreover, analysis suggests a need to better understand how innovations may impact the mental health of people at risk of psychosis and other mental health conditions. Conclusions. Clinical experts and lived experience experts need to be informed about and collaborate with future research and development of technology, specifically artificial intelligence and machine learning, early in the development cycle. This concurs with other artificial intelligence research recommendations calling for design attention to the development and implementation of technological innovation applied in a mental health context.
Training Deep Neural Networks for Detecting Drinking Glasses Using Synthetic Images
Publisher: Springer International Publishing
Date: 2017
Scene perception using pareidolia of faces and expressions of emotion
Publisher: IEEE
Date: 04-2013
Group emotion recognition in the wild by combining deep neural networks for facial expression classification and scene-context analysis
Publisher: ACM
Date: 03-11-2017
Multimodal Emotion Recognition Based on Speech and Physiological Signals Using Deep Neural Networks
Publisher: Springer International Publishing
Date: 2021
Comparing Computing Platforms for Deep Learning on a Humanoid Robot
Publisher: Springer International Publishing
Date: 2018
The Impact of Image Resolution on Facial Expression Analysis with CNNs
Publisher: IEEE
Date: 07-2019
Regime type and international conflict: Towards a general model
Publisher: SAGE Publications
Date: 11-2008
Abstract: The authors take a new look at the relationship between regime type and deadly militarized conflict among pairs of states (dyads) in the international system. With the goal of describing the general functional form, they evaluate three perspectives: democratic peace, regime similarity and regime rationality. They employ both standard logistic regression (logit) and a recently developed machine learning technique, a support vector machine (SVM). Logit is dependent on assumptions that limit flexibility and make it difficult to discern the appropriate functional form. SVM estimation, on the other hand, is highly flexible and appears capable of discovering a relationship that is contingent on other variables in the model. SVM results indicate that regime similarity and joint democracy are important in most dyadic interactions. However, for the special but important case of the most dangerous dyads, regime rationality plays a role and the democratic peace effect is dominant. The results suggest that models of international conflict excluding distinct indicators for political similarity, joint democracy and joint autocracy may be misspecified. SVMs are an especially useful complement to conventional statistical methods.
Segmenting Salient Objects in 3D Point Clouds of Indoor Scenes Using Geodesic Distances
Publisher: Scientific Research Publishing, Inc.
Date: 2013
A component based approach improves classification of discrete facial expressions over a holistic approach
Publisher: IEEE
Date: 07-2010
INCREMENTAL LEARNING IN BIOLOGICAL AND MACHINE LEARNING SYSTEMS
Publisher: World Scientific Pub Co Pte Lt
Date: 12-2002
DOI: 10.1142/S0129065702001308
Abstract: Incremental learning concepts are reviewed in machine learning and neurobiology. They are identified in evolution, neurodevelopment and learning. A timeline of qualitative axon, neuron and synapse development summarizes the review on neurodevelopment. A discussion of experimental results on data incremental learning with recurrent artificial neural networks reveals that incremental learning often seems to be more efficient or powerful than standard learning but can produce unexpected side effects. A characterization of incremental learning is proposed which takes the elaborated biological and machine learning concepts into account.
Testing the Robustness of Manifold Learning on Examples of Thinned-Out Data
Publisher: IEEE
Date: 07-2019
A Smart Meter Firmware Update Strategy Through Network Coding for AMI Network
Publisher: Springer International Publishing
Date: 30-12-2018
Recognition of emotion from speech using evolutionary cepstral coefficients
Publisher: Springer Science and Business Media LLC
Date: 18-09-2020
Comparing Ellipse Detection and Deep Neural Networks for the Identification of Drinking Glasses in Images
Publisher: Springer International Publishing
Date: 2019
Performance of evolutionary wavelet neural networks in acrobot control tasks
Publisher: Springer Science and Business Media LLC
Date: 11-07-2019
An experimental evaluation of pairwise adaptive support vector machines
Publisher: IEEE
Date: 06-2012
NUClear: A Loosely Coupled Software Architecture for Humanoid Robot Systems
Publisher: Frontiers Media SA
Date: 25-04-2016
Improving the reliability of implicit averaging methods using new conditional operators for robust optimization
Publisher: Elsevier BV
Date: 12-2019
Addressing deadlock in large‐scale, complex rail networks via multi‐agent deep reinforcement learning
Publisher: Wiley
Date: 04-05-2023
DOI: 10.1111/EXSY.13315
Interpretations of Innovation: The intersection of technological advancement and psychosis (Preprint)
Publisher: JMIR Publications Inc.
Date: 03-02-2022
Abstract: he prominence of technology in modern life cannot be understated. However, for some people these innovations or their related plausible advancements, can be associated with perceptual misinterpretation and/or incorporation into delusional concepts. his paper aims to explore the intersection of technological advancement and experiencing psychosis. We present a discussion about the explanation seeking that incorporates the concept, that for some people, of technological innovation becoming intertwined with delusional symptoms over the past 100 years. longitudinal review of the literature was conducted to synthesise and draw these concepts together, mapping them to a timeline that aligns computing science and healthcare expertise and presents the significant technological changes of the modern era charted against mental health milestones and reports of technology-related delusions. t is possible for technology to be incorporated in the content of delusions with evidence supporting a link between the rate of technological change, the content of delusions and the use of technology as a way of seeking an explanation. Moreover, analysis suggests a need to better understand how innovations may impact the mental health of people at risk of psychosis and other mental health conditions. linical experts and lived experience experts need to be informed about and collaborate with future research and development of technology, specifically artificial intelligence and machine learning, early in the development cycle. This concurs with other artificial intelligence research recommendations calling for design attention to the development and implementation of technological innovation applied in a mental health context.
A study on validating non-linear dimensionality reduction using persistent homology
Publisher: Elsevier BV
Date: 12-2017
Visual Mesh: Real-Time Object Detection Using Constant Sample Density
Publisher: Springer International Publishing
Date: 2019
Parallel LSTM Architectures for Non-Intrusive Load Monitoring in Smart Homes
Publisher: IEEE
Date: 12-2020
Wayfinding: A method for the empirical evaluation of structural saliency using 3D Isovists
Publisher: Informa UK Limited
Date: 08-2013
Representations of streetscape perceptions through manifold learning in the space of hough arrays
Publisher: IEEE
Date: 04-2007
RoboCup Junior in the Hunter Region: Driving the Future of Robotic STEM Education
Publisher: Springer International Publishing
Date: 2019
A study on detecting three-dimensional balls using boosted classifiers
Publisher: ACM
Date: 02-2016
From Face Recognition to Facial Pareidolia: Analysing Hidden Neuron Activations in CNNs for Cross-Depiction Recognition
Publisher: IEEE
Date: 07-2019
Monocular ORB-SLAM on a Humanoid Robot for Localization Purposes
Publisher: Springer International Publishing
Date: 2018
An automatic HyLoggerTM mineral mapping method using a machine-learning-based computer vision technique
Publisher: Informa UK Limited
Date: 05-11-1919
Quadratic Leaky Integrate-and-Fire Neural Network Tuned with an Evolution-Strategy for a Simulated 3D Biped Walking Controller
Publisher: IEEE
Date: 09-2008
DOI: 10.1109/HIS.2008.146
Bulk Anatomical Density Based Dose Calculation for Patient-Specific Quality Assurance of MRI-Only Prostate Radiotherapy
Publisher: Frontiers Media SA
Date: 02-10-2019
A computational approach to fractal analysis of a cityscape's skyline
Publisher: Informa UK Limited
Date: 06-2009
Artificial Life and Computational Intelligence
Publisher: Springer International Publishing
Date: 2015
Machine Learning in the Four-Legged League
Publisher: Elsevier BV
Date: 09-2004
Intelligent evaluation of urban streetscape designs by analysing pedestrian body dynamics
Publisher: IEEE
Date: 08-2010
Kernel Methods in Finance
Publisher: Springer Berlin Heidelberg
Date: 2008
Hill climbing in recurrent neural networks for learning the anbncn language
Publisher: IEEE
Date: 1999
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