ORCID Profile
0000-0002-7886-3653
Current Organisation
The University of Newcastle
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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.
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 |
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
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2014
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.
Publisher: ACM
Date: 02-12-2014
Publisher: IEEE
Date: 2003
Publisher: Springer Singapore
Date: 2020
Publisher: IEEE
Date: 1999
Publisher: Elsevier BV
Date: 2013
Publisher: IEEE
Date: 06-2009
Publisher: Springer Science and Business Media LLC
Date: 05-07-2015
Publisher: ACM Press
Date: 2006
Publisher: Informa UK Limited
Date: 05-2011
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.
Publisher: Springer Science and Business Media LLC
Date: 06-07-2021
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.
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 06-2012
Publisher: IEEE
Date: 06-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2007
Publisher: Elsevier BV
Date: 05-2021
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 07-2020
Publisher: IEEE
Date: 04-2013
Publisher: Informa UK Limited
Date: 06-2007
Publisher: Informa UK Limited
Date: 12-2005
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: Nanyang Technol. Univ
Date: 2002
Publisher: Springer International Publishing
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2018
Publisher: Springer Singapore
Date: 2020
Publisher: Springer Science and Business Media LLC
Date: 10-05-2008
Publisher: Wiley
Date: 08-08-2020
DOI: 10.1002/ACM2.12992
Publisher: IEEE
Date: 07-2019
Publisher: IEEE
Date: 07-2020
Publisher: IEEE
Date: 04-2018
Publisher: Springer International Publishing
Date: 2015
Publisher: ACM Press
Date: 2008
Publisher: Elsevier BV
Date: 09-2003
Publisher: IEEE
Date: 10-2018
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.
Publisher: Springer International Publishing
Date: 2017
Publisher: IEEE
Date: 04-2013
Publisher: ACM
Date: 03-11-2017
Publisher: Springer International Publishing
Date: 2021
Publisher: Springer International Publishing
Date: 2018
Publisher: IEEE
Date: 07-2019
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.
Publisher: Scientific Research Publishing, Inc.
Date: 2013
Publisher: IEEE
Date: 07-2010
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.
Publisher: IEEE
Date: 07-2019
Publisher: Springer International Publishing
Date: 30-12-2018
Publisher: Springer Science and Business Media LLC
Date: 18-09-2020
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer Science and Business Media LLC
Date: 11-07-2019
Publisher: IEEE
Date: 06-2012
Publisher: Frontiers Media SA
Date: 25-04-2016
Publisher: Elsevier BV
Date: 12-2019
Publisher: Wiley
Date: 04-05-2023
DOI: 10.1111/EXSY.13315
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.
Publisher: Elsevier BV
Date: 12-2017
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 12-2020
Publisher: Informa UK Limited
Date: 08-2013
Publisher: IEEE
Date: 04-2007
Publisher: Springer International Publishing
Date: 2019
Publisher: ACM
Date: 02-2016
Publisher: IEEE
Date: 07-2019
Publisher: Springer International Publishing
Date: 2018
Publisher: Informa UK Limited
Date: 05-11-1919
Publisher: IEEE
Date: 09-2008
DOI: 10.1109/HIS.2008.146
Publisher: Frontiers Media SA
Date: 02-10-2019
Publisher: Informa UK Limited
Date: 06-2009
Publisher: Springer International Publishing
Date: 2015
Publisher: Elsevier BV
Date: 09-2004
Publisher: IEEE
Date: 08-2010
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: IEEE
Date: 1999
Start Date: 2007
End Date: 2009
Funder: Australian Research Council
View Funded ActivityStart Date: 2021
End Date: 2024
Funder: Australian Research Council
View Funded ActivityStart Date: 2003
End Date: 2003
Funder: University of Newcastle Australia
View Funded ActivityStart Date: 2010
End Date: 2012
Funder: Australian Research Council
View Funded ActivityStart Date: 2016
End Date: 2016
Funder: Department of Industry, Innovation and Science
View Funded ActivityStart Date: 2020
End Date: 2021
Funder: Australian Coal Research Limited
View Funded ActivityStart Date: 2021
End Date: 2024
Funder: Australian Research Council
View Funded ActivityStart Date: 2019
End Date: 2020
Funder: Australian Mathematical Sciences Institute
View Funded ActivityStart Date: 2020
End Date: 2022
Funder: Australian Research Council
View Funded ActivityStart Date: 2003
End Date: 2003
Funder: Australian Research Council
View Funded ActivityStart Date: 01-2007
End Date: 12-2010
Amount: $275,829.00
Funder: Australian Research Council
View Funded ActivityStart Date: 07-2021
End Date: 06-2024
Amount: $400,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 02-2010
End Date: 01-2014
Amount: $486,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 05-2021
End Date: 05-2024
Amount: $545,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 03-2021
End Date: 03-2024
Amount: $411,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 01-2004
End Date: 11-2004
Amount: $20,000.00
Funder: Australian Research Council
View Funded Activity