ORCID Profile
0000-0002-4959-5274
Current Organisation
Central Queensland University
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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.
Animal Breeding | Neural, Evolutionary and Fuzzy Computation | Genetics | Quantitative Genetics (incl. Disease and Trait Mapping Genetics)
Expanding Knowledge in the Biological Sciences | Beef Cattle | Sheep - Meat |
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: SPIE
Date: 15-11-2007
DOI: 10.1117/12.774719
Publisher: Wiley
Date: 10-03-2021
DOI: 10.1002/ECE3.7344
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: MDPI AG
Date: 08-04-2021
DOI: 10.3390/S21082611
Abstract: Image data is one of the primary sources of ecological data used in bio ersity conservation and management worldwide. However, classifying and interpreting large numbers of images is time and resource expensive, particularly in the context of camera trapping. Deep learning models have been used to achieve this task but are often not suited to specific applications due to their inability to generalise to new environments and inconsistent performance. Models need to be developed for specific species cohorts and environments, but the technical skills required to achieve this are a key barrier to the accessibility of this technology to ecologists. Thus, there is a strong need to democratize access to deep learning technologies by providing an easy-to-use software application allowing non-technical users to train custom object detectors. U-Infuse addresses this issue by providing ecologists with the ability to train customised models using publicly available images and/or their own images without specific technical expertise. Auto-annotation and annotation editing functionalities minimize the constraints of manually annotating and pre-processing large numbers of images. U-Infuse is a free and open-source software solution that supports both multiclass and single class training and object detection, allowing ecologists to access deep learning technologies usually only available to computer scientists, on their own device, customised for their application, without sharing intellectual property or sensitive data. It provides ecological practitioners with the ability to (i) easily achieve object detection within a user-friendly GUI, generating a species distribution report, and other useful statistics, (ii) custom train deep learning models using publicly available and custom training data, (iii) achieve supervised auto-annotation of images for further training, with the benefit of editing annotations to ensure quality datasets. Broad adoption of U-Infuse by ecological practitioners will improve ecological image analysis and processing by allowing significantly more image data to be processed with minimal expenditure of time and resources, particularly for camera trap images. Ease of training and use of transfer learning means domain-specific models can be trained rapidly, and frequently updated without the need for computer science expertise, or data sharing, protecting intellectual property and privacy.
Publisher: IEEE
Date: 11-2015
Publisher: Springer Science and Business Media LLC
Date: 18-02-2012
Publisher: IEEE
Date: 11-2013
Publisher: Springer Science and Business Media LLC
Date: 06-11-2014
Publisher: Elsevier BV
Date: 10-2014
DOI: 10.1016/J.ACTATROPICA.2014.03.021
Abstract: Agent-based modelling has proven to be a promising approach for developing rich simulations for complex phenomena that provide decision support functions across a broad range of areas including biological, social and agricultural sciences. This paper demonstrates how high performance computing technologies, namely General-Purpose Computing on Graphics Processing Units (GPGPU), and commercial Geographic Information Systems (GIS) can be applied to develop a national scale, agent-based simulation of an incursion of Old World Screwworm fly (OWS fly) into the Australian mainland. The development of this simulation model leverages the combination of massively data-parallel processing capabilities supported by NVidia's Compute Unified Device Architecture (CUDA) and the advanced spatial visualisation capabilities of GIS. These technologies have enabled the implementation of an in idual-based, stochastic lifecycle and dispersal algorithm for the OWS fly invasion. The simulation model draws upon a wide range of biological data as input to stochastically determine the reproduction and survival of the OWS fly through the different stages of its lifecycle and dispersal of gravid females. Through this model, a highly efficient computational platform has been developed for studying the effectiveness of control and mitigation strategies and their associated economic impact on livestock industries can be materialised.
Publisher: Springer Science and Business Media LLC
Date: 24-11-2015
Publisher: World Scientific Pub Co Pte Lt
Date: 09-2009
DOI: 10.1142/S0218001409007478
Abstract: Using local data information, the recently proposed local Fisher Discriminant Analysis (LFDA) algorithm 18 provides a new way of handling the multimodal issues within classes where the conventional Fisher Discriminant Analysis (FDA) algorithm fails. Like the FDA algorithm (global counterpart), the LFDA suffers when it is applied to the higher dimensional data sets. In this paper, we propose a new formulation by which a robust algorithm can be formed. The new algorithm offers more robust results for higher dimensional data sets when compared with the LFDA in most cases. By extensive simulation studies, we have demonstrated the practical usefulness and robustness of our new algorithm in data visualization.
Publisher: Elsevier BV
Date: 06-2016
Publisher: Informa UK Limited
Date: 25-04-2019
Publisher: Elsevier BV
Date: 07-2018
Publisher: IEEE
Date: 07-2014
Publisher: Springer Berlin Heidelberg
Date: 2003
Publisher: Inderscience Publishers
Date: 2011
Publisher: Elsevier BV
Date: 03-2017
Publisher: Public Library of Science (PLoS)
Date: 30-05-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Wiley
Date: 13-10-2014
DOI: 10.1002/CEM.2683
Publisher: Addleton Academic Publishers
Date: 2018
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Elsevier BV
Date: 03-2010
Publisher: IGI Global
Date: 2013
DOI: 10.4018/978-1-4666-3994-2.CH040
Abstract: In the last decade, many computer-aided diagnosis (CAD) systems that utilize a broad range of diagnostic techniques have been proposed. Due to both the inherently complex structure of the breast tissues and the low intensity contrast found in most mammographic images, CAD systems that are based on conventional techniques have been shown to have missed malignant masses in mammographic images that would otherwise be treatable. On the other hand, systems based on fuzzy image processing techniques have been found to be able to detect masses in cases where conventional techniques would have failed. In the current chapter, recent advances in fuzzy image segmentation techniques as applied to mass detection in digital mammography are reviewed. Image segmentation is an important step in CAD systems since the quality of its outcome will significantly affect the processing downstream that can involve both detection and classification of benign versus malignant masses.
Publisher: Australasian Society for Computers in Learning in Tertiary Education
Date: 21-03-2019
DOI: 10.14742/AJET.3340
Abstract: This study aimed to investigate how the use of a smartphone clicker app by a group of 390 Saudi Arabian male undergraduate students would impact their learning performance while participating in a computer science class. The smartphone clicker app was used by the students during peer group discussions and to respond to teacher questions. A conceptual framework identified teacher-student and student-student interactions, collaborative learning, and student engagement as three primary practices that could improve student performance when a smartphone clicker app was used. The relationships between these factors were tested empirically by participant completion of a self-administered online survey. This study found the use of a smartphone clicker app promoted increased teacher-student and student-student interactivity, leading to active collaboration learning by students and improved learning performance. No positive relationship was found between the smartphone clicker app use and increased student engagement. These results demonstrated the role of the smartphone clicker app in enhancing the learning experience of the Saudi undergraduate students included in this study, but not the overall student engagement. Further research into how use of a smartphone clicker app in classroom settings might promote student engagement to improve the overall learning performance is needed.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 12-2010
Publisher: Wiley
Date: 23-07-2018
DOI: 10.1002/ECE3.4286
Publisher: Cold Spring Harbor Laboratory
Date: 15-05-2020
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Frontiers Media SA
Date: 19-03-2015
Publisher: World Scientific Pub Co Pte Lt
Date: 02-2010
Publisher: IGI Global
Date: 2012
DOI: 10.4018/978-1-4666-1830-5.CH018
Abstract: Agent-based modelling is becoming a widely used approach for simulating complex phenomena. By making use of emergent behaviour, agent based models can simulate systems right down to the most minute interactions that affect a system’s behaviour. In order to capture the level of detail desired by users, many agent based models now contain hundreds of thousands and even millions of interacting agents. The scale of these models makes them computationally expensive to operate in terms of memory and CPU time, limiting their practicality and use. This chapter details the techniques for applying Dynamic Hierarchical Agent Compression to agent based modelling systems, with the aim of reducing the amount of memory and number of CPU cycles required to manage a set of agents within a model. The scheme outlined extracts the state data stored within a model’s agents and takes advantage of redundancy in this data to reduce the memory required to represent this information. The techniques show how a hierarchical data structure can be used to achieve compression of this data and the techniques for implementing this type of structure within an existing modelling system. The chapter includes a case study that outlines the practical considerations related to the application of this scheme to Australia’s National Model for Emerging Livestock Disease Threats that is currently being developed.
Publisher: Elsevier BV
Date: 07-2019
Publisher: IEEE
Date: 12-2013
Publisher: Public Library of Science (PLoS)
Date: 06-02-2017
Publisher: ACM
Date: 27-10-2017
Publisher: IEEE
Date: 08-2014
Publisher: IEEE
Date: 11-2011
Publisher: Springer Berlin Heidelberg
Date: 2002
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: IGI Global
Date: 04-2010
Publisher: Informa UK Limited
Date: 21-04-2020
Publisher: MDPI AG
Date: 09-08-2022
Abstract: Results of recent studies have suggested that intensive methods of delivery might improve engagement, attendance, and achievement for students from erse backgrounds. Contributing to this area of inquiry, this study assesses how students perceived their experience studying a certificate course that was delivered in an online intensive block mode and flipped classroom (BMFC), pedagogy amidst COVID-19 restrictions. The subjects were students enrolled at Melbourne Institute of Technology between July 2021 and January 2022 across four certificate courses, three at postgraduate and one at undergraduate level. These certificate courses differed from normal degree courses in several aspects: (a) a shorter 4-week (undergraduate) or 5-week (postgraduate), instead of a 12-week duration, (b) subjects were taken sequentially instead of concurrently as in a normal semester, (c) taught using an online flipped classroom rather than the in-class approach, and (d) open to both high-school leavers and mature aged students who did not study full-time. A questionnaire involving 10 perception-based questions was used to survey students’ satisfaction with the BMFC delivery, in relation to their learning and engagement experience. The mean, median, and mode calculated from the responses revealed that students regarded the BMFC approach as more satisfied than not on a 5-star rating scale in 7 out of the 10 questions. This is further supported by high correlations among the questions (the lowest at r = 0.48 and the highest at r = 0.87). Multiple regression analysis using the first nine questions as predictors of the 10th question (overall satisfaction) revealed that six of these are statistically significant predictors (p 0.05) of the overall satisfaction, implying that an increase in the overall satisfaction can potentially be achieved by improving these key factors of the BMFC delivered certificate courses. Our findings correlate with existing research that student learning and engagement might be improved by intensive modes of delivery. Furthermore, the BMFC pedagogy proposed in our study differentiates us from existing research, where block scheduling was used only in a face-to-face delivery in pre COVID-19 environment. Our study, therefore, contributes a novel delivery method for learning and teaching that is suitable for both online and face-to-face mode in a post COVID-19 era.
Publisher: Elsevier BV
Date: 09-2002
Publisher: IGI Global
Date: 2012
Publisher: ACM
Date: 26-11-2012
Publisher: IGI Global
Date: 2012
DOI: 10.4018/978-1-4666-1830-5.CH021
Abstract: In the last decade, many computer-aided diagnosis (CAD) systems that utilize a broad range of diagnostic techniques have been proposed. Due to both the inherently complex structure of the breast tissues and the low intensity contrast found in most mammographic images, CAD systems that are based on conventional techniques have been shown to have missed malignant masses in mammographic images that would otherwise be treatable. On the other hand, systems based on fuzzy image processing techniques have been found to be able to detect masses in cases where conventional techniques would have failed. In the current chapter, recent advances in fuzzy image segmentation techniques as applied to mass detection in digital mammography are reviewed. Image segmentation is an important step in CAD systems since the quality of its outcome will significantly affect the processing downstream that can involve both detection and classification of benign versus malignant masses.
Publisher: IEEE
Date: 11-2014
Publisher: IEEE
Date: 12-2006
DOI: 10.1109/AIDM.2006.18
Publisher: Elsevier BV
Date: 2009
Publisher: MDPI AG
Date: 20-06-2022
Publisher: IEEE
Date: 08-2007
Publisher: IEEE
Date: 11-2017
Publisher: IEEE
Date: 12-2010
Publisher: MDPI AG
Date: 22-11-2021
Abstract: Livestock welfare and management could be greatly enhanced by the replacement of branding or ear tagging with less invasive visual biometric identification methods. Biometric identification of cattle from muzzle patterns has previously indicated promising results. Significant barriers exist in the translation of these initial findings into a practical precision livestock monitoring system, which can be deployed at scale for large herds. The objective of this study was to investigate and address key limitations to the autonomous biometric identification of cattle. The contributions of this work are fourfold: (1) provision of a large publicly-available dataset of cattle face images (300 in idual cattle) to facilitate further research in this field, (2) development of a two-stage YOLOv3-ResNet50 algorithm that first detects and extracts the cattle muzzle region in images and then applies deep transfer learning for biometric identification, (3) evaluation of model performance across a range of cattle breeds, and (4) utilizing few-shot learning (five images per in idual) to greatly reduce both the data collection requirements and duration of model training. Results indicated excellent model performance. Muzzle detection accuracy was 99.13% (1024 × 1024 image resolution) and biometric identification achieved 99.11% testing accuracy. Overall, the two-stage YOLOv3-ResNet50 algorithm proposed has substantial potential to form the foundation of a highly accurate automated cattle biometric identification system, which is applicable in livestock farming systems. The obtained results indicate that utilizing livestock biometric monitoring in an advanced manner for resource management at multiple scales of production is possible for future agriculture decision support systems, including providing useful information to forecast acceptable stocking rates of pastures.
Publisher: Association for Computing Machinery (ACM)
Date: 28-07-2017
DOI: 10.1145/3012287
Abstract: Annotations provide a valuable perspective on the semantic information present in digital heritage collections, and in recent years they've been employed in a number of innovative, user-centric techniques that can personalise a user's experience of heritage materials, such as by actively adapting exhibits as a user reveals their interests, or by guiding users to explore collections which are meaningfully linked to what they have previously encountered. Despite the captivating opportunities offered by these techniques, collecting annotations for a large heritage collection is no trivial task. A significant amount of work is required to manually annotate large quantities of heritage materials, and automated, computational approaches leave much to be desired regarding the level of insight and semantic richness that they can currently provide. By analysing the emergent relationships between the initial annotations in a collection, we propose a metadata-driven algorithm for assisting and augmenting the annotation process. This algorithm, called SAGA (Semantically-Annotated Graph Analysis), allows for semi-automatic annotation, which balances the value of the contributions of human annotators with the time and effort-saving benefits of an automatic, suggestion-driven process. SAGA uses an entity relationship-driven approach to make annotation suggestions. It is used in the context of a web-based infrastructure called SAGE (Semantic Annotation by Group Exploration), a multiagent environment which assists groups of experts in creating comprehensive annotation sets for heritage collections. SAGA and SAGE are evaluated from the perspectives of suggestion accuracy, explicit user acceptance and implicit user acceptance, and demonstrate strong results in each evaluation.
Publisher: IEEE
Date: 08-2014
Publisher: Springer New York
Date: 28-08-2012
Publisher: Springer Science and Business Media LLC
Date: 10-05-2018
Publisher: Wiley
Date: 2018
DOI: 10.1002/JSC.2176
Publisher: IEEE
Date: 11-2011
Publisher: Wiley
Date: 2018
DOI: 10.1002/JSC.2175
Publisher: Elsevier BV
Date: 2015
Publisher: IEEE
Date: 12-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2008
Publisher: SPIE
Date: 15-11-2007
DOI: 10.1117/12.749777
Publisher: Wiley
Date: 19-04-2019
DOI: 10.1002/PS.5420
Abstract: Ommatissus lybicus de Bergevin (Hemiptera: Tropiduchidae) (Dubas Bug, DB) is an insect pest attacking date palms. It occurs in Arab countries including Oman. In this paper, the logistic, ordinary least square, and geographical weighted regressions were applied to model the absence resence and density of DB against climate factors. A method is proposed for modelling spatially correlated prorations annually over the study period, based on annual and seasonal outbreaks. The historical 2006-2015 climate data were obtained from weather stations located in nine governorates in northern Oman, while dataloggers collected the 2017 microclimate data in eight of these nine governorates. Logistic regression model showed the percentages of correctly predicted values using a cut-off point of 0.5 were 90%, 88% and 84%, indicating good classification accuracy. OLS and GWR models showed an overall trend of strong linear correlation between DB infestation levels and short- and long-term climate factors. The three models suggested that precipitation, elevation, temperature, humidity, wind direction and wind speed are important in influencing the spatial distribution and the presence/absence of dense DB populations. The results provide an improved understanding of climate factors that impact DB's spread and is considered useful for managing DB infestations in date palm plantations. © 2019 Society of Chemical Industry.
Publisher: IEEE
Date: 2007
DOI: 10.1109/ICBBE.2007.4
Publisher: PeerJ
Date: 31-08-2017
DOI: 10.7717/PEERJ.3752
Abstract: In order to understand the distribution and prevalence of Ommatissus lybicus (Hemiptera: Tropiduchidae) as well as analyse their current biographical patterns and predict their future spread, comprehensive and detailed information on the environmental, climatic, and agricultural practices are essential. The spatial analytical techniques such as Remote Sensing and Spatial Statistics Tools, can help detect and model spatial links and correlations between the presence, absence and density of O. lybicus in response to climatic, environmental, and human factors. The main objective of this paper is to review remote sensing and relevant analytical techniques that can be applied in mapping and modelling the habitat and population density of O. lybicus . An exhaustive search of related literature revealed that there are very limited studies linking location-based infestation levels of pests like the O. lybicus with climatic, environmental, and human practice related variables. This review also highlights the accumulated knowledge and addresses the gaps in this area of research. Furthermore, it makes recommendations for future studies, and gives suggestions on monitoring and surveillance methods in designing both local and regional level integrated pest management strategies of palm tree and other affected cultivated crops.
Publisher: Inderscience Publishers
Date: 2015
Publisher: Elsevier BV
Date: 10-2012
Publisher: Elsevier BV
Date: 03-2016
Publisher: Elsevier BV
Date: 04-2020
Publisher: IEEE
Date: 11-2016
Publisher: Cold Spring Harbor Laboratory
Date: 02-10-2020
Publisher: Royal Society of Chemistry (RSC)
Date: 2014
DOI: 10.1039/C3AY42196A
Publisher: ACM
Date: 04-06-2016
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: MDPI AG
Date: 31-10-2021
Abstract: 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 postgraduate business unit, and one undergraduate and two postgraduate units in networking. Our study is distinguished from previous research in two novel aspects. First, the subject matter concerns capstone project units which are taken by students in the final year of their degree. In these units, students are expected to apply a variety of knowledge and skills that they have acquired thus far in carrying out an industry-based project of substantial complexity. The learning outcomes (LOs) require students to apply skills and knowledge that they have learned across completed units and connect them with real-world problems. Second, the FL approach has been applied wholly in an online virtual classroom setting due to the social distancing restrictions enforced by local authorities in response to the COVID-19 pandemic. Our hypothesis is that FL has positively influenced the perception of learners in their attaining the GAs. We tested this hypothesis by using data collected by an online survey administered to the student cohorts of the five chosen units at the end of Trimester 1 of 2021. The survey, which comprised 14 questions, assesses a student’s perception of achieving the LOs through developments in three dimensions, including cognitive, affective, and behavioural, acquired in a real-world client setting. Statistical analyses of the survey data reveal that the FL approach resulted in a positive perception by students of their attaining the GAs through achieving the LOs of the capstone project units, which in turn is supported by the responses to the three measured dimensions.
Publisher: IEEE
Date: 10-2007
Publisher: World Scientific Pub Co Pte Lt
Date: 08-2011
Publisher: IGI Global
Date: 2013
Publisher: Elsevier BV
Date: 02-2019
Publisher: Elsevier BV
Date: 03-2003
Publisher: World Scientific Pub Co Pte Lt
Date: 2013
Start Date: 2013
End Date: 03-2017
Amount: $246,000.00
Funder: Australian Research Council
View Funded Activity