ORCID Profile
0000-0002-5360-7514
Current Organisation
University of Tasmania
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Publisher: Elsevier BV
Date: 10-2018
DOI: 10.1016/J.JBI.2018.08.007
Abstract: Instruments rating risk of harm to self and others are widely used in inpatient forensic psychiatry settings. A potential alternate or supplementary means of risk prediction is from the automated analysis of case notes in Electronic Health Records (EHRs) using Natural Language Processing (NLP). This exploratory study rated presence or absence and frequency of words in a forensic EHR dataset, comparing four reference dictionaries. Seven machine learning algorithms and different time periods of EHR analysis were used to probe which dictionary and which time period were most predictive of risk assessment scores on validated instruments. The EHR dataset comprised de-identified forensic inpatient notes from the Wilfred Lopes Centre in Tasmania. The data comprised unstructured free-text case note entries and serial ratings of three risk assessment scales: Historical Clinical Risk Management-20 (HCR-20), Short-Term Assessment of Risk and Treatability (START) and Dynamic Appraisal of Situational Aggression (DASA). Four NLP dictionary word lists were selected: 6865 mental health symptom words from the Unified Medical Language System (UMLS), 455 DSM-IV diagnoses from UMLS repository, 6790 English positive and negative sentiment words, and 1837 high frequency words from the Corpus of Contemporary American English (COCA). Seven machine learning methods Bagging, J48, Jrip, Logistic Model Trees (LMT), Logistic Regression, Linear Regression and Support Vector Machine (SVM) were used to identify the combination of dictionaries and algorithms that best predicted risk assessment scores. The most accurate prediction was attained on the DASA dataset using the sentiment dictionary and the LMT and SVM algorithms. NLP, used in conjunction with NLP dictionaries and machine learning, predicted risk ratings on the HCR-20, START, and DASA, based on EHR content. Further research is required to ascertain the utility of NLP approaches in predicting endpoints of actual self-harm, harm to others or victimisation.
Publisher: Elsevier BV
Date: 2022
Publisher: Elsevier BV
Date: 05-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: ACM
Date: 12-07-2011
Publisher: MDPI AG
Date: 13-09-2023
Publisher: IEEE
Date: 07-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Springer Berlin Heidelberg
Date: 2002
Publisher: IEEE
Date: 05-2009
Publisher: Elsevier BV
Date: 10-2020
Publisher: MDPI AG
Date: 20-01-2022
DOI: 10.3390/F13020153
Abstract: Unoccupied Aircraft Systems (UAS) are beginning to replace conventional forest plot mensuration through their use as low-cost and powerful remote sensing tools for monitoring growth, estimating biomass, evaluating carbon stocks and detecting weeds however, physical s les remain mostly collected through time-consuming, expensive and potentially dangerous conventional techniques. Such conventional techniques include the use of arborists to climb the trees to retrieve s les, shooting branches with firearms from the ground, canopy cranes or the use of pole-mounted saws to access lower branches. UAS hold much potential to improve the safety, efficiency, and reduce the cost of acquiring canopy s les. In this work, we describe and demonstrate four iterations of 3D printed canopy s ling UAS. This work includes detailed explanations of designs and how each iteration informed the design decisions in the subsequent iteration. The fourth iteration of the aircraft was tested for the collection of 30 canopy s les from three tree species: eucalyptus pulchella, eucalyptus globulus and acacia dealbata trees. The collection times ranged from 1 min and 23 s, up to 3 min and 41 s for more distant and challenging to capture s les. A vision for the next iteration of this design is also provided. Future work may explore the integration of advanced remote sensing techniques with UAS-based canopy s ling to progress towards a fully-automated and holistic forest information capture system.
Publisher: Public Library of Science (PLoS)
Date: 03-08-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2020
Publisher: Springer Science and Business Media LLC
Date: 28-02-2023
DOI: 10.1007/S11269-023-03472-6
Abstract: Climate change is impacting people’s lives, with management of water resources and food security being major concerns for the future of many countries. In this paper, future water availability, crop water needs, yields, market costs and returns of current crops in a case study area in Australia are evaluated under future climatic conditions. The predictive methods on which the work is based have the advantage of being robust—they are able to simultaneously consider many climate change models—giving greater confidence in determining what the future will hold in this regard. The results indicate business as usual, in terms of the quantity and types of crops that can be grown presently, will not be sustainable in the medium and long term future. Instead, modelling indicates that changes in production and land use to maximise revenue per megalitre of water will be needed to adapt to future conditions and deliver climate-smart agriculture.
Publisher: IEEE
Date: 18-07-2022
Publisher: Springer Berlin Heidelberg
Date: 2002
Publisher: IEEE
Date: 12-2013
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 07-2019
Publisher: Public Library of Science (PLoS)
Date: 21-10-2019
Publisher: Springer International Publishing
Date: 2018
Publisher: IEEE
Date: 06-2019
Publisher: World Scientific Pub Co Pte Lt
Date: 06-2003
DOI: 10.1142/S1469026803000938
Abstract: Ant colony optimization techniques are usually guided by pheromone and heuristic cost information when choosing the next element to add to a solution. However, while an in idual element may be attractive, usually its long term consequences are neither known nor considered. For instance, a short link in a traveling salesman problem may be incorporated into an ant's solution, yet, as a consequence of this link, the rest of the path may be longer than if another link was chosen. The Accumulated Experience Ant Colony uses the previous experiences of the colony to guide in the choice of elements. This is in addition to the normal pheromone and heuristic costs. Two versions of the algorithm are presented, the original and an improved AEAC that makes greater use of accumulated experience. The results indicate that the original algorithm finds improved solutions on problems with less than 100 cities, while the improved algorithm finds better solutions on larger problems.
Publisher: Elsevier BV
Date: 06-2022
Publisher: IEEE
Date: 07-2010
Publisher: IEEE
Date: 07-2016
Publisher: IEEE
Date: 11-2015
Publisher: IEEE
Date: 08-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: IEEE
Date: 06-2017
Publisher: Elsevier BV
Date: 09-2020
Publisher: IEEE Comput. Soc
Date: 2000
Publisher: IEEE
Date: 2010
Publisher: Elsevier BV
Date: 12-2021
Publisher: Elsevier BV
Date: 04-2020
Publisher: IEEE
Date: 06-2019
Publisher: MDPI AG
Date: 06-08-2018
Abstract: Online social network users share their information in different social sites to establish connections with in iduals with whom they want to be a friend. While users share all their information to connect to other in iduals, they need to hide the information that can bring about privacy risks for them. As user participation in social networking sites rises, the possibility of sharing information with unknown users increases, and the probability of privacy breaches for the user mounts. This work addresses the challenges of sharing information in a safe manner with unknown in iduals. Currently, there are a number of available methods for preserving privacy in order to friending (the act of adding someone as a friend), but they only consider a single source of data and are more focused on users’ security rather than privacy. Consequently, a privacy-preserving friending mechanism should be considered for information shared in multiple online social network sites. In this paper, we propose a new privacy-preserving friending method that helps users decide what to share with other in iduals with the reduced risk of being exploited or re-identified. In this regard, the first step is to calculate the sensitivity score for in iduals using Bernstein’s polynomial theorem to understand what sort of information can influence a user’s privacy. Next, a new model is applied to anonymise the data of users who participate in multiple social networks. Anonymisation helps to understand to what extent a piece of information can be shared, which allows information sharing with reduced risks in privacy. Evaluation indicates that measuring the sensitivity of information besides anonymisation provides a more accurate outcome for the purpose of friending, in a computationally efficient manner.
Publisher: Elsevier BV
Date: 2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: MDPI AG
Date: 03-11-2022
Abstract: Student persistence and retention in STEM disciplines is an important yet complex and multi-dimensional issue confronting universities. Considering the rapid evolution of online pedagogy and virtual learning environments, we must rethink the factors that impact students’ decisions to stay or leave the current course. Learning analytics has demonstrated positive outcomes in higher education contexts and shows promise in enhancing academic success and retention. However, the retention factors in learning analytics practice for STEM education have not been fully reviewed and revealed. The purpose of this systematic review is to contribute to this research gap by reviewing the empirical evidence on factors affecting student persistence and retention in STEM disciplines in higher education and how these factors are measured and quantified in learning analytics practice. By analysing 59 key publications, seven factors and associated features contributing to STEM retention using learning analytics were comprehensively categorised and discussed. This study will guide future research to critically evaluate the influence of each factor and evaluate relationships among factors and the feature selection process to enrich STEM retention studies using learning analytics.
Publisher: MDPI AG
Date: 19-11-2021
DOI: 10.3390/RS13224677
Abstract: Forest mensuration remains critical in managing our forests sustainably, however, capturing such measurements remains costly, time-consuming and provides minimal amounts of information such as diameter at breast height (DBH), location, and height. Plot scale remote sensing techniques show great promise in extracting detailed forest measurements rapidly and cheaply, however, they have been held back from large-scale implementation due to the complex and time-consuming workflows required to utilize them. This work is focused on describing and evaluating an approach to create a robust, sensor-agnostic and fully automated forest point cloud measurement tool called the Forest Structural Complexity Tool (FSCT). The performance of FSCT is evaluated using 49 forest plots of terrestrial laser scanned (TLS) point clouds and 7022 destructively s led manual diameter measurements of the stems. FSCT was able to match 5141 of the reference diameter measurements fully automatically with mean, median and root mean squared errors (RMSE) of 0.032 m, 0.02 m, and 0.103 m respectively. A video demonstration is also provided to qualitatively demonstrate the ersity of point cloud datasets that the tool is capable of measuring. FSCT is provided as open source, with the goal of enabling plot scale remote sensing techniques to replace most structural forest mensuration in research and industry. Future work on this project will seek to make incremental improvements to this methodology to further improve the reliability and accuracy of this tool in most high-resolution forest point clouds.
Publisher: Elsevier BV
Date: 08-2019
Publisher: Institution of Engineering and Technology
Date: 04-07-2019
Publisher: ACM
Date: 12-07-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2022
Publisher: IEEE
Date: 18-07-2022
No related grants have been discovered for James Montgomery.