ORCID Profile
0000-0001-8384-9509
Current Organisations
International School of Engineering (INSOFE) + Systems Wisdom
,
Australian Research Data Commons
,
University of Technology Sydney
,
Systems Wisdom Corp
,
University of Canterbury
,
National Institute of Technology (RECT/ BDU)
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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.
Health Information Systems (incl. Surveillance) | Medical Devices | Software Engineering | Computer Software
Behaviour and Health | Integrated Circuits and Devices | Application Software Packages (excl. Computer Games) |
Publisher: Elsevier BV
Date: 02-2018
Publisher: Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
Date: 29-08-2019
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Springer International Publishing
Date: 03-11-2019
Publisher: Wiley
Date: 22-02-2012
Publisher: SAGE Publications
Date: 31-08-2019
Abstract: We have been modeling an ever-increasing scale of applications with agents that simulate the pattern of life (PoL) and real-world human behaviors in erse regions of the world. The goal is to support sociocultural training and analysis. To measure progress, we propose the definition of a measure of goodness for such simulated agents, and review the issues and challenges associated with first-generation (1G) agents. Then we present a second generation (2G) agent hybrid approach that seeks to improve realism in terms of emergent daily activities, social awareness, and micro-decision making in simulations. We offer a PoL case study with a mix of 1G and 2G approaches that was able to replace the pucksters and avatar operators needed in large-scale immersion exercises. We conclude by observing that a 1G PoL simulation might still be best where large-scale, pre-scripted training scenarios will suffice, while the 2G approach will be important for analysis or if it is vital to learn about adaptive opponents or unexpected or emergent effects of actions. Lessons are shared about ways to blend 1G and 2G approaches to get the best of each.
Publisher: IEEE
Date: 12-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2007
Publisher: Civil-Comp Press
Date: 2010
DOI: 10.4203/CCP.93.340
Publisher: IEEE
Date: 18-07-2021
Publisher: MDPI AG
Date: 16-11-2021
DOI: 10.3390/ELECTRONICS10222810
Abstract: The rise in web and social media interactions has resulted in the efortless proliferation of offensive language and hate speech. Such online harassment, insults, and attacks are commonly termed cyberbullying. The sheer volume of user-generated content has made it challenging to identify such illicit content. Machine learning has wide applications in text classification, and researchers are shifting towards using deep neural networks in detecting cyberbullying due to the several advantages they have over traditional machine learning algorithms. This paper proposes a novel neural network framework with parameter optimization and an algorithmic comparative study of eleven classification methods: four traditional machine learning and seven shallow neural networks on two real world cyberbullying datasets. In addition, this paper also examines the effect of feature extraction and word-embedding-techniques-based natural language processing on algorithmic performance. Key observations from this study show that bidirectional neural networks and attention models provide high classification results. Logistic Regression was observed to be the best among the traditional machine learning classifiers used. Term Frequency-Inverse Document Frequency (TF-IDF) demonstrates consistently high accuracies with traditional machine learning techniques. Global Vectors (GloVe) perform better with neural network models. Bi-GRU and Bi-LSTM worked best amongst the neural networks used. The extensive experiments performed on the two datasets establish the importance of this work by comparing eleven classification methods and seven feature extraction techniques. Our proposed shallow neural networks outperform existing state-of-the-art approaches for cyberbullying detection, with accuracy and F1-scores as high as ~95% and ~98%, respectively.
Publisher: CRC Press
Date: 08-10-2009
Publisher: MDPI AG
Date: 20-10-2023
DOI: 10.3390/INFO14100577
Publisher: Springer Nature Singapore
Date: 2022
Publisher: Springer Singapore
Date: 2021
Publisher: Springer Science and Business Media LLC
Date: 21-08-2017
Publisher: Springer Science and Business Media LLC
Date: 17-10-2023
Publisher: SAGE Publications
Date: 22-08-2013
Abstract: The philosophical perspectives on model evaluation can be broadly classified into reductionist/logical positivist and relativist/holistic. In this paper, we outline some of our past efforts in, and challenges faced during, evaluating models of social systems with cognitively detailed agents. Owing to richness in the model, we argue that the holistic approach and consequent continuous improvement are essential to evaluating complex social system models such as these. A social system built primarily of cognitively detailed agents can provide multiple levels of correspondence, both at observable and abstract aggregated levels. Such a system can also pose several challenges, including large feature spaces, issues in information elicitation with database, experts and news feeds, counterfactuals, fragmented theoretical base, and limited funding for validation. We subscribe to the view that no model can faithfully represent reality, but detailed, descriptive models are useful in learning about the system and bringing about a qualitative jump in understanding of the system it attempts to model – provided they are properly validated. Our own approach to model evaluation is to consider the entire life cycle and assess the validity under two broad dimensions of (1) internally focused validity/quality achieved through structural, methodological, and ontological evaluations and (2) external validity consisting of micro validity, macro validity, and qualitative, causal and narrative validity. In this paper, we also elaborate on selected validation techniques that we have employed in the past. We recommend a triangulation of multiple validation techniques, including methodological soundness, qualitative validation techniques, such as face validation by experts and narrative validation, and formal validation tests, including correspondence testing.
Publisher: Springer Science and Business Media LLC
Date: 19-06-2007
Publisher: AIP Publishing
Date: 2022
DOI: 10.1063/5.0070769
Publisher: Elsevier BV
Date: 10-2012
Publisher: Elsevier BV
Date: 2023
DOI: 10.2139/SSRN.4358507
Publisher: IEEE
Date: 03-2019
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Defense Technical Information Center
Date: 19-06-2008
DOI: 10.21236/ADA483617
Publisher: Korea Society of Management Information Systems
Date: 31-03-2023
Publisher: MDPI AG
Date: 17-01-2021
DOI: 10.3390/BUILDINGS11010032
Abstract: Occupant behaviors are one of the most dominant factors that influence building energy use. Understanding the influences from building occupants can promote the development of energy–efficient buildings. This paper quantifies the impact of different occupant behavior information on building energy model (BEM) from multiple perspectives. For this purpose, an occupant behavior model that uses agent–based modeling (ABM) approach is implemented via co-simulation with a BEM of an existing commercial building. Then, actual occupant behavior data in correspondence to ABM output, including operations on window, door, and blinds in selected thermal zones of the building are recorded using survey logs. A simulation experiment is conducted by creating three BEMs with constant, actual, and modeled occupant behavioral inputs. The analysis of the simulation results among these scenarios helps us gain an in–depth understanding of how occupant behaviors influence building performance. This study aims to facilitate robust building design and operation with human–in–the–loop system optimization.
Publisher: CRC Press
Date: 19-09-2016
Publisher: Informa UK Limited
Date: 12-2014
Publisher: Elsevier BV
Date: 08-2023
Publisher: Computers, Materials and Continua (Tech Science Press)
Date: 2022
Publisher: IOP Publishing
Date: 07-2021
Publisher: Society for Modeling and Simulation International (SCS)
Date: 2018
Publisher: Wiley
Date: 29-03-2010
Publisher: MIT Press - Journals
Date: 04-2006
DOI: 10.1162/PRES.2006.15.2.163
Abstract: Many producers and consumers of legacy training simulator and game environments are beginning to envision a new era where psycho-socio-physiologic models could be interoperated to enhance their environments' simulation of human agents. This paper explores whether we could embed our behavior modeling framework (described in the companion paper, Part 1) behind a legacy first person shooter 3D game environment to recreate portions of the Black Hawk Down scenario. Section 1 lifies the interoperability needs and challenges confronting the field, presents the questions that are examined, and describes the test scenario. Sections 2 and 3 review the software and knowledge engineering methodology, respectively, needed to create the system and populate it with bots. Results (Section 4) and discussion (Section 5) reveal that we were able to generate plausible and adaptive recreations of Somalian crowds, militia, women acting as shields, suicide bombers, and more. Also, there are specific lessons learned about ways to advance the field so that such interoperabilities will become more affordable and widespread.
Publisher: Wiley
Date: 07-2017
Publisher: Elsevier BV
Date: 10-2021
Publisher: IEEE
Date: 07-2020
Publisher: AIP Publishing
Date: 2022
DOI: 10.1063/5.0109707
Publisher: CRC Press
Date: 03-06-2009
Publisher: Springer Science and Business Media LLC
Date: 24-06-2023
DOI: 10.1007/S10462-023-10505-4
Abstract: The increasing interest from technology enthusiasts and organisational practitioners in big data applications in the supply chain has encouraged us to review recent research development. This paper proposes a systematic literature review to explore the available peer-reviewed literature on how big data is widely optimised and managed within the supply chain management context. Although big data applications in supply chain management appear to be often studied and reported in the literature, different angles of big data optimisation and management technologies in the supply chain are not clearly identified. This paper adopts the explanatory literature review involving bibliometric analysis as the primary research method to answer two research questions, namely: (1) How to optimise big data in supply chain management? and (2) What tools are most used to manage big data in supply chain management? A total of thirty-seven related papers are reviewed to answer the two research questions using the content analysis method. The paper also reveals some research gaps that lead to prospective future research directions.
Publisher: Elsevier BV
Date: 04-2022
Publisher: Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
Date: 29-08-2019
Publisher: Springer International Publishing
Date: 2022
Publisher: IEEE
Date: 12-2010
Publisher: Springer Science and Business Media LLC
Date: 29-02-2008
Publisher: Elsevier BV
Date: 07-2023
Publisher: OAE Publishing Inc.
Date: 2021
Publisher: Elsevier BV
Date: 02-2015
DOI: 10.1016/J.ARTMED.2014.08.006
Abstract: Explore whether agent-based modeling and simulation can help healthcare administrators discover interventions that increase population wellness and quality of care while, simultaneously, decreasing costs. Since important dynamics often lie in the social determinants outside the health facilities that provide services, this study thus models the problem at three levels (in iduals, organizations, and society). The study explores the utility of translating an existing (prize winning) software for modeling complex societal systems and agent's daily life activities (like a Sim City style of software), into a desired decision support system. A case study tests if the 3 levels of system modeling approach is feasible, valid, and useful. The case study involves an urban population with serious mental health and Philadelphia's Medicaid population (n=527,056), in particular. Section 3 explains the models using data from the case study and thereby establishes feasibility of the approach for modeling a real system. The models were trained and tuned using national epidemiologic datasets and various domain expert inputs. To avoid co-mingling of training and testing data, the simulations were then run and compared (Section 4.1) to an analysis of 250,000 Philadelphia patient hospital admissions for the year 2010 in terms of re-hospitalization rate, number of doctor visits, and days in hospital. Based on the Student t-test, deviations between simulated vs. real world outcomes are not statistically significant. Validity is thus established for the 2008-2010 timeframe. We computed models of various types of interventions that were ineffective as well as 4 categories of interventions (e.g., reduced per-nurse caseload, increased check-ins and stays, etc.) that result in improvement in well-being and cost. The 3 level approach appears to be useful to help health administrators sort through system complexities to find effective interventions at lower costs.
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Elsevier BV
Date: 2021
Publisher: Springer International Publishing
Date: 2020
Publisher: Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
Date: 29-08-2019
Publisher: Elsevier BV
Date: 09-2019
Publisher: Elsevier BV
Date: 10-2023
Publisher: MDPI AG
Date: 05-07-2023
DOI: 10.20944/PREPRINTS202307.0351.V1
Abstract: This paper proposes a concise literature review aimed at identifying the current body of knowledge on the adoption of Social Networks in Crisis Management. The major input is a structured research question based on the initial reading around the topic. Before the recent pan-demic, most literature has focused on local crises, with a relatively low number of exceptions. Additionally, self-organizing system are spontaneous established between people who are af-fected by a crisis. Among the identified challenges, there is the need to integrate official commu-nication by emergency agencies with citizen-generated contents in a contest of credibility and trustworthiness. In certain cases, it has been reported a lack of specific competence, knowledge, and expertise, as well as a lack of sufficient policies and guidelines in the use of Social Networks. Those challenges need to be framed by considering the classic difficulties to provide timely and accurate information, to deal with fake news, unverified or misleading information, and infor-mation overload. Bridging major gaps though advanced analytics and AI-based technology is expected to provide a key contribution to establish and safely enable in practice an effective and efficient communication, to contrast dissonant mental models, which are often fostered by Social Networks, and to enable a shared situational awareness.
Publisher: IEEE
Date: 07-2012
Publisher: Elsevier BV
Date: 12-2022
Publisher: Springer Science and Business Media LLC
Date: 13-02-2021
Publisher: MDPI AG
Date: 21-09-2023
Publisher: OAE Publishing Inc.
Date: 2021
Location: United States of America
Location: United States of America
Location: Australia
Start Date: 03-2018
End Date: 10-2023
Amount: $2,962,655.00
Funder: Australian Research Council
View Funded Activity