Diarrhoeal disease surveillance in Papua New Guinea : findings and challenges
- Abdad, Mohammad, Soli, Kevin, Pham, Bang, Bande, Grace, Maure, Tobias, Jonduo, Marinjo, Kisa, Debbie, Rai, Glennis, Phuanukoonnon, Suparat, Siba, Peter, Horwood, Paul, Greenhill, Andrew
- Authors: Abdad, Mohammad , Soli, Kevin , Pham, Bang , Bande, Grace , Maure, Tobias , Jonduo, Marinjo , Kisa, Debbie , Rai, Glennis , Phuanukoonnon, Suparat , Siba, Peter , Horwood, Paul , Greenhill, Andrew
- Date: 2020
- Type: Text , Journal article
- Relation: Western Pacific Surveillance and Response Vol. 11, no. 1 (Jan-Mar 2020), p. 6
- Full Text:
- Reviewed:
- Description: Diarrhoeal diseases are among the leading causes of morbidity and mortality in the Western Pacific Region. However, data on the major causes of infectious diarrhoea are limited in many countries within the Region, including Papua New Guinea. In 2013-2014, we conducted surveillance for acute diarrhoeal illness in four provinces in Papua New Guinea. One rural health clinic from each province participated in the surveillance activity. Samples were sent to central laboratories and batch analysed for bacterial and viral gastrointestinal pathogens that are commonly associated with diarrhoea. Across the four sites, the most commonly detected pathogens were Shigella spp., Campylobacter spp. and rotavirus. In this paper, we report the results of the surveillance activity and the challenges that we faced. The lessons learnt may be applicable to other parts of the Region with a similar socioeconomic status.
- Authors: Abdad, Mohammad , Soli, Kevin , Pham, Bang , Bande, Grace , Maure, Tobias , Jonduo, Marinjo , Kisa, Debbie , Rai, Glennis , Phuanukoonnon, Suparat , Siba, Peter , Horwood, Paul , Greenhill, Andrew
- Date: 2020
- Type: Text , Journal article
- Relation: Western Pacific Surveillance and Response Vol. 11, no. 1 (Jan-Mar 2020), p. 6
- Full Text:
- Reviewed:
- Description: Diarrhoeal diseases are among the leading causes of morbidity and mortality in the Western Pacific Region. However, data on the major causes of infectious diarrhoea are limited in many countries within the Region, including Papua New Guinea. In 2013-2014, we conducted surveillance for acute diarrhoeal illness in four provinces in Papua New Guinea. One rural health clinic from each province participated in the surveillance activity. Samples were sent to central laboratories and batch analysed for bacterial and viral gastrointestinal pathogens that are commonly associated with diarrhoea. Across the four sites, the most commonly detected pathogens were Shigella spp., Campylobacter spp. and rotavirus. In this paper, we report the results of the surveillance activity and the challenges that we faced. The lessons learnt may be applicable to other parts of the Region with a similar socioeconomic status.
Differences in personality and the sharing of managerial tacit knowledge: an empirical analysis of public sector managers in Malaysia
- Abdul Manaf, Halimah, Harvey, William, Armstrong, Steven, Lawton, Alan
- Authors: Abdul Manaf, Halimah , Harvey, William , Armstrong, Steven , Lawton, Alan
- Date: 2020
- Type: Text , Journal article
- Relation: Journal of Knowledge Management Vol. 24, no. 5 (2020), p. 1177-1199
- Full Text:
- Reviewed:
- Description: Purpose: This study aims to identify differences in knowledge-sharing mechanisms and personality among expert, typical and novice managers within the Malaysian public sector. Strengthening knowledge sharing function is essential for enabling public institutions around the world to be more productive. Design/methodology/approach: This quantitative study involves 308 employees from management and professional groups within 98 local authorities in the Malaysian local government. Stratified random sampling techniques were used and the sampling frame comprised 1,000 staff using postal surveys. Data analyses were carried out using analysis of variance and correlations to test the research hypotheses. Findings: The findings reveal that expert managers are more proactive in sharing their knowledge, particularly those with the personality traits of conscientiousness and openness. These two personality traits were also related to expert behaviours such as thoroughness, responsibility and persistence, which led to work competency and managerial success. Originality/value: This study provides theoretical insights into how managerial tacit knowledge differs and can accumulate, depending on the personality traits of middle managers. The paper shows the different mechanisms of knowledge sharing, tacit knowledge and personality among expert, typical and novice managers. Practically, this study is important for guiding senior managers in their attempts to identify the most appropriate personalities of their middle managers. This study found that the expert group was higher in conscientiousness, openness and overall personality traits compared with the typical and novice groups. The paper also highlights the value of sharing managerial tacit knowledge effectively. © 2020, Emerald Publishing Limited.
- Authors: Abdul Manaf, Halimah , Harvey, William , Armstrong, Steven , Lawton, Alan
- Date: 2020
- Type: Text , Journal article
- Relation: Journal of Knowledge Management Vol. 24, no. 5 (2020), p. 1177-1199
- Full Text:
- Reviewed:
- Description: Purpose: This study aims to identify differences in knowledge-sharing mechanisms and personality among expert, typical and novice managers within the Malaysian public sector. Strengthening knowledge sharing function is essential for enabling public institutions around the world to be more productive. Design/methodology/approach: This quantitative study involves 308 employees from management and professional groups within 98 local authorities in the Malaysian local government. Stratified random sampling techniques were used and the sampling frame comprised 1,000 staff using postal surveys. Data analyses were carried out using analysis of variance and correlations to test the research hypotheses. Findings: The findings reveal that expert managers are more proactive in sharing their knowledge, particularly those with the personality traits of conscientiousness and openness. These two personality traits were also related to expert behaviours such as thoroughness, responsibility and persistence, which led to work competency and managerial success. Originality/value: This study provides theoretical insights into how managerial tacit knowledge differs and can accumulate, depending on the personality traits of middle managers. The paper shows the different mechanisms of knowledge sharing, tacit knowledge and personality among expert, typical and novice managers. Practically, this study is important for guiding senior managers in their attempts to identify the most appropriate personalities of their middle managers. This study found that the expert group was higher in conscientiousness, openness and overall personality traits compared with the typical and novice groups. The paper also highlights the value of sharing managerial tacit knowledge effectively. © 2020, Emerald Publishing Limited.
A global optimisation approach to classification in medical diagnosis and prognosis
- Bagirov, Adil, Rubinov, Alex, Yearwood, John, Stranieri, Andrew
- Authors: Bagirov, Adil , Rubinov, Alex , Yearwood, John , Stranieri, Andrew
- Date: 2001
- Type: Text , Conference paper
- Relation: Paper presented at 34th Hawaii International Conference on System Sciences, HICSS-34, Maui, Hawaii, USA : 3rd-6th January 2001
- Full Text:
- Description: In this paper global optimisation-based techniques are studied in order to increase the accuracy of medical diagnosis and prognosis with FNA image data from the Wisconsin Diagnostic and Prognostic Breast Cancer databases. First we discuss the problem of determining the most informative features for the classification of cancerous cases in the databases under consideration. Then we apply a technique based on convex and global optimisation to breast cancer diagnosis. It allows the classification of benign cases and malignant ones and the subsequent diagnosis of patients with very high accuracy. The third application of this technique is a method that calculates centres of clusters to predict when breast cancer is likely to recur in patients for which cancer has been removed. The technique achieves higher accuracy with these databases than reported elsewhere in the literature.
- Description: 2003003950
- Authors: Bagirov, Adil , Rubinov, Alex , Yearwood, John , Stranieri, Andrew
- Date: 2001
- Type: Text , Conference paper
- Relation: Paper presented at 34th Hawaii International Conference on System Sciences, HICSS-34, Maui, Hawaii, USA : 3rd-6th January 2001
- Full Text:
- Description: In this paper global optimisation-based techniques are studied in order to increase the accuracy of medical diagnosis and prognosis with FNA image data from the Wisconsin Diagnostic and Prognostic Breast Cancer databases. First we discuss the problem of determining the most informative features for the classification of cancerous cases in the databases under consideration. Then we apply a technique based on convex and global optimisation to breast cancer diagnosis. It allows the classification of benign cases and malignant ones and the subsequent diagnosis of patients with very high accuracy. The third application of this technique is a method that calculates centres of clusters to predict when breast cancer is likely to recur in patients for which cancer has been removed. The technique achieves higher accuracy with these databases than reported elsewhere in the literature.
- Description: 2003003950
Educational big data : predictions, applications and challenges
- Bai, Xiaomei, Zhang, Fuli, Li, Jinzhou, Guo, Teng, Xia, Feng
- Authors: Bai, Xiaomei , Zhang, Fuli , Li, Jinzhou , Guo, Teng , Xia, Feng
- Date: 2021
- Type: Text , Journal article , Review
- Relation: Big Data Research Vol. 26, no. (2021), p.
- Full Text:
- Reviewed:
- Description: Educational big data is becoming a strategic educational asset, exceptionally significant in advancing educational reform. The term educational big data stems from the rapidly growing educational data development, including students' inherent attributes, learning behavior, and psychological state. Educational big data has many applications that can be used for educational administration, teaching innovation, and research management. The representative examples of such applications are student academic performance prediction, employment recommendation, and financial support for low-income students. Different empirical studies have shown that it is possible to predict student performance in the courses during the next term. Predictive research for the higher education stage has become an attractive area of study since it allowed us to predict student behavior. In this survey, we will review predictive research, its applications, and its challenges. We first introduce the significance and background of educational big data. Second, we review the students' academic performance prediction research, such as factors influencing students' academic performance, predicting models, evaluating indices. Third, we introduce the applications of educational big data such as prediction, recommendation, and evaluation. Finally, we investigate challenging research issues in this area. This discussion aims to provide a comprehensive overview of educational big data. © 2021 Elsevier Inc. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Feng Xia” is provided in this record**
- Authors: Bai, Xiaomei , Zhang, Fuli , Li, Jinzhou , Guo, Teng , Xia, Feng
- Date: 2021
- Type: Text , Journal article , Review
- Relation: Big Data Research Vol. 26, no. (2021), p.
- Full Text:
- Reviewed:
- Description: Educational big data is becoming a strategic educational asset, exceptionally significant in advancing educational reform. The term educational big data stems from the rapidly growing educational data development, including students' inherent attributes, learning behavior, and psychological state. Educational big data has many applications that can be used for educational administration, teaching innovation, and research management. The representative examples of such applications are student academic performance prediction, employment recommendation, and financial support for low-income students. Different empirical studies have shown that it is possible to predict student performance in the courses during the next term. Predictive research for the higher education stage has become an attractive area of study since it allowed us to predict student behavior. In this survey, we will review predictive research, its applications, and its challenges. We first introduce the significance and background of educational big data. Second, we review the students' academic performance prediction research, such as factors influencing students' academic performance, predicting models, evaluating indices. Third, we introduce the applications of educational big data such as prediction, recommendation, and evaluation. Finally, we investigate challenging research issues in this area. This discussion aims to provide a comprehensive overview of educational big data. © 2021 Elsevier Inc. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Feng Xia” is provided in this record**
An evaluation methodology for interactive reinforcement learning with simulated users
- Bignold, Adam, Cruz, Francisco, Dazeley, Richard, Vamplew, Peter, Foale, Cameron
- Authors: Bignold, Adam , Cruz, Francisco , Dazeley, Richard , Vamplew, Peter , Foale, Cameron
- Date: 2021
- Type: Text , Journal article
- Relation: Biomimetics Vol. 6, no. 1 (2021), p. 1-15
- Full Text:
- Reviewed:
- Description: Interactive reinforcement learning methods utilise an external information source to evaluate decisions and accelerate learning. Previous work has shown that human advice could significantly improve learning agents’ performance. When evaluating reinforcement learning algorithms, it is common to repeat experiments as parameters are altered or to gain a sufficient sample size. In this regard, to require human interaction every time an experiment is restarted is undesirable, particularly when the expense in doing so can be considerable. Additionally, reusing the same people for the experiment introduces bias, as they will learn the behaviour of the agent and the dynamics of the environment. This paper presents a methodology for evaluating interactive reinforcement learning agents by employing simulated users. Simulated users allow human knowledge, bias, and interaction to be simulated. The use of simulated users allows the development and testing of reinforcement learning agents, and can provide indicative results of agent performance under defined human constraints. While simulated users are no replacement for actual humans, they do offer an affordable and fast alternative for evaluative assisted agents. We introduce a method for performing a preliminary evaluation utilising simulated users to show how performance changes depending on the type of user assisting the agent. Moreover, we describe how human interaction may be simulated, and present an experiment illustrating the applicability of simulating users in evaluating agent performance when assisted by different types of trainers. Experimental results show that the use of this methodology allows for greater insight into the performance of interactive reinforcement learning agents when advised by different users. The use of simulated users with varying characteristics allows for evaluation of the impact of those characteristics on the behaviour of the learning agent. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Bignold, Adam , Cruz, Francisco , Dazeley, Richard , Vamplew, Peter , Foale, Cameron
- Date: 2021
- Type: Text , Journal article
- Relation: Biomimetics Vol. 6, no. 1 (2021), p. 1-15
- Full Text:
- Reviewed:
- Description: Interactive reinforcement learning methods utilise an external information source to evaluate decisions and accelerate learning. Previous work has shown that human advice could significantly improve learning agents’ performance. When evaluating reinforcement learning algorithms, it is common to repeat experiments as parameters are altered or to gain a sufficient sample size. In this regard, to require human interaction every time an experiment is restarted is undesirable, particularly when the expense in doing so can be considerable. Additionally, reusing the same people for the experiment introduces bias, as they will learn the behaviour of the agent and the dynamics of the environment. This paper presents a methodology for evaluating interactive reinforcement learning agents by employing simulated users. Simulated users allow human knowledge, bias, and interaction to be simulated. The use of simulated users allows the development and testing of reinforcement learning agents, and can provide indicative results of agent performance under defined human constraints. While simulated users are no replacement for actual humans, they do offer an affordable and fast alternative for evaluative assisted agents. We introduce a method for performing a preliminary evaluation utilising simulated users to show how performance changes depending on the type of user assisting the agent. Moreover, we describe how human interaction may be simulated, and present an experiment illustrating the applicability of simulating users in evaluating agent performance when assisted by different types of trainers. Experimental results show that the use of this methodology allows for greater insight into the performance of interactive reinforcement learning agents when advised by different users. The use of simulated users with varying characteristics allows for evaluation of the impact of those characteristics on the behaviour of the learning agent. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Being smart and being green : Entrepreneurial innovation in challenging times
- Braun, Patrice, Lowe, Julian
- Authors: Braun, Patrice , Lowe, Julian
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at 32nd Institute for Small Business & Entrepreneurship Conference, ISBE 2009, Liverpool, UK : 3rd-6th November 2009
- Full Text:
- Description: In difficult times business operators are looking for clever and affordable ways to grow their enterprises. This paper seeks to make a contribution to a better understanding of proactive environmental and innovation strategies for SMEs and the interaction between demand and supply towards sustainable and innovative business practices. The paper discusses the combined outcomes of the exit survey of a greening small business 2008 pilot program and the entry survey for the 2009 online training and networking version of the program, which fuses environmental, business and ICT- enabled skilling to enhance both SME entrepreneurship and innovation. The study suggests that SME business sustainability cannot be reduced to an oversimplified business case and that pro-environmental strategy adoption and behaviour, and particularly behavioural change, is highly complex. The outcomes of this research are expected to contribute to good practice in environmental and innovation skilling for SMEs, especially skilling that differentiates between supply and demand side skilling and brings together the two sides in a proactive resource acquisition, knowledge transfer and networking environment.
- Description: 2003007572
- Authors: Braun, Patrice , Lowe, Julian
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at 32nd Institute for Small Business & Entrepreneurship Conference, ISBE 2009, Liverpool, UK : 3rd-6th November 2009
- Full Text:
- Description: In difficult times business operators are looking for clever and affordable ways to grow their enterprises. This paper seeks to make a contribution to a better understanding of proactive environmental and innovation strategies for SMEs and the interaction between demand and supply towards sustainable and innovative business practices. The paper discusses the combined outcomes of the exit survey of a greening small business 2008 pilot program and the entry survey for the 2009 online training and networking version of the program, which fuses environmental, business and ICT- enabled skilling to enhance both SME entrepreneurship and innovation. The study suggests that SME business sustainability cannot be reduced to an oversimplified business case and that pro-environmental strategy adoption and behaviour, and particularly behavioural change, is highly complex. The outcomes of this research are expected to contribute to good practice in environmental and innovation skilling for SMEs, especially skilling that differentiates between supply and demand side skilling and brings together the two sides in a proactive resource acquisition, knowledge transfer and networking environment.
- Description: 2003007572
Going green : Women entrepreneurs and the environment
- Authors: Braun, Patrice
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at 32nd Institute for Small Business & Entrepreneurship Conference, ISBE 2009, Liverpool, UK : 3rd-6th November 2009
- Full Text:
- Description: In economically challenging times business operators are looking for clever and affordable ways to grow their enterprises. This paper discusses the role of women entrepreneurs’ in proactively greening their small business. The paper highlights the combined outcomes of the exit survey of a greening small business 2008 pilot program and the entry survey for the 2009 online version of the training and networking program, which fuses environmental, business and ICT-enabled skilling to enhance both SME entrepreneurship and innovation. The study suggests that while reported environmental attitudes between male and female entrepreneurs do not differ significantly, women’s motivations differ from male entrepreneurs in terms of greening their business; and women are more proactive in pursuing green networking opportunities, where they can interact with like-minded businesses, access more clients, source alternative resources and expand their green business networks.
- Description: 2003007573
- Authors: Braun, Patrice
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at 32nd Institute for Small Business & Entrepreneurship Conference, ISBE 2009, Liverpool, UK : 3rd-6th November 2009
- Full Text:
- Description: In economically challenging times business operators are looking for clever and affordable ways to grow their enterprises. This paper discusses the role of women entrepreneurs’ in proactively greening their small business. The paper highlights the combined outcomes of the exit survey of a greening small business 2008 pilot program and the entry survey for the 2009 online version of the training and networking program, which fuses environmental, business and ICT-enabled skilling to enhance both SME entrepreneurship and innovation. The study suggests that while reported environmental attitudes between male and female entrepreneurs do not differ significantly, women’s motivations differ from male entrepreneurs in terms of greening their business; and women are more proactive in pursuing green networking opportunities, where they can interact with like-minded businesses, access more clients, source alternative resources and expand their green business networks.
- Description: 2003007573
Networking tourism SMEs : E-commerce and e-marketing issues in regional Australia
- Authors: Braun, Patrice
- Date: 2002
- Type: Text , Journal article
- Relation: Information Technology and Tourism Vol. 5, no. 1 (2002), p. 13-23
- Full Text:
- Reviewed:
- Description: Networks, knowledge, and relationships have become crucial assets to business survival in the new economy. Research indicates that network building is a major new source of competitive advantage and an essential regional and indeed global management requirement. Because regional policies encourage interfirm alliances and the development of regional economic communities, the fostering of a culture of connectivity, networking, learning, and trust between regional Australian small and medium- size tourism enterprises (SMTEs) may offer a potential solution to the possible loss of competitive advantage for Australian tourism enterprises. It is suggested that SMTEs would benefit from increased information flow through regional networking and cooperative e-marketing campaigns to enhance market visibility, global positioning, and strategic leverage in the new economy.
- Description: C1
- Description: 2003000256
- Authors: Braun, Patrice
- Date: 2002
- Type: Text , Journal article
- Relation: Information Technology and Tourism Vol. 5, no. 1 (2002), p. 13-23
- Full Text:
- Reviewed:
- Description: Networks, knowledge, and relationships have become crucial assets to business survival in the new economy. Research indicates that network building is a major new source of competitive advantage and an essential regional and indeed global management requirement. Because regional policies encourage interfirm alliances and the development of regional economic communities, the fostering of a culture of connectivity, networking, learning, and trust between regional Australian small and medium- size tourism enterprises (SMTEs) may offer a potential solution to the possible loss of competitive advantage for Australian tourism enterprises. It is suggested that SMTEs would benefit from increased information flow through regional networking and cooperative e-marketing campaigns to enhance market visibility, global positioning, and strategic leverage in the new economy.
- Description: C1
- Description: 2003000256
Local contrast as an effective means to robust clustering against varying densities
- Chen, Bo, Ting, Kaiming, Washio, Takashi, Zhu, Ye
- Authors: Chen, Bo , Ting, Kaiming , Washio, Takashi , Zhu, Ye
- Date: 2018
- Type: Text , Journal article
- Relation: Machine Learning Vol. 107, no. 8-10 (2018), p. 1621-1645
- Full Text:
- Reviewed:
- Description: Most density-based clustering methods have difficulties detecting clusters of hugely different densities in a dataset. A recent density-based clustering CFSFDP appears to have mitigated the issue. However, through formalising the condition under which it fails, we reveal that CFSFDP still has the same issue. To address this issue, we propose a new measure called Local Contrast, as an alternative to density, to find cluster centers and detect clusters. We then apply Local Contrast to CFSFDP, and create a new clustering method called LC-CFSFDP which is robust in the presence of varying densities. Our empirical evaluation shows that LC-CFSFDP outperforms CFSFDP and three other state-of-the-art variants of CFSFDP. © 2018, The Author(s).
- Authors: Chen, Bo , Ting, Kaiming , Washio, Takashi , Zhu, Ye
- Date: 2018
- Type: Text , Journal article
- Relation: Machine Learning Vol. 107, no. 8-10 (2018), p. 1621-1645
- Full Text:
- Reviewed:
- Description: Most density-based clustering methods have difficulties detecting clusters of hugely different densities in a dataset. A recent density-based clustering CFSFDP appears to have mitigated the issue. However, through formalising the condition under which it fails, we reveal that CFSFDP still has the same issue. To address this issue, we propose a new measure called Local Contrast, as an alternative to density, to find cluster centers and detect clusters. We then apply Local Contrast to CFSFDP, and create a new clustering method called LC-CFSFDP which is robust in the presence of varying densities. Our empirical evaluation shows that LC-CFSFDP outperforms CFSFDP and three other state-of-the-art variants of CFSFDP. © 2018, The Author(s).
Understanding personal use of the Internet at work: An integrated model of neutralization techniques and general deterrence theory
- Cheng, Lijiao, Li, Wenli, Zhai, Qingguo, Smyth, Russell
- Authors: Cheng, Lijiao , Li, Wenli , Zhai, Qingguo , Smyth, Russell
- Date: 2014
- Type: Text , Journal article
- Relation: Computers in Human Behavior Vol. 38, no. (September 2014 2014), p. 220-228
- Full Text:
- Reviewed:
- Description: This paper examines the influence of neutralization techniques, perceived sanction severity, perceived detection certainty and perceived benefits of using the Internet for personal purposes on intention to use the Internet at work for personal use. To do so, we draw on a conceptual framework integrating neutralization theory and general deterrence theory. The study finds that both neutralization techniques and perceived benefits have a positive effect on personal use of the Internet. Perceived detection certainty is found to have a negative effect on personal use of the Internet, while the effect of perceived sanctions severity on personal use of the Internet is not significant. The effect of neutralization and perceived benefits are much stronger than perceived detection certainty. The findings suggest that people may think more about neutralization and perceived benefits than they do about costs, when deciding whether to use the Internet at work for personal purposes.
- Description: C1
- Authors: Cheng, Lijiao , Li, Wenli , Zhai, Qingguo , Smyth, Russell
- Date: 2014
- Type: Text , Journal article
- Relation: Computers in Human Behavior Vol. 38, no. (September 2014 2014), p. 220-228
- Full Text:
- Reviewed:
- Description: This paper examines the influence of neutralization techniques, perceived sanction severity, perceived detection certainty and perceived benefits of using the Internet for personal purposes on intention to use the Internet at work for personal use. To do so, we draw on a conceptual framework integrating neutralization theory and general deterrence theory. The study finds that both neutralization techniques and perceived benefits have a positive effect on personal use of the Internet. Perceived detection certainty is found to have a negative effect on personal use of the Internet, while the effect of perceived sanctions severity on personal use of the Internet is not significant. The effect of neutralization and perceived benefits are much stronger than perceived detection certainty. The findings suggest that people may think more about neutralization and perceived benefits than they do about costs, when deciding whether to use the Internet at work for personal purposes.
- Description: C1
Data mining with combined use of optimization techniques and self-organizing maps for improving risk grouping rules : Application to prostate cancer patients
- Churilov, Leonid, Bagirov, Adil, Schwartz, Daniel, Smith, Kate, Dally, Michael
- Authors: Churilov, Leonid , Bagirov, Adil , Schwartz, Daniel , Smith, Kate , Dally, Michael
- Date: 2005
- Type: Text , Journal article
- Relation: Journal of Management Information Systems Vol. 21, no. 4 (2005), p. 85-100
- Full Text:
- Reviewed:
- Description: Data mining techniques provide a popular and powerful tool set to generate various data-driven classification systems. In this paper, we investigate the combined use of self-organizing maps (SOM) and nonsmooth nonconvex optimization techniques in order to produce a working case of a data-driven risk classification system. The optimization approach strengthens the validity of SOM results, and the improved classification system increases both the quality of prediction and the homogeneity within the risk groups. Accurate classification of prostate cancer patients into risk groups is important to assist in the identification of appropriate treatment paths. We start with the existing rules and aim to improve classification accuracy by identifying inconsistencies utilizing self-organizing maps as a data visualization tool. Then, we progress to the study of assigning prostate cancer patients into homogenous groups with the aim to support future clinical treatment decisions. Using the case of prostate cancer patients grouping, we demonstrate strong potential of data-driven risk classification schemes for addressing the risk grouping issues in more general organizational settings. © 2005 M.E. Sharpe, Inc.
- Description: C1
- Description: 2003001265
- Authors: Churilov, Leonid , Bagirov, Adil , Schwartz, Daniel , Smith, Kate , Dally, Michael
- Date: 2005
- Type: Text , Journal article
- Relation: Journal of Management Information Systems Vol. 21, no. 4 (2005), p. 85-100
- Full Text:
- Reviewed:
- Description: Data mining techniques provide a popular and powerful tool set to generate various data-driven classification systems. In this paper, we investigate the combined use of self-organizing maps (SOM) and nonsmooth nonconvex optimization techniques in order to produce a working case of a data-driven risk classification system. The optimization approach strengthens the validity of SOM results, and the improved classification system increases both the quality of prediction and the homogeneity within the risk groups. Accurate classification of prostate cancer patients into risk groups is important to assist in the identification of appropriate treatment paths. We start with the existing rules and aim to improve classification accuracy by identifying inconsistencies utilizing self-organizing maps as a data visualization tool. Then, we progress to the study of assigning prostate cancer patients into homogenous groups with the aim to support future clinical treatment decisions. Using the case of prostate cancer patients grouping, we demonstrate strong potential of data-driven risk classification schemes for addressing the risk grouping issues in more general organizational settings. © 2005 M.E. Sharpe, Inc.
- Description: C1
- Description: 2003001265
Addressing the complexities of big data analytics in healthcare : The diabetes screening case
- De Silva, Daswin, Burstein, Frada, Jelinek, Herbert, Stranieri, Andrew
- Authors: De Silva, Daswin , Burstein, Frada , Jelinek, Herbert , Stranieri, Andrew
- Date: 2015
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 19, no. (2015), p. S99-S115
- Full Text:
- Reviewed:
- Description: The healthcare industry generates a high throughput of medical, clinical and omics data of varying complexity and features. Clinical decision-support is gaining widespread attention as medical institutions and governing bodies turn towards better management of this data for effective and efficient healthcare delivery and quality assured outcomes. Amass of data across all stages, from disease diagnosis to palliative care, is further indication of the opportunities and challenges to effective data management, analysis, prediction and optimization techniques as parts of knowledge management in clinical environments. Big Data analytics (BDA) presents the potential to advance this industry with reforms in clinical decision-support and translational research. However, adoption of big data analytics has been slow due to complexities posed by the nature of healthcare data. The success of these systems is hard to predict, so further research is needed to provide a robust framework to ensure investment in BDA is justified. In this paper we investigate these complexities from the perspective of updated Information Systems (IS) participation theory. We present a case study on a large diabetes screening project to integrate, converge and derive expedient insights from such an accumulation of data and make recommendations for a successful BDA implementation grounded in a participatory framework and the specificities of big data in healthcare context. © 2015 De Silva, Burstein, Jelinek, Stranieri.
- Authors: De Silva, Daswin , Burstein, Frada , Jelinek, Herbert , Stranieri, Andrew
- Date: 2015
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 19, no. (2015), p. S99-S115
- Full Text:
- Reviewed:
- Description: The healthcare industry generates a high throughput of medical, clinical and omics data of varying complexity and features. Clinical decision-support is gaining widespread attention as medical institutions and governing bodies turn towards better management of this data for effective and efficient healthcare delivery and quality assured outcomes. Amass of data across all stages, from disease diagnosis to palliative care, is further indication of the opportunities and challenges to effective data management, analysis, prediction and optimization techniques as parts of knowledge management in clinical environments. Big Data analytics (BDA) presents the potential to advance this industry with reforms in clinical decision-support and translational research. However, adoption of big data analytics has been slow due to complexities posed by the nature of healthcare data. The success of these systems is hard to predict, so further research is needed to provide a robust framework to ensure investment in BDA is justified. In this paper we investigate these complexities from the perspective of updated Information Systems (IS) participation theory. We present a case study on a large diabetes screening project to integrate, converge and derive expedient insights from such an accumulation of data and make recommendations for a successful BDA implementation grounded in a participatory framework and the specificities of big data in healthcare context. © 2015 De Silva, Burstein, Jelinek, Stranieri.
A survey on context awareness in big data analytics for business applications
- Dinh, Loan, Karmakar, Gour, Kamruzzaman, Joarder
- Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder
- Date: 2020
- Type: Text , Journal article
- Relation: Knowledge and Information Systems Vol. 62, no. 9 (2020), p. 3387-3415
- Full Text:
- Reviewed:
- Description: The concept of context awareness has been in existence since the 1990s. Though initially applied exclusively in computer science, over time it has increasingly been adopted by many different application domains such as business, health and military. Contexts change continuously because of objective reasons, such as economic situation, political matter and social issues. The adoption of big data analytics by businesses is facilitating such change at an even faster rate in much complicated ways. The potential benefits of embedding contextual information into an application are already evidenced by the improved outcomes of the existing context-aware methods in those applications. Since big data is growing very rapidly, context awareness in big data analytics has become more important and timely because of its proven efficiency in big data understanding and preparation, contributing to extracting the more and accurate value of big data. Many surveys have been published on context-based methods such as context modelling and reasoning, workflow adaptations, computational intelligence techniques and mobile ubiquitous systems. However, to our knowledge, no survey of context-aware methods on big data analytics for business applications supported by enterprise level software has been published to date. To bridge this research gap, in this paper first, we present a definition of context, its modelling and evaluation techniques, and highlight the importance of contextual information for big data analytics. Second, the works in three key business application areas that are context-aware and/or exploit big data analytics have been thoroughly reviewed. Finally, the paper concludes by highlighting a number of contemporary research challenges, including issues concerning modelling, managing and applying business contexts to big data analytics. © 2020, Springer-Verlag London Ltd., part of Springer Nature.
- Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder
- Date: 2020
- Type: Text , Journal article
- Relation: Knowledge and Information Systems Vol. 62, no. 9 (2020), p. 3387-3415
- Full Text:
- Reviewed:
- Description: The concept of context awareness has been in existence since the 1990s. Though initially applied exclusively in computer science, over time it has increasingly been adopted by many different application domains such as business, health and military. Contexts change continuously because of objective reasons, such as economic situation, political matter and social issues. The adoption of big data analytics by businesses is facilitating such change at an even faster rate in much complicated ways. The potential benefits of embedding contextual information into an application are already evidenced by the improved outcomes of the existing context-aware methods in those applications. Since big data is growing very rapidly, context awareness in big data analytics has become more important and timely because of its proven efficiency in big data understanding and preparation, contributing to extracting the more and accurate value of big data. Many surveys have been published on context-based methods such as context modelling and reasoning, workflow adaptations, computational intelligence techniques and mobile ubiquitous systems. However, to our knowledge, no survey of context-aware methods on big data analytics for business applications supported by enterprise level software has been published to date. To bridge this research gap, in this paper first, we present a definition of context, its modelling and evaluation techniques, and highlight the importance of contextual information for big data analytics. Second, the works in three key business application areas that are context-aware and/or exploit big data analytics have been thoroughly reviewed. Finally, the paper concludes by highlighting a number of contemporary research challenges, including issues concerning modelling, managing and applying business contexts to big data analytics. © 2020, Springer-Verlag London Ltd., part of Springer Nature.
Lifting student engagement in marketing classes
- Authors: Errey, Robert
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at Australian and New Zealand Marketing Academy (ANZMAC) Conference 2007 : 3Rs - Reputation, Responsibility & Relevance, University of Otago, Dunedin, New Zealand : 3rd-5th December 2007 p. 3235-3240
- Full Text:
- Description: High levels of student engagement have been linked with better student learning outcomes, such as the quality of their output. With marketing students accounting for a high percentage of business school undergraduates, it is important that the level of engagement is determined and drivers of engagement identified. Marketing has traditionally been delivered in a teachercentric model, as opposed to a student-centric model which better encourages independent learning. Important aspects of the latter model are interactivity, active and collaborative learning, and enriching educational experiences. The author conducted focus groups with business students, and preliminary findings reveal that the instructor’s approach and the nature of the assignments do affect student engagement. A preliminary model of student engagement is proposed which will be tested in the quantitative research phase.
- Description: 2003005151
- Authors: Errey, Robert
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at Australian and New Zealand Marketing Academy (ANZMAC) Conference 2007 : 3Rs - Reputation, Responsibility & Relevance, University of Otago, Dunedin, New Zealand : 3rd-5th December 2007 p. 3235-3240
- Full Text:
- Description: High levels of student engagement have been linked with better student learning outcomes, such as the quality of their output. With marketing students accounting for a high percentage of business school undergraduates, it is important that the level of engagement is determined and drivers of engagement identified. Marketing has traditionally been delivered in a teachercentric model, as opposed to a student-centric model which better encourages independent learning. Important aspects of the latter model are interactivity, active and collaborative learning, and enriching educational experiences. The author conducted focus groups with business students, and preliminary findings reveal that the instructor’s approach and the nature of the assignments do affect student engagement. A preliminary model of student engagement is proposed which will be tested in the quantitative research phase.
- Description: 2003005151
Intelligent energy prediction techniques for fog computing networks
- Farooq, Umar, Shabir, Muhammad, Javed, Muhammad, Imran, Muhammad
- Authors: Farooq, Umar , Shabir, Muhammad , Javed, Muhammad , Imran, Muhammad
- Date: 2021
- Type: Text , Journal article
- Relation: Applied Soft Computing Vol. 111, no. (2021), p.
- Full Text:
- Reviewed:
- Description: Energy Efficiency is a key concern for future fog-enabled Internet of Things (IoT). Since Fog Nodes (FNs) are energy-constrained devices, task offloading techniques must consider the energy consumption of the FNs to maximize the performance of IoT applications. In this context, accurate energy prediction can enable the development of intelligent energy-aware task offloading techniques. In this paper, we present two energy prediction techniques, the first one is based on the Recursive Least Square (RLS) filter and the second one uses the Artificial Neural Network (ANN). Both techniques use inputs such as the number of tasks and size of the tasks to predict the energy consumption at different fog nodes. Simulation results show that both techniques have a root mean square error of less than 3%. However, the ANN-based technique shows up to 20% less root mean square error as compared to the RLS-based technique. © 2021 Elsevier B.V.
- Authors: Farooq, Umar , Shabir, Muhammad , Javed, Muhammad , Imran, Muhammad
- Date: 2021
- Type: Text , Journal article
- Relation: Applied Soft Computing Vol. 111, no. (2021), p.
- Full Text:
- Reviewed:
- Description: Energy Efficiency is a key concern for future fog-enabled Internet of Things (IoT). Since Fog Nodes (FNs) are energy-constrained devices, task offloading techniques must consider the energy consumption of the FNs to maximize the performance of IoT applications. In this context, accurate energy prediction can enable the development of intelligent energy-aware task offloading techniques. In this paper, we present two energy prediction techniques, the first one is based on the Recursive Least Square (RLS) filter and the second one uses the Artificial Neural Network (ANN). Both techniques use inputs such as the number of tasks and size of the tasks to predict the energy consumption at different fog nodes. Simulation results show that both techniques have a root mean square error of less than 3%. However, the ANN-based technique shows up to 20% less root mean square error as compared to the RLS-based technique. © 2021 Elsevier B.V.
An intelligent learning environment for traditional Chinese medicine practitioners and students
- Jia, Long, Stranieri, Andrew, Shen, J
- Authors: Jia, Long , Stranieri, Andrew , Shen, J
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at HIC 2008 Australia's Health Informatics Conference; The Person in the Centre, Brunswick East, Victoria : 31st August - 2nd September 2008
- Full Text:
- Description: Objectives: This study aims to support the training of Traditional Chinese Medicine practitioners by embedding an expert diagnostic model for arthritis into an Intelligent Interactive Learning Environment (IILE). Background: The increasing prevalence of Traditional Chinese Medicine (TCM) outside China is characterised by the emergence of university level practitioner training and stringent regulatory requirements. TCM differential diagnosis is a difficult task that was traditionally taught by exposure to large numbers of patients in a master-apprentice context. In university degree programs, students and novice diagnosticians cannot have the exposure to cases possible in the traditional context. An online system that engages students in the interactive construction of a virtual case and provides immediate feedback on the appropriateness of student actions and the accuracy of diagnostic conclusions can enhance student learning. The system, an Intelligent Interactive Learning Environment (IILE) is based on an approach that has been shown to improve learning outcomes in intensive care nurse training. Methods: An expert model of diagnostic reasoning elicited from TCM expert practitioners lies at the core of the IILE. The knowledge acquisition is performed using an argumentation tree representation that has been shown to be effective in structuring complex knowledge and facilitating engineer - expert interactions. Problems associated with keeping knowledge bases up to date are mitigated with the use of a knowledge model known as ripple down rules permits dynamic updating of knowledge so that knowledge bases evolve over time. A simple narrative model builds up the virtual case study as user interaction proceeds. Results and discussion: This article reports preliminary results in the study that includes an overview of TCM differential diagnosis, the argument tree, the ripple down rule representation and the narrative based IILE. Segments of the knowledge model based solely on TCM literature are illustrated.
- Description: 2003006755
- Authors: Jia, Long , Stranieri, Andrew , Shen, J
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at HIC 2008 Australia's Health Informatics Conference; The Person in the Centre, Brunswick East, Victoria : 31st August - 2nd September 2008
- Full Text:
- Description: Objectives: This study aims to support the training of Traditional Chinese Medicine practitioners by embedding an expert diagnostic model for arthritis into an Intelligent Interactive Learning Environment (IILE). Background: The increasing prevalence of Traditional Chinese Medicine (TCM) outside China is characterised by the emergence of university level practitioner training and stringent regulatory requirements. TCM differential diagnosis is a difficult task that was traditionally taught by exposure to large numbers of patients in a master-apprentice context. In university degree programs, students and novice diagnosticians cannot have the exposure to cases possible in the traditional context. An online system that engages students in the interactive construction of a virtual case and provides immediate feedback on the appropriateness of student actions and the accuracy of diagnostic conclusions can enhance student learning. The system, an Intelligent Interactive Learning Environment (IILE) is based on an approach that has been shown to improve learning outcomes in intensive care nurse training. Methods: An expert model of diagnostic reasoning elicited from TCM expert practitioners lies at the core of the IILE. The knowledge acquisition is performed using an argumentation tree representation that has been shown to be effective in structuring complex knowledge and facilitating engineer - expert interactions. Problems associated with keeping knowledge bases up to date are mitigated with the use of a knowledge model known as ripple down rules permits dynamic updating of knowledge so that knowledge bases evolve over time. A simple narrative model builds up the virtual case study as user interaction proceeds. Results and discussion: This article reports preliminary results in the study that includes an overview of TCM differential diagnosis, the argument tree, the ripple down rule representation and the narrative based IILE. Segments of the knowledge model based solely on TCM literature are illustrated.
- Description: 2003006755
The gene of scientific success
- Kong, Xiangjie, Zhang, Jun, Zhang, Da, Bu, Yi, Ding, Ying, Xia, Feng
- Authors: Kong, Xiangjie , Zhang, Jun , Zhang, Da , Bu, Yi , Ding, Ying , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: ACM Transactions on Knowledge Discovery from Data Vol. 14, no. 4 (2020), p.
- Full Text:
- Reviewed:
- Description: This article elaborates how to identify and evaluate causal factors to improve scientific impact. Currently, analyzing scientific impact can be beneficial to various academic activities including funding application, mentor recommendation, discovering potential cooperators, and the like. It is universally acknowledged that high-impact scholars often have more opportunities to receive awards as an encouragement for their hard work. Therefore, scholars spend great efforts in making scientific achievements and improving scientific impact during their academic life. However, what are the determinate factors that control scholars' academic success? The answer to this question can help scholars conduct their research more efficiently. Under this consideration, our article presents and analyzes the causal factors that are crucial for scholars' academic success. We first propose five major factors including article-centered factors, author-centered factors, venue-centered factors, institution-centered factors, and temporal factors. Then, we apply recent advanced machine learning algorithms and jackknife method to assess the importance of each causal factor. Our empirical results show that author-centered and article-centered factors have the highest relevancy to scholars' future success in the computer science area. Additionally, we discover an interesting phenomenon that the h-index of scholars within the same institution or university are actually very close to each other. © 2020 ACM.
- Authors: Kong, Xiangjie , Zhang, Jun , Zhang, Da , Bu, Yi , Ding, Ying , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: ACM Transactions on Knowledge Discovery from Data Vol. 14, no. 4 (2020), p.
- Full Text:
- Reviewed:
- Description: This article elaborates how to identify and evaluate causal factors to improve scientific impact. Currently, analyzing scientific impact can be beneficial to various academic activities including funding application, mentor recommendation, discovering potential cooperators, and the like. It is universally acknowledged that high-impact scholars often have more opportunities to receive awards as an encouragement for their hard work. Therefore, scholars spend great efforts in making scientific achievements and improving scientific impact during their academic life. However, what are the determinate factors that control scholars' academic success? The answer to this question can help scholars conduct their research more efficiently. Under this consideration, our article presents and analyzes the causal factors that are crucial for scholars' academic success. We first propose five major factors including article-centered factors, author-centered factors, venue-centered factors, institution-centered factors, and temporal factors. Then, we apply recent advanced machine learning algorithms and jackknife method to assess the importance of each causal factor. Our empirical results show that author-centered and article-centered factors have the highest relevancy to scholars' future success in the computer science area. Additionally, we discover an interesting phenomenon that the h-index of scholars within the same institution or university are actually very close to each other. © 2020 ACM.
Community-diversified influence maximization in social networks
- Li, Jianxin, Cai, Taotao, Deng, Ke, Wang, Xinjue, Sellis, Timos, Xia, Feng
- Authors: Li, Jianxin , Cai, Taotao , Deng, Ke , Wang, Xinjue , Sellis, Timos , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: Information Systems Vol. 92, no. (2020), p.
- Full Text:
- Reviewed:
- Description: To meet the requirement of social influence analytics in various applications, the problem of influence maximization has been studied in recent years. The aim is to find a limited number of nodes (i.e., users) which can activate (i.e. influence) the maximum number of nodes in social networks. However, the community diversity of influenced users is largely ignored even though it has unique value in practice. For example, the higher community diversity reduces the risk of marketing campaigns as you should not put all your eggs in one basket; the diversity can also prolong the effect of a marketing campaign in the future promotion. Motivated by this observation, this paper investigates Community-diversified Influence Maximization (CDIM) problem to efficiently find k nodes such that, if a message is initiated and spread by the k nodes, the number as well as the community diversity of the activated nodes will be maximized at the end of propagation process. This work proposes a metric to measure the community-diversified influence and addresses a series of computational challenges. Two algorithms and an innovative CPSP-Tree index have been developed. This study also investigates the situation that community definition is not specified. The effectiveness and efficiency of the proposed solutions have been verified through extensive experimental studies on five real-world social network datasets. © 2020 Elsevier Ltd
- Authors: Li, Jianxin , Cai, Taotao , Deng, Ke , Wang, Xinjue , Sellis, Timos , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: Information Systems Vol. 92, no. (2020), p.
- Full Text:
- Reviewed:
- Description: To meet the requirement of social influence analytics in various applications, the problem of influence maximization has been studied in recent years. The aim is to find a limited number of nodes (i.e., users) which can activate (i.e. influence) the maximum number of nodes in social networks. However, the community diversity of influenced users is largely ignored even though it has unique value in practice. For example, the higher community diversity reduces the risk of marketing campaigns as you should not put all your eggs in one basket; the diversity can also prolong the effect of a marketing campaign in the future promotion. Motivated by this observation, this paper investigates Community-diversified Influence Maximization (CDIM) problem to efficiently find k nodes such that, if a message is initiated and spread by the k nodes, the number as well as the community diversity of the activated nodes will be maximized at the end of propagation process. This work proposes a metric to measure the community-diversified influence and addresses a series of computational challenges. Two algorithms and an innovative CPSP-Tree index have been developed. This study also investigates the situation that community definition is not specified. The effectiveness and efficiency of the proposed solutions have been verified through extensive experimental studies on five real-world social network datasets. © 2020 Elsevier Ltd
Tracing the Pace of COVID-19 research : topic modeling and evolution
- Liu, Jiaying, Nie, Hansong, Li, Shihao, Ren, Jing, Xia, Feng
- Authors: Liu, Jiaying , Nie, Hansong , Li, Shihao , Ren, Jing , Xia, Feng
- Date: 2021
- Type: Text , Journal article
- Relation: Big Data Research Vol. 25, no. (2021), p.
- Full Text:
- Reviewed:
- Description: COVID-19 has been spreading rapidly around the world. With the growing attention on the deadly pandemic, discussions and research on COVID-19 are rapidly increasing to exchange latest findings with the hope to accelerate the pace of finding a cure. As a branch of information technology, artificial intelligence (AI) has greatly expedited the development of human society. In this paper, we investigate and visualize the on-going advancements of early scientific research on COVID-19 from the perspective of AI. By adopting the Latent Dirichlet Allocation (LDA) model, this paper allocates the research articles into 50 key research topics pertinent to COVID-19 according to their abstracts. We present an overview of early studies of the COVID-19 crisis at different scales including referencing/citation behavior, topic variation and their inner interactions. We also identify innovative papers that are regarded as the cornerstones in the development of COVID-19 research. The results unveil the focus of scientific research, thereby giving deep insights into how the academic society contributes to combating the COVID-19 pandemic. © 2021 Elsevier Inc. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Jing Ren and Feng Xia" is provided in this record**
- Description: COVID-19 has been spreading rapidly around the world. With the growing attention on the deadly pandemic, discussions and research on COVID-19 are rapidly increasing to exchange latest findings with the hope to accelerate the pace of finding a cure. As a branch of information technology, artificial intelligence (AI) has greatly expedited the development of human society. In this paper, we investigate and visualize the on-going advancements of early scientific research on COVID-19 from the perspective of AI. By adopting the Latent Dirichlet Allocation (LDA) model, this paper allocates the research articles into 50 key research topics pertinent to COVID-19 according to their abstracts. We present an overview of early studies of the COVID-19 crisis at different scales including referencing/citation behavior, topic variation and their inner interactions. We also identify innovative papers that are regarded as the cornerstones in the development of COVID-19 research. The results unveil the focus of scientific research, thereby giving deep insights into how the academic society contributes to combating the COVID-19 pandemic. © 2021 Elsevier Inc.
- Authors: Liu, Jiaying , Nie, Hansong , Li, Shihao , Ren, Jing , Xia, Feng
- Date: 2021
- Type: Text , Journal article
- Relation: Big Data Research Vol. 25, no. (2021), p.
- Full Text:
- Reviewed:
- Description: COVID-19 has been spreading rapidly around the world. With the growing attention on the deadly pandemic, discussions and research on COVID-19 are rapidly increasing to exchange latest findings with the hope to accelerate the pace of finding a cure. As a branch of information technology, artificial intelligence (AI) has greatly expedited the development of human society. In this paper, we investigate and visualize the on-going advancements of early scientific research on COVID-19 from the perspective of AI. By adopting the Latent Dirichlet Allocation (LDA) model, this paper allocates the research articles into 50 key research topics pertinent to COVID-19 according to their abstracts. We present an overview of early studies of the COVID-19 crisis at different scales including referencing/citation behavior, topic variation and their inner interactions. We also identify innovative papers that are regarded as the cornerstones in the development of COVID-19 research. The results unveil the focus of scientific research, thereby giving deep insights into how the academic society contributes to combating the COVID-19 pandemic. © 2021 Elsevier Inc. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Jing Ren and Feng Xia" is provided in this record**
- Description: COVID-19 has been spreading rapidly around the world. With the growing attention on the deadly pandemic, discussions and research on COVID-19 are rapidly increasing to exchange latest findings with the hope to accelerate the pace of finding a cure. As a branch of information technology, artificial intelligence (AI) has greatly expedited the development of human society. In this paper, we investigate and visualize the on-going advancements of early scientific research on COVID-19 from the perspective of AI. By adopting the Latent Dirichlet Allocation (LDA) model, this paper allocates the research articles into 50 key research topics pertinent to COVID-19 according to their abstracts. We present an overview of early studies of the COVID-19 crisis at different scales including referencing/citation behavior, topic variation and their inner interactions. We also identify innovative papers that are regarded as the cornerstones in the development of COVID-19 research. The results unveil the focus of scientific research, thereby giving deep insights into how the academic society contributes to combating the COVID-19 pandemic. © 2021 Elsevier Inc.
Measuring perceptions of service quality within the visitor attractions sector
- Authors: Lynch, David
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at Australian and New Zealand Marketing Academy (ANZMAC) Conference 2007 : 3Rs - Reputation, Responsibility & Relevance, University of Otago, Dunedin, New Zealand : 3rd-5th December 2007 p. 64-72
- Full Text:
- Description: The attraction sector’s ability to enhance service quality is fundamentally important to its future sustainability. Attempts to enhance performance within the sector have suffered from the lack of a standard instrument for measuring service quality perceptions. This study sought to address this issue by piloting an instrument designed to measure visitor levels of perceived service quality. The instrument was piloted on 133 visitors to four purpose built attractions in Victoria, Australia. Analysis of the data resulted in a purified 17-item instrument, called ATTRACTQUAL and proposed that two dimensions, interactions and outcomes, underlie attraction visitors’ perceptions of service quality.
- Description: 2003005150
- Authors: Lynch, David
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at Australian and New Zealand Marketing Academy (ANZMAC) Conference 2007 : 3Rs - Reputation, Responsibility & Relevance, University of Otago, Dunedin, New Zealand : 3rd-5th December 2007 p. 64-72
- Full Text:
- Description: The attraction sector’s ability to enhance service quality is fundamentally important to its future sustainability. Attempts to enhance performance within the sector have suffered from the lack of a standard instrument for measuring service quality perceptions. This study sought to address this issue by piloting an instrument designed to measure visitor levels of perceived service quality. The instrument was piloted on 133 visitors to four purpose built attractions in Victoria, Australia. Analysis of the data resulted in a purified 17-item instrument, called ATTRACTQUAL and proposed that two dimensions, interactions and outcomes, underlie attraction visitors’ perceptions of service quality.
- Description: 2003005150