ORCID Profile
0000-0001-6756-4415
Current Organisation
University of South Australia
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Informa UK Limited
Date: 2012
Publisher: Hindawi Limited
Date: 13-08-2012
DOI: 10.1155/2012/719237
Abstract: We propose a dependent hidden Markov model of credit quality. We suppose that the "true" credit quality is not observed directly but only through noisy observations given by posted credit ratings. The model is formulated in discrete time with a Markov chain observed in martingale noise, where "noise" terms of the state and observation processes are possibly dependent. The model provides estimates for the state of the Markov chain governing the evolution of the credit rating process and the parameters of the model, where the latter are estimated using the EM algorithm. The dependent dynamics allow for the so-called "rating momentum" discussed in the credit literature and also provide a convenient test of independence between the state and observation dynamics.
Publisher: IGI Global
Date: 13-05-2022
DOI: 10.4018/978-1-6684-6291-1.CH043
Abstract: In this chapter, machine learning techniques are applied to examine consumer food choices, specifically purchasing patterns in relation to fresh fruit and vegetables. This product category contributes some of the highest profit margins for supermarkets, making understanding consumer choices in that category important not just for health but also economic reasons. Several unsupervised and supervised machine learning techniques, including hierarchical clustering, latent class analysis, linear regression, artificial neural networks, and deep learning neural networks, are illustrated using Nielsen Consumer Panel Dataset, a large and high-quality source of information on consumer purchases in the United States. The main finding from the clustering analysis is that households who buy less fresh produce are those with children – an important insight with significant public health implications. The main outcome from predictive modelling of spending on fresh fruit and vegetables is that contrary to expectations, neural networks failed to outperform a linear regression model.
Publisher: Springer International Publishing
Date: 17-11-2016
Publisher: WORLD SCIENTIFIC
Date: 10-2012
Publisher: IGI Global
Date: 2017
DOI: 10.4018/978-1-5225-2512-7.CH005
Abstract: Technological advances have led to increasingly more data becoming available, a phenomenon known as Big Data. The volume of Big Data is to the order of zettabytes, offering the promise of valuable insights with visualisation the key to unlocking these insights, however the size and variety of Big Data poses significant challenges. The fundamental principles behind tried-and-tested methods for visualising data are still as relevant as ever, although the emphasis necessarily shifts to why visualisation is being attempted. This chapter outlines the use of graph semiotics to build data visualisations for exploration and decision-making and the formulation of elementary, intermediate- and overall-level analytical questions. The public scanner database Dominick's Finer Foods, consisting of approximately 98 million observations, is used as a demonstrative case study. Common Big Data analytic tools (SAS, R and Python) are used to produce visualisations and exemplars of student work are presented, based on the outlined visualisation approach.
Publisher: Springer New York
Date: 2014
Publisher: Elsevier BV
Date: 04-2013
Publisher: Elsevier BV
Date: 2013
Publisher: IGI Global
Date: 2020
DOI: 10.4018/978-1-7998-0106-1.CH018
Abstract: In this chapter, machine learning techniques are applied to examine consumer food choices, specifically purchasing patterns in relation to fresh fruit and vegetables. This product category contributes some of the highest profit margins for supermarkets, making understanding consumer choices in that category important not just for health but also economic reasons. Several unsupervised and supervised machine learning techniques, including hierarchical clustering, latent class analysis, linear regression, artificial neural networks, and deep learning neural networks, are illustrated using Nielsen Consumer Panel Dataset, a large and high-quality source of information on consumer purchases in the United States. The main finding from the clustering analysis is that households who buy less fresh produce are those with children – an important insight with significant public health implications. The main outcome from predictive modelling of spending on fresh fruit and vegetables is that contrary to expectations, neural networks failed to outperform a linear regression model.
Publisher: Springer International Publishing
Date: 2021
Publisher: Elsevier BV
Date: 12-2008
Publisher: University of South Australia Library
Date: 2023
DOI: 10.59453/JMTN6001
Abstract: This article examines the potential impact of large language models (LLMs) on higher education, using the integration of ChatGPT in Australian universities as a case study. Drawing on the experience of the first 100 days of integration, the authors conducted a content analysis of university websites and quotes from spokespeople in the media. Despite the potential benefits of LLMs in transforming teaching and learning, early media coverage has primarily focused on the obstacles to their adoption. The authors argue that the lack of official recommendations for Artificial Intelligence (AI) implementation has further impeded progress. Several recommendations for successful AI integration in higher education are proposed to address these challenges. These include developing a clear AI strategy that aligns with institutional goals, investing in infrastructure and staff training, and establishing guidelines for the ethical and transparent use of AI. The importance of involving all stakeholders in the decision-making process to ensure successful adoption is also stressed. This article offers valuable insights for policymakers and university leaders interested in harnessing the potential of AI to improve the quality of education and enhance the student experience. LIFT Learning: Engage further with the authors and the issues surrounding the first 100 days of ChatGPT in universities at the companion LIFT Learning site. Hear the authors grapple with some of the pressing challenges and opportunities that this technology brings through this panel style interview. The LIFT Learning site is available at apps.lift.c3l.ai/learning/course/coursev1:LEARNINGLETTERS+0101+2023
No related grants have been discovered for Malgorzata Korolkiewicz.