ORCID Profile
0000-0003-1422-0648
Current Organisation
The University of Newcastle
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Publisher: Association for Computing Machinery (ACM)
Date: 05-2013
Abstract: The advent of digital environments has generated significant benefits for businesses, organizations, governments, academia and societies in general. Today, over millions of transactions take place on the Internet. Although the widespread use of digital environments has generally provided opportunities for societies, a number of threats have limited their adoption. The de-facto standard today is for certification authorities to authenticate the identity of service providers while trust on the provided services is implied. This approach has certain shortcomings, for ex le, single point of failure, implied trust rather than explicit trust and others. One approach for minimizing such threats is to introduce an effective and resilient trust mechanism that is capable of determining the trustworthiness of service providers in providing their services. Determining the trustworthiness of services reduces invalid transactions in digital environments and further encourages collaborations. Evaluating trustworthiness of a service provider without any prior historical transactions (i.e. the initial transaction) pose a number of challenging issues. This article presents TIDE - a decentralized reputation trust mechanism that determines the initial trustworthiness of entities in digital environments. TIDE improves the precision of trust computation by considering raters’ feedback, number of transactions, credibility, incentive to encourage raters’ participation, strategy for updating raters’ category, and safeguards against dynamic personalities. Furthermore, TIDE classifies raters into three categories and promotes the flexibility and customization through its parameters. Evaluation of TIDE against several attack vectors demonstrates its accuracy, robustness and resilience.
Publisher: IEEE
Date: 06-2013
Publisher: ACTAPRESS
Date: 2012
Publisher: Scientific Research Publishing, Inc.
Date: 2012
Publisher: IEEE
Date: 24-11-2021
Publisher: Informa UK Limited
Date: 27-05-2018
Publisher: Audio Engineering Society
Date: 03-06-2021
Publisher: IEEE
Date: 11-2014
Publisher: Science and Engineering Publishing Company
Date: 2015
Publisher: Wiley
Date: 21-06-2023
DOI: 10.1002/ENG2.12699
Abstract: Today, educational institutions produce large amounts of data with the deployment of learning management systems. These large datasets provide an untapped potential to support and enhance decision‐making and operations. In recent times, machine learning (ML) has been applied to develop models utilizing this “big” data to assist in decision‐making. This study presents a systematic literature review into the application of ML to predict student performance. A total of 162 research articles from January 2010 to October 2022 were critically reviewed and analyzed by applying Kitchenham's systematic literature review approach. Our analysis categorized the literature predicting students' academic performance into two categories: (i) predicting student performance in assessments, courses or programs, and identifying students at‐risk of failing their course rogram (129 studies) and (ii) predicting student dropout or retention in a course or program (33 studies). Classification is the most commonly used approach for predicting student performance (138 studies), followed by regression (25 studies) and clustering (9 studies). Supervised learning methods are used more often than semi‐supervised learning. Five most popular ML algorithms include the Decision Tree, Random Forest, Naïve Bayes, Artificial Neural Network, and Support Vector Machine. Historical records of students' grades and class performance, academic related data from learning management systems, and students' demographics are the most common features used for predicting students' performance. The most common methods used for feature selection are Information Gain‐based selection algorithms, Correlation‐based feature selection, and Gain Ratio. The general platforms/tools/libraries used in the studies include WEKA, Python, R, Rapid Miner, and MATLAB. We also investigated possible actions considered in the literature to help at‐risk students. We only found very few studies that deployed remedial actions and evaluated their impact on students' performance. In conclusion, ML has shown great potential in the prediction of student performance, but also has many areas of further research.
Publisher: Springer Science and Business Media LLC
Date: 1996
DOI: 10.1007/BF00643598
Publisher: Zenodo
Date: 2022
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: SAGE Publications
Date: 2009
Publisher: IEEE
Date: 08-2012
Publisher: IEEE
Date: 10-2008
DOI: 10.1109/UMC.2008.17
Publisher: ACTAPRESS
Date: 2012
Publisher: Trans Tech Publications, Ltd.
Date: 02-2012
DOI: 10.4028/WWW.SCIENTIFIC.NET/AMR.463-464.811
Abstract: Information Technology innovations have strongly affected today’s businesses and the way we work. This effect involves different industries, and the healthcare industry is one of them. Various healthcare information systems have been introduced to manage and share patient records and information. However, based on the reviewed literatures, the healthcare knowledge management system does not have the same focus and attention. It is found that there is no system that is able to manage the tacit healthcare knowledge and innovation. As a result, this paper aims to introduce a theoretical framework that enables healthcare tacit knowledge management and global sharing. Digital Ecosystem is found to be the most suitable technology to achieve this aim specifically with the wiki environment as it is most suitable for the healthcare industry requirements.
Publisher: Springer Science and Business Media LLC
Date: 21-12-2015
Publisher: International Academy Publishing (IAP)
Date: 2015
Publisher: Informa UK Limited
Date: 21-06-2022
Publisher: IEEE
Date: 24-11-2021
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: IEEE
Date: 2006
DOI: 10.1109/CIT.2006.192
Publisher: IEEE
Date: 10-2018
Publisher: IEEE
Date: 05-2011
Publisher: ACM
Date: 20-10-2005
Publisher: IEEE
Date: 12-2006
Publisher: ACM
Date: 21-11-2011
Publisher: IEEE
Date: 06-2019
Publisher: SCITEPRESS - Science and and Technology Publications
Date: 2015
Publisher: Inderscience Publishers
Date: 2016
Publisher: IEEE
Date: 12-2014
Publisher: ACTAPRESS
Date: 2012
Publisher: IEEE
Date: 09-2014
Publisher: Springer Science and Business Media LLC
Date: 29-06-2016
Publisher: IEEE
Date: 06-2009
No related grants have been discovered for Rukshan Athauda.