A framework for data-driven solutions with covid-19 illustrations

dc.contributor.author Mwitondi, Kassim S.
dc.contributor.author Said, Raed A.
dc.date.accessioned 2021-12-05T16:58:38Z
dc.date.available 2021-12-05T16:58:38Z
dc.date.copyright © 2021
dc.date.issued 2021
dc.description This article is not available at CUD collection. The version of scholarly record of this article is published in Data Science Journal (2021), available online at: http://doi.org/10.5334/dsj-2021-036 en_US
dc.description.abstract Data–driven solutions have long been keenly sought after as tools for driving the world’s fast changing business environment, with business leaders seeking to enhance decision making processes within their organisations. In the current era of Big Data, applications of data tools in addressing global, regional and national challenges have steadily grown in almost all fields across the globe. However, working in silos has continued to impede research progress, creating knowledge gaps and challenges across geographical borders, legislations, sectors and fields. There are many examples of the challenges the world faces in tackling global issues, including the complex interactions of the 17 Sustainable Development Goals (SDG) and the spatio–temporal variations of the impact of the on-going COVID–19 pandemic. Both challenges can be seen as non–orthogonal, strongly correlated and requiring an interdisciplinary approach to address. We present a generic framework for filling such gaps, based on two data-driven algorithms that combine data, machine learning and interdisciplinarity to bridge societal knowledge gaps. The novelty of the algorithms derives from their robust built–in mechanics for handling data randomness. Animation applications on structured COVID–19 related data obtained from the European Centre for Disease Prevention and Control (ECDC) and the UK Office of National Statistics exhibit great potentials for decision-support systems. Predictive findings are based on unstructured data–a large COVID–19 X–Ray data, 3181 image files, obtained from GitHub and Kaggle. Our results exhibit consistent performance across samples, resonating with cross-disciplinary discussions on novel paths for data-driven interdisciplinary research. © 2021, Ubiquity Press. All rights reserved. en_US
dc.description.sponsorship Council for Scientific and Industrial Research, South Africa Council of Scientific and Industrial Research, India en_US
dc.identifier.citation Mwitondi, K. S., & Said, R. A. (2021). A framework for data-driven solutions with covid-19 illustrations. Data Science Journal, 20(1). http://doi.org/10.5334/dsj-2021-036 en_US
dc.identifier.issn 16831470
dc.identifier.uri http://doi.org/10.5334/dsj-2021-036
dc.identifier.uri http://hdl.handle.net/20.500.12519/473
dc.language.iso en en_US
dc.publisher Ubiquity Press en_US
dc.relation Authors Affiliations : Mwitondi, K.S., Sheffield Hallam University, College of Business, Technology & Engineering, GB, United Kingdom; Said, R.A., Canadian University Dubai, Faculty of Management, United Arab Emirates
dc.relation.ispartofseries Data Science Journal;Volume 20, Issue 1
dc.rights.holder Copyright : © 2021, Ubiquity Press. All rights reserved.
dc.rights.uri Creative Common Attribution 4.0 International License (CC BY 4.0)
dc.rights.uri http://creativecommons.org/licenses/by/4.0
dc.subject Animation en_US
dc.subject Big Data en_US
dc.subject Convolutional Neural Networks en_US
dc.subject COVID-19 en_US
dc.subject Data Science en_US
dc.subject Sustainable Development Goals (SDG) en_US
dc.title A framework for data-driven solutions with covid-19 illustrations en_US
dc.type Article en_US
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