Yes / The emerging information revolution makes it necessary to manage vast amounts of unstructured data rapidly. As the world is
increasingly populated by IoT devices and sensors that can sense their surroundings and communicate with each other, a digital
environment has been created with vast volumes of volatile and diverse data. Traditional AI and machine learning techniques
designed for deterministic situations are not suitable for such environments. With a large number of parameters required by each
device in this digital environment, it is desirable that the AI is able to be adaptive and self-build (i.e. self-structure, self-configure,
self-learn), rather than be structurally and parameter-wise pre-defined. This study explores the benefits of self-building AI and
machine learning with unsupervised learning for empowering big data analytics for smart city environments. By using the
growing self-organizing map, a new suite of self-building AI is proposed. The self-building AI overcomes the limitations of
traditional AI and enables data processing in dynamic smart city environments. With cloud computing platforms, the selfbuilding AI can integrate the data analytics applications that currently work in silos. The new paradigm of the self-building AI
and its value are demonstrated using the IoT, video surveillance, and action recognition applications. / Supported by the Data to Decisions Cooperative Research Centre (D2D CRC) as part of their analytics and decision support program and a La Trobe University Postgraduate Research Scholarship.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/18003 |
| Date | 19 August 2020 |
| Creators | Alahakoon, D., Nawaratne, R., Xu, Y., De Silva, D., Sivarajah, Uthayasankar, Gupta, B. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, Published version |
| Rights | © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
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