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Self-building Artificial Intelligence and machine learning to empower big data analytics in smart cities
Alahakoon, D. Nawaratne, R. Xu, Y. De Silva, D. Gupta, B.
Alahakoon, D.
Nawaratne, R.
Xu, Y.
De Silva, D.
Gupta, B.
Publication Date
2023
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14/08/2020
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Abstract
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.
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Published version
Citation
Alahakoon D, Nawaratne R, Xu Y et al (2023) Self-building Artificial Intelligence and machine learning to empower big data analytics in smart cities. Information Systems Frontiers. 25: 221-240.
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Article