Self-building artificial intelligence and machine learning to empower big data analytics in smart cities

Damminda Alahakoon, Rashmika Nawaratne, Yan Xu, Daswin de Silva, Uthayasankar Sivarajah and Bhumika Gupta ()
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Damminda Alahakoon: La Trobe University
Rashmika Nawaratne: La Trobe University
Yan Xu: NPU - Northernwest Polytechnical University [Xi'an]
Daswin de Silva: La Trobe University
Uthayasankar Sivarajah: School of Management [Bradford] - University of Bradford
Bhumika Gupta: IMT-BS - MMS - Département Management, Marketing et Stratégie - TEM - Télécom Ecole de Management - IMT - Institut Mines-Télécom [Paris] - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris], LITEM - Laboratoire en Innovation, Technologies, Economie et Management (EA 7363) - UEVE - Université d'Évry-Val-d'Essonne - Université Paris-Saclay - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris]

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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 self-building 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.

Keywords: Big data analytics; Self-building AI; Machine learning; Smart cities; Self-organizing maps; Sustainable Development Goals (search for similar items in EconPapers)
Date: 2023-02
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Published in Information Systems Frontiers, 2023, Special Issue on Artificial Intelligence (AI) and Information Systems: Perspectives to Responsible AI, 25 (1), pp.221-240. ⟨10.1007/s10796-020-10056-x⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03613397

DOI: 10.1007/s10796-020-10056-x

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