Abstract
Through the Internet of things (IoT), as promoted by smart cities, there is an emergence of big data accentuating the use of artificial intelligence through various components of urban planning, management, and design. One such system is that of artificial neural networks (ANNs), a component of machine learning that boasts similitude with brain neurological networks and its functioning. However, the development of ANN was done in singular fashion, whereby processes are rendered in sequence in a unidimensional perspective, contrasting with the functions of the brain to which ANN boasts similitude, and in particular to the concept of neuroplasticity which encourages unique complex interactions in self-learning fashion, thereby encouraging more inclusive urban processes and render urban coherence. This paper takes inspiration from Christopher Alexander’s Nature of Order and dwells in the concept of complexity theory; it also proposes a theoretical model of how ANN can be rendered with the same plastic properties as brain neurological networks with multidimensional interactivity in the context of smart cities through the use of big data and its emerging complex networks. By doing so, this model caters to the creation of stronger, richer, and more complex patterns that support Alexander’s concept of “wholeness” through the connection of overlapping networks. This paper is aimed toward engineers with interdisciplinary interest looking at creating more complex and intricate ANN models, and toward urban planners and urban theorists working on the emerging contemporary concept of smart cities.
Highlights
Cities all over the world are confronted with the challenge of rapid urbanization and population increase, with over two-thirds of the population projected to be living in cities by 2050 [1]
There ought to be a collaborative and interdisciplinary approach to avoid the pitfalls extensively discussed by Alexander in his book “Pattern Language” [87] and his extensive volumes “The Order of Order” [98]. He pointed out the concept of rigidity and a unidimensional approach, as confirmed by other authors [73,74,75], which affects the current technologies employed to enable the implementation of smart cities portrayed by current artificial neural networks (ANNs) networks and structures
This paper explored, through a multi-disciplinary perspective, how to transcend the limitations of artificial neural networks to achieve neuroplasticity through smart cities by increasing the complexity of its geometrical entities
Summary
Cities all over the world are confronted with the challenge of rapid urbanization and population increase, with over two-thirds of the population projected to be living in cities by 2050 [1]. One of the salient features of the smart city concept is its reliance on technologies such as Internet of things (IoT) [9,10], big data [11,12], blockchain [13,14,15], and artificial intelligence (AI) [16,17], which transformed the design, planning, and management of urban life These technologies allowed for speedy, quality, efficient, and real-time processes due to their ability to allow for massive data gathering and analysis [18,19]. They allowed communication between different components of the city within the network; services and activities run seamlessly with little negative impact on the environment, the economy, and the available resources [20]
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