Achieving neuroplasticity in artificial neural networks through smart cities

Smart Cities

Volumn 2, Issue 2, 2019, Pages 118-134

  Allam, Zaheer ^a  

^a CURTIN UNIVERSITY   (Australia)

Author keywords

Artificial intelligence; Artificial neural networks (ANNs); Brain; Complexity; Geometry; Machine learning; Neuroplasticity; Smart cities

Indexed keywords

EID: 85070956572     PISSN: None     EISSN: 26246511     Source Type: Journal    
DOI: 10.3390/smartcities2020009     Document Type: Article

References (105)

1. OECD. Cities and climate change: National governments enabling local action. In OECD Policy Perspectives; Organisation for Economic Co-operation and Development; OECD: Paris, France, 2014; pp. 1–21.
2. United Nations. Sustainable Development Goals Report; UN: New York, NY, USA, 2018; p. 40.
3. UNDP. Sustainable Urbanization Strategy: Undp’s Support to Sustainable, Inclusive and Resilient Cites in the Developing World; United Nations Development Programme: New York, NY, USA, 2016.
4. Sepasgozar, S.M.E.; Hawken, S.; Sargolzaei, S.; Foroozanfa, M. Implementing citizen centric technology in developing smart cities: A model for predicting the acceptance of urban technologies. Technol. Forecast. Soc. Chang. 2018, 142, 115–116. [CrossRef]
5. Allam, M.Z. Redefining the Smart City: Culture, Metabolism and Governance. Case Study of Port Louis, Mauritius; Curtin University: Perth, Australia, 2018.
6. Allam, Z. Contextualising the smart city for sustainability and inclusivity. New Des. Ideas 2018, 2, 124–127.
7. Allam, Z.; Newman, P. Redefining the smart city: Culture, metabolism & governance. Smart Cities 2018, 1, 4–25.
8. Barns, S.; Cosgrave, E.; Acuto, M.; Mcneill, D. Digital infrastructures and urban governance. Urban Policy Res. 2016, 35, 20–31. [CrossRef]
9. Gil, D.; Ferrández, A.; Moramora, H.; Peral, J. Internet of things: A review of surveys based on context aware intelligent services. Sensors 2016, 16, 1069. [CrossRef]
10. Bibri, S.E. The ioT for smart sustainable cities of the future: An analytical framework for sensor-based big data applications for environmental sustainability. Sustain. Cities Soc. 2018, 38, 230–253. [CrossRef]
11. Bassoo, V.; Ramnarain-Seetohul, V.; Hurbungs, V.; Fowdur, T.P.; Beeharry, Y. Big data analytics for smart cities. In Internet of Things and Big Data Analytics toward Next-Generation Intelligence. Studies in Big Data; Dey, N., Hassanien, A., Bhatt, C., Stapathy, S., Eds.; Springer: Cham, Switzerland, 2018; Volume 30.
12. Batty, M. Big data and the city. Built Environ. 2016, 42, 321–337. [CrossRef]
13. Al-Saqaf, W.; Seidler, N. Blockchain technology for social impact: Opportunities and challenges ahead. J. Cyber Policy 2017, 2, 338–354. [CrossRef]
14. Christidis, K.; Devetsikiotis, M. Blockchains and smart contracts for the internet of things. IEEE Access 2016, 4, 2292–2303. [CrossRef]
15. Allam, Z. On smart contracts and organisational performance: A review of smart contracts through the blockchain technology. Rev. Econ. Bus. Stud. 2018, 11, 137–156. [CrossRef]
16. Bini, S.A. Artificial intelligence, machine learning, deep learning, and cognitive computing: What do these terms mean and how will they impact health care? J. Arthroplast. 2018, 33, 2358–2361. [CrossRef] [PubMed]
17. Guo, K.; Lu, Y.; Gao, H.; Cao, R. Artificial intelligence-based semantic internet of things in a user-centric smart city. Sensors 2018, 18, 1341. [CrossRef]
18. Aguilera, U.; Peña, O.; Belmonte, O.; López-de-Ipiña, D. Citizen-centric data services for smarter cities. Future Gener. Comput. Syst. 2017, 76, 234–247. [CrossRef]
19. Khan, M.; Babar, M.; Ahmed, S.H.; Shad, S.C.; Han, K. Smart city designing and planning based on big data analytics. Sustain. Cities Soc. 2017, 35, 271–279. [CrossRef]
20. Silva, B.N.; Khan, M.; Han, K. Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities. Sustain. Cities Soc. 2018, 38, 697–713. [CrossRef]
21. Mathaini, S.; Jain, R.K.; Arora, M.K. An artificial neural network based approach for modelling urban spatial growth. ITPI J. 2007, 4, 43–51.
22. Mathaini, S. A neural network based urban growth model of an Indian city. J. Indian Soc. Remote Sens. 2009, 37, 363–376. [CrossRef]
23. Yeh, A.G.; Li, X. Urban simulation using neural networks and cellular automata for land use planning. In Proceedings of the Symposium on Geospatial Theory, Processing and Applications, Ottawa, ON, Canada, 9–12 July 2002.
24. Sorda, K.R. Artificial Neural Networks—The Future of Smart Cities. Available online: http://www. itwebafrica.com/home-pagex/opinion/244318-artificial-neural-networks--the-future-of-smart-cities (accessed on 1 January 2019).
25. Dubey, A.; Naik, N.; Parikh, D.; Raskar, R.; Hidalgo, C.A. Deep learning the city: Quantifying urban perception at a global scale. In Proceedings of the ECCV European Conference on Computer Vision, Amsterdam, The Netherlands, 11–14 October 2016.
26. Kropp, J. A neural network approach to the analysis of city systems. Appl. Geogr. 1998, 18, 83–96. [CrossRef]
27. Zorins, A.; Grabusts, P. Artificial Neural Networks and Human Brain: Survey of Improvement Possibilities of Learning. In Proceedings of the 10th International Scientific and Practical Conference, Rezekne, Latvia, 18–20 June 2015; pp. 228–231.
28. Graupe, D. Principles of Artificial Neural Networks: Advanced Series on Circuits and Systems, 2nd ed.; World Scientific Publishing Co. Ltd.: Singapore, 2007; Volume 6.
29. Jain, L.; Fanelli, A.M. Recent Advances in Artificial Neural Networks: Design and Applications; CRC Press LLC: Boca Raton, FL, USA, 2000.
30. Mehrotra, K.; Mohan, C.K.; Ranka, S. Elements of Artificial Neural Networks; MIT Press: Cambridge, MA, USA, 1997.
31. Botta, A.; Donato, W.D.; Persico, V.; Pescapé, A. Integration of cloud computing and internet of things: A survey. Future Gener. Comput. Syst. 2016, 56, 684–700. [CrossRef]
32. Solomon, A.S.; Henri, C.; Charles, S.; Melwin, P.; Ninoshka, T. Neuron the memory unit of the brain. IOSR J. Comput. Eng. 2015, 17, 48–61.
33. Haykin, S. Neural Networks and Learning Machines, 3rd ed.; McMaster University Hamilton: Ontario, ON, Canada, 2009.
34. He, H.; Garcia, E.A. Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 2009, 21, 1260–1284.
35. Sidiropulou, K.; Pissadaki, E.K.; Poirazi, P. Inside the brain of a neuron. EMBO Rep. 2006, 7, 886–892. [CrossRef]
36. Perwej, Y.; Parwej, F. A neuroplasticity (brain plasticity) approach to use in artificial neural network. Int. J. Sci. Eng. Res. 2012, 3, 1–9.
37. Abiodun, O.I.; Jantan, A.; Omolara, A.E.; Dada, K.V.; Mahamed, N.A.; Arshad, H. State-of-the-art in artificial neural network applications: A survey. Heliyon 2018, 4, e00938–e00979. [CrossRef] [PubMed]
38. Huang, T.-J. Imitating the brain with neurocomputer: A “new” way towards artificial general intelligence. Int. J. Autom. Comput. 2017, 14, 520–531. [CrossRef]
39. GK. How Neural Networks Work. Available online: https://chatbotslife.com/how-neural-networks-work-ff4c7ad371f7 (accessed on 2 January 2019).
40. Kurama, V. Deep Learning with Python. Available online: https://towardsdatascience.com/deep-learning-with-python-703e26853820 (accessed on 3 January 2019).
41. Fukumizu, K. Chapter 17—Geometry of neural networks: Natural gradient for learning. In Handbook of Biological Physics; Moss, F., Gielen, S., Eds.; Elsevier: Amsterdam, The Netherlands, 2001; pp. 731–769.
42. Hauser, M.; Ray, A. Principles of Riemannian geometry in neural networks. In Proceedings of the 31st Conference on Neural Information Processing Systems NIPS, Long Beach, CA, USA, 4–9 December 2017.
43. Triantakonstantis, D.; Stathakis, D. Urban growth prediction in Athens, Greece, using artificial neural networks. Int. J. Archit. Environ. Eng. 2015, 9, 234–238.
44. Jafar, R.; Shahrour, I.; Juran, I. Application of artificial neural networks (ANN) to model the failure of urban water mains. Math. Comput. Model. 2010, 51, 1170–1180. [CrossRef]
45. Lee, K.Y.; Chung, N.; Hwang, S. Application of an artificial neural network (ANN) model for predicting mosquito abundances in urban areas. Ecol. Inform. 2016, 36, 172–180. [CrossRef]
46. Liu, W.; Seto, K.C. Using the ART-MMAP neural network to model and predict urban growth: A spatio-temporal data mining approach. Environ. Plan. B Plan. Des. 2008, 35, 296–317. [CrossRef]
47. Alizadeh, M.; Ngah, I.; Hashim, M.; Pradhan, B.; Pour, A.B. A hybrid analytic network process and artificial neural network (ANP-ANN) model for urban earthquake vulnerability assessment. Remote Sens. 2018, 10, 975. [CrossRef]
48. Sasmita, A.O.; Kuruvilla, J.; Ling, A.P.K. Harnessing neuroplasticity: Modern approaches and clinical future. Int. J. Neurosci. 2018, 128, 1061–1077. [CrossRef]
49. Aleksandra, C. How Is a Neuron Adapted to Perform Its Function. Available online: https://socratic.org/questions/how-is-a-neuron-adapted-to-perform-its-function (accessed on 3 January 2019).
50. Pascual-Leone, A.; Amedi, A.; Fregni, F.; Merabet, L.B. The plastic human brain cortex. Annual Review of Neuroscience 2005, 28, 377–401. [CrossRef]
51. Shaffer, J. Neuroplasticity and clinical practice: Building brain power for health. Front. Psychol. 2016, 7, 1118. [CrossRef]
52. Schaefer, N.; Rotermund, C.; Blumrich, E.-M.; Lourenco, M.V.; Joshi, P.; Hegemann, R.U.; Jamwal, S.; Ali, N.; Romero, E.M.G. The malleable brain: Plasticity of neural circuits and behavior—A review from students to students. J. Neurochem. 2017, 142, 790–811. [CrossRef]
53. Bryck, R.L.; Fisher, P.A. Training the brain: Practical applications of neural plasticity from the intersection of cognitive neuroscience, developmental psychology, and prevention science. Am. Psychol. 2012, 67, 87–100. [CrossRef]
54. Hertzog, C.; Kramer, A.F.; Wilson, R.S.; Lindenberger, U. Enrichment effects on adult cognitive development: Can the functional capacity of older adults be preserved and enhanced? Psychol. Sci. Public Interest J. Am. Psychol. Soc. 2008, 9, 1–65. [CrossRef]
55. Mahncke, H.W.; Bronstone, A.; Merzenich, M.M. Brain plasticity and functional losses in the aged: Scientific bases for a novel intervention. Prog. Brain Res. 2006, 157, 81–109.
56. Yang, M. Mapping the Brain’s Traffic. Available online: https://engineering.cmu.edu/news-events/news/2018/09/06-mapping-brain-traffic.html (accessed on 3 January 2019).
57. Salingaros, N.A. Theory of the urban web. J. Urban Des. 1998, 3, 53–71. [CrossRef]
58. Alexander, C. A city is not a tree. Archit. Forum 1965, 122, 58–61.
59. Molotch, H. The city as a growth machine: Toward a political economy of place. Am. J. Sociol. 1976, 82, 309–332. [CrossRef]
60. Batty, M.; Longley, P. Fractal Cities: A Geometry of Form and Function; Academic Press: Cambridge, MA, USA, 1994.
61. Allam, Z. Building a conceptual framework for smarting an existing city in Mauritius: The case of Port Louis. J. Biourbanism 2017, 4, 103–121.
62. Allam, Z.; Newman, P. Economically incentivising smart urban regeneration. Case study of Port Louis, Mauritius. Smart Cities 2018, 1, 53–74. [CrossRef]
63. Kim, T.-h.; Ramos, C.; Mohammed, S. Smart city and IoT. Future Gener. Comput. Syst. 2017, 76, 159–162. [CrossRef]
64. Qin, Y.; Sheng, Q.Z.; Falkner, N.J.; Dustdar, S.; Wang, H.; Vasilakos, A.V. When things matter: A survey on data-centric internet of things. J. Netw. Comput. Appl. 2016, 64, 137–153. [CrossRef]
65. Deshpande, A.; Pitale, P.; Sanap, S. Industrial automation using internet of things (IoT). Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 2016, 5, 266–269.
66. Tzafestas, S.G. Synergy of IoT and AI in modern society: The robotics and automation case. Robot. Autom. Eng. J. 2018, 31, 1–15. [CrossRef]
67. Čolaković, A.; Hadžialić, M. Internet of things (IoT): A review of enabling technologies, challenges, and open research issues. Comput. Netw. 2018, 144, 17–39. [CrossRef]
68. Kotenko, I.; Saenko, I.; Skorik, F.; Bushuev, S. Neural network approach to forecast the state of the internet of things elements. In Proceedings of the XVIII International Conference on Soft Computing and Measurements (SCM), St. Petersburg, Russia, 19–21 May 2015; pp. 133–135.
69. Mahdavinejad, M.S.; Rezvan, M.; Barekatain, M.; Adabi, P.; Barnaghi, P.; Sheth, A.P. Machine learning for internet of things data analysis: A survey. Digit. Commun. Netw. 2018, 4, 161–175. [CrossRef]
70. Forsyth, R. Expert Systems; Chapman and Hall: Boca Raton, FL, USA, 2017.
71. Naganathan, V.; Rao, R.K. The evolution of internet of things: Bringing the power of artificial intelligence to iot, its opportunities and challenges. Int. J. Comput. Sci. Trends ADN Technol. 2018, 6, 94–108.
72. Patel, K.K.; Patel, S.M. Internet of things-IoT: Definition, characteristics, architecture, enabling technologies, applications and future challenges. Int. J. Eng. Sci. Comput. 2016, 6, 6122–6131.
73. Zhang, N.; Chen, J.; Chen, X.; Chen, J. Semantic framework of internet of things for smart cities: Case studies. Sensors 2016, 16, 1501. [CrossRef]
74. Barnaghi, P.; Bermudez-Edo, M.; Tonjes, R. Challenges for quality of data in smart cities. J. Data Inf. Qual. 2015, 6, 6. [CrossRef]
75. Keenan, M. The future of data with the rise of the IoT. RFID J. 2018. Available online: https://www. rfidjournal.com/articles/view?17954 (accessed on 25 January 2019).
76. Khaled, A.E.; Halal, S. Interoperable communication framework for bridging restful and topic—Based communication in IoT. Future Gener. Comput. Syst. 2019, 92, 628–643. [CrossRef]
77. Al-Fugaha, A.; Guizani, A.M.; Mehdi, M.; Mohammed, A.; Moussa, A. Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 2015, 17, 2347–2376. [CrossRef]
78. Espada, J.P.; Yager, R.; Yu, Z. Communications, collaborations and services in networks of embedded devices. Future Gener. Comput. Syst. 2019, 92, 560–563. [CrossRef]
79. Bačić, Ž.; Jogun, T.; Majić, I. Integrated sensor systems for smart cities. Teh. Vjesn. 2018, 25, 277–284.
80. Alexander, C. A New Theory of Urban Design; Oxford University Press: New York, NY, USA, 1987.
81. Alexander, C. The Process of Creating Life: An Essay on the Art of Building and the Nature of the Universe; Center for Environmental Structure: Berkeley, CA, USA, 2002.
82. Alexander, C. The Phenomenon of Life: An Essay on the Art of Building and the Nature of the Universe; Center for Environmental Structure: Berkeley, CA, USA, 2002.
83. Alexander, C. The Luminous Ground: An Essay on the Art of Building and the Nature of the Universe; Center for Environmental Structure: Berkeley, CA, USA, 2002.
84. Alexander, C. A Vision of a Living World: An Essay on the Art of Building and the Nature of the Universe; Center for Environmental Structure: Berkeley, CA, USA, 2002.
85. Salingaros, N.A. Complexity in architecture and design. Oz J. 2014, 36, 18–25. [CrossRef]
86. Bhat, R. Understanding complexity through pattern languages in biological and man-made architectures. Archnet Int. J. Archit. Res. 2014, 8, 8–19. [CrossRef]
87. Alexander, C.; Ishikawa, S.; Silverstein, M. A Pattern Language; Oxford University Press: New York, NY, USA, 1977.
88. Salingaros, N.A. Connecting the fractal city. In 5th Biennial of Towns and Town Planners in Europe; PLANUM: Barcelona, Spain, 2003; pp. 78–101.
89. Alexander, C. The Timeless Way of Building; Oxford University Press: New York, NY, USA, 1979.
90. Dawes, M.J.; Ostwald, M.J. Christopher Alexander’s a pattern language: Analysing, mapping and classifying the critical response. City Territ. Archit. 2017, 4, 1–14. [CrossRef]
91. Salingaros, N.A. Biophilia and Healing Environments; Off the Common Books: Amherst, MA, USA, 2015.
92. Kruhl, J.H. Fractal-geometry techniques in the quantification of complex rock structures: A special view on scaling regimes, inhomogeneity and anisotropy. J. Struct. Geol. 2013, 46, 2–21. [CrossRef]
93. Salingaros, N.A. Fractals and Christopher Alexander’s “fifteen fundamental properties”. In Conscious Cities Anthology 2018; Human-Centred Design: London, UK, 2018.
94. Seamon, D. Christopher Alexander and a phenomenology of wholeness. In Christopher Alexander Annual Meeting of the Environmental Design Research Association; EDRA: Sacramento, CA, USA, 2007.
95. Grabow, S. Christopher Alexander and the Search for a New Paradigm in Architecture; Oriel Press: London, UK, 1983.
96. Delaney, S. Fractal Scaling. Available online: https://medium.com/@sdelaney32/fractal-scaling-6dc8fa1773a9 (accessed on 3 January 2019).
97. Salingaros, N.A.; Fractals in the new architecture. Archimagazine 2001. Available online: http://zeta.math. utsa.edu/~{}yxk833/fractals.html (accessed on 25 January 2019).
98. Alexander, C. The Nature of Order: The Process of Creating Life; The Centre for Environmental Structure: Berkeley, CA, USA, 2002.
99. Jiang, B. A new kind of beauty out of the underlying scaling of geographic space. Prof. Geogr. 2014, 66, 676–686. [CrossRef]
100. Jiang, B. Wholeness as a hierarchical graph to capture the nature of space. Int. J. Geogr. Inf. Sci. 2015, 29, 1632–1648. [CrossRef]
101. Salingaros, N.A. Complexity and urban coherence. J. Urban Des. 2000, 5, 291–316. [CrossRef]
102. Jacobs, J. The Death and Life of Great American Cities; Random House: New York, NY, USA, 1961.
103. Allam, Z.; Dhunny, A.; Siew, G.; Jones, D. Towards smart urban regeneration: Findings of an urban footprint survey in Port Louis, Mauritius. Smart Cities 2018, 1, 121–133. [CrossRef]
104. Allam, Z.; Dhunny, Z.A. On big data, artificial intelligence and smart cities. Cities 2019, 89, 80–91. [CrossRef]
105. Allam, Z.; Jones, D.S. The potential of blockchain within air rights development as a prevention measure against urban sprawl. Urban Science 2019, 3, 38. [CrossRef]