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Machine Learning Approaches for Sustainable Cities Using Internet of Things

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The Effect of Information Technology on Business and Marketing Intelligence Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1056))

Abstract

As these tech-based cities continue to emerge, so do the experts dive deeper into the research on the various Internet of Things methods, and the most plausible machine learning techniques. What is Machine Learning? What role does it play in IoT-based platforms? How does it contribute to the continuous advancement of smart cities? What is IoT? How has it changed the lives of urban households residing in smart cities? How does IoT interact with machine learning to ensure more efficient cities? This research paper delves into the various existing literature in a bid to respond to these questions as effectively as possible. It looks the research that have handled issues of IoT and machine learning. The search criteria are set out in the methodology section that has inclusion and exclusion criteria for the articles considered in this paper. There is systematic evidence showing that IOT and machine learning can be used for the management of the future cities. The results suggest that IoT and machine learning are at the core of the development, maintenance, and sustainability of smart cities. Further studies are required on how IOT can be securely used by analysis some of the security vulnerabilities faced by these systems.

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Authors and Affiliations

  1. Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebansaan Malaysia (UKM), 43600, Bangi, Selangor, Malaysia

    Taher M. Ghazal & Mohammad Kamrul Hasan

  2. School of Information Technology, Skyline University College, Sharjah, UAE

    Taher M. Ghazal

  3. School of Computer Science, National College of Business Administration & Economics, Lahore, Pakistan

    Munir Ahmad

  4. School of Business, Skyline University College, Sharjah, UAE

    Haitham M. Alzoubi & Haitham M. Alzoubi

  5. Department of Marketing, School of Business, University of Jordan, Amman, Jordan

    Muhammad Alshurideh

  6. Department of Management, College of Business Administration, University of Sharjah, Amman, UAE

    Muhammad Alshurideh

Authors
  1. Taher M. Ghazal
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  2. Mohammad Kamrul Hasan
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  3. Munir Ahmad
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  4. Haitham M. Alzoubi
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  5. Muhammad Alshurideh
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Corresponding author

Correspondence to Haitham M. Alzoubi .

Editor information

Editors and Affiliations

  1. Dept of Mngmnt, Cllg of Busness Admin, University of Sharjah, Sharjah, United Arab Emirates

    Muhammad Alshurideh

  2. Department of Business Administration, Faculty of Economics and Administrative Sciences, Hashemite University, Zarqa, Jordan

    Barween Hikmat Al Kurdi

  3. Management Information Systems Department, School of Business, University of Jordan, Aqaba, Jordan

    Ra’ed Masa’deh

  4. Skyline University College, Sharjah, United Arab Emirates

    Haitham M. Alzoubi

  5. Research Institute of Sciences and Engineering, University of Sharjah, Sharjah, United Arab Emirates

    Said Salloum

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Ghazal, T.M., Hasan, M.K., Ahmad, M., Alzoubi, H.M., Alshurideh, M. (2023). Machine Learning Approaches for Sustainable Cities Using Internet of Things. In: Alshurideh, M., Al Kurdi , B.H., Masa’deh, R., Alzoubi , H.M., Salloum, S. (eds) The Effect of Information Technology on Business and Marketing Intelligence Systems. Studies in Computational Intelligence, vol 1056. Springer, Cham. https://doi.org/10.1007/978-3-031-12382-5_108

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  • DOI: https://doi.org/10.1007/978-3-031-12382-5_108

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