IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v16y2020i10p1550147720965473.html
   My bibliography  Save this article

An architectural framework for information integration using machine learning approaches for smart city security profiling

Author

Listed:
  • Adnan Abid
  • Ansar Abbas
  • Adel Khelifi
  • Muhammad Shoaib Farooq
  • Razi Iqbal
  • Uzma Farooq

Abstract

In the past few decades, the whole world has been badly affected by terrorism and other law-and-order situations. The newspapers have been covering terrorism and other law-and-order issues with relevant details. However, to the best of our knowledge, there is no existing information system that is capable of accumulating and analyzing these events to help in devising strategies to avoid and minimize such incidents in future. This research aims to provide a generic architectural framework to semi-automatically accumulate law-and-order-related news through different news portals and classify them using machine learning approaches. The proposed architectural framework discusses all the important components that include data ingestion, preprocessor, reporting and visualization, and pattern recognition. The information extractor and news classifier have been implemented, whereby the classification sub-component employs widely used text classifiers for a news data set comprising almost 5000 news manually compiled for this purpose. The results reveal that both support vector machine and multinomial Naïve Bayes classifiers exhibit almost 90% accuracy. Finally, a generic method for calculating security profile of a city or a region has been developed, which is augmented by visualization and reporting components that maps this information onto maps using geographical information system.

Suggested Citation

  • Adnan Abid & Ansar Abbas & Adel Khelifi & Muhammad Shoaib Farooq & Razi Iqbal & Uzma Farooq, 2020. "An architectural framework for information integration using machine learning approaches for smart city security profiling," International Journal of Distributed Sensor Networks, , vol. 16(10), pages 15501477209, October.
    • Handle: RePEc:sae:intdis:v:16:y:2020:i:10:p:1550147720965473
    DOI: 10.1177/1550147720965473
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1550147720965473
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1550147720965473?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Razi Iqbal & Talal Ashraf Butt & Muhammad Afzaal & Khaled Salah, 2019. "Trust management in social Internet of vehicles: Factors, challenges, blockchain, and fog solutions," International Journal of Distributed Sensor Networks, , vol. 15(1), pages 15501477198, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ayodeji Falayi & Qianlong Wang & Weixian Liao & Wei Yu, 2023. "Survey of Distributed and Decentralized IoT Securities: Approaches Using Deep Learning and Blockchain Technology," Future Internet, MDPI, vol. 15(5), pages 1-28, May.
    2. Riya Sapra & Parneeta Dhaliwal, 2021. "Blockchain: The Perspective Future of Technology," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 16(2), pages 1-20, April.
    3. Manju Biswas & Debashis Das & Sourav Banerjee & Amrit Mukherjee & Waleed AL-Numay & Utpal Biswas & Yudong Zhang, 2023. "Blockchain-Enabled Communication Framework for Secure and Trustworthy Internet of Vehicles," Sustainability, MDPI, vol. 15(12), pages 1-19, June.
    4. Muntadher Sallal & Ruairí de Fréin & Ali Malik, 2023. "PVPBC: Privacy and Verifiability Preserving E-Voting Based on Permissioned Blockchain," Future Internet, MDPI, vol. 15(4), pages 1-27, March.
    5. Juyeon Son & Wonyoung Choi & Sang-Min Choi, 2020. "Trust information network in social Internet of things using trust-aware recommender systems," International Journal of Distributed Sensor Networks, , vol. 16(4), pages 15501477209, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:intdis:v:16:y:2020:i:10:p:1550147720965473. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.