IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i14p10977-d1193134.html
   My bibliography  Save this article

An Intelligent Decision Support System Based on Multi Agent Systems for Business Classification Problem

Author

Listed:
  • Mais Haj Qasem

    (Department of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa 13110, Jordan)

  • Mohammad Aljaidi

    (Department of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa 13110, Jordan)

  • Ghassan Samara

    (Department of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa 13110, Jordan)

  • Raed Alazaidah

    (Department of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa 13110, Jordan)

  • Ayoub Alsarhan

    (Department of Information Technology, Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, The Hashemite University, Zarqa 13116, Jordan)

  • Mohammed Alshammari

    (Faculty of Computing and Information Technology, Northern Border University, Rafha 91431, Saudi Arabia)

Abstract

The development of e-systems has given consumers and businesses access to a plethora of information, which has complicated the process of decision making. Document classification is one of the main decisions that any business adopts in their decision making to categorize documents into groups according to their structure. In this paper, we combined multi-agent systems (MASs), which is one of the IDSS systems, with Bayesian-based classification to filter out the specialization, collaboration, and privacy of distributed business sources to produce an efficient distributed classification system. Bayesian classification made use of MAS to eliminate distributed sources’ specialization and privacy. Therefore, incorporating the probabilities of various sources is a practical and swift solution to such a problem, where this method works the same when all the data are merged into a single source. Each intelligent agent can collaborate and ask for help from other intelligent agents in classifying cases that are difficult to classify locally. The results demonstrate that our proposed technique is more accurate than those of the non-communicated classification, where the results proved the ability of the utilized productive distributed classification system.

Suggested Citation

  • Mais Haj Qasem & Mohammad Aljaidi & Ghassan Samara & Raed Alazaidah & Ayoub Alsarhan & Mohammed Alshammari, 2023. "An Intelligent Decision Support System Based on Multi Agent Systems for Business Classification Problem," Sustainability, MDPI, vol. 15(14), pages 1-14, July.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:10977-:d:1193134
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/14/10977/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/14/10977/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Galimkair Mutanov & Zhanar Omirbekova & Aijaz A. Shaikh & Zhansaya Issayeva, 2023. "Sustainability-Driven Green Innovation: Revolutionising Aerospace Decision-Making with an Intelligent Decision Support System," Sustainability, MDPI, vol. 16(1), pages 1-16, December.

    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:gam:jsusta:v:15:y:2023:i:14:p:10977-:d:1193134. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.