IDEAS home Printed from https://ideas.repec.org/a/ovi/oviste/vxxiy2021i2p743-748.html
   My bibliography  Save this article

Management Based on Data Analysis. Part Two. Artificial Intelligence Data Modeling

Author

Listed:
  • Constantin Ilie

    (“Ovidius†University of Constanta)

  • Andreea-Daniela Moraru

    (“Ovidius†University of Constanta)

Abstract

The use of the artificial neural networks in management is common. Employing artificial intelligence allows businesses to gain a commercial advantage. The objective of the present paper is to apply an artificial neural network as a modeling and simulation technique, to determine the importance of influences of four input data on the output data in human resources evaluation. The data are organized in 14,999 datasets: satisfaction level of the employee, the average monthly hours worked, the project number that the employee participated in, and time spent by company for employee care. The used neural network is of feed forward type, with three layers (1 input, 1 hidden, 1 output) and with 9 (13) hidden neurons. Several functions for activation and solver were applied for finding the most accurate one. The simulations concluded that the most important influence is that of the average monthly hours followed closely by the employee level of satisfaction.

Suggested Citation

  • Constantin Ilie & Andreea-Daniela Moraru, 2021. "Management Based on Data Analysis. Part Two. Artificial Intelligence Data Modeling," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(2), pages 743-748, December.
  • Handle: RePEc:ovi:oviste:v:xxi:y:2021:i:2:p:743-748
    as

    Download full text from publisher

    File URL: https://stec.univ-ovidius.ro/html/anale/RO/2021-2/Section%204/25.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    management; artificial neural networks; human resources; evaluation;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

    Statistics

    Access and download statistics

    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:ovi:oviste:v:xxi:y:2021:i:2:p:743-748. 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: Gheorghiu Gabriela (email available below). General contact details of provider: https://edirc.repec.org/data/feoviro.html .

    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.