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IJMLC 2021 Vol.11(2): 130-136 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.2.1025

A Conceptual Artificial Neural Network Model in Warehouse Receiving Management

Judy X Yang, Lily D Li, and Mohammad G. Rasul

Abstract—The purpose of this research is to explore a suitable Artificial Neural Network (ANN) method applying to warehouse receiving management. A conceptual ANN model is proposed to perform identification and counting of components. The proposed model consists of a standard image library, an ANN system to present objects for identification from the real-time images and to count the number of objects in the image. The authors adopted four basic mechanical design shapes as the attributes of images for shape analysis and pre-defined features; the joint probability from Bayes theorem and image pixel values for object counting is applied in this research. Compared to other ANNs, the proposed conceptual model is straightforward to perform classification and counting. The model is tested by employing a mini image dataset which is industrial enterprise relevant. The initial result shows that the proposed model has achieved an accuracy rate of 80% in classification and a 97% accuracy rate in counting. The development of the model is associated with a few challenges, including exploring algorithms to enhance the accuracy rate for component identification and testing the model in a larger dataset.

Index Terms—Classification, counting, contour, template matching, warehouse management.

The authors are with the School of Engineering and Technology, CQUniversity, Rockhampton, QLD 4702, Australia (e-mail: j.yang@cqu.edu.au, l.li@cqu.edu.au, m.rasul@cqu.edu.au).

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Cite: Judy X Yang, Lily D Li, and Mohammad G. Rasul, "A Conceptual Artificial Neural Network Model in Warehouse Receiving Management," International Journal of Machine Learning and Computing vol. 11, no. 1, pp. 130-136, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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