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: email@example.com, firstname.lastname@example.org, email@example.com).
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).