Title: Design and supply chain management of intelligent logistics system using cloud computing under internet of things

Authors: Minzhi Wang

Addresses: School of Public Finance and Taxation, Henan Finance University, Zhengzhou, Henan, China

Abstract: Image recognition is the key to smart logistics systems. Traditional handwriting feature extraction is difficult to meet the requirements of image recognition. Deep learning is used for image recognition. Firstly, Convolutional Neural Network (CNN) and deep Boltzmann machines under deep learning are introduced. Secondly, cellular neural networks are used to perform feature recognition and extraction on images. Finally, a Parzen classifier is used to classify the obtained image features. The novelty is that through the structural design and research of the intelligent logistics system, the CNN is combined to construct a management system of supply chain logistics of image recognition and information processing. Experiments show that the time for the improved algorithm to achieve high recognition accuracy on the Mixed National Institute of Standards and Technology mixed data set is 198.85 s. When the improved algorithm achieves the same recognition accuracy as the unimproved algorithm, the time is 159.65 s.

Keywords: intelligent logistics system; internet of things; deep learning; convolutional neural network.

DOI: 10.1504/IJGUC.2023.131007

International Journal of Grid and Utility Computing, 2023 Vol.14 No.2/3, pp.216 - 228

Received: 07 Apr 2022
Received in revised form: 23 Jun 2022
Accepted: 09 Aug 2022
Published online: 18 May 2023 *

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