RETRACTED: The design for supply chain management of intelligent logistics system using cloud computing and the internet of things
Retraction(s) for this article
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RETRACTION: The Design for Supply Chain Management of Intelligent Logistics System Using Cloud Computing and the Internet of Things
- Volume 42Issue 2Expert Systems
- First Published online: November 7, 2024
Corresponding Author
Huan Wang
College of Economics and Management, Hubei University of Automotive Technology, Shiyan, China
Correspondence
Huan Wang, College of Economics and Management, Hubei University of Automotive Technology, Shiyan 442002, China.
Email: [email protected]
Search for more papers by this authorYuanxing Yin
College of Economics and Management, Hubei University of Automotive Technology, Shiyan, China
Search for more papers by this authorXinyu Wang
College of Economics and Management, Hubei University of Automotive Technology, Shiyan, China
Search for more papers by this authorCorresponding Author
Huan Wang
College of Economics and Management, Hubei University of Automotive Technology, Shiyan, China
Correspondence
Huan Wang, College of Economics and Management, Hubei University of Automotive Technology, Shiyan 442002, China.
Email: [email protected]
Search for more papers by this authorYuanxing Yin
College of Economics and Management, Hubei University of Automotive Technology, Shiyan, China
Search for more papers by this authorXinyu Wang
College of Economics and Management, Hubei University of Automotive Technology, Shiyan, China
Search for more papers by this authorAbstract
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. Second, 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. The experimental results show that the recognition accuracy time of the proposed improved fusion algorithm on the Mixed National Institute of Standards and Technology data set is 198.85 s. When the improved algorithm achieves the same recognition accuracy, it takes 159.65 s. The recognition efficiency of the improved algorithm is 19.71% higher than that of the unimproved algorithm. In addition, when the unimproved algorithm reaches the maximum number of iterations, the error rate is 2.47%. The error rate of the improved algorithm is only 0.74%. This study provides a basis for improving the image recognition accuracy and has certain practical value.
Open Research
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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