The Impact of SIPOC on Process Reengineering and Sustainability of Enterprise Procurement Management in E-Commerce Environments Using Deep Learning

Zhang, Hui; Fan, Lijun; Chen, Min; Qiu, Chen

doi:10.4018/joeuc.306270

Cited by 36 publications

(29 citation statements)

References 22 publications

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“…In the first three sets of data auditing process, the searchable encryption algorithm takes the least execution time. When the number of elements reaches 150, the execution time of the searchable encryption algorithm is nearly the same as that of the homomorphic encryption algorithm (Zhang et al, 2022). The execution time of the database security audit algorithm based on bypass monitoring is the highest from beginning to end.…”

Section: Discussionmentioning

confidence: 98%

Ciphertext Database Audit Technology Under Searchable Encryption Algorithm and Blockchain Technology

Jin¹

2022

Journal of Global Information Management

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The study aims to solve the problems in auditing ciphertext data, improve audit efficiency, and increase the security of audit data in the audit server. First, the existing encryption algorithms are analyzed. Second, the searchable encryption algorithm is proposed to audit the ciphertext data, and an audit server scheme is made based on blockchain technology (BT). Finally, the two schemes are compared with the traditional audit technology. The results show that the server's inspection efficiency of the searchable encryption algorithm is higher.

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Section: Discussionmentioning

confidence: 98%

Ciphertext Database Audit Technology Under Searchable Encryption Algorithm and Blockchain Technology

Jin¹

2022

Journal of Global Information Management

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“…The data are input into the BPNN, processed by the hidden layer after they go through the input layer, transmitted to the output layer, and processed and output by the output layer. When the output result and the expected result differ significantly, the error data will propagate back, returning the error along the original path, adjusting the network threshold and weight, and repeating this operation until the error reaches the allowable range or the most significant number of iterations of the algorithm (Park et al, 2020; Zhang et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

The genetic algorithm and BP neural network in financial supply chain management under information sharing

2023

Expert Systems

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In today's global business competition environment, the significant changes in the market environment make it very difficult for supply chain enterprises to gain a place in the fierce market only by their strength. This article will do the following research work to effectively play the role of supply chain management. First, the concepts, and characteristics, related applications of supply chain information sharing (IS) and genetic algorithm back propagation (GABP) neural networks, are expounded. Then, the feasibility of applying BP neural network (BPNN) to evaluate supply chain IS is analysed. Next, the assessment indicator system for IS is constructed from four aspects: IS infrastructure, IS member characteristics, IS content, and the quality of supply chain enterprises. Finally, the principle and algorithm of BPNN are analysed, the network model is constructed, the BPNN process based on GA is designed, and every step in the process is explained in detail. The sample input is provided for the network model by obtaining and sorting data. The supply chain IS assessment model based on the GABP neural network is constructed. The results show that the assessment model of supply chain IS based on the GABP neural network has high accuracy, and the highest network accuracy reaches 95.01%, which proves that the assessment model based on BPNN has certain advantages for the supply chain IS of the financial industry.

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“…Modelling and coding. Through the feature extraction and coding of the sample data and the architecture analysis of the confusion matrix, the vector quantization programming can be quickly carried out on the Matlab platform to accelerate the operation speed (Zhang et al, 2022). 6.…”

Section: Construction Of Ims Evaluation Modelmentioning

confidence: 99%

The evaluation of enterprise supply chain intelligent manufacturing system for agricultural interconnection data based on machine learning

Yu¹,

Li²,

Li³

et al. 2023

Expert Systems

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The present work aims to promote the modernization of the real economy and facilitate the manufacturing industry to construct an intelligent manufacturing system. Analysis and evaluation are performed on the intelligent manufacturing system (IMS) of enterprises under the Internet of Things (IoT). First, the connotation and characteristics of machine learning (ML) in the era of IoT are explained, followed by the introduction to neural networks. Second, the optimization process of the neural network is described. Since it has no memory, the traditional deep neural network (DNN) needs to be improved. Then, the Generative Adversarial Network (GAN) is utilized to expand the sample data. Moreover, the confusion matrix is used in the indicator evaluation. The backpropagation neural network (BPNN) is optimized by the genetic algorithm (GA) to improve the network configuration and model performance. In addition, the connotation and development status of IMS are analysed, and the feasibility of applying deep learning to the evaluation of IMS is discussed. Finally, the evaluation model of IMS is designed and built via a neural network, which is ultimately verified in the simulation experiment. The experimental results indicate that the BPNN‐GA algorithm has advantages in predicting fitting accuracy, taking up less memory, and shortening training time; it can achieve generalization and accuracy in the forefront of optimal Pareto value, and the fitting accuracy attains 95.3%. The horizontal evaluation of IMS proves the effectiveness and feasibility of the evaluation model. This shows that the BPNN‐GA algorithm has an excellent evaluation effect on IMS. And the IMS evaluation model is applied to assess the IMS of sample enterprises. The evaluation results demonstrate that there are problems in the current manufacturing industry. For example, the transactions are challenging to be completed smoothly, information technology is updated slowly, and IMS is dependent on collecting resources.

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The Impact of SIPOC on Process Reengineering and Sustainability of Enterprise Procurement Management in E-Commerce Environments Using Deep Learning

Cited by 36 publications

References 22 publications

Ciphertext Database Audit Technology Under Searchable Encryption Algorithm and Blockchain Technology

Ciphertext Database Audit Technology Under Searchable Encryption Algorithm and Blockchain Technology

The genetic algorithm and BP neural network in financial supply chain management under information sharing

The evaluation of enterprise supply chain intelligent manufacturing system for agricultural interconnection data based on machine learning

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