Application Value Evaluation of Blockchain Technology in Innovation and Entrepreneurship Information Platform for College Students

• Published in: Security and communication networks Vol. 2022; pp. 1 - 9
• Main Author: Li, Shixiao
• Format: Journal Article
• Language: English
• Published: London Hindawi 08.06.2022; Hindawi Limited
• Subjects: Access control; Artificial intelligence; Artificial neural networks; Blockchain; College campuses; College students; Colleges & universities; Convolution; Cooperation; Cryptography; Efficiency; Entrepreneurs; Entrepreneurship; Information management; Information storage; Innovations; Interdisciplinary subjects; Internet of Things; Learning; Privacy; Students
• ISSN: 1939-0114; 1939-0122
• DOI: 10.1155/2022/4862606

The current collegiate innovation and entrepreneurship information network works in a centralized manner, and there is a centralized trust dilemma. Malicious administrators can use their own rights to achieve public and private purposes. To solve this problem, a blockchain technology based on decentralization was introduced into the innovation as well as an entrepreneurship information platform for college students. It is critical to understand how to assess the usefulness of blockchain in innovation and entrepreneurship information platforms. This research mixes it with the current popular artificial intelligence trend and offers a neural network to assess the value of blockchain technology in terms of creativity and as an entrepreneurship information platform. The contents are as follows: (1) an application value evaluation method with an improved residual neural network is proposed. First, an improved data pooling layer is constructed by using three consecutive convolutional layers in series. The approach then has a significant feature learning ability by increasing the receptive field, thanks to an atrous residual block that combines atrous convolution and the residual block. Finally, the dropout method is introduced to avoid the negative impact of overfitting. (2) An application value evaluation method based on skip connection and residual network is proposed. With the inception module, this method creates a better data pooling layer and adds residual connections. The skip connection line is built in the residual block, which improves the residual block’s learning efficiency for feature information. The ordinary convolution in the residual block with a skip connection line is replaced with atrous convolution, and an atrous residual block with a skip connection line is designed. Finally, to construct a neural network, the two designed leftover blocks are connected end-to-end.