Title: A transfer learning-based model for assessing university students' innovation and entrepreneurship

Authors: Yiping Zheng

Addresses: Cooperative School of International Education, Tianjin University of Commerce, Tianjin, 300134, China

Abstract: In the current trend of innovation and entrepreneurship, the number of students who start their own innovation and entrepreneurship is increasing. In order to enable university students to assess their own innovation and entrepreneurship ability and avoid greater risks, the study starts with a convolutional neural network (CNN) based on migration learning, which is used to establish an assessment model of innovation and entrepreneurship ability. The model is based on migration learning and adversarial networks to strengthen the learning ability and incorporate game theory, with similar adversarial learning of classifiers and generators in the migration, so that the model has stronger learning ability and faster computing speed, and avoids problems such as over-convergence and excessive degrees of freedom. A variational self-encoder is used on the encoder to further improve the accuracy and precision of the input data recognition by compressing the information bottleneck.

Keywords: transfer learning; convolutional neural network; CNN; evaluation model; innovation and entrepreneurship.

DOI: 10.1504/IJCSYSE.2024.137446

International Journal of Computational Systems Engineering, 2024 Vol.8 No.1/2, pp.66 - 74

Received: 28 Nov 2022
Accepted: 01 Mar 2023
Published online: 19 Mar 2024 *

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