Work

Assessment and Prediction of Enterprise's Innovation Capability Based on Variational AutoEncoder and Long Short-Term Memory Network

Year: 2024

Type: conference-paper

Abstract: Under the background of innovation capability as the key driving force of enterprise development, this paper proposes an enterprise innovation capability assessment and prediction method based on Variational AutoEncoder (VAE) and Long Short-Term Memory Network (LSTM). And the effectiveness of the proposed method is verified by the data of 133 listed companies, and the proposed method can realize the quantitative assessment and prediction of enterprise innovation capability.

Source: International Conference on Artificial Intelligence, Systems and Network Security (AISNS)

DOI: 10.1145/3714334.3714347

Authors: Zhisen Chen, Xiaochen Zhang, Lingling Tang

Institutions: Fujian Normal University, Guangdong University of Foreign Studies

Language: English

Topic: Evaluation and Optimization Models

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: bronze

Funder: Guangdong University of Foreign Studies