集合(抽象数据类型)
人工神经网络
质量(理念)
计算机科学
人工智能
数据挖掘
数据科学
机器学习
哲学
认识论
程序设计语言
作者
Feiteng Yi,Xuan Zhang,Jiali Zhang,Yongchang Wei
出处
期刊:Systems
[Multidisciplinary Digital Publishing Institute]
日期:2024-07-15
卷期号:12 (7): 257-257
标识
DOI:10.3390/systems12070257
摘要
Standards play significant roles in the development of technology and economics, while the cooperation between drafters directly determines the quality of standard systems. The cooperation prediction is a significant while challenging problem for seeking new cooperation chances between drafting units due to their differences in experience and professional ability. In this study, an integrated artificial intelligence method is proposed for cooperation prediction using the link prediction method, text analysis, and network modeling. Specifically, we develop a multi-layer standard network formed by standard citation relationships and cooperation relationships between drafters. Then, a set of novel metrics is designed for predicting the cooperation between drafters considering the knowledge, experience, and professional capability. These metrics are further integrated into a neural network to improve the prediction accuracy. The priorities of our method in terms of prediction accuracy are verified with realistic data of Chinese environmental health standards. The prediction results provide strong support for the selection of drafters and further optimize the structure of standard systems.
科研通智能强力驱动
Strongly Powered by AbleSci AI