Data-knowledge co-driven innovations in engineering and management.

IF 6.7 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Patterns Pub Date : 2024-12-13 DOI:10.1016/j.patter.2024.101114
Yingji Xia, Xiqun Michael Chen, Sudan Sun
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引用次数: 0

Abstract

Modern intelligent engineering and management scenarios require advanced data utilization methodologies. Here, we propose and discuss data-knowledge co-driven innovations that could address emerging challenges, and we advocate for the adoption of interdisciplinary methodologies in numerous engineering and management applications.

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来源期刊
Patterns
Patterns Decision Sciences-Decision Sciences (all)
CiteScore
10.60
自引率
4.60%
发文量
153
审稿时长
19 weeks
期刊介绍:
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Data-knowledge co-driven innovations in engineering and management. Integration of large language models and federated learning. Decorrelative network architecture for robust electrocardiogram classification. Best holdout assessment is sufficient for cancer transcriptomic model selection. The recent Physics and Chemistry Nobel Prizes, AI, and the convergence of knowledge fields.
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