Detecting technological maturity from bibliometric patterns

作者：

Highlights：

• Identify emergent technologies from open-source indicators using binary classifier.

• Transition between emergence and growth is encoded in the shape of the curve.

• Artificial neural network learns technology maturity based on derivatives of curve.

• Data augmentation improves classifier performance by increasing training corpus.

摘要

•Identify emergent technologies from open-source indicators using binary classifier.•Transition between emergence and growth is encoded in the shape of the curve.•Artificial neural network learns technology maturity based on derivatives of curve.•Data augmentation improves classifier performance by increasing training corpus.

论文关键词：Technology life cycle,Machine learning,Artificial neural network,Data augmentation

论文评审过程：Received 24 May 2021, Revised 18 February 2022, Accepted 1 April 2022, Available online 8 April 2022, Version of Record 4 May 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117177