Title: Identifying emerging technologies to foresee the future of intelligent ships: a machine learning approach to patent data

Authors: Weiwei Liu; Jingyi Yao; Kexin Bi

Addresses: School of Economics and Management, Harbin Engineering University, Harbin, 150001, China ' School of Economics and Management, Harbin Engineering University, Harbin, 150001, China ' School of Economics and Management, Harbin Engineering University, Harbin, 150001, China

Abstract: The development of emerging technologies for intelligent ships is related to the transformation and modernisation of the shipping industry. However, identifying and predicting emerging technologies in the field of intelligent ships is an imminent yet overlooked task. In this paper, we propose a machine learning-based framework for identifying and predicting emerging technologies based on the patent data. The Latent Dirichlet allocation (LDA) model is used to identify technology topics within the field of intelligent ships, we then construct an indicator system to recognise emerging technology topics. Ultimately, the long short-term memory (LSTM) network is adopted to predict the development trends of these emerging technologies. The results show that intelligent control systems, lane design and optimisation, and ship equipment intelligence are emerging technology topics for intelligent ships. The research not only provides insights for sustainable development and innovation of intelligent ships, but also offers research avenues for accurately identifying emerging technology topics and forecasting their development trends.

Keywords: emerging technology topics; machine learning approach; intelligent ship; LSTM network; LDA model.

DOI: 10.1504/IJTPM.2024.143525

International Journal of Technology, Policy and Management, 2024 Vol.24 No.4, pp.417 - 442

Received: 10 Apr 2023
Accepted: 25 Feb 2024
Published online: 30 Dec 2024 *

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