Work

Application research on deep learning algorithms supporting cross-border low-carbon IoT systems in manufacturing—taking Guangdong, China, as an example

Year: 2024

Type: article

Abstract: With the rapid advancement of new quality productive forces, the manufacturing industry faces increasing pressure for green transformation. This study, focused on Dongguan City, explores the role of deep learning in enabling cross-border, low-carbon Internet of Things (IoT) systems to enhance global competitiveness. A novel CNN–GRU–Attention deep learning model processes logistics data, capturing spatial and temporal features while highlighting key information. Combined with a three-tier low-carbon IoT system, this approach optimizes energy consumption and reduces carbon emissions. Empirical analysis from Dongguan’s logistics data demonstrates improved prediction accuracy and efficiency.

Source: International Journal of Low-Carbon Technologies

DOI: 10.1093/ijlct/ctae298

Authors: Jianzhong Li, Qiang Wan, Juan Zhang, Liangrui Zhang, Zhiming Ou

Institutions: Hanseo University, Guangdong Innovative Technical College, South China University of Technology

Language: English

Topic: Impact of AI and Big Data on Business and Society

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDGs: Industry, innovation and infrastructure, Climate action, Responsible consumption and production

Open Access status: gold

Estimated APC paid: $3,030