Advanced Deep Learning Methods for Enhancing Information Compliance Checking
| dc.contributor.author | Liu, Chongyi | |
| dc.contributor.supervisor | Honglei Xu | en_US |
| dc.contributor.supervisor | Xiangyu Wang | en_US |
| dc.date.accessioned | 2024-05-15T06:50:56Z | |
| dc.date.available | 2024-05-15T06:50:56Z | |
| dc.date.issued | 2023 | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.11937/95037 | |
| dc.description.abstract |
The study in this thesis enhances information checking algorithm challenges, such as CAD drawings comliance checking which is time-consuming and error-prone, by focusing on the development and refinement of advanced deep learning algorithms, primarily in the Natural Language Processing (NLP) sphere, as innovative methods for higher accuracy and time-saving solution. | en_US |
| dc.publisher | Curtin University | en_US |
| dc.title | Advanced Deep Learning Methods for Enhancing Information Compliance Checking | en_US |
| dc.type | Thesis | en_US |
| dcterms.educationLevel | PhD | en_US |
| curtin.department | School of Electrical Engineering, Computing and Mathematical Sciences | en_US |
| curtin.accessStatus | Open access | en_US |
| curtin.faculty | Science and Engineering | en_US |
| curtin.contributor.orcid | Liu, Chongyi [0000-0003-1898-061X] | en_US |