Time-evolving data science and artificial intelligence for Advanced Open Environmental Science (TAIAO) programme
| dc.contributor.author | Koh, Yun Sing | |
| dc.contributor.author | Bifet, Albert | |
| dc.contributor.author | Bryan, Karin R. | |
| dc.contributor.author | Cassales, Guilherme | |
| dc.contributor.author | Graffeuille, Olivier | |
| dc.contributor.author | Lim, Nick Jin Sean | |
| dc.contributor.author | Mourot, Phil | |
| dc.contributor.author | Ning, Ding | |
| dc.contributor.author | Pfahringer, Bernhard | |
| dc.contributor.author | Vetrova, Varvara | |
| dc.contributor.author | Murilo Gomes, Heitor | |
| dc.contributor.editor | Larson, K | |
| dc.coverage.spatial | Jeju, Korea | |
| dc.date.accessioned | 2024-10-11T03:07:03Z | |
| dc.date.available | 2024-10-11T03:07:03Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | New Zealand's unique ecosystems face increasing threats from climate change, impacting biodiversity and posing challenges to safety, livelihoods, and well-being. To tackle these complex issues, advanced data science and artificial intelligence techniques can provide unique solutions. Currently, in its fourth year of a seven-year program, TAIAO focuses on methods for analyzing environmental datasets. Recognizing this urgency, the open-source TAIAO platform was developed. This platform enables new artificial intelligence research for environmental data and offers an open-access repository to enhance reproducibility in the field. This paper will showcase four environmental case studies, artificial intelligence research, platform implementation details, and future development plans. | |
| dc.identifier.citation | Koh, Y. S., Bifet, A., Bryan, K., Weigert Cassales, G., Graffeuille, O., Lim, N., Mourot, P., Ning, D., Pfahringer, B., Vetrova, V., & Murilo Gomes, H. (2024). Time-evolving data science and artificial intelligence for Advanced Open Environmental Science (TAIAO) programme. IJCAI International Joint Conference on Artificial Intelligence, 7314-7322. https://doi.org/10.24963/ijcai.2024/809 | |
| dc.identifier.doi | 10.24963/ijcai.2024/809 | |
| dc.identifier.uri | https://hdl.handle.net/10289/16982 | |
| dc.publisher | International Joint Conferences on Artificial Intelligence (IJCAI) | |
| dc.relation.isPartOf | Proc 33rd International Joint Conference on Artificial Intelligence (IJCAI 24) | |
| dc.rights | © 2024 International Joint Conferences on Artificial Intelligence (IJCAI). | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.source | IJCAI 2024, the 33rd International Joint Conference on Artificial Intelligence | |
| dc.subject | computer science | |
| dc.subject | machine learning | |
| dc.subject.anzsrc2020 | 46 Information and Computing Sciences | |
| dc.subject.anzsrc2020 | 4611 Machine Learning | |
| dc.title | Time-evolving data science and artificial intelligence for Advanced Open Environmental Science (TAIAO) programme | |
| dc.type | Conference Contribution |