Theme Session J – Moving the latest developments from machine learning and AI closer to exploiting mountains and lakes of data
Book of abstracts of theme session J:
Moving the latest developments from machine learning and AI closer to exploiting mountains and lakes of data
Conveners: Conor Delaney (Belgium), Neil Holdsworth (ICES Secretariat), Laura Uusitalo (Finland)
- CM 684: Automatic mapping of aquaculture activity in the Atlantic Ocean
- CM 685: Fishery catch records support machine learning-based prediction of illegal fishing off United States West Coast
- CM 727: Enhancing large language models that are pre trained with generation that is augmented by retrieval (Retrieval-augmented generation - RAG for Marine science and fishing)
- CM 744: Age determination of fish otoliths by Deep Learning; do humans outperform computers?
- CM 811: Towards the development of guidelines for trustworthy artificial intelligence to support international and EU biodiversity policies
- CM 812: Unlocking Eel Movement: A Lake-Specific Traceability Tool for Conservation and Management in the Shannon Catchment
- CM 841: Estimating Interactions between Fisheries and Offshore Energy Developments in the North Sea
- CM 842: Developing automation in image processing of marine fish
- CM 848: From black boxes to transparent pyramids: development and integration of machine learning methods for routine fish age interpretation
- CM 860: Automatic Antarctic Krill (Euphausia Superba) staging and target strength analysis from high resolution image pairs
- CM 866: Comparing the effectiveness of Random Forest and Gradient Boosting Machine algorithms for the prediction of the sale price of Northeast Atlantic mackerel landed in Ireland
- CM 894: AI for image-based age reading from fish otoliths
- CM 922: Constructing an open library containing a curated digital catalogue of individual sound signatures from the marine underwater soundscape in shallow seas
- CM 939: Developing edge-ai pipelines and cloud based solutions for in situ imaging data
- CM 986: Advancing in Underwater Sound Signature Classification: A Deep Learning approach
- CM 987: Analysing large amounts of echosounder data using cloud based data access combined with deep learning models
- CM 989: Advancing in Underwater Sound Signature Classification: A Deep Learning approach
- CM 990: What to do with ICES historical data archive: a call for ideas and inspiration
- CM 1019: Start the ball rolling: sharing annotated images to make automatic detection of bycatch possible in EM programmes
- CM 1034: Detection and classification of bycatch events from EM video feeds
- CM 1059: Synthetic Solutions for Real-world Challenges: Bridging Data Gaps with Virtual Fish
- CM 1072: Otolith data science: developing image capture and GPU-accelerated computer vision for otolith-based stock discrimination
- CM 1075: Leveraging Deep Learning for Automated Data Standardization and Analysis of Angler-Collected Fish Images
- CM 1080: Filling the gap: Improving the spatio-temporal coverage of small pelagic survey data through modelling approaches
- CM 1082: Avoiding confusion: modelling image identification surveys with classification errors
- CM 1125: Decoding female mimicry in a fish using deep learning
- CM 1149: SmartDots AI: Neural Network-Powered Fish Age Determination via Web Service
- CM 1216: Navigating the Moral Landscape: The Ethics of AI and its Societal Implications on Research & Marine Science
