title: Theme Session J – ​Moving the latest developments from machine learning and AI closer to exploiting mountains and lakes of data
Creators: name: ICES
Publisher: name: ASC 2024
Year: 2024
Subjects: Technologies and data
Fisheries and aquaculture

description: <b>Book of abstracts of theme session J:</b>Moving the latest developments from machine learning and AI closer to exploiting mountains and lakes of dataConveners: Conor Delaney (Belgium), Neil Holdsworth (ICES Secretariat), Laura Uusitalo (Finland)CM 684: Automatic mapping of aquaculture activity in the Atlantic OceanCM 685: Fishery catch records support machine learning-based prediction of illegal fishing off United States West CoastCM 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 policiesCM 812: Unlocking Eel Movement: A Lake-Specific Traceability Tool for Conservation and Management in the Shannon CatchmentCM 841: Estimating Interactions between Fisheries and Offshore Energy Developments in the North SeaCM 842: Developing automation in image processing of marine fishCM 848: From black boxes to transparent pyramids: development and integration of machine learning methods for routine fish age interpretationCM 860: Automatic Antarctic Krill (<i>Euphausia Superba</i>) staging and target strength analysis from high resolution image pairsCM 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 IrelandCM 894: AI for image-based age reading from fish otolithsCM 922: Constructing an open library containing a curated digital catalogue of individual sound signatures from the marine underwater soundscape in shallow seasCM 939: Developing edge-ai pipelines and cloud based solutions for in situ imaging dataCM 986: Advancing in Underwater Sound Signature Classification: A Deep Learning approachCM 987: Analysing large amounts of echosounder data using cloud based data access combined with deep learning modelsCM 989: Advancing in Underwater Sound Signature Classification: A Deep Learning approachCM 990: What to do with ICES historical data archive: a call for ideas and inspirationCM 1019: Start the ball rolling: sharing annotated images to make automatic detection of bycatch possible in EM programmesCM 1034: Detection and classification of bycatch events from EM video feedsCM 1059: Synthetic Solutions for Real-world Challenges: Bridging Data Gaps with Virtual FishCM 1072: Otolith data science: developing image capture and GPU-accelerated computer vision for otolith-based stock discriminationCM 1075: Leveraging Deep Learning for Automated Data Standardization and Analysis of Angler-Collected Fish ImagesCM 1080: Filling the gap: Improving the spatio-temporal coverage of small pelagic survey data through modelling approachesCM 1082: Avoiding confusion: modelling image identification surveys with classification errorsCM 1125: Decoding female mimicry in a fish using deep learningCM 1149: SmartDots AI: Neural Network-Powered Fish Age Determination via Web ServiceCM 1216: Navigating the Moral Landscape: The Ethics of AI and its Societal Implications on Research &amp; Marine Science