Blar i Department of Informatics på emneord "Machine learning"
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EDAM-bioimaging: the ontology of bioimage informatics operations, topics, data, and formats (update 2020)
(Others, 2020)EDAM is a well-established ontology of operations, topics, types of data, and data formats that are used in bioinformatics and its neighbouring fields [1,2] . EDAM-bioimaging is an extension of EDAM dedicated to bioimage ... -
Emerging Era of Biomolecular Membrane Simulations: Automated Physically-Justified Force Field Development and Quality-Evaluated Databanks
(Journal article; Peer reviewed, 2022)Molecular simulations of biological membranes and proxies thereof are entering a new era characterized by several key aspects. Progress starts with the realization that the outcome of the simulations can only be as good ... -
Employing Deep Learning for Fish Recognition
(Master thesis, 2018-08-21)Underwater imagery processing is in high demand, but the unrestricted environment makes it difficult to develop methods for analyzing it. Not only is obtaining a dataset for a single species difficult, but there are reported ... -
Features impacting the mesopelagic layer in the ocean: a machine learning-based approach
(Master thesis, 2022-09-01)Context: Recently the United Nations proclaimed a Decade of Ocean Science for Sustainable Development (2021–2030) due to threats to the productivity and health of the ocean due to human impact. The One Ocean Expedition ... -
Online learning through Reinforcement learning in a high-fidelity physics simulator
(Master thesis, 2022-11-21) -
SAR and Optical Satellite Imagery Automated Matching using Machine Learning
(Master thesis, 2022-11-21) -
Specially designed random forest loss function for high energy physics
(Master thesis, 2022-06-01)The purpose of the ATLAS experiment at CERN is to provide a better understand of the underlying principles of fundamental particles and to potentially discover new ones, such as dark matter. The process of doing so is long ... -
Towards prediction of CML Treatment Outcomes with Machine Learning and CyTOF Data
(Master thesis, 2024-06-03)Predicting responses to therapy for patients with Chronic Myeloid Leukemia (CML) is crucial for optimizing treatment strategies. This study utilizes machine learning models with mass cytometry (CyTOF) data from samples ...