Blar i Faculty of Mathematics and Natural Sciences på emneord "Machine learning"
Viser treff 1-11 av 11
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ALADDIN: Docking Approach Augmented by Machine Learning for Protein Structure Selection Yields Superior Virtual Screening Performance
(Peer reviewed; Journal article, 2019-10)Protein flexibility and solvation pose major challenges to docking algorithms and scoring functions. One established strategy for addressing these challenges is to use multiple protein conformations for docking (all‐against‐all ... -
Convolutional Neural Networks for Malaria Detection
(Master thesis, 2019-12-13)Together with doctors at Haukeland University Hospital in Bergen, we wanted to research how the diagnosis of malaria can be improved. We propose a method that can detect malaria parasites (Plasmodium falciparum) in microscope ... -
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 ... -
A novel approach to computing super observations for probabilistic wave model validation
(Peer reviewed; Journal article, 2019-07)In the field of wave model validation, the use of super observations is a common strategy to smooth satellite observations and match the simulated spatiotemporal scales. An approach based on averaging along track is widely ... -
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 ...