Browsing Bergen Open Research Archive by Subject "Machine learning"
Now showing items 1-18 of 18
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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 ... -
Deep Neural Nets and the Language Instinct
(Master thesis, 2023-12-01)In this masters thesis we will conduct a series of translation experiments with sentences that deviate from the principles of universal grammar on human participants and the machine learning models NLLB200 and ChatGPT. ... -
Diversity in Stakeholder Preferences Regarding EU Policy: The Effect of Survey Elements within Processes of Open Public Consultation
(Master thesis, 2022-06-22)Since the "European Governance A White Paper" launched in 2001, the European Union has worked to improve accountability and transparency within the organization. Preferences voiced by the public are one of the most important ... -
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 ... -
Exercise-induced Laryngeal Obstruction Diagnostics Using Machine Learning
(Master thesis, 2024-06-03)Exercise-induced laryngeal obstruction (EILO), characterized by laryngeal narrowing during physical exercise, poses a significant challenge, especially for athletes and active youth, impacting performance and quality of ... -
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 ... -
Machine learning applications in proteomics research: How the past can boost the future
(Peer reviewed; Journal article, 2014)Machine learning is a subdiscipline within artificial intelligence that focuses on algorithms that allow computers to learn solving a (complex) problem from existing data. This ability can be used to generate a solution ... -
Machine learning vs logistic regression in credit scoring: A trade-off between accuracy and interpretability?
(Master thesis, 2021-06-15)In this thesis, I compare logistic regression to the machine learning models k-nearest neighbor, decision trees, random forest, and gradient booster by creating different credit models. By using data from an anonymous ... -
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) -
An overview of deep learning in medical imaging focusing on MRI
(Peer reviewed; Journal article, 2018-12-13)What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started ... -
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 ...