Browsing Fakultet for informasjonsteknologi og elektroteknikk (IE) by Journals "Scientific Reports"
Now showing items 41-46 of 46
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Two-dimensional CNN-based distinction of human emotions from EEG channels selected by Multi-Objective evolutionary algorithm
(Peer reviewed; Journal article, 2022)In this study we explore how different levels of emotional intensity (Arousal) and pleasantness (Valence) are reflected in electroencephalographic (EEG) signals. We performed the experiments on EEG data of 32 subjects from ... -
Unfolded deep kernel estimation-attention UNet-based retinal image segmentation
(Peer reviewed; Journal article, 2023)Retinal vessel segmentation is a critical process in the automated inquiry of fundus images to screen and diagnose diabetic retinopathy. It is a widespread complication of diabetes that causes sudden vision loss. Automated ... -
Using machine learning model explanations to identify proteins related to severity of meibomian gland dysfunction
(Peer reviewed; Journal article, 2023)Meibomian gland dysfunction is the most common cause of dry eye disease and leads to significantly reduced quality of life and social burdens. Because meibomian gland dysfunction results in impaired function of the tear ... -
Using machine learning model explanations to identify proteins related to severity of meibomian gland dysfunction
(Peer reviewed; Journal article, 2023)Meibomian gland dysfunction is the most common cause of dry eye disease and leads to significantly reduced quality of life and social burdens. Because meibomian gland dysfunction results in impaired function of the tear ... -
Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems
(Peer reviewed; Journal article, 2022)A central challenge in the computational modeling and simulation of a multitude of science applications is to achieve robust and accurate closures for their coarse-grained representations due to underlying highly nonlinear ... -
Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography
(Journal article; Peer reviewed, 2022)