Faculty of Engineering and Science
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Enhancing multiclass COVID-19 prediction with ESN-MDFS: Extreme smart network using mean dropout feature selection technique
(Peer reviewed; Journal article, 2024)Deep learning and artificial intelligence offer promising tools for improving the accuracy and efficiency of diagnosing various lung conditions using portable chest x-rays (CXRs). This study explores this potential by ... -
Networking the documentational approach and Valsiner's zone theory
(Chapter, 2023)The documentational approach to didactics (DAD) and Valsiner’s zone theory are combined to increase explanatory power when exploring pre-service teachers’ professional development. The strategy of combining is applied, ... -
Multivariate singular spectrum analysis and detrended fluctuation analysis for plant-wide oscillations denoising
(Journal article; Peer reviewed, 2024)Oscillations are considered the most important indicator of poorly performing control loops. However, noise and other disturbances conceal these oscillations, thus making the detection task quite difficult. Furthermore, ... -
Stiction detection in industrial control valves using Poincaré plot-based convolutional neural networks
(Peer reviewed; Journal article, 2023)Valve stiction is one of the major causes of poorly performing industrial control loops. Stiction occurs when the static friction exceeds the dynamic friction during a direction change or when the stem is at rest. Recently, ... -
Technological creep masks continued decline in a lobster (Homarus gammarus) fishery over a century
(Peer reviewed; Journal article, 2022)Fishery-dependent data are frequently used to inform management decisions. However, inferences about stock development based on commercial data such as Catch-Per-Unit-Effort (CPUE) can be severely biased due to a phenomenon ... -
The International Union for Conservation of Nature Red List does not account for intraspecific diversity
(Peer reviewed; Journal article, 2024)The International Union for Conservation of Nature (IUCN) Red List identifies threatened and endangered species and is a key instrument in global biodiversity conservation efforts. Our understanding of the structure and ... -
Successful growth of coastal marine microalgae in wastewater from a salmon recirculating aquaculture system
(Journal article; Peer reviewed, 2024)As global demand for seafood increases, recirculating aquaculture systems (RAS) have gained prominence for sustainable fish rearing. The sustainability of RAS still requires improvement, particularly managing the fish ... -
Genetic monitoring uncovers long-distance marine feeding coupled with strong spatial segregation in sea trout (Salmo trutta L.) consistent at annual and decadal time scales
(Journal article; Peer reviewed, 2024)Genetic data have greatly increased means to understand fish marine migration behaviours at large spatial scale within a quantitative framework. The anadromous sea trout is a prized target of recreational fishery and an ... -
“Imagine, maths is used anywhere, and we don’t get to know this”—upper secondary students and the relevance of advanced mathematics
(Peer reviewed; Journal article, 2024)People are more motivated to put effort into learning when they know they will be able to put the learnt content to use. These relevance perceptions play a motivating role in the learning of mathematics, a subject renowned ... -
Coherent long-term body-size responses across all Northwest Atlantic herring populations to warming and environmental change despite contrasting harvest and ecological factors
(Peer reviewed; Journal article, 2024)Body size is a key component of individual fitness and an important factor in the structure and functioning of populations and ecosystems. Disentangling the effects of environmental change, harvest and intra- and inter-specific ... -
Deep Learning-Based Brain Tumor Detection in Privacy-Preserving Smart Health Care Systems
(Peer reviewed; Journal article, 2024)Deep learning has been widely used in medical image processing, which has sparked the development of a wide range of applications and led to a notable increase in the number of therapeutic and diagnostic options available ... -
Understanding the challenges of the secondary-tertiary transition in mathematics for economics in higher education: a literature review
(Peer reviewed; Journal article, 2024)This review paper examines the issues identified by research regarding students transitioning from school mathematics to service mathematics modules within economics education at the tertiary level. Literature was gathered ... -
Condition factor tailored to lumpfish (Cyclopterus lumpus) used as cleaner fish in salmonid farms
(Peer reviewed; Journal article, 2024)Lumpfish (Cyclopterus lumpus) are extensively used as part of the control measures against salmon lice in fish farms. In recent years, there has been an increased focus on lumpfish welfare, and how to increase survival of ... -
Predictive Modeling of Semi-Submersible Floater Motion Using Bi-LSTM Model
(Peer reviewed; Journal article, 2024)Floating offshore wind turbines (FOWTs) are emerging as a promising renewable energy solution, tapping into the vast wind resources in deep-sea locations. Accurate predictions of heave, pitch, sway, roll, yaw, and surge ... -
Comparing recurrent neural networks using principal component analysis for electrical load predictions
(Chapter, 2021)Electrical demand forecasting is essential for power generation capacity planning and integrating environment-friendly energy sources. In addition, load predictions will help in developing demand-side management in ... -
Haulout Patterns of Harbour Seal Colonies in the Norwegian Skagerrak, as Monitored through Time-Lapse Camera Surveys
(Peer reviewed; Journal article, 2024)Harbour seals (Phoca vitulina) are part of the Norwegian coastal ecosystem and can be observed on skerries, islands, and sandbanks along the coastline, sometimes in close proximity to inhabited areas. In this study, we ... -
Explainable Deep Learning for Human Behaviour Understanding: Sleep Monitoring, Human Activity Recognition, and Future Opportunities for Healthcare
(Doctoral dissertations at University of Agder;no. 511, Doctoral thesis, 2024)This Ph.D. thesis investigates the transformative potential of explainable deep learning in human behavior analysis, focusing on sleep monitoring and human activity recognition. The research addresses a critical need in ... -
An Interpretable Deep Learning-based Feature Reduction in Video-Based Human Activity Recognition
(Peer reviewed; Journal article, 2024)This paper presents a human activity recognition framework tailored for healthcare applications, emphasizing the essential balance between accuracy and interpretability required for medical monitoring. The model utilizes ... -
Sleep Stage Identification based on Single-Channel EEG Signals using 1-D Convolutional Autoencoders
(Chapter, 2022)Automatic sleep stage classification can play a vital role when measuring sleep quality and diagnosing different sleep-related ailments. Several automated sleep stage identification algorithms have been proposed using ... -
Automatic Sleep Stage Identification with Time Distributed Convolutional Neural Network
(Peer reviewed; Journal article, 2021)Polysomnography (PSG), the gold standard for sleep stage classification, requires a sleep expert for scoring and is both resource-intensive and expensive. Many researchers currently focus on the real-time classification ...
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