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Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-01-23)Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To ... -
GridWatch: A Smart Network for Smart Grid
(Chapter; Bokkapittel, 2024-11-04)The adoption of decentralized energy market models facilitates the exchange of surplus power among local nodes in peer-to-peer settings. However, decentralized energy transactions within untrusted and non-transparent energy markets in modern Smart Grids expose vulnerabilities and are susceptible to attacks. One such attack is the False Data Injection Attack, where malicious entities intentionally ... -
AC Microgrid Modeling and Adaptive Control Using Biomimetic Valence Learning: An AI-Based Approach
(Chapter; Bokkapittel, 2024-11-04)AC microgrids play a crucial role in integrating distributed energy resources and facilitating localized power management in contemporary power networks. Nevertheless, conventional droop control methods in these microgrids have constraints in guaranteeing precise power distribution, stability of voltage/frequency, and flexibility in response to changing operating conditions. This study introduces ... -
AI-Based Cropping of Sport Videos Using SmartCrop
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-08-27)In the rapidly evolving landscape of digital platforms, the need for optimizing media representations to cater to various aspect ratios is palpable. In this paper, we pioneer an approach that utilizes object detection, scene detection, outlier detection, and interpolation for smart cropping. Using soccer as a case study, our primary goal is to capture the frame salience using object (player and ball) ... -
GUNet++: guided-U-Net-based compact image representation with an improved reconstruction mechanism
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-09-26)The invention of microscopy- and nanoscopy-based imaging technology opened up different research directions in life science. However, these technologies create the need for larger storage space, which has negative impacts on the environment. This scenario creates the need for storing such images in a memory-efficient way. Compact image representation (CIR) can solve the issue as it targets storing ... -
Pushing the Limits of Gradient Descent for Efficient Learning on Large Images
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024)Traditional deep learning models are trained and tested on relatively low-resolution images (< 300 px), and cannot be directly operated on large-scale images due to compute and memory constraints. We propose Patch Gradient Descent (PatchGD), an effective learning strategy that allows us to train the existing CNN and transformer architectures (hereby referred to as deep learning models) on large-scale ... -
Understanding metric-related pitfalls in image analysis validation
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-02-12)Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical ... -
Metrics reloaded: recommendations for image analysis validation
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-02-12)Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework ... -
Operationalizing AI/ML in Future Networks: A Bird's Eye View from the System Perspective
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-09-09)Modern artificial intelligence (AI) technologies, led by machine learning (ML), have gained unprecedented momentum over the past decade. Following this wave of "AI summer," the network research community has also embraced AI/ML algorithms to address many problems related to network operations and management. However, compared to their counterparts in other domains, most ML-based solutions have yet ... -
Mining Profitability in Bitcoin: Calculations of User-Miner Equilibria and Cost of Mining
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-06-12)This paper examines the equilibrium between user transaction fees and miner profitability within proof-of-work-based blockchains, specifically focusing on Bitcoin. We analyze the dependency of mining profit on factors such as transaction fee adjustments and operational costs, particularly electricity. By applying a multidimensional profitability model and performing a sensitivity analysis, we evaluate ... -
Misconfiguration of Cluster and IoT Systems Recovery: Extended Experiments
(Chapter; Bokkapittel, 2024-08-15)Containerized cluster systems and edge devices are vulnerable to security breaches when configuration errors occur. In this paper, we propose more experiments on the self-healing approach for misconfigurations that adopt a Markov Decision Process to determine the optimal recovery action or policy that maximizes performance metrics. The experiments conducted are an improvement on our previous ... -
What are end-users’ needs and preferences for a comprehensive e-health program for type 2 diabetes? – A qualitative user preference study
(Journal article; Tidsskriftartikkel; Peer reviewed, 2025-03-03)Introduction - Type 2 diabetes (T2D) prevalence is rising, which imposes a significant burden on individuals, healthcare systems, and economies worldwide. Lifestyle factors contribute significantly to the escalating incidence of T2D. Consequently, there is an increasing need for interventions that not only target at-risk populations for prevention but also empower individuals with T2D to achieve ... -
Comparison of Optimal Reactive Power Dispatch Methods in IEEE 30 Bus System
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-10-19)It has been noted that a number of metaheuristics are applied with success to power system optimal reactive power dispatch (ORPD) challenges. These algorithms’ convergence rates are also determined to be low, and the results they provide are deemed to be inadequate. It suggests that there is insufficient investigation and exploitation in the algorithm. Therefore, an appropriate approach is needed ... -
Influence of Accelerometer Calibration on the Estimation of Objectively Measured Physical Activity: The Tromsø Study
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-01-11)Accelerometers are increasingly used to observe human behavior such as physical activity under free-living conditions. An important prerequisite to obtain reliable results is the correct calibration of the sensors. However, accurate calibration is often neglected, leading to potentially biased results. Here, we demonstrate and quantify the effect of accelerometer miscalibration on the estimation of ... -
An UltraMNIST classification benchmark to train CNNs for very large images
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-07-12)Current convolutional neural networks (CNNs) are not designed for large scientific images with rich multi-scale features, such as in satellite and microscopy domain. A new phase of development of CNNs especially designed for large images is awaited. However, application-independent high-quality and challenging datasets needed for such development are still missing. We present the ‘UltraMNIST ... -
Online Learning under Haphazard Input Conditions: A Comprehensive Review and Analysis
(Journal article; Tidsskriftartikkel, 2024-04-07)The domain of online learning has experienced multifaceted expansion owing to its prevalence in real-life applications. Nonetheless, this progression operates under the assumption that the input feature space of the streaming data remains constant. In this survey paper, we address the topic of online learning in the context of haphazard inputs, explicitly foregoing such an assumption. We discuss, ... -
NLPOps: A Case Study on Automation and Integration of Norwegian Medical Entity Recognition Model in Clinical Environment.
(Mastergradsoppgave; Master thesis, 2024-11-19)Integrating Machine Learning (ML) models into healthcare system, especially by Natural Language Processing (NLP), holds tremendous potential for improving hospital workflows and patient outcomes. This thesis presents the challenges and deployment of an NLP model, Automated Medical Entity Recognition (AMER), designed to extract medical entities from clinical text, within in cloud-based Machine Learning ... -
Agent-based modeling: Insights into consumer behavior, urban dynamics, grid management, and market interactions
(Journal article; Tidsskriftartikkel; Peer reviewed, 2025-01-03)A future sustainable energy system is expected to be digital, de-central, de-carbonized, and democratized. As the transition unfolds, new and diverse actors of various sizes will emerge in different segments. Thereby, the future energy system could shift its attention to the actors’ behavior than finding an optimum based on the physical system. Agent based modeling tools can reflect decisions from ... -
INTEND: Human-Like Intelligence for Intent-Based Data Operations in the Cognitive Computing Continuum
(Chapter; Bokkapittel, 2024)This paper outlines a research roadmap of the INTEND project towards the development of a cognitive computing continuum that leverages human-like intelligence for intent-based data operations. The primary objective of this initiative is to create a system that can adapt dynamically to varying contexts by understanding and acting upon the intents of stakeholders in a decentralised and strategic manner. ... -
Towards the Best Solution for Complex System Reliability: Can Statistics Outperform Machine Learning?
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-12-11)Studying the reliability of complex systems using machine learning techniques involves facing a series of technical and practical challenges, ranging from the intrinsic nature of the system and data to the difficulties in modeling and effectively deploying models in real-world scenarios. This study compares the effectiveness of classical statistical techniques and machine learning methods for ...
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