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norskBlar i tittel Artikler, rapporter og annet (informatikk)
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Comparison of deep learning models for multivariate prediction of time series wind power generation and temperature
(Journal article; Tidsskriftartikkel; Peer reviewed, 2020-03-02)Wind power experienced a substantial growth over the past decade especially because it has been seen as one of the best ways towards meeting climate change and emissions targets by many countries. Since wind power is not fully dispatchable, the accuracy of wind forecasts is a key element for the electric system operators, as it strongly affects the decision-making processes. The planning horizon can ... -
Compliant Sharing of Sensitive Data with Dataverse and Lohpi
(Conference object; Konferansebidrag, 2021-06) -
A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-02-19)Gastrointestinal (GI) endoscopy has been an active field of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. The high missed rate of such abnormalities during endoscopy is thus a critical bottleneck. Lack of attentiveness due to tiring procedures, and requirement of training are few contributing factors. ... -
Comprehensive Analysis of Solar Panel Performance and Correlations with Meteorological Parameters
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-12-08)To mitigate the adverse effects of fossil fuel-based energy, mankind is in constant search of clean and cost-effective sources of energy, such as solar energy. The economic viability of a power plant to harness solar energy mostly depends on the efficiency of solar panels. Investigations over the years show that the solar panel efficiency significantly depends on the different meteorological parameters. ... -
Consumer-Based Activity Trackers as a Tool for Physical Activity Monitoring in Epidemiological Studies During the COVID-19 Pandemic: Development and Usability Study
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-04-23)Background: Consumer-based physical activity trackers have increased in popularity. The widespread use of these devices and the long-term nature of the recorded data provides a valuable source of physical activity data for epidemiological research. The challenges include the large heterogeneity between activity tracker models in terms of available data types, the accuracy of recorded data, and how ... -
Continuation-passing enactment of distributed recoverable workflows
(Research report; Forskningsrapport, 2006)Scalability, reliability and adaptability are among the key requirements for the enactment of distributed workflows. In addition, system resources should be efficiently utilized. Central workflow engines and static analysis of workflow specifications are some of the important obstacles to meeting these requirements. We propose a fully decentralized approach to workflow enactment that is not subject ... -
Convolutional Neural Network for Breathing Phase Detection in Lung Sounds
(Journal article; Tidsskriftartikkel; Peer reviewed, 2019-04-15)We applied deep learning to create an algorithm for breathing phase detection in lung sound recordings, and we compared the breathing phases detected by the algorithm and manually annotated by two experienced lung sound researchers. Our algorithm uses a convolutional neural network with spectrograms as the features, removing the need to specify features explicitly. We trained and evaluated the ... -
Cooperation through information interchange in StormCast
(Research report; Forskningsrapport, 1990)This paper addresses the cooperation between different expert system modules in a networking environment. StormCast - a distributed artificial intelligence application for severe storm forecasting is used as a case to obtain practical results. Two key aspects is investigated, first the representation of knowledge in this kind of environment is outlined. Then the cooperating nature of a group of ... -
Counterfactual Explainable Gastrointestinal and Colonoscopy Image Segmentation
(Journal article; Tidsskriftartikkel; Peer reviewed, 2022-04-26)Segmenting medical images accurately and reliably is crucial for disease diagnosis and treatment. Due to the wide assortment of objects’ sizes, shapes, and scanning modalities, it has become more challenging. Many convolutional neural networks (CNN) have recently been designed for segmentation tasks and achieved great success. This paper presents an optimized deep learning solution using DeepLabv3+ ... -
Criteria for Assessing and Recommending Digital Diabetes Tools: A Delphi Study
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021)Diabetes self-management, an integral part of diabetes care, can be improved with the help of digital self-management tools such as apps, sensors, websites, and social media. The study objective was to reach a consensus on the criteria required to assess and recommend digital diabetes self-management tools targeting those with diabetes in Norway. Healthcare professionals working with diabetes care ... -
CSP at the Cyber-Physical Edge
(Chapter; Bokkapittel, 2019)Today, to do ground-based in-situ observations of the arctic tundra, researchers carry wild life cameras and other observation units into the field, manually configure the devices while on the arctic tundra, and fetch the collected data several months later. This approach does not scale. Instead, observing and reporting of data must be automated using a distributed wireless network of autonomous ... -
Data Analysis Techniques for Smart Nudging
(Conference object; Konferansebidrag, 2021)<p>Nudge principles and techniques are significant in communications, marketing, and groups’ motivation to improve personal health, wealth, and well-being. We make numerous decisions in online situations. People’s health and well-being have garnered widespread interest and concern in this wearable’s age. Smart nudging is defined as “digital nudging, where the guidance of user behavior is tailored ... -
Data collection and analysis methods for smart nudging to promote physical activity: Protocol for a mixed methods study
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023)New digital technologies like activity trackers, nudge concepts, and approaches can inspire and improve personal health. There is increasing interest in employing such devices to monitor people’s health and well-being. These devices can continually gather and examine health-related information from people and groups in their familiar surroundings. Context-aware nudges can assist people in self-managing ... -
Data collection and smart nudging to promote physical activity and a healthy lifestyle using wearable devices
(Chapter; Bokkapittel, 2022-08-22)Nudge principles and techniques can motivate and improve personal health through emerging digital devices, such as activity trackers. Tracking people's health and well-being using such devices have earned widespread interest. These devices can continuously capture and analyze health-related data from individuals and communities in their everyday environment. Providing context-aware nudges can help ... -
Data-Driven and Artificial Intelligence (AI) Approach for Modelling and Analyzing Healthcare Security Practice: A Systematic Review
(Journal article; Tidsskriftartikkel; Peer reviewed, 2020-03)Data breaches in healthcare continue to grow exponentially, calling for a rethinking into better approaches of security measures towards mitigating the menace. Traditional approaches including technological measures, have significantly contributed to mitigating data breaches but what is still lacking is the development of the “human firewall,” which is the conscious care security practices of the ... -
Data-driven blood glucose pattern classification and anomalies detection: Machine-learning applications in Type 1 diabetes
(Journal article; Tidsskriftartikkel; Peer reviewed, 2019-05-01)<p><i>Background - </i>Diabetes mellitus is a chronic metabolic disorder that results in abnormal blood glucose (BG) regulations. The BG level is preferably maintained close to normality through self-management practices, which involves actively tracking BG levels and taking proper actions including adjusting diet and insulin medications. BG anomalies could be defined as any undesirable reading ... -
Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes
(Journal article; Tidsskriftartikkel; Peer reviewed, 2019-07-26)<i>Background</i>: Diabetes mellitus (DM) is a metabolic disorder that causes abnormal blood glucose (BG) regulation that might result in short and long-term health complications and even death if not properly managed. Currently, there is no cure for diabetes. However, self-management of the disease, especially keeping BG in the recommended range, is central to the treatment. This includes actively ... -
Data-intensive computing infrastructure systems for unmodified biological data analysis pipelines
(Journal article; Tidsskriftartikkel; Peer reviewed, 2015-11-18)Biological data analysis is typically implemented using a deep pipeline that combines a wide array of tools and databases. These pipelines must scale to very large datasets, and consequently require parallel and distributed computing. It is therefore important to choose a hardware platform and underlying data management and processing systems well suited for processing large datasets. There are many ... -
Dataset of Consumer-Based Activity Trackers as a Tool for Physical Activity Monitoring in Epidemiological Studies During the COVID-19 Pandemic
(Journal article; Tidsskriftartikkel; Peer reviewed, 2022-03-01)Physical activity (PA) data were downloaded from 113 participants who owned a Garmin or Fitbit activity tracker in 2019 and 2020. Upon participant authorization, data were automatically downloaded from the Garmin and Fitbit cloud storages. The mSpider tool, a solution for automatic and continuous data extraction from activity tracker providers, were used to download participant data. Available ... -
Dataset of fitness trackers and smartwatches to measuring physical activity in research
(Journal article; Tidsskriftartikkel; Peer reviewed, 2022-07-16)Objectives: Accelerometer-based wrist-worn ftness trackers and smartwatches (wearables) appeared on the consumer market in 2011. Many wearable devices have been released since. The objective of this data paper is to describe a dataset of 423 wearables released before July 2017.<p> <p>Data description: We identifed wearables and extracted information from six online and ofine databases. We ...
