Viser treff 258-277 av 389

    • Predicting Meibomian Gland Dropout and Feature Importance Analysis with Explainable Artificial Intelligence 

      Fineide, Fredrik; Storås, Andrea; Riegler, Michael Alexander; Utheim, Tor Paaske (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-07-17)
      Dry eye disease is a common and potentially debilitating medical condition. Meibum secreted from the meibomian glands is the largest contributor to the outermost, protective lipid layer of the tear film. Dysfunction of the meibomian glands is the most common cause of dry eye disease. As meibomian gland dysfunction progresses, gradual atrophy of the glands is observed. The meibomian glands are ...
    • Predicting peek readiness-to-train of soccer players using long short-term memory recurrent neural networks 

      Wiik, Theodor; Johansen, Håvard D.; Pettersen, Svein Arne; Matias Do Vale Baptista, Ivan Andre; Kupka, Tomas; Johansen, Dag; Riegler, Michael; Halvorsen, Pål (Conference object; Konferansebidrag, 2019-10-21)
      We are witnessing the emergence of a myriad of hardware and software systems that quantifies sport and physical activities. These are frequently touted as game changers and important for future sport developments. The vast amount of generated data is often visualized in graphs and dashboards, for use by coaches and other sports professionals to make decisions on training and match strategies. Modern ...
    • Predicting Tacrolimus Exposure in Kidney Transplanted Patients Using Machine Learning 

      Storås, Andrea; Åsberg, Anders; Halvorsen, Pål; Riegler, Michael Alexander; Strumke, Inga (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-08-31)
      Tacrolimus is one of the cornerstone immunosup-pressive drugs in most transplantation centers worldwide following solid organ transplantation. Therapeutic drug monitoring of tacrolimus is necessary in order to avoid rejection of the transplanted organ or severe side effects. However, finding the right dose for a given patient is challenging, even for experienced clinicians. Consequently, a tool that ...
    • Predicting Transaction Latency with Deep Learning in Proof-of-Work Blockchains 

      Tedeschi, Enrico; Nordmo, Tor-Arne Schmidt; Johansen, Dag; Johansen, Håvard D. (Peer reviewed; Chapter, 2019)
      Proof-of-work based cryptocurrencies, like Bitcoin, have a fee market where transactions are included in the blockchain according to a first-price auction for block space. Many attempts have been made to adjust and predict the fee volatility, but even well-formed transactions sometimes experience delays and evictions unless an enormous fee is paid. % In this paper, we present a novel ...
    • Prediction of cloud fractional cover using machine learning 

      Svennevik, Hanna; Riegler, Michael A.; Hicks, Steven; Storelvmo, Trude; Hammer, Hugo L. (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-11-03)
      Climate change is stated as one of the largest issues of our time, resulting in many unwanted effects on life on earth. Cloud fractional cover (CFC), the portion of the sky covered by clouds, might affect global warming and different other aspects of human society such as agriculture and solar energy production. It is therefore important to improve the projection of future CFC, which is usually ...
    • Predictive analytics beyond time series: Predicting series of events extracted from time series data 

      Mishra, Sambeet; Bordin, Chiara; Taharaguchi, Kota; Purkayastha, Adri (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-06-07)
      Realizing carbon neutral energy generation creates the challenge of accurately predicting time-series generation data for long-term capacity planning and for short-term operational decisions. The key challenges for adopting data-driven decision-making, specifically predictive analytics, can be attributed to data volume and velocity. Data volume poses challenges for data storage and retrieval. Data ...
    • Preliminary Evaluation of a mHealth Coaching Conversational Artificial Intelligence for the Self-Care Management of People with Sickle-Cell Disease 

      Issom, David-Zacharie; Rochat, Jessica; Hartvigsen, Gunnar; Lovis, Christian (Journal article; Tidsskriftartikkel; Peer reviewed, 2020)
      Adherence to the complex set of recommended self-care practices among people with Sickle-Cell Disease (SCD) positively impacts health outcomes. However, few patients possess the required skills (i.e. disease-specific knowledge, adequate levels of self-efficacy). Consequently, adherence rates remain low and only 1% of patients are empowered enough to master the self-care practices. Health coaching ...
    • Prescriptive analytics for optimal multi-use battery energy storage systems operation: State-of-the-art and research directions 

      Haug, Martin; Bordin, Chiara; Mishra, Sambeet; Moisan, Julien (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-12-08)
      This paper presents the state-of-the-art and latest advances in implementing multi-use practices on BESS applications to the power system grid. Representative papers on modeling and optimization methods were selected, most of them working with realistic use cases, but none reporting on real-world implementations. Some major findings from reviewing key representative papers are that current optimization ...
    • Privacy Concerns Related to Data Sharing for European Diabetes Devices 

      Randine, Pietro; Pocs, Matthias; Cooper, John Graham; Tsolovos, Dimitrios; Muzny, Miroslav; Besters, Rouven; Årsand, Eirik (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-11-13)
      Background: Individuals with diabetes rely on medical equipment (eg, continuous glucose monitoring (CGM), hybrid closed-loop systems) and mobile applications to manage their condition, providing valuable data to health care providers. Data sharing from this equipment is regulated via Terms of Service (ToS) and Privacy Policy documents. The introduction of the Medical Devices Regulation (MDR) and In ...
    • Privacy Perceptions and Concerns in Image-Based Dietary Assessment Systems: Questionnaire-Based Study 

      Sharma, Aakash; Czerwinska, Katja P; Brenna, Lars; Johansen, Dag; Johansen, Håvard D. (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-10-15)
      Background: Complying with individual privacy perceptions is essential when processing personal information for research. Our specific research area is performance development of elite athletes, wherein nutritional aspects are important. Before adopting new automated tools that capture such data, it is crucial to understand and address the privacy concerns of the research subjects that are to be ...
    • Privacy preserving distributed computation of community health research data 

      Andersen, Anders; Saus, Merete (Journal article; Tidsskriftartikkel; Peer reviewed, 2017-09-19)
      Research in community health introduces challenges regarding analysis of the research data. It involves multiple actors in a varity of arenas, and it is often directed towards the local community and children and their families. The legal, ethical and privacy issues involved introduce constraints upon the analysis performed. SNOOP combined with the D2Worm declarative modelling and infrastructure ...
    • Pro-Anorexia and Pro-Recovery Photo Sharing: A Tale of Two Warring Tribes 

      Yom-Tov, Elad; Fernandez Luque, Luis; Weber, Ingmar; Crain, Steven P. (Journal article; Tidsskriftartikkel; Peer reviewed, 2012)
      There is widespread use of the Internet to promote anorexia as a lifestyle choice. Pro-anorexia content can be harmful for people affected or at risk of having anorexia. That movement is actively engaged in sharing photos on social networks such as Flickr. Objective: To study the characteristics of the online communities engaged in disseminating content that encourages eating disorders (known as ...
    • Proceedings of the 18th Scandinavian Conference on Health Informatics 

      Henriksen, André; Gabarron, Elia; Vimarlund, Vivian (Book; Bok, 2022-08-22)
      This proceeding presents the papers presented at the 18th Scandinavian Conference on Health Informatics - SHI 2022 in Tromsø, Norway on August 22-24, 2022.
    • A programmable structure for pervasive computing 

      Arntzen, Ingar Mæhlum; Johansen, Dag (Research report; Forskningsrapport, 2004)
      This exstended abstract presents an asymmetric and programmable (extensible) approach to pervasive computing. The idea is to off-load computations from light portable clients into a back-bone of seamlessly integrated servers. This way, a user can extend and personalize his pervasive computational environment by installing computations following his trajectory throughout the day. Focus on this extended ...
    • Prototyping a Diet Self-management System for People with Diabetes with Cultural Adaptable User Interface Design 

      Lee, Eunji; Årsand, Eirik; Choi, Yoon-Hee; Østengen, Geir; Sato, Keiichi; Hartvigsen, Gunnar (Chapter; Bokkapittel, 2014-08-22)
      Diet management is a critical part of diabetes selfmanagement. This project developed a working prototype application on Android-based mobile phone called SMART CARB that assists people with diabetes to self-manage their diet. The system particularly focused on monitoring carbohydrate intake in order to control their blood glucose levels. The project was positioned as a research extension to the ...
    • pVD - Personal Video Distribution 

      Su, Fei; Bjørndalen, John Markus; Ha, Hoai Phuong; Anshus, Otto (Journal article; Tidsskriftartikkel, 2013-11-25)
      A user has several personal computers, including mobile phones, tablets, and laptops, and needs to watch live camera feeds from and videos stored at any of these computers at one or more of the others. Industry solutions designed for many users, computers, and videos can be complicated and slow to apply. The user must typically rely on a third party service or at least log in. The Personal Video ...
    • pyndl: Naïve discriminative learning in python 

      Sering, Konstantin; Weitz, Marc; Shafaei-Bajestan, Elnaz; Künstle, David-Elias (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-11-15)
      The pyndl package implements Naïve Discriminative Learning (NDL) in Python. NDL is an incremental learning algorithm grounded in the principles of discrimination learning (Rescorla & Wagner, 1972; Widrow & Hoff, 1960) and motivated by animal and human learning research (e.g. Baayen et al., 2011; Rescorla, 1988). Lately, NDL has become a popular tool in language research to examine large corpora and ...
    • PyPSA meets Africa: Developing an open source electricity network model of the African continent 

      Kirli, Desen; Hampp, Johannes; van Greevenbroek, Koen; Grant, Rebecca; Mahmood, Matin; Parzen, Maximilian; Kiprakis, Aristides (Chapter; Bokkapittel, 2021-10-25)
      Electricity network modelling and grid simulations form a key enabling element for the integration of newer and cleaner technologies such as renewable energy generation and electric vehicles into the existing grid and energy system infrastructure. This paper reviews the models of the African electricity systems and highlights the gaps in the open model landscape. Using PyPSA (an open Power System ...
    • QoS applied to security in mobile computing 

      Fallmyr, Terje; Stabell-Kulø, Tage (Research report; Forskningsrapport, 1997-06-30)
      Hand-held mobile computers have the potential to become important communication tools for roaming users. As such, they will also become very personal. They will be used under a wide range of operating conditions, and tight user control will be enforced on issues like power consumption, consistency control, and trust management. Their ability to adapt will be the key to their success. In this paper ...
    • Quantified Soccer Using Positional Data: A Case Study 

      Pettersen, Svein Arne; Johansen, Håvard D.; Baptista, Ivan; Halvorsen, Pål; Johansen, Dag (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-07-06)
      Performance development in international soccer is undergoing a silent revolution fueled by the rapidly increasing availability of athlete quantification data and advanced analytics. Objective performance data from teams and individual players are increasingly being collected automatically during practices and more recently also in matches after FIFA's 2015 approval of wearables in electronic ...