Now showing items 141-160 of 640

    • Client Selection in Federated Learning under Imperfections in Environment 

      Kumari, Arti; Rai, Sumit; Prasad, Dilip K. (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-02-25)
      Federated learning promises an elegant solution for learning global models across distributed and privacy-protected datasets. However, challenges related to skewed data distribution, limited computational and communication resources, data poisoning, and free riding clients affect the performance of federated learning. Selection of the best clients for each round of learning is critical in alleviating ...
    • The Impossible, the Unlikely, and the Probable Nudges: A Classification for the Design of Your Next Nudge 

      Karlsen, Randi; Andersen, Anders (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-10-22)
      Nudging provides a way to gently influence people to change behavior towards a desired goal, e.g., by moving towards a healthier or more environmentally friendly lifestyle. Personalized and context-aware digital nudging (named smart nudging) can be a powerful tool for efficient nudging by tailoring nudges to the current situation of each individual user. However, designing smart nudges is challenging, ...
    • A Framework for AI-enabled Proactive mHealth with Automated Decision-making for a User’s Context 

      Sulaiman, Muhammad; Håkansson, Anne; Karlsen, Randi (Journal article; Tidsskriftartikkel; Peer reviewed, 2022)
      Health promotion is to enable people to take control over their health. Digital health with mHealth empowers users to establish proactive health, ubiquitously. The users shall have increased control over their health to improve their life by being proactive. To develop proactive health with the principles of prediction, prevention, and ubiquitous health, artificial intelligence with mHealth can ...
    • Influence of Biosynthesized Nanoparticles Addition and Fibre Content on the Mechanical and Moisture Absorption Behaviour of Natural Fibre Composite 

      Lakshmaiya, Natrayan; Ganesan, Velmurugan; Paramasivam, Prabhu; Dhanasekaran, Seshathiri (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-12-19)
      This study looks at how incorporating nanofiller into sisal/flax-fibre-reinforced epoxy-based hybrid composites affects their mechanical and water absorption properties. The green Al2O3 NPs are generated from neem leaves in a proportion of leaf extract to an acceptable aluminium nitrate combination. Both natural fibres were treated with different proportions of NaOH to eliminate moisture absorption. ...
    • Nudging according to user’s preferences 

      Saleem, Sufyan (Mastergradsoppgave; Master thesis, 2022-11-15)
      Physical inactivity has been identified as a global pandemic, physical inactivity causes multiple health outcomes in different demographic group such as coronary heart disease, type 2 diabetes, colon, and breast cancer. A physical inactive person takes less than 5000 steps a day. To try to reduce physical inactivity along individual for healthy lifestyle, this thesis provides personalized digital ...
    • Virtual labeling of mitochondria in living cells using correlative imaging and physics-guided deep learning 

      Somani, Ayush; Sekh, Arif Ahmed; Opstad, Ida Sundvor; Birgisdottir, Åsa birna; Myrmel, Truls; Ahluwalia, Balpreet Singh; Horsch, Alexander; Agarwal, Krishna; Prasad, Dilip K. (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-09-28)
      Mitochondria play a crucial role in cellular metabolism. This paper presents a novel method to visualize mitochondria in living cells without the use of fluorescent markers. We propose a physics-guided deep learning approach for obtaining virtually labeled micrographs of mitochondria from bright-field images. We integrate a microscope’s point spread function in the learning of an adversarial neural ...
    • Exploration of Different Time Series Models for Soccer Athlete Performance Prediction 

      Kulakou, Siarhei; Ragab, Nourhan; Midoglu, Cise; Boeker, Matthias; Johansen, Dag; Riegler, Michael; Halvorsen, Pål (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-06-29)
      Professional sports achievements combine not only the individual physical abilities of athletes but also many modern technologies in areas such as medicine, equipment production, nutrition, and physical and mental health monitoring. In this work, we address the problem of predicting soccer players’ ability to perform, from subjective self-reported wellness parameters collected using a commercially ...
    • Intersecting near-optimal spaces: European power systems with more resilience to weather variability 

      Grochowicz, Aleksander; van Greevenbroek, Koen; Benth, Fred Espen; Zeyringer, Marianne (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-01-03)
      We suggest a new methodology for designing robust energy systems. For this, we investigate so-called near-optimal solutions to energy system optimisation models; solutions whose objective values deviate only marginally from the optimum. Using a refined method for obtaining explicit geometric descriptions of these near-optimal feasible spaces, we find designs that are as robust as possible to ...
    • Effectiveness of LRB in Curved Bridge Isolation: A Numerical Study 

      Gupta, Praveen Kumar; Ghosh, Goutam; Kumar, Virendra; Paramasivam, Prabhu; Dhanasekaran, Seshathiri (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-11-07)
      Lead Rubber Bearings (LRBs) represent one of the most widely employed devices for the seismic protection of structures. However, the effectiveness of the same in the case of curved bridges has not been judged well because of the complexity involved in curved bridges, especially in controlling torsional moments. This study investigates the performance of an LRB-isolated horizontally curved ...
    • Towards the Neuroevolution of Low-level artificial general intelligence 

      Pontes Filho, Sidney; Olsen, Kristoffer; Yazidi, Anis; Riegler, Michael; Halvorsen, Pål; Nichele, Stefano (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-10-14)
      In this work, we argue that the search for Artificial General Intelligence should start from a much lower level than human-level intelligence. The circumstances of intelligent behavior in nature resulted from an organism interacting with its surrounding environment, which could change over time and exert pressure on the organism to allow for learning of new behaviors or environment models. Our ...
    • TFHE-rs: A library for safe and secure remote computing using fully homomorphic encryption and trusted execution environments 

      Brenna, Lars; Singh, Isak Sunde; Johansen, Håvard D.; Johansen, Dag (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-01-20)
      Fully Homomorphic Encryption (FHE) and Trusted Execution Environ-ments (TEEs) are complementing approaches that can both secure computa-tions running remotely on a public cloud. Existing FHE schemes are, however, malleable by design and lack integrity protection, making them susceptible to integrity breaches where an adversary could modify the data and corrupt the output. This paper describes how ...
    • Predicting an unstable tear film through artificial intelligence 

      Fineide, Fredrik; Chen, Xiangjun; Magnø, Morten Schjerven; Yazidi, Anis; Riegler, Michael; Utheim, Tor Paaske; Storås, Andrea Marheim (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-12-10)
      Dry eye disease is one of the most common ophthalmological complaints and is defined by a loss of tear film homeostasis. Establishing a diagnosis can be time-consuming, resource demanding and unpleasant for the patient. In this pilot study, we retrospectively included clinical data from 431 patients with dry eye disease examined in the Norwegian Dry Eye Clinic to evaluate how artificial intelligence ...
    • Multi-Objective Optimal Scheduling of a Microgrid Using Oppositional Gradient-Based Grey Wolf Optimizer 

      Rajagopalan, Arul; Nagarajan, Karthik; Montoya, Oscar Danilo; Dhanasekaran, Seshathiri; Abdul Kareem, Inayathullah; Sendraya Perumal, Angalaeswari; Lakshmaiya, Natrayan; Paramasivam, Prabhu (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-11-29)
      Optimal energy management has become a challenging task to accomplish in today’s advanced energy systems. If energy is managed in the most optimal manner, tremendous societal benefits can be achieved such as improved economy and less environmental pollution. It is possible to operate the microgrids under grid-connected, as well as isolated modes. The authors presented a new optimization algorithm, ...
    • FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation 

      Jha, Debesh; Riegler, Michael; Johansen, Håvard D.; Johansen, Dag; Rittscher, Jens; Halvorsen, Pål; Ali, Sharib (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-03-25)
      The increase of available large clinical and experimental datasets has contributed to a substantial amount of important contributions in the area of biomedical image analysis. Image segmentation, which is crucial for any quantitative analysis, has especially attracted attention. Recent hardware advancement has led to the success of deep learning approaches. However, although deep learning models are ...
    • Utilizing Alike Neighbor Influenced Similarity Metric for Efficient Prediction in Collaborative Filter-Approach-Based Recommendation System 

      Singh, Raushan Kumar; Singh, Pradeep Kumar; Singh, Juginder Pal; Singh, Akhilesh Kumar; Dhanasekaran, Seshathiri (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-11-17)
      The most popular method collaborative filter approach is primarily used to handle the information overloading problem in E-Commerce. Traditionally, collaborative filtering uses ratings of similar users for predicting the target item. Similarity calculation in the sparse dataset greatly influences the predicted rating, as less count of co-rated items may degrade the performance of the collaborative ...
    • Automatic algorithm for determining bone and soft-tissue factors in dual-energy subtraction chest radiography 

      Do, Quan; Seo, Wontaek; Shin, Choul Woo (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-11-11)
      Lung cancer is currently the first leading cause of worldwide cancer deaths since the early stage of lung cancer detection is still a challenge. In lung diagnosis, nodules sometimes overlap with ribs and tissues on lung chest radiographic images, which are complex for doctors and radiologists. Dual-energy subtraction (DES) is a suitable solution to solve those issues. This article will develop an ...
    • Designing, implementing, and testing a modern electronic clinical study management system – the HUBRO system 

      Muzny, Miroslav; Bradway, Meghan; Blixgård, Håvard Kvalvåg; Årsand, Eirik (Journal article; Tidsskriftartikkel, 2022-08-22)
      Clinical trials need to adapt to the rapid development of today’s digital health technologies. The fast phase these technologies are changing today, make the clinical study administration demanding. To meet this challenge, new and more efficient platforms for performing clinical trials in this domain need to be designed. Since the process of following up such trials is very time-consuming, it calls ...
    • Fish AI: Sustainable Commercial Fishing Challenge 

      Nordmo, Tor-Arne Schmidt; Kvalsvik, Ove; Kvalsund, Svein Ove; Hansen, Birte; Halvorsen, Pål; Hicks, Steven; Johansen, Dag; Johansen, Håvard D.; Riegler, Michael Alexander (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-06-02)
      FishAI: Sustainable Commercial Fishingis the second chal-lenge at theNordic AI Meetfollowing the successful MedAI,which had a focus on medical image segmentation and trans-parency in machine learning (ML)-based systems. FishAI fo-cuses on a new domain, namely, commercial fishing and howto make it more sustainable with the help of machine learning.A range of public available datasets is used to tackle ...
    • H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images 

      Pedersen, André; Smistad, Erik; Rise, Tor Vikan; Dale, Vibeke Grotnes; Pettersen, Henrik P Sahlin; Nordmo, Tor-Arne Schmidt; Bouget, David Nicolas Jean-Mar; Reinertsen, Ingerid; Valla, Marit (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-09-14)
      Over the past decades, histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic methods for evaluation of histopathological cancer sections would be of value. In this study, we used 624 whole slide images (WSIs) of breast cancer from a Norwegian cohort. We propose a cascaded ...
    • Triggering the next nudge 

      Ottestad, Thomas Forsgren (Mastergradsoppgave; Master thesis, 2022-08-26)
      The aim of this thesis is to establish if a smart nudging system using triggers could be used to better understand the user and their situation as well as aiding in selecting a target activity as part of a complete smart nudging system. Explore how such a system can be used to determine when a user should be nudged and how it can learn from feedback from the user to make changes to itself and ...