Now showing items 21-37 of 37

    • FishNet: A Unified Embedding for Salmon Recognition 

      Meidell, Espen; Sjøblom, Edvard Schreiner (Master thesis, 2019)
      Dagens metoder for markering og sporing av oppdrettslaks baserer seg på fysisk kontakt med fisken. Denne prosessen er både ineffektiv, stressende og potensielt skadelig for laksen. Bruken av dyp læring har de siste årene ...
    • FishNet: A Unified Embedding For Salmon Recognition 

      Meidell, Espen; Sjøblom, Edvard Schreiner (Master thesis, 2019)
      Dagens metoder for markering og sporing av oppdrettslaks baserer seg p˚a fysisk kontakt med fisken. Denne prosessen er b˚ade ineffektiv, stressende og potensielt skadelig for laksen. Bruken av dyp læring har de siste ˚arene ...
    • FishNet: A Unified Embedding for Salmon Recognition 

      Mathisen, Bjørn Magnus; Bach, Kerstin; Meidell, Espen; Måløy, Håkon; Sjøblom, Edvard Schreiner (Chapter, 2020)
      Identifying individual salmon can be very beneficial for the aquaculture industry as it enables monitoring and analyzing fish behavior and welfare. For aquaculture researchers identifying indi- vidual salmon is imperative ...
    • From data to context-aware decision making: Challenges and opportunities 

      Bach, Kerstin (Journal article; Peer reviewed, 2018)
      This extended abstract presents the research challenges for developing context-aware, explainable artificial intelligence challenges and briefly presents the opportunities ahead when utilizing the vast amount of data generated.
    • Human Activity Recognition With Two Body-Worn Accelerometer Sensors 

      Hessen, Hans-Olav; Tessem, Astrid Johnsen (Master thesis, 2016)
      Data used in studies about physical activity is primarily collected from questionnaires and other subjective methods, which may lead to biased and inaccurate data. As subjective data collection methods have shown to be ...
    • Information-Driven Adaptive Sensing Based on Deep Reinforcement Learning 

      Murad, Abdulmajid; Kraemer, Frank Alexander; Bach, Kerstin; Taylor, Gavin (Chapter, 2020)
      In order to make better use of deep reinforcement learning in the creation of sensing policies for resource-constrained IoT devices, we present and study a novel reward function based on the Fisher information value. This ...
    • IoT Sensor Gym: Training Autonomous IoT Devices with Deep Reinforcement Learning 

      Murad, Abdulmajid Abdullah Yahya; Kraemer, Frank Alexander; Bach, Kerstin; Taylor, Gavin (Chapter, 2019)
      We describe IoT Sensor Gym, a framework to train the behavior of constrained IoT devices using deep reinforcement learning. We focus on the main architectural choices to align problems from the IoT domain with cutting-edge ...
    • Learning similarity measures from data 

      Mathisen, Bjørn Magnus; Aamodt, Agnar; Langseth, Helge; Bach, Kerstin (Journal article; Peer reviewed, 2019)
      Defining similarity measures is a requirement for some machine learning methods. One such method is case-based reasoning (CBR) where the similarity measure is used to retrieve the stored case or a set of cases most similar ...
    • Machine learning methods for sleep-wake classification using two body-worn accelerometers 

      Hay, Aileen (Master thesis, 2019)
      Søvn er en viktig faktor for beskytte en persons fysiske og mentale trivsel. Derfor utføres mange studier som fokuserer på forebygge, diagnostisere og behandle søvnforstyrrelser. En avgjørende del av disse studiene er ...
    • Mobile User Interface for a patient-centered health care application 

      Stokvik, Linn Kristin (Master thesis, 2017)
      Low back pain is a prevalent issue in the developed world as people adopt a more sedentary lifestyle. This thesis gives insight to available interactive health communication applications for self-managing non-specific low ...
    • Modelling Similarity for Comparing Physical Activity Profiles - A Data-Driven Approach 

      Verma, Deepika; Bach, Kerstin; Mork, Paul Jarle (Journal article; Peer reviewed, 2018)
      Objective measurements of physical behaviour are an interesting research field from the public health and computer science perspective. While for public health research, measurements with a high quality and feasible setup ...
    • Scalability of mobile health backends: Analysis, configuration and evaluation of the selfBACK systems data storage 

      Rynning-Tønnesen, Kasper (Master thesis, 2018)
      Internett vokser og rekker lenger og lenger, med bedre hastighet enn noen sinne. Apper med lav responstid er derfor viktig for brukeropplevelsen. Hvis denne responstiden øker nok, vil brukere gå lei, og ende opp med å ...
    • Similarity measure development for case-based reasoning?a data-driven approach 

      Verma, Deepika; Bach, Kerstin; Mork, Paul Jarle (Peer reviewed; Journal article, 2019)
      In this paper, we demonstrate a data-driven methodology for modelling the local similarity measures of various attributes in a dataset. We analyse the spread in the numerical attributes and estimate their distribution using ...
    • Similarity Measure Development for Case-Based Reasoning–A Data-Driven Approach 

      Verma, Deepika; Bach, Kerstin; Mork, Paul Jarle (Chapter, 2019)
      In this paper, we demonstrate a data-driven methodology for modelling the local similarity measures of various attributes in a dataset. We analyse the spread in the numerical attributes and estimate their distribution using ...
    • The 25th International Conference on Case-Based Reasoning 

      Aha, David; Bach, Kerstin; Gundersen, Odd Erik; Lieber, Jean (Journal article; Peer reviewed, 2018)
    • Usability and Acceptability of an App (SELFBACK) to Support Self-Management of Low Back Pain: Mixed Methods Study 

      Nordstoga, Anne Lovise; Bach, Kerstin; Sani, Sadiq; Wiratunga, Nirmalie; Mork, Paul Jarle; Villumsen, Morten; Cooper, Kay (Peer reviewed; Journal article, 2020)
      Background: Self-management is the key recommendation for managing nonspecific low back pain (LBP). However, there are well-documented barriers to self-management; therefore, methods of facilitating adherence are required. ...
    • Visual analytics for exploring air quality data in an AI-enhanced IoT environment 

      Kalamaras, Ilias; Xygonakis, Ioannis; Glykos, Konstantinos; Akselsen, Sigmund; Munch-Ellingsen, Arne; Nguyen, Hai Thanh; Jacobsen Lepperod, Andreas; Bach, Kerstin; Votis, Konstantinos; Tzovaras, Dimitrios (Chapter, 2019)
      Visual analytics have an important role in the exploration and analysis of large amounts of data in IoT applications. Data visualizations can provide overviews of different aspects of data and user interaction can assist ...