Blar i Fakultet for informasjonsteknologi og elektroteknikk (IE) på emneord "Machine learning"

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    • Automated well log depth matching: Late fusion multimodal deep learning 

      Torres Caceres, Veronica Alejandra; Duffaut, Kenneth; Yazidi, Anis; Westad, Frank Ove; Johansen, Yngve Bolstad (Peer reviewed; Journal article, 2022)
      Petrophysical interpretation and optimal correlation extraction of different measurements require accurate well log depth matching. We have developed a supervised multimodal machine learning alternative for the task of ...

    • A character-based analysis of impacts of dialects on end-to-end Norwegian ASR 

      Parsons, Phoebe; Kvale, Knut; Svendsen, Torbjørn Karl; Salvi, Giampiero (Chapter, 2023)
      We present a method for analyzing character errors for use with character-based, end-to-end ASR systems, as used herein for investigating dialectal speech. As end-to-end systems are able to produce novel spellings, there ...

    • A combined informative and representative active learning approach for plankton taxa labeling 

      Haug, Martin Lund; Saad, Aya; Stahl, Annette (Peer reviewed; Journal article, 2021)
      With an ever-increasing amount of image data, the manual labeling process has become the bottleneck in many machine learning applications. Plankton taxa labeling is especially a challenge due to its complex nature, and the ...

    • Data driven case base construction for prediction of success of marine operations 

      Mathisen, Bjørn Magnus; Aamodt, Agnar; Langseth, Helge (Journal article; Peer reviewed, 2017)
      It is a common situation to have lots of recorded data that you want to use for improving a process in your organization or make use of this data to provide new services or products. Starting with one primary data set we ...

    • Deep learning based keypoint rejection system for underwater visual ego-motion estimation 

      Leonardi, Marco; Fiori, Luca; Stahl, Annette (Peer reviewed; Journal article, 2020)
      Most visual odometry (VO) and visual simultaneous localization and mapping (VSLAM) systems rely heavily on robust keypoint detection and matching. With regards to images taken in the underwater environment, phenomena like ...

    • Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk 

      Groos, Daniel; Adde, Lars; Aubert, Sindre Aarnes; Boswell, Lynn; De Regnier, Raye-Ann; Fjørtoft, Toril Larsson; Gaebler-Spira, Deborah; Haukeland, Andreas; Loennecken, Marianne; Msall, Michael; Moinichen, Unn Inger; Pascal, Aurelie; Peyton, Colleen; Ramampiaro, Heri; Schreiber, Michael D.; Silberg, Inger Elisabeth; Songstad, Nils Thomas; Thomas, Niranjan; van den Broeck, Christine; Øberg, Gunn Kristin; Ihlen, Espen Alexander F.; Støen, Ragnhild (Peer reviewed; Journal article, 2022)
      Importance Early identification of cerebral palsy (CP) is important for early intervention, yet expert-based assessments do not permit widespread use, and conventional machine learning alternatives lack validity. Objective ...

    • Extracting news events from microblogs 

      Øystein, Repp; Ramampiaro, Heri (Journal article; Peer reviewed, 2018)
      Twitter stream has become a large source of information, but the magnitude of tweets posted and the noisy nature of its content makes harvesting of knowledge from Twitter has challenged researchers for long time. Aiming ...

    • Forecasting Hourly Ambulance Demand for Oslo, Norway: A Neuro-Symbolic Method 

      Van De Weijer, Erling; Owren, Odd André; Mengshoel, Ole Jakob (Journal article; Peer reviewed, 2023)
      Forecasting ambulance demand is critical for emergency medical services to allocate their resources as efficiently as possible. This work uses data from Norway's Oslo University Hospital (OUH) to forecast hourly ambulance ...

    • Forecasting Intra-Hour Imbalances in Electric Power Systems 

      Saleh Salem, Tárik; Kathuria, Karan; Ramampiaro, Heri; Langseth, Helge (Journal article; Peer reviewed, 2019)
      Keeping the electricity production in balance with the actual demand is becoming a difficult and expensive task in spite of an involvement of experienced human operators. This is due to the increasing complexity of the ...

    • 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 ...

    • MOG: a background extraction approach for data augmentation of time-series images in deep learning segmentation 

      Borgersen, Jonas Nagell; Saad, Aya; Stahl, Annette (Journal article; Peer reviewed, 2021)
      Image segmentation is one of the key components in systems performing computer vision recognition tasks. Various algorithms for image segmentation have been developed in the literature. Among them, more recently, deep ...

    • Reliable Unmanned Autonomous Systems: Conceptual Framework for Warning Identification during Remote Operations 

      Spahić, Rialda; Hepsø, Vidar; Lundteigen, Mary Ann (Journal article, 2021)
      In the offshore industry, unmanned autonomous systems are expected to have a permanent role in future operations. During offshore operations, the unmanned autonomous system needs definite instructions on evaluating the ...

    • Using neural networks to support high-quality evidence mapping 

      Røst, Thomas Brox; Slaughter, Laura; Nytrø, Øystein; Muller, Ashley Elizabeth; Vist, Gunn Elisabeth (Peer reviewed; Journal article, 2021)
      Abstract Background: The Living Evidence Map Project at the Norwegian Institute of Public Health (NIPH) gives an updated overview of research results and publications. As part of NIPH’s mandate to inform evidence-based ...