Blar i Institutt for datateknologi og informatikk 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 ...

    • 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 ...

    • 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 ...

    • 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 ...