• m - f - neuter: kjønnsparadokser på nett 

      Lie, Merete (Chapter; Peer reviewed, 2002)
    • M.H. Bjørk og medarbeidere svarer 

      Bjørk, Marte-Helene; Gerstner, Thorsten Alfons; Taubøll, Erik (Peer reviewed; Journal article, 2020)
    • Machine Learning Aided Static Malware Analysis: A Survey and Tutorial 

      Shalaginov, Andrii; Banin, Sergii; Dehghantanha, Ali; Franke, Katrin (Chapter, 2018)
      Malware analysis and detection techniques have been evolving during the last decade as a reflection to development of different malware techniques to evade network-based and host-based security protections. The fast growth ...
    • Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury 

      Gravesteijn, BY; Nieboer, Daan; Ercole, Ari; Lingsma, Hester F; Nelson, David; Van Calster, Ben; Steyerberg, Ewout W; Andelic, Nada; Anke, Audny; Frisvold, Shirin; Helseth, Eirik; Røe, Cecilie; Røise, Olav; Skandsen, Toril; Vik, Anne (Peer reviewed; Journal article, 2020)
      Objective We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting We performed logistic regression ...
    • A Machine Learning Approach to Investigate Fronto-Parietal Neural Ensemble Dynamics During Complex 

      Gheorghiu, Medorian; Ciuparu, Andrei; Mimica, Bartul; Whitlock, Jonathan; Mureşan, Raul (Chapter, 2020)
      Brain circuits exhibit very complex dynamics, where individual neurons fire action potentials determining coordinated activity patterns. During behavior, a multitude of brain areas are engaged in planning and execution. A ...
    • Machine Learning Based Heuristic Technique for Multi-response Machining Process 

      Ghosh, Tamal; Martinsen, Kristian (Peer reviewed; Journal article, 2020)
      Manufacturing process variables influence the quality of products substantially. It is unquestionably difficult to model the manufacturing processes that include a large number of variables and responses. Development of ...
    • Machine Learning Based Prediction of Nanoscale Ice Adhesion on Rough Surfaces 

      Ringdahl, Simen; Xiao, Senbo; He, Jianying; Zhang, Zhiliang (Peer reviewed; Journal article, 2021)
      It is widely recognized that surface roughness plays an important role in ice adhesion strength, although the correlation between the two is far from understood. In this paper, two approaches, molecular dynamics (MD) ...
    • Machine Learning for Hydropower Scheduling: State of the Art and Future Research Directions 

      Bordin, Chiara; Skjelbred, Hans Ivar; Kong, Jiehong; Yang, Zhirong (Peer reviewed; Journal article, 2020)
      This paper investigates and discusses the current and future role of machine learning (ML) within the hydropower sector. An overview of the main applications of ML in the field of hydropower operations is presented to show ...
    • Machine Learning for Smart Building Applications: Review and Taxonomy 

      Djenouri, Djamel; Laidi, Roufaida; Djenouri, Youcef; Balasingham, Ilangko (Journal article; Peer reviewed, 2019)
      The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first class groups solutions ...
    • Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives 

      Schweidtmann, Artur M.; Clayton, Adam D; Holmes, Nicholas; Bradford, Eric; Richard A, Bourne; Lapkin, AA (Journal article; Peer reviewed, 2018)
      Automated development of chemical processes requires access to sophisticated algorithms for multi-objective optimization, since single-objective optimization fails to identify the trade-offs between conflicting performance ...
    • Machine learning methods for prediction of hot water demands in integrated R744 system for hotels 

      Zhang, Zhanluo; Smitt, Silje Marie; Eikevik, Trygve Magne; Hafner, Armin (Chapter, 2020)
      Load forecasting can help modern energy systems achieve more efficient operation by means of more accurate peak power shaving and more reliable control. This paper proposes a framework based on machine learning algorithms ...
    • Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study 

      Ihlen, Espen Alexander F.; Støen, Ragnhild; Boswell, Lynn; de-Regnier, Raye-Ann; Fjørtoft, Toril Larsson; Gaebler-spira, Deborah; Labori, Cathrine; Loennecken, Marianne; Msall, Me; Møinicken, Unn inger; Peyton, Colleen; Schreiber, Me; Silberg, Inger Elisabeth; Songstad, Nils Thomas; Vaagen, Randi; Øberg, Gunn Kristin; Adde, Lars (Journal article; Peer reviewed, 2019)
      Background: Early identification of cerebral palsy (CP) during infancy will provide opportunities for early therapies and treatments. The aim of the present study was to present a novel machine-learning model, the ...
    • Machine Learning-based Occupancy Estimation Using Multivariate Sensor Nodes 

      Singh, Adarsh Pal; Jain, Vivek; Chaudhari, Sachin; Kraemer, Frank Alexander; Werner, Stefan; Garg, Vishal (Chapter, 2019)
      In buildings, a large chunk of energy is spent on heating, ventilation and air conditioning systems. One way to optimize their usage is to make them demand-driven depending on human occupancy. This paper focuses on accurately ...
    • Machine-learning-based estimation and rendering of scattering in virtual reality 

      Pulkki, Ville; Svensson, U. Peter (Journal article; Peer reviewed, 2019)
      In this work, a technique to render the acoustic effect of scattering from finite objects in virtual reality is proposed, which aims to provide a perceptually plausible response for the listener, rather than a physically ...
    • MACPET: model-based analysis for ChIA-PET 

      Vardaxis, Ioannis; Drabløs, Finn; Rye, Morten Beck; Lindqvist, Bo Henry (Journal article; Peer reviewed, 2019)
      We present model-based analysis for ChIA-PET (MACPET), which analyzes paired-end read sequences provided by ChIA-PET for finding binding sites of a protein of interest. MACPET uses information from both tags of each PET ...
    • Macroalgal browsing on a heavily degraded, urbanized equatorial reef system 

      Bauman, Andrew G; Hoey, Andrew; Dunshea, Glenn; Feary, David A.; Low, Jeffery; Todd, Peter A. (Peer reviewed; Journal article, 2017)
      The removal of macroalgal biomass is critical to the health of coral reef ecosystems. Previous studies on relatively intact reefs with diverse and abundant fish communities have quantified rapid removal of macroalgae by ...
    • MacroBiomass En kompetansebase for industriell taredyrking 

      Forbord, Silje; Handå, Aleksander; Broch, Ole Jacob; Arff, Johanne; Dahle, Stine Veronica Wiborg; Fredriksen, Stein; Reitan, Kjell Inge; Steinhovden, Kristine; Størseth, Trond Røvik; Tangen, Karl; Lüning, Klaus (Research report, 2013)
      MacroBiomass Prosjektet MacroBiomass har hatt som mål å bygge en kompetansebase med fokus på biologiske utfordringer i storskala dyrking av makroalger (tare) til bioenergi. Prosjektet har jobbet med hele produksjonssyklusen ...
    • Macrocell corrosion in carbonated Portland and Portland-fly ash concrete - Contribution and mechanism 

      Belda Revert, Andres; Hornbostel, Karla; De Weerdt, Klaartje; Geiker, Mette Rica (Journal article, 2019)
      The corrosion of reinforcement in carbonated concrete with high moisture state was measured with and without electrical connection to reinforcement in non-carbonated concrete. The impact of the fly ash content and the ...
    • Macroeconomic costs of the unmet burden of surgical disease in Sierra Leone: A retrospective economic analysis 

      Grimes, Caris E.; Quaife, Matthew; Kamara, Thaim B.; Lavy, Christopher B.D.; Leather, Andy J.M.; Bolkan, Håkon Angell (Journal article; Peer reviewed, 2018)
      Objectives The Lancet Commission on Global Surgery estimated that low/middle-income countries will lose an estimated cumulative loss of US$12.3 trillion from gross domestic product (GDP) due to the unmet burden of surgical ...