Browsing Fakultet for teknologi, kunst og design (TKD) by Subject "Machine learning"

Now showing items 41-45 of 45

    • Soccer athlete performance prediction using time series analysis 

      Ragab, Nourhan (ACIT;2022, Master thesis, 2022)
      Regardless of the sport you prefer, your favorite athlete has almost certainly disappointed you at some point. Did you jump to a conclusion and dismissed it as "not their day"? Or, did you consider the underlying causes for ...

    • Toadstool: a dataset for training emotional intelligent machines playing Super Mario Bros 

      Svoren, Henrik; Thambawita, Vajira; Halvorsen, Pål; Jakobsen, Petter; Garcia-Ceja, Enrique; Noori, Farzan Majeed; Hammer, Hugo Lewi; Lux, Mathias; Riegler, Michael; Hicks, Steven (MMSys: Multimedia Systems;MMSys '20: Proceedings of the 11th ACM Multimedia Systems Conference, Conference object, 2020)
      Games are often defined as engines of experience, and they are heavily relying on emotions, they arouse in players. In this paper, we present a dataset called Toadstool as well as a reproducible methodology to extend ...

    • Towards AI-powered Cybersecurity Attack Modeling with Simulation Tools: Review of Attack Simulators 

      Alzarqawee, Aws Naser Jaber; Fritsch, Lothar (Lecture Notes in Networks and Systems;Volume 571, Conference object, 2023-10-18)
      Cybersecurity currently focuses primarily on defenses that detect and prevent cyber-attacks. However, it is more important to regularly verify an organization’s security posture to reinforce its cybersecurity defenses as ...

    • Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook 

      Goodwin, Morten; Halvorsen, Kim Aleksander Tallaksen; Jiao, Lei; Knausgård, Kristian Muri; Martin, Angela Helen; Moyano, Marta; Oomen, Rebekah Alice; Rasmussen, Jeppe Have; Sørdalen, Tonje Knutsen; Thorbjørnsen, Susanna Huneide (ICES Journal of Marine Science;, Peer reviewed; Journal article, 2022-01-14)
      The deep learning (DL) revolution is touching all scientific disciplines and corners of our lives as a means of harnessing the power of big data. Marine ecology is no exception. New methods provide analysis of data from ...

    • Unraveling the Impact of Land Cover Changes on Climate Using Machine Learning and Explainable Artificial Intelligence 

      Kolevatova, Anastasiia; Riegler, Michael; Cherubini, Francesco; Hu, Xiangping; Hammer, Hugo Lewi (Big Data and Cognitive Computing;Volume 5, Issue 4, Peer reviewed; Journal article, 2021-10-15)
      A general issue in climate science is the handling of big data and running complex and computationally heavy simulations. In this paper, we explore the potential of using machine learning (ML) to spare computational time ...