Browsing TKD - Institutt for informasjonsteknologi by Subject "Machine learning"

Now showing items 21-32 of 32

    • A Learning Automaton-based Scheme for Scheduling Domestic Shiftable Loads in Smart Grids 

      Thapa, Rajan; Lei, Jiao; Oommen, John; Yazidi, Anis (Journal article; Peer reviewed, 2017)
      In this paper, we consider the problem of scheduling shiftable loads, over multiple users, in smart electrical grids. We approach the problem, which is becoming increasingly pertinent in our present energy-thirsty society, ...

    • A Machine Learning-based Tool for Passive OS Fingerprinting with TCP Variant as a Novel Feature 

      Hagos, Desta Haileselassie; Yazidi, Anis; Kure, Øivind; Engelstad, Paal (IEEE Internet of Things Journal;Volume: 8, Issue: 5, Peer reviewed; Journal article, 2020-09-15)
      With the emergence of Internet of Things (IoT), securing and managing large, complex enterprise network infrastructure requires capturing and analyzing network traffic traces in real-time. An accurate passive Operating ...

    • Manifold feature fusion with dynamical feature selection for cross-subject emotion recognition 

      Hua, Yue; Zhong, Xiaolong; Zhang, Bingxue; Yin, Zhong; Zhang, Jianhua (Brain Sciences;Volume 11 / Issue 11, Peer reviewed; Journal article, 2021-10-23)
      Affective computing systems can decode cortical activities to facilitate emotional human– computer interaction. However, personalities exist in neurophysiological responses among different users of the brain–computer ...

    • A neural network model and framework for an automatic evaluation of image descriptions based on NCAM image accessibility guidelines 

      Shrestha, Raju (Chapter; Peer reviewed, 2021)
      Millions of people who are either blind or visually impaired have difficulty understanding the content in an image. To address the problem textual image descriptions or captions are provided separately or as alternative ...

    • PMData: a sports logging dataset 

      Thambawita, Vajira; Hicks, Steven; Borgli, Hanna; Stensland, Håkon Kvale; Jha, Debesh; Svensen, Martin Kristoffer; Pettersen, Svein Arne; Johansen, Dag; Johansen, Håvard D.; Pettersen, Susann Dahl; Nordvang, Simon; Pedersen, Sigurd; Gjerdrum, Anders Tungeland; Grønli, Tor-Morten; Fredriksen, Per Morten; Eg, Ragnhild; Hansen, Kjeld S.; Fagernes, Siri; Claudi, Christine; Biørn-Hansen, Andreas; Dang Nguyen, Duc Tien; Kupka, Tomas; Hammer, Hugo Lewi; Jain, Ramesh; Riegler, Michael; Halvorsen, Pål (MM: International Multimedia Conference;MMSys '20: Proceedings of the 11th ACM Multimedia Systems Conference, Conference object, 2020-05-27)
      In this paper, we present PMData: a dataset that combines traditional lifelogging data with sports-activity data. Our dataset enables the development of novel data analysis and machine-learning applications where, for ...

    • Predicting High Delays in Mobile Broadband Networks 

      Mohamed Ahmed, Azza Hassan; Hicks, Steven; Riegler, Michael; Elmokashfi, Ahmed Mustafa Abdalla (IEEE Access;Volume 9: 2021, Peer reviewed; Journal article, 2021-12-24)
      The number of applications that run over mobile networks, expecting bounded end-to-end delay, is increasing steadily. However, the stochastic and shared nature of the wireless medium makes providing such guarantees ...

    • Prediction of cloud fractional cover using machine learning 

      Svennevik, Hanna; Riegler, Michael A.; Hicks, Steven; Storelvmo, Trude; Hammer, Hugo L. (Big Data and Cognitive Computing;Volume 5 / Issue 4, Peer reviewed; Journal article, 2021-11-03)
      Climate change is stated as one of the largest issues of our time, resulting in many unwanted effects on life on earth. Cloud fractional cover (CFC), the portion of the sky covered by clouds, might affect global warming ...

    • SemML: Facilitating development of ML models for condition monitoring with semantics 

      Zhou, Baifan; Svetashova, Yulia; Silva Gusmao, Andre; Soylu, Ahmet; Cheng, Gong; Miku, Ralf; Waaler, Arild Torolv Søetorp; Kharlamov, Evgeny (Journal of Web Semantics;Volume 71, November 2021, 100664, Peer reviewed; Journal article, 2021-10-22)
      Monitoring of the state, performance, quality of operations and other parameters of equipment and production processes, which is typically referred to as condition monitoring, is an important common practice in many ...

    • 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 ...