Blar i Publikasjoner fra Cristin på emneord "Deep learning"

Viser treff 1-19 av 19

    • Bringing deep learning to the Surface: A systematic mapping review of 48 years of research in primary and secondary education 

      Winje, Øystein; Løndal, Knut (Nordic Journal of Comparative and International Education;Vol. 4 No. 2 (2020): General issue - Comparative and international education in a changing world, Peer reviewed; Journal article, 2020-07-01)
      Deep learning is a key term in current educational discourses worldwide and used by researchers, policymakers, stakeholders, politicians, organisations and the media with different definitions and, consequently, much ...

    • Cross-Subject emotion recognition from EEG using Convolutional Neural Networks 

      Zhong, Xiaolong; Yin, Zhong; Zhang, Jianhua (Chinese Control Conference (CCC);2020 39th Chinese Control Conference (CCC), Peer reviewed; Journal article, 2020-09-09)
      Using electroencephalogram (EEG) signals for emotion detection has aroused widespread research concern. However, across subjects emotional recognition has become an insurmountable gap which researchers cannot step across ...

    • Deep Learning for Classifying Physical Activities from Accelerometer Data 

      Nunavath, Vimala; Johansen, Sahand; Johannessen, Tommy Sandtorv; Jiao, Lei; Hansen, Bjørge Hermann; Stølevik, Sveinung Berntsen; Goodwin, Morten (Sensors;Volume 21 / Issue 16, Peer reviewed; Journal article, 2021-08-18)
      Physical inactivity increases the risk of many adverse health conditions, including the world’s major non-communicable diseases, such as coronary heart disease, type 2 diabetes, and breast and colon cancers, shortening ...

    • A deep learning-based tool for automatic brain extraction from functional magnetic resonance images of rodents 

      Gulden Dahl, Annelene; Nichele, Stefano; Mello, Gustavo (Peer reviewed; Journal article, 2021)
      Removing skull artifacts from functional magnetic images (fMRI) is a well understood and frequently encountered problem. Because the fMRI field has grown mostly due to human studies, many new tools were developed to handle ...

    • Deep Tower Networks for Efficient Temperature Forecasting from Multiple Data Sources 

      Eide, Siri Sofie; Riegler, Michael; Hammer, Hugo Lewi; Bremnes, John Bjørnar (Sensors;Volume 22 / Issue 7, Peer reviewed; Journal article, 2022-04-06)
      Many data related problems involve handling multiple data streams of different types at the same time. These problems are both complex and challenging, and researchers often end up using only one modality or combining them ...

    • DivergentNets: Medical Image Segmentation by Network Ensemble 

      Thambawita, Vajira L B; Hicks, Steven; Halvorsen, Pål; Riegler, Michael (CEUR Workshop Proceedings;Vol-2886 - Proceedings of the 3rd International Workshop and Challenge on Computer Vision in Endoscopy (EndoCV 2021), Conference object, 2021)
      Detection of colon polyps has become a trending topic in the intersecting fields of machine learning and gastrointestinal endoscopy. The focus has mainly been on per-frame classification. More recently, polyp segmentation ...

    • DoS and DDoS mitigation using Variational Autoencoders 

      Bårli, Eirik Molde; Yazidi, Anis; Viedma, Enrique Herrera; Haugerud, Hårek (Computer Networks;Volume 199, 9 November 2021, 108399, Peer reviewed; Journal article, 2021-09-01)
      DoS and DDoS attacks have been growing in size and number over the last decade and existing solutions to mitigate these attacks are largely inefficient. Compared to other types of malicious cyber attacks, DoS and DDoS ...

    • Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review 

      Zhang, Jianhua; Yin, Zhong; Chen, Peng; Nichele, Stefano (Information Fusion;Volume 59, July 2020, Peer reviewed; Journal article, 2020-01-31)
      In recent years, the rapid advances in machine learning (ML) and information fusion has made it possible to endow machines/computers with the ability of emotion understanding, recognition, and analysis. Emotion recognition ...

    • Interpretable intrusion detection for next generation of Internet of Things 

      Djenouri, Youcef; Belhadi, Asma; Srivastava, Gautam; Lin, Jerry Chun-Wei; Yazidi, Anis (Peer reviewed; Journal article, 2023)
      This paper presents a new framework for intrusion detection in the next-generation Internet of Things. MinMax normalization strategy is used to collect and preprocess data. The Marine Predator algorithm is then used to ...

    • Kvasir-Instruments and Polyp Segmentation Using UNet 

      Keprate, Arvind (Nordic Machine Intelligence (NMI);Vol. 1 No. 1 (2021): MedAI: Transparency in Medical Image Segmentation, Peer reviewed; Journal article, 2021-11-01)
      This paper aims to describe the methodology used to develop, fine-tune and analyze a UNet model for creating masks for two datasets: Polyp Segmentation and Instrument Segmentation, which are part of MedAI challenge. For ...

    • Learning through modelling in science: Reflections by pre-service teachers 

      Aalbergsjø, Siv Gundrosen; Sollid, Per Øyvind (Nordic Studies in Science Education;volume 17, issue 2, Peer reviewed; Journal article, 2021-04-28)
      This study analyses pre-service science teachers’ (PSTs’) experiences of working with models and modelling and their ideas about their usefulness in science education. Although several studies have investigated pre- and ...

    • LiDAR-Based Obstacle Detection and Distance Estimation in Navigation Assistance for Visually Impaired 

      Kuriakose, Bineeth; Shrestha, Raju; Sandnes, Frode Eika (Lecture Notes in Computer Science;Volume 13309, Conference object, 2022-06-16)
      People with visual impairments can face challenges with independent navigation and therefore may use traditional aids such as guide dogs, white canes, or a travel companion for navigation assistance. In recent years, ...

    • LightLayers: Parameter Efficient Dense and Convolutional Layers for Image Classification 

      Jha, Debesh; Yazidi, Anis; Riegler, Michael Alexander; Johansen, Dag; Johansen, Håvard D.; Halvorsen, Pål (Lecture Notes in Computer Science;Volume 12606, Conference object, 2021-02-21)
      Deep Neural Networks (DNNs) have become the de-facto standard in computer vision, as well as in many other pattern recognition tasks. A key drawback of DNNs is that the training phase can be very computationally expensive. ...

    • 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 ...

    • Predicting Remaining Fatigue Life of Topside Piping Using Deep Learning 

      Keprate, Arvind; Chatterjee, Supratik (International Conference on Applied Artificial Intelligence (ICAPAI);2021 International Conference on Applied Artificial Intelligence (ICAPAI), Conference object, 2021-06-29)
      Topside piping is the most commonly failed equipment in the Petroleum and Maritime industry. The prominent degradation mechanism causing piping failure is fatigue which results in unnecessary hydrocarbon release from these ...

    • 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 ...

    • Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning 

      Jha, Debesh; Ali, Sharib; Tomar, Nikhil Kumar; Johansen, Håvard D.; Johansen, Dag; Rittscher, Jens; Riegler, Michael A.; Halvorsen, Pal (IEEE Access;Volume: 9, Peer reviewed; Journal article, 2021-03-04)
      Computer-aided detection, localization, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of ...

    • SceneRecog: A Deep Learning Scene Recognition Model for Assisting Blind and Visually Impaired Navigate using Smartphones 

      Kuriakose, Bineeth; Shrestha, Raju; Sandnes, Frode Eika (IEEE International Conference on Systems, Man and Cybernetics;2021 IEEE International Conference on Systems, Man, and Cybernetics, Conference object, 2022-01-06)
      Deep learning models have recently gained popularity in the research community due to their high classification success rates. In this paper, we proposed an EfficientNet-Lite based scene recognition model for scene recognition ...

    • ‘Wow! is that a birch leaf? In the picture it looked totally different’: a pragmatist perspective on deep learning in Norwegian ‘uteskole’ 

      Winje, Øystein; Løndal, Knut (Education 3-13: International Journal of Primary, Elementary and Early Years Education;, Peer reviewed; Journal article, 2021-07-22)
      This study investigates pupils’ experiences with learning outside the classroom and discusses how these experiences might contribute to ‘deep learning’ according to a pragmatist theoretical framework and a situated perspective ...