Viser treff 41-60 av 389

    • ML-Peaks: Chip-seq peak detection pipeline using machine learning techniques 

      Sheshkal, Sajad Amouei; Riegler, Michael; Hammer, Hugo Lewi (Chapter; Bokkapittel, 2023-07-17)
      CHIP-Seq data is critical for identifying the locations where proteins bind to DNA, offering valuable insights into disease molecular mechanisms and potential therapeutic targets. However, identifying regions of protein binding, or peaks, in CHIP-seq data can be challenging due to limitations in peak detection methods. Current computational tools often require manual human inspection using data ...
    • Approximate Bayesian Inference Based on Expected Evaluations 

      Hammer, Hugo Lewi; Riegler, Michael (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-02-01)
      Approximate Bayesian computing (ABC) and Bayesian Synthetic likelihood (BSL) are two popular families of methods to evaluate the posterior distribution when the likelihood function is not available or tractable. For existing variants of ABC and BSL, the focus is usually first put on the simulation algorithm, and after that the form of the resulting approximate posterior distribution comes as a ...
    • Fishing Trawler Event Detection: An Important Step Towards Digitization of Sustainable Fishing 

      Nordmo, Tor-Arne Schmidt; Ovesen, Aril Bernhard; Dagenborg, Håvard; Halvorsen, Pål; Riegler, Michael Alexander; Johansen, Dag (Chapter; Bokkapittel, 2023-08-02)
      Detection of anomalies within data streams is an important task that is useful for different important societal challenges such as in traffic control and fraud detection. To be able to perform anomaly detection, unsupervised analysis of data is an important key factor, especially in domains where obtaining labelled data is difficult or where the anomalies that should be detected are often ...
    • ScopeSense: An 8.5-Month Sport, Nutrition, and Lifestyle Lifelogging Dataset 

      Riegler, Michael Alexander; Thambawita, Vajira; Chatterjee, Ayan; Nguyen, Thu; Hicks, Steven; Telle-Hansen, Vibeke; Pettersen, Svein Arne; Johansen, Dag; Jain, Ramesh; Halvorsen, Pål (Chapter; Bokkapittel, 2023-03-29)
      Nowadays, most people have a smartphone that can track their everyday activities. Furthermore, a significant number of people wear advanced smartwatches to track several vital biomarkers in addition to activity data. However, it is still unclear how these data can actually be used to improve certain aspects of people’s lives. One of the key challenges is that the collected data is often massive and ...
    • Capturing Nutrition Data for Sports: Challenges and Ethical Issues 

      Sharma, Aakash; Czerwinska, Katja P; Johansen, Dag; Dagenborg, Håvard Johansen (Chapter; Bokkapittel, 2023)
      Nutrition plays a key role in an athlete’s performance, health, and mental well-being. Capturing nutrition data is crucial for analyzing those relations and performing necessary interventions. Using traditional methods to capture long-term nutritional data requires intensive labor, and is prone to errors and biases. Artificial Intelligence (AI) methods can be used to remedy such problems by using ...
    • Auxiliary Network: Scalable and agile online learning for dynamic system with inconsistently available inputs 

      Agarwal, Rohit; Agarwal, Krishna; Horsch, Alexander; Prasad, Dilip K. (Journal article; Tidsskriftartikkel, 2022-04-13)
      Streaming classification methods assume the number of input features is fixed and always received. But in many real-world scenarios, some features are reliable while others are unreliable or inconsistent. We propose a novel online deep learning-based model called Auxiliary Network (Aux-Net), which is scalable and agile and can handle any number of inputs at each time instance. The Aux-Net model is ...
    • Quantifying the variability of power and energy consumption for IoT edge nodes 

      Tofaily, Salma; Rais, Issam; Anshus, Otto Johan (Chapter; Bokkapittel, 2023)
      For IoT and edge systems, measuring, predicting and optimizing energy consumption is an open field. It is important to accurately and precisely characterize power and energy consumption of edge nodes, as energy can be a scarce and key resource. However, there are no fine-grain studies that aim at understanding the potential variability of power and energy consumption of edge nodes. Existing research ...
    • Predicting in-hospital death from derived EHR trajectory features 

      Bopche, Rajeev; Gustad, Lise Tuset; Afset, Jan Egil; Damås, Jan Kristian; Nytrø, Øystein (Chapter; Bokkapittel, 2023)
      Medical histories of patients can provide insight into the immediate future of a patient. While most studies propose to predict survival from vital signs and hospital tests within one episode of care, we carry out selective feature engineering from longitudinal historical medical records in this study to develop a dataset with derived features. We then train multiple machine learning models for the ...
    • Autostrata: Improved Automatic Stratification for Coarsened Exact Matching 

      Arnes, Jo Inge; Hapfelmeier, Alexander; Horsch, Alexander (Chapter; Bokkapittel, 2022-08-22)
      We commonly adjust for confounding factors in analytical observational epidemiologyto reduce biases that distort the results. Stratification and matching are standard methods for reducing confounder bias. Coarsened exact matching (CEM) is a recent method using stratification to coarsen variables into categorical variables to enable exact matching of exposed and nonexposed ...
    • The Beauty of Complex Designs 

      Arnes, Jo Inge; Bongo, Lars Ailo (Chapter; Bokkapittel, 2020-12-08)
      The increasing use of omics data in epidemiology enables many novel study designs, but also introduces challenges for data analysis. We describe the possibilities for systems epidemiological designs in the Norwegian Women and Cancer (NOWAC) study and show how the complexity of NOWAC enables many beautiful new study designs. We discuss the challenges of implementing designs and analyzing data. Finally, ...
    • LightLayers: Parameter Efficient Dense and Convolutional Layers for Image Classification 

      Jha, Debesh; Yazidi, Anis; Riegler, Michael Alexander; Johansen, Dag; Johansen, Håvard D.; Halvorsen, Pål (Chapter; Bokkapittel, 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. Organizations or individuals that cannot afford purchasing state-of-the-art hardware or tapping into cloud hosted infrastructures may face a long waiting time before the training ...
    • Automatic Unsupervised Clustering of Videos of the Intracytoplasmic Sperm Injection (ICSI) Procedure 

      Storås, Andrea; Riegler, Michael Alexander; Haugen, Trine B.; Thambawita, Vajira L B; Hicks, Steven Alexander; Hammer, Hugo Lewi; Kakulavarapu, Radhika; Halvorsen, Pål; Stensen, Mette Haug (Chapter; Bokkapittel, 2023-02-02)
      The in vitro fertilization procedure called intracytoplasmic sperm injection can be used to help fertilize an egg by injecting a single sperm cell directly into the cytoplasm of the egg. In order to evaluate, refine and improve the method in the fertility clinic, the procedure is usually observed at the clinic. Alternatively, a video of the procedure can be examined and labeled in a time-consuming ...
    • Real-Time Detection of Events in Soccer Videos using 3D Convolutional Neural Networks 

      Rognved, Olav; Hicks, Steven; Lasantha Bandara Thambawita, Vajira; Stensland, Håkon Kvale; Zouganeli, Evi; Johansen, Dag; Riegler, Michael A.; Halvorsen, Pål (Chapter; Bokkapittel, 2020)
      In this paper, we present an algorithm for automatically detecting events in soccer videos using 3D convolutional neural networks. The algorithm uses a sliding window approach to scan over a given video to detect events such as goals, yellow/red cards, and player substitutions. We test the method on three different datasets from SoccerNet, the Swedish Allsvenskan, and the Norwegian Eliteserien. ...
    • Algorithms that forget: Machine unlearning and the right to erasure 

      Juliussen, Bjørn Aslak; Rui, Jon Petter; Johansen, Dag (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-09-22)
      rticle 17 of the General Data Protection Regulation (GDPR) contains a right for the data subject to obtain the erasure of personal data. The right to erasure in the GDPR gives, however, little clear guidance on how controllers processing personal data should erase the personal data to meet the requirements set out in Article 17. Machine Learning (ML) models that have been trained on personal data ...
    • Data Analysis Techniques for Smart Nudging 

      Dhanasekaran, Seshathiri; Andersen, Anders; Karlsen, Randi; Håkansson, Anne (Conference object; Konferansebidrag, 2021)
      <p>Nudge principles and techniques are significant in communications, marketing, and groups’ motivation to improve personal health, wealth, and well-being. We make numerous decisions in online situations. People’s health and well-being have garnered widespread interest and concern in this wearable’s age. Smart nudging is defined as “digital nudging, where the guidance of user behavior is tailored ...
    • UX-based personalization of timed media experiences 

      Arntzen, Ingar M; Borch, Njål Trygve (Conference object; Konferansebidrag, 2021)
      A client-side approach brings great opportunities for flexible personalization of timed media experiences. This, turns it into a challenge of UX development. However, UX development does not support time-driven rendering or shared interactivity, which are required by media related scenarios. Moreover, UX development is already quite complicated, so adding support for timed rendering and shared ...
    • Pedagogical Perspectives of Interdisciplinary Teaching and Research: An Energy System Modelling Outlook in Relation to Energy Informatics 

      Bordin, Chiara; Mishra, Sambeet; Benth, Fred Espen (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-08-02)
      The purpose of this paper is to present and discuss pedagogical frameworks and approaches to developing, delivering, and evaluating a new interdisciplinary course within the domain of energy informatics at both Master’s and PhD levels. This study is needed because many papers on sustainable energy engineering education concentrate on course content but provide very little information on the ...
    • Single image dehazing for a variety of haze scenarios using back projected pyramid network 

      Singh, Ayush; Bhave, Ajay; Prasad, Dilip K. (Conference object; Konferansebidrag, 2020)
      Learning to dehaze single hazy images, especially using a small training dataset is quite challenging. We propose a novel generative adversarial network architecture for this problem, namely back projected pyramid network (BPPNet), that gives good performance for a variety of challenging haze conditions, including dense haze and inhomogeneous haze. Our architecture incorporates learning of multiple ...
    • GEMM-eMFIS (FRI/E): A Novel General Episodic Memory Mechanism for Fuzzy Neural Networks 

      Pang, SW; Quek, Chai; Prasad, Dilip K. (Conference object; Konferansebidrag, 2020)
      In fields such as finance, medicine, engineering, and science, making real-time predictions during transient periods characterized by sudden and large changes is a hard challenge for machine learning. Humans keep memory of these transient events, abstractly learn the most relevant rules and reuse them when similar events occur, which stems from episodic memory that allows storage and recall of similar ...
    • Trusted Computing on Privacy Sensitive Data with Diggi 

      Gjerdrum, Anders Tungeland; Pettersen, Robert; Johansen, Håvard D.; Van Renesse, Robbert; Johansen, Dag (Conference object; Konferansebidrag, 2017)