Viser treff 161-180 av 483

    • Unsupervised and supervised learning for the reliability analysis of complex systems 

      Gámiz, María Luz; Navas-Gómez, Fernando; Nozal Cañadas, Rafael; Raya-Miranda, Rocío (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-03-18)
      In this paper, a strategy to deal with high-dimensional reliability systems with multiple correlated components is proposed. The goal is to construct a state function that enables the classification of the states of components in one of two categories, that is, failure and operative, in case of dealing with a large number of units in the system. To this end, it is proposed a new algorithm that ...
    • The third country problem under the GDPR: enhancing protection of data transfers with technology 

      Juliussen, Bjørn Aslak; Kozyri, Elisavet; Johansen, Dag; Rui, Jon Petter (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-07-19)
      The overall objective of the General Data Protection Regulation (GDPR)1 is two-fold: To contribute to the protection of privacy and personal data and to promote the free flow of personal data within the protected area2 through uniform regulations and homogenized interpretations of those regulations.<p> <p>If a controller or processor in the protected area (the exporter) transfers personal data ...
    • An investigation of combined effect of infill pattern, density, and layer thickness on mechanical properties of 3D printed ABS by fused filament fabrication 

      Agrawal, Anant Prakash; Kumar, Virendra; Kumar, Jitendra; Paramasivam, Prabhu; Dhanasekaran, Seshathiri; Prasad, Lalta (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-05-23)
      Additive manufacturing technology and its benefits have a significant impact on different industrial applications. The 3D printing technologies help manufacture lightweight intricate geometrical designs with enhanced strengths. The present study investigates the blended effects of previously recommended parameters of different infill patterns (line, triangle, and concentric) and infill densities ...
    • Analyzing Mitochondrial Morphology Through Simulation Supervised Learning 

      Punnakkal, Abhinanda Ranjit; Godtliebsen, Gustav; Somani, Ayush; Acuna Maldonado, Sebastian Andres; Birgisdottir, Åsa birna; Prasad, Dilip K.; Horsch, Alexander; Agarwal, Krishna (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-03-03)
      The quantitative analysis of subcellular organelles such as mitochondria in cell fluorescence microscopy images is a demanding task because of the inherent challenges in the segmentation of these small and morphologically diverse structures. In this article, we demonstrate the use of a machine learning-aided segmentation and analysis pipeline for the quantification of mitochondrial morphology in ...
    • Framework of Transactive Energy Market Strategies for Lucrative Peer-to-Peer Energy Transactions 

      Loganathan, Arun S.; Ramachandran, Vijayapriya; Perumal, Angalaeswari Sendraya; Dhanasekaran, Seshathiri; Lakshmaiya, Natrayan; Paramasivam, Prabhu (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-12-20)
      Leading to the enhancement of smart grid implementation, the peer-to-peer (P2P) energy transaction concept has grown dramatically in recent years allowing the end-users to successfully exchange their excess generation and demand in a more profitable way. This paper presents local energy market (LEM) architecture with various market strategies for P2P energy trading among a set of end-users ...
    • Method for Designing Semantic Annotation of Sepsis Signs in Clinical Text 

      Yan, Melissa Y.; Gustad, Lise Tuset; Høvik, Lise Husby; Nytrø, Øystein (Chapter; Bokkapittel, 2023)
      Annotated clinical text corpora are essential for machine learning studies that model and predict care processes and disease progression. However, few studies describe the necessary experimental design of the annotation guideline and annotation phases. This makes replication, reuse, and adoption challenging. Using clinical questions about sepsis, we designed a semantic annotation guideline to ...
    • Selection of Response Reduction Factor Considering Resilience Aspect 

      Prasanth, S.; Ghosh, Goutam; Gupta, Praveen Kumar; Kumar, Virendra; Paramasivam, Prabhu; Dhanasekaran, Seshathiri (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-02-27)
      The selection of an adequate response reduction factor (R) in the seismic design of a reinforced concrete building is critical to the building’s seismic response. To construct a robust structure, the R factor should be chosen based on the building’s resilience performance. Since no background was provided for the selection of R factors, the study focuses on the right selection of R factors in relation ...
    • A Telemedicine System Intervention for Patients With Type 1 Diabetes: Pilot Feasibility Crossover Intervention Study 

      Vlasakova, Martina; Muzik, Jan; Holubova, Anna; Fiala, Dominik; Årsand, Eirik; Urbanová, Jana; Žďárská, Denisa Janíčková; Brabec, Marek; Brož, Jan (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-04-28)
      Background: Today’s diabetes-oriented telemedicine systems can gather and analyze many parameters like blood glucose levels, carbohydrate intake, insulin doses, and physical activity levels (steps). Information collected can be presented to patients in a variety of graphical outputs. Despite the availability of several technical means, a large percentage of patients do not reach the goals established ...
    • Quantifying athlete wellness: Investigating the predictive potential of subjective wellness reports through a player monitoring system 

      Johansen, Dag; Pettersen, Susann Dahl; Alexandersen, Andreas (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-05-25)
      This study investigated the potential of self-reported wellness data from a player monitoring system and its predictive power of individual match performance among a female professional football player cohort. Using longitudinal data collected from the Pm Reporter Pro mobile application and corresponding individual performance scores (InStat Index), the study investigated if pre-match perceived ...
    • Health research requires efficient platforms for data collection from personal devices 

      Johannessen, Erlend; Henriksen, André; Årsand, Eirik; Horsch, Alexander; Johansson, Jonas; Hartvigsen, Gunnar (Journal article; Tidsskriftartikkel; Peer reviewed, 2023)
      Data from consumer-based devices for collecting personal health-related data could be useful in diagnostics and treatment. This requires a flexible and scalable software and system architecture to handle the data. This study examines the existing mSpider platform, addresses shortcomings in security and development, and suggests a full risk analysis, a more loosely coupled component- based system for ...
    • Data collection and analysis methods for smart nudging to promote physical activity: Protocol for a mixed methods study 

      Dhanasekaran, Seshathiri; Andersen, Anders; Karlsen, Randi; Håkansson, Anne; Henriksen, André (Journal article; Tidsskriftartikkel; Peer reviewed, 2023)
      New digital technologies like activity trackers, nudge concepts, and approaches can inspire and improve personal health. There is increasing interest in employing such devices to monitor people’s health and well-being. These devices can continually gather and examine health-related information from people and groups in their familiar surroundings. Context-aware nudges can assist people in self-managing ...
    • Capturing Nutrition Data for Sports: Challenges and Ethical Issues 

      Sharma, Aakash; Czerwinska, Katja P; Johansen, Dag; Dagenborg, Håvard (Conference object; Konferansebidrag, 2023-01)
      Nutritionplaysakeyroleinanathlete’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 Image-Based ...
    • Using machine learning to provide automatic image annotation for wildlife camera traps in the Arctic 

      Thom, Håvard; Bjørndalen, John Markus; Kleiven, Eivind Flittie; Soininen, Eeva M; Killengreen, Siw Turid; Ehrich, Dorothee; Ims, Rolf Anker; Anshus, Otto; Horsch, Alexander (Chapter; Bokkapittel, 2017)
      The arctic tundra is considered the terrestrial biome expected to be most impacted by climate change, with temperatures projected to increase as much as 10 °C by the turn of the century. The Climate-ecological Observatory for Arctic Tundra (COAT) project monitors the climate and ecosystems using several sensor types. We report on results from projects that automate image annotations from two of the ...
    • Prototyping a Diet Self-management System for People with Diabetes with Cultural Adaptable User Interface Design 

      Lee, Eunji; Årsand, Eirik; Choi, Yoon-Hee; Østengen, Geir; Sato, Keiichi; Hartvigsen, Gunnar (Chapter; Bokkapittel, 2014-08-22)
      Diet management is a critical part of diabetes selfmanagement. This project developed a working prototype application on Android-based mobile phone called SMART CARB that assists people with diabetes to self-manage their diet. The system particularly focused on monitoring carbohydrate intake in order to control their blood glucose levels. The project was positioned as a research extension to the ...
    • Counterfactual Explainable Gastrointestinal and Colonoscopy Image Segmentation 

      Singh, Divij; Somani, Ayush; Horsch, Alexander; Prasad, Dilip K. (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-04-26)
      Segmenting medical images accurately and reliably is crucial for disease diagnosis and treatment. Due to the wide assortment of objects’ sizes, shapes, and scanning modalities, it has become more challenging. Many convolutional neural networks (CNN) have recently been designed for segmentation tasks and achieved great success. This paper presents an optimized deep learning solution using DeepLabv3+ ...
    • A Self-Configuration Controller To Detect, Identify, and Recover Misconfiguration At IoT Edge Devices and Containerized Cluster System 

      Elgazazz, Areeg Samir Ahmed; Dagenborg, Håvard (Journal article; Tidsskriftartikkel; Peer reviewed, 2023)
      Securing workloads and information flow against misconfiguration in container-based clusters and edge medical devices is an important part of overall system security. This paper presented a controller that analyzes the misconfiguration, maps the observation to its hidden misconfiguration type, and selects the optimal recovery policy to maximize the performance of defined metrics. In the future, we ...
    • Including patient-generated health data in electronic health records – a solution for CGM-data 

      Randine, Pietro; Pape-Haugaard, Louise; Hartvigsen, Gunnar; Årsand, Eirik (Journal article; Tidsskriftartikkel; Peer reviewed, 2023)
      Patients with diabetes and health personnel do not have an optimal way of interacting. Health personnel must use multiple ICT systems, such as third-party companies’ services, to access health-related data from diverse vendors’ CGM platforms and Electronic Health Record (EHR). Furthermore, other health-related data like physical activities, quality of life or well-being is often discussed but ...
    • Technical Viewpoint of Challenges, Opportunities, and Future Directions of Policy Change and Information-Flow in Digital Healthcare Systems 

      Elgazazz, Areeg Samir Ahmed; Johansen, Håvard D. (Chapter; Bokkapittel, 2022)
      Digital healthcare systems often run on heterogeneous devices in a distributed multi-cluster environment, and maintain their healthcare policies for managing data, securing information flow, and controlling interactions among systems components. As healthcare systems become more digitally distributed, lack of integration and safe interpretation between heterogeneous systems clusters become ...
    • Automatic thumbnail selection for soccer videos using machine learning 

      Husa, Andreas; Midoglu, Cise; Hammou, Malek; Hicks, Steven; Johansen, Dag; Kupka, Tomas; Riegler, Michael; Halvorsen, Pål (Chapter; Bokkapittel, 2022-08-05)
      Thumbnail selection is a very important aspect of online sport video presentation, as thumbnails capture the essence of important events, engage viewers, and make video clips attractive to watch. Traditional solutions in the soccer domain for presenting highlight clips of important events such as goals, substitutions, and cards rely on the manual or static selection of thumbnails. However, such ...
    • Predicting Tacrolimus Exposure in Kidney Transplanted Patients Using Machine Learning 

      Storås, Andrea; Åsberg, Anders; Halvorsen, Pål; Riegler, Michael Alexander; Strumke, Inga (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-08-31)
      Tacrolimus is one of the cornerstone immunosup-pressive drugs in most transplantation centers worldwide following solid organ transplantation. Therapeutic drug monitoring of tacrolimus is necessary in order to avoid rejection of the transplanted organ or severe side effects. However, finding the right dose for a given patient is challenging, even for experienced clinicians. Consequently, a tool that ...