Viser treff 21-40 av 436

    • In-network monitoring strategies for HPC cloud 

      Hemmatpour, Masoud; Larsen, Tore Heide; Kumar, Nikshubha; Gran, Ernst Gunnar (Conference object; Konferansebidrag, 2024)
      The optimized network architectures and interconnect technologies employed in high-performance cloud computing environments introduce challenges when it comes to developing monitoring solutions that effectively capture relevant network metrics. Moreover, network monitoring often involves capturing and analyzing a large volume of network traffic data. This process can introduce additional overhead ...
    • In-hospital Mortality, Readmission, and Prolonged Length of Stay Risk Prediction Leveraging Historical Electronic Patient Records 

      Bopche, Rajeev; Gustad, Lise Tuset; Afset, Jan Egil; Ehrnström, Birgitta; Damås, Jan Kristian; Nytrø, Øystein (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-09-14)
      Objective - This study aimed to investigate the predictive capabilities of historical patient records to predict patient adverse outcomes such as mortality, readmission, and prolonged length of stay (PLOS).<p> <p>Methods - Leveraging a de-identified dataset from a tertiary care university hospital, we developed an eXplainable Artificial Intelligence (XAI) framework combining tree-based and ...
    • Inquiry-Based Linear Algebra Teaching and Learning in a Flipped Classroom Framework: A Case Study 

      Fredriksen, Helge Ingvart; Rebenda, Josef; Rensaa, Ragnhild Johanne; Pettersen, Petter (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-07-17)
      Flipped Classroom (FC) approaches, which utilize video distribution via modern internet platforms, have recently gained interest as a pedagogical framework. Inquiry Based Mathematics Education (IBME) has proven to be a valid form of task design to motivate active learning and enhance classroom interactivity. This article presents a practical combination of introductory videos and inquiry-based class ...
    • A review of information sources and analysis methods for data driven decision aids in child and adolescent mental health services 

      Koochakpour, Kaban; Nytrø, Øystein; Leventhal, Bennett L.; Westbye, Odd Sverre; Røst, Thomas Brox; Koposov, Roman Alexandriovich; Frodl, Thomas; Clausen, Carolyn Elizabeth; Stien, Line Mærvoll; Skokauskas, Norbert (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-05-13)
      Objective: Clinical data analysis relies on effective methods and appropriate data. Recognizing distinctive clinical services and service functions may lead to improved decision-making. Our first objective is to categorize analytical methods, data sources, and algorithms used in current research on information analysis and decision support in child and adolescent mental health services (CAMHS). ...
    • pNNCLR: Stochastic pseudo neighborhoods for contrastive learning based unsupervised representation learning problems 

      Biswas, Momojit; Buckchash, Himanshu; Prasad, Dilip Kumar (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-05-11)
      Nearest neighbor (NN) sampling provides more semantic variations than predefined transformations for selfsupervised learning (SSL) based image recognition problems. However, its performance is restricted by the quality of the support set, which holds positive samples for the contrastive loss. In this work, we show that the quality of the support set plays a crucial role in any nearest neighbor based ...
    • Data note for gender perspectives on a flipped classroom environment 

      Fredriksen, Helge Ingvart; Rensaa, Ragnhild Johanne (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-08-20)
      The present paper is a data note related to the paper “Gender perspectives on a flipped classroom environment”. The paper drew on an in-depth analysis of the interview with a single female student called Sofia and attempted to analyze different gender related interactions between students in group work situations experienced by this student. The transcript from this interview was translated from ...
    • Using One App Only – Collecting a Comprehensive Set of Health-Related Data for Prevention of Chronic Conditions 

      Årsand, Eirik; Muzny, Miroslav; Rishaug, Tina; Wägner, Anna M.; Betancort, Carmelo; Granja, Conceicao; Soguero-Ruiz, Cristina; Hartvigsen, Gunnar; Namolosanu, Mihai; Rinnetmaki, Mikael; Henriksen, André (Journal article; Tidsskriftartikkel; Peer reviewed, 2024)
      This paper presents the design, implementation and early tests of an app that collects a comprehensive set of health-related data, as part of the EU-project WARIFA. To achieve the main aim of the project – using AI to prevent chronic conditions – a wide range of data needs to be collected and stored at a backend server for processing. The methods and elements for creating this system are presented, ...
    • Control-Driven Media: A Unifying Model for Consistent, Cross-platform Multimedia Experiences 

      Arntzen, Ingar M; Borch, Njål Trygve; Andersen, Anders (Journal article; Tidsskriftartikkel; Peer reviewed, 2024)
      Many media providers offer complementary prod-ucts on different platforms to target a diverse consumer base. Online sports coverage, for instance, may include professionally produced audio and video channels, as well as Web pages and native apps offering live statistics, maps, data visualizations, social commentary and more. Many consumers also engage in parallel usage, setting up streaming products ...
    • Social robots in research on social and cognitive development in infants and toddlers: A scoping review 

      Flatebø, Solveig; Tran, Ngoc Nha Vi; Wang, Catharina Elisabeth Arfwedson; Bongo, Lars Ailo Aslaksen (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-05-15)
      There is currently no systematic review of the growing body of literature on using social robots in early developmental research. Designing appropriate methods for early childhood research is crucial for broadening our understanding of young children’s social and cognitive development. This scoping review systematically examines the existing literature on using social robots to study social and ...
    • A Robust Framework for Distributional Shift Detection Under Sample-Bias 

      Torpmann-Hagen, Birk Sebastian Frostelid; Riegler, Michael; Halvorsen, Pål; Johansen, Dag (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-04-24)
      Deep Neural Networks have been shown to perform poorly or even fail altogether when deployed in real-world settings, despite exhibiting excellent performance on initial benchmarks. This typically occurs due to relative changes in the nature of the production data, often referred to as distributional shifts. In an attempt to increase the transparency, trustworthiness, and overall utility of deep ...
    • Deepfake detection using deep feature stacking and meta-learning 

      Naskar, Gourab; Mohiuddin, Sk; Malakar, Samir; Cuevas, Erik; Sarkar, Ram (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-02-15)
      Deepfake is a type of face manipulation technique using deep learning that allows for the replacement of faces in videos in a very realistic way. While this technology has many practical uses, if used maliciously, it can have a significant number of bad impacts on society, such as spreading fake news or cyberbullying. Therefore, the ability to detect deepfake has become a pressing need. This ...
    • Digital Psychosocial Follow-up for Childhood Critical Illness Survivors: A Qualitative Interview Study on Health Professionals' Perspectives 

      Hagen, Marte Hoff; Hartvigsen, Gunnar; Jaccheri, Maria Letizia; Papavlasopoulou, Sofia (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-07-18)
      Background: Digital solutions have been reported to provide positive psychological and social outcomes to childhood critical illness survivors, a group with an increased risk for long-term adverse psychosocial effects. Objective: To explore health professionals’ perspectives on the potential of digital psychosocial follow-up for childhood critical illness survivors.<p> <p>Methods: Using a ...
    • Concatenated Modified LeNet Approach for Classifying Pneumonia Images 

      Jaganathan, Dhayanithi; Balsubramaniam, Sathiyabhama; Sureshkumar, Vidhushavarshini; Dhanasekaran, Seshathiri (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-03-21)
      Pneumonia remains a critical health concern worldwide, necessitating efficient diagnostic tools to enhance patient care. This research proposes a concatenated modified LeNet classifier to classify pneumonia images accurately. The model leverages deep learning techniques to improve the diagnosis of Pneumonia, leading to more effective and timely treatment. Our modified LeNet architecture incorporates ...
    • Diagnostics analysis of partial discharge events of the power cables at various voltage levels using ramping behavior analysis method 

      Mishra, Sambeet; Singh, Praveen Prakash; Kiitam, Ivar; Shafiq, Muhammad; Palu, Ivo; Bordin, Chiara (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-11-16)
      Partial discharge events can occur in high-voltage cables. It can be caused by defects in the cable insulation, contamination, or a combination of both. Partial discharge in cables can lead to insulation failure and cable failure. This investigation aims to identify the trends and patterns in the internal partial discharge (PD) occurrences in the power cables when exposed to different voltage ...
    • Analyzing the MHD Bioconvective Eyring–Powell Fluid Flow over an Upright Cone/Plate Surface in a Porous Medium with Activation Energy and Viscous Dissipation 

      Peter, Francis; Sambath, Paulsamy; Dhanasekaran, Seshathiri (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-03-04)
      In the field of heat and mass transfer applications, non-Newtonian fluids are potentially considered to play a very important role. This study examines the magnetohydrodynamic (MHD) bioconvective Eyring–Powell fluid flow on a permeable cone and plate, considering the viscous dissipation (0.3 ≤ E<sub>c</sub> ≤ 0.7), the uniform heat source/sink (−0.1 ≤ Q<sub>0</sub> ≤ 0.1), and the activation energy ...
    • Incidental Data: A Survey towards Awareness on Privacy-Compromising Data Incidentally Shared on Social Media 

      Kutschera, Stefan; Slany, Wolfgang; Ratschiller, Patrick; Gursch, Sarina; Deininger, Patrick; Dagenborg, Håvard Johansen (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-02-23)
      Sharing information with the public is becoming easier than ever before through the usage of the numerous social media platforms readily available today. Once posted online and released to the public, information is almost impossible to withdraw or delete. More alarmingly, postings may carry sensitive information far beyond what was intended to be released, so-called incidental data, which raises ...
    • Revolutionizing Breast Cancer Diagnosis: A Concatenated Precision through Transfer Learning in Histopathological Data Analysis 

      Jaganathan, Dhayanithi; Balasubramaniam, Sathiyabhama; Sureshkumar, Vidhushavarshini; Dhanasekaran, Seshathiri (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-02-14)
      Breast cancer remains a significant global public health concern, emphasizing the critical role of accurate histopathological analysis in diagnosis and treatment planning. In recent years, the advent of deep learning techniques has showcased notable potential in elevating the precision and efficiency of histopathological data analysis. The proposed work introduces a novel approach that harnesses ...
    • An Improved Long Short-Term Memory Algorithm for Cardiovascular Disease Prediction 

      Revathi, T.K.; Balasubramaniam, Sathiyabhama; Sureshkumar, Vidhushavarshini; Dhanasekaran, Seshathiri (Journal article; Tidsskriftartikkel, 2024-01-23)
      Cardiovascular diseases, prevalent as leading health concerns, demand early diagnosis for effective risk prevention. Despite numerous diagnostic models, challenges persist in network configuration and performance degradation, impacting model accuracy. In response, this paper introduces the Optimally Configured and Improved Long Short-Term Memory (OCI-LSTM) model as a robust solution. Leveraging the ...
    • Response to “Microdosing: A Conceptual Framework for Use as Programming Strategy for Resistance Training in Team Sports” 

      Afonso, José; Nakamura, Fàbio Yuzo; Matias Do Vale Baptista, Ivan Andre; Rendeiro-Pinho, Gonçalo; Brito, Joao; Figueiredo, Pedro (Journal article; Tidsskriftartikkel, 2024-07-19)
      <p>This letter was written in response to the article “Microdosing: A conceptual framework for use as programming strategy for resistance training in team sports”, recently published in the <i>Strength and Conditioning Journal</i>. The article proposes a framework for implementing microdosing of resistance training across several training and competitive contexts and presents a comprehensive proposal ...
    • The Complexity of Defining and Assessing the Most Demanding Periods of Play in Team Sports: A Current Opinion 

      Lino Mesquita, Joao; Baptista, Ivan; Nakamura, Fàbio Yuzo; Casanova, Filipe; Yousefian, Farzad; Travassos, Bruno; Afonso, José (Journal article; Tidsskriftartikkel, 2024-07-19)
      In the context of training load monitoring, the most demanding periods of play (MDPs) have increasingly caught the interest of researchers. However, the MDPs analysis is currently embryonic, raising some conceptual and methodological questions. This current opinion synthesizes the methods used for the MDPs analysis while highlighting conceptual and methodological gaps and proposing a broader perspective ...