Blar i NTNU Open på forfatter "Bach, Kerstin"
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Explaining CBR Systems Through Retrieval and Similarity Measure Visualizations: A Case Study
Marin Veites, Paola; Bach, Kerstin (Chapter, 2022)Explainability in AI is becoming increasingly important as we delegate more safety-critical tasks to intelligent decision support systems. Case-Based Reasoning (CBR) systems are one way to build such systems. Understanding ... -
Explaining your Neighbourhood: A CBR Approach for Explaining Black-Box Models
Bayrak, Betül; Marin Veites, Paola; Bach, Kerstin (Peer reviewed; Journal article, 2022) -
Exploratory application of machine learning methods on patient reported data in the development of supervised models for predicting outcomes
Verma, Deepika; Jansen, Duncan; Bach, Kerstin; Poel, Mannes; Mork, Paul Jarle; Oude Nijeweme d’Hollosy, Wendy (Peer reviewed; Journal article, 2022)Background Patient-reported outcome measurements (PROMs) are commonly used in clinical practice to support clinical decision making. However, few studies have investigated machine learning methods for predicting PROMs ... -
External validation of prediction models for patient-reported outcome measurements collected using the selfBACK mobile app
Verma, Deepika; Bach, Kerstin; Mork, Paul Jarle (Peer reviewed; Journal article, 2023) -
Factors influencing the use of an artificial intelligence-based app (selfBACK) for tailored self-management support among adults with neck and/or low back pain
Hurmuz, Marian; Jansen-Kosterink, Stephanie; Bach, Kerstin; Mork, Paul Jarle; Hermens, HJ (Journal article; Peer reviewed, 2024)Purpose: Tailored self-management support is recommended as first-line treatment for neck and low back pain, for which mHealth applications could be promising. However, there is limited knowledge about factors influencing ... -
FishNet: A Unified Embedding For Salmon Recognition
Meidell, Espen; Sjøblom, Edvard Schreiner (Master thesis, 2019)Dagens metoder for markering og sporing av oppdrettslaks baserer seg p˚a fysisk kontakt med fisken. Denne prosessen er b˚ade ineffektiv, stressende og potensielt skadelig for laksen. Bruken av dyp læring har de siste ˚arene ... -
FishNet: A Unified Embedding for Salmon Recognition
Meidell, Espen; Sjøblom, Edvard Schreiner (Master thesis, 2019)Dagens metoder for markering og sporing av oppdrettslaks baserer seg på fysisk kontakt med fisken. Denne prosessen er både ineffektiv, stressende og potensielt skadelig for laksen. Bruken av dyp læring har de siste årene ... -
FishNet: A Unified Embedding for Salmon Recognition
Mathisen, Bjørn Magnus; Bach, Kerstin; Meidell, Espen; Måløy, Håkon; Sjøblom, Edvard Schreiner (Chapter, 2020)Identifying individual salmon can be very beneficial for the aquaculture industry as it enables monitoring and analyzing fish behavior and welfare. For aquaculture researchers identifying indi- vidual salmon is imperative ... -
From a Low-Cost Air Quality Sensor Network to Decision Support Services: Steps towards Data Calibration and Service Development
Veiga, Tiago Santos; Munch-Ellingsen, Arne; Papastergiopoulos, Christoforos; Tzovaras, Dimitrios; Kalamaras, Ilias; Bach, Kerstin; Votis, Konstantinos; Akselsen, Sigmund (Peer reviewed; Journal article, 2021)Air pollution is a widespread problem due to its impact on both humans and the environment. Providing decision makers with artificial intelligence based solutions requires to monitor the ambient air quality accurately and ... -
From data to context-aware decision making: Challenges and opportunities
Bach, Kerstin (Journal article; Peer reviewed, 2018)This extended abstract presents the research challenges for developing context-aware, explainable artificial intelligence challenges and briefly presents the opportunities ahead when utilizing the vast amount of data generated. -
Harth: A human activity recognition dataset for machine learning
Logacjov, Aleksej; Bach, Kerstin; Kongsvold, Atle; Bårdstu, Hilde Bremseth; Mork, Paul Jarle (Peer reviewed; Journal article, 2021)Existing accelerometer-based human activity recognition (HAR) benchmark datasets that were recorded during free living suffer from non-fixed sensor placement, the usage of only one sensor, and unreliable annotations. We ... -
Human Activity Recognition With Two Body-Worn Accelerometer Sensors
Hessen, Hans-Olav; Tessem, Astrid Johnsen (Master thesis, 2016)Data used in studies about physical activity is primarily collected from questionnaires and other subjective methods, which may lead to biased and inaccurate data. As subjective data collection methods have shown to be ... -
Improving Labour Inspection Efficiency via Machine Learning
Flogard, Eirik Lund (Doctoral theses at NTNU;2024:130, Doctoral thesis, 2024)Labour inspections are carried out nationwide by governmental agencies in countries that have ratified the International Labour Organization’s Labour Inspection Convention (1947), to enforce decent working conditions and ... -
Individually tailored self-management app-based intervention (selfBACK) versus a self-management web-based intervention (e-Help) or usual care in people with low back and neck pain referred to secondary care: protocol for a multiarm randomised clinical trial
Marcuzzi, Anna; Bach, Kerstin; Nordstoga, Anne Lovise; Bertheussen, Gro Falkener; Ashikhmin, Ilya; Boldermo, Nora; Kvarner, Else-Norun; Nilsen, Tom Ivar Lund; Marchand, Gunn Hege; Ose, Solveig Osborg; Aasdahl, Lene; Kaspersen, Silje Lill; Bardal, Ellen Marie; Børke, Janne-Birgitte; Mork, Paul Jarle; Gismervik, Sigmund Østgård (Peer reviewed; Journal article, 2021)Introduction: Low back pain (LBP) and neck pain (NP) are common and costly conditions. Self-management is a key element in the care of persistent LBP and NP. Artificial intelligence can be used to support and tailor ... -
Information-Driven Adaptive Sensing Based on Deep Reinforcement Learning
Murad, Abdulmajid; Kraemer, Frank Alexander; Bach, Kerstin; Taylor, Gavin (Chapter, 2020)In order to make better use of deep reinforcement learning in the creation of sensing policies for resource-constrained IoT devices, we present and study a novel reward function based on the Fisher information value. This ... -
IoT Sensor Gym: Training Autonomous IoT Devices with Deep Reinforcement Learning
Murad, Abdulmajid Abdullah Yahya; Kraemer, Frank Alexander; Bach, Kerstin; Taylor, Gavin (Chapter, 2019)We describe IoT Sensor Gym, a framework to train the behavior of constrained IoT devices using deep reinforcement learning. We focus on the main architectural choices to align problems from the IoT domain with cutting-edge ... -
Large-Scale Pre-Training for Dual-Accelerometer Human Activity Recognition
Logacjov, Aleksej; Herland, Sverre; Ustad, Astrid; Bach, Kerstin (Peer reviewed; Journal article, 2023) -
Learning neural representations for the processing of temporal data in deep neutral networks
Måløy, Håkon (Doctoral theses at NTNU;2023:6, Doctoral thesis, 2023)Ever since the third spring of artificial intelligence a decadeago, representation learning through deep neural networks hasbeen the dominating approach for most research in machinelearning. However, typical deep neural ... -
Learning similarity measures from data
Mathisen, Bjørn Magnus; Aamodt, Agnar; Langseth, Helge; Bach, Kerstin (Journal article; Peer reviewed, 2019)Defining similarity measures is a requirement for some machine learning methods. One such method is case-based reasoning (CBR) where the similarity measure is used to retrieve the stored case or a set of cases most similar ... -
A Machine Learning Classifier for Detection of Physical Activity Types and Postures During Free-Living
Bach, Kerstin; Kongsvold, Atle Austnes; Bårdstu, Hilde Bremseth; Bardal, Ellen Marie; Kjærnli, Håkon Slåtten; Herland, Sverre; Logacjov, Aleksej; Mork, Paul Jarle (Journal article; Peer reviewed, 2021)Accelerometer-based measurements of physical activity types are commonly used to replace self-reports. To advance the field, it is desirable that such measurements allow accurate detection of key daily physical activity ...