Browsing Fakultet for medisin og helsevitenskap (MH) by Subject "Machine learning"
Now showing items 1-5 of 5
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Assisted Cement Log Interpretation Using Machine Learning
(Peer reviewed; Journal article, 2022)The Assisted Cement Log Interpretation Project has used machine learning (ML) to create a tool that interprets cement logs by predicting a predefined set of annular condition codes used in the cement log interpretation ... -
Automatic interpretation of cement evaluation logs from cased boreholes using supervised deep neural networks
(Peer reviewed; Journal article, 2020)The integrity of cement in cased boreholes is typically evaluated using well logging. However, well logging results are complex and can be ambiguous, and decisions associated with significant risks may be taken based on ... -
Better Automatic Interpretation of Cement Evaluation Logs through Feature Engineering
(Chapter, 2021)We build systems to automatically interpret cement evaluation logs using supervised machine learning (ML). Such systems can provide instant rough interpretations that may then be used as a basis for human interpretation. ... -
Better Automatic Interpretation of Cement Evaluation Logs through Feature Engineering
(Journal article; Peer reviewed, 2021)We investigate systems to automatically interpret cement evaluation logs using supervised machine learning (ML). Such systems can provide instant rough interpretations that may then be used as a basis for human interpretation. ... -
Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk
(Peer reviewed; Journal article, 2022)Importance Early identification of cerebral palsy (CP) is important for early intervention, yet expert-based assessments do not permit widespread use, and conventional machine learning alternatives lack validity. Objective ...