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Machine learning for fuel property predictions : a multi-task and transfer learning approach Universiteit Gent
Prediction of delayed graft function after kidney transplantation : comparison between logistic regression and machine learning methods Universiteit Gent
Root cause analysis of compressor failure by machine learning Universiteit Gent
Deep learning for infrared thermal image based machine health monitoring Universiteit Gent
Incorporating unmodeled dynamics into first-principles models through machine learning Universiteit Gent
Anomaly Detection in Multi-Wavelength Photoplethysmography Using Lightweight Machine Learning Algorithms Vrije Universiteit Brussel
Over the past few years, there has been increased interest in photoplethysmography (PPG) technology, which has revealed that, in addition to heart rate and oxygen saturation, the pulse shape of the PPG signal contains much more valuable information. Lately, the wearable market has shifted towards a multi-wavelength and multichannel approach to increase signal robustness and facilitate the extraction of other intrinsic information from the ...
Adding interpretability to predictive maintenance by machine learning on sensor data Universiteit Gent
Toward an integrated machine learning model of a proteomics experiment Universiteit Gent Vlaams Instituut voor Biotechnologie
In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluate and explore machine learning applications for realistic modeling of data from multidimensional mass spectrometry-based proteomics analysis of any sample or organism. Following this sample-to-data ...
ProteomicsML : an online platform for community-curated data sets and tutorials for machine learning in proteomics Universiteit Gent Vlaams Instituut voor Biotechnologie
Data set acquisition and curation are often the most difficult and time-consuming parts of a machine learning endeavor. This is especially true for proteomics-based liquid chromatography (LC) coupled to mass spectrometry (MS) data sets, due to the high levels of data reduction that occur between raw data and machine learning-ready data. Since predictive proteomics is an emerging field, when predicting peptide behavior in LC-MS setups, each lab ...