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Small data materials design with machine learning : when the average model knows best Ghent University Hasselt University
Machine learning is quickly becoming an important tool in modern materials design. Where many of its successes are rooted in huge datasets, the most common applications in academic and industrial materials design deal with datasets of at best a few tens of data points. Harnessing the power of machine learning in this context is, therefore, of considerable importance. In this work, we investigate the intricacies introduced by these small ...
Machine learning algorithms for stratigraphy classification on uranium deposits Ghent University
Machine learning today becomes more and more effective instrument to solve many particular problems, where there are difficulties to apply well known and described math model. In other words - it is a great tool to describe non-linear phenomena. We tried to use this technique to improve existing process of stratigraphy, and reduce costs on site by applying computer leaded predictions on the basis of existing on-field collected data. Article ...
Prediction of delayed graft function after kidney transplantation : comparison between logistic regression and machine learning methods Ghent University
Root cause analysis of compressor failure by machine learning Ghent University
Incorporating unmodeled dynamics into first-principles models through machine learning Ghent University
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 Ghent University
Toward an integrated machine learning model of a proteomics experiment Ghent University Flanders Institute for Biotechnology
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 ...