In the process industry, streaming data is characterized of high dimensionality, non-
stationarity, and nonlinearity. Furthermore, hybrid recurring concept drifts occur due to …

We propose an extension of the Cross Industry Standard Process for Data Mining
(CRISPDM) which addresses specific challenges of machine learning and data mining for …

We describe a systematic approach called reframing, defined as the process of preparing a
machine learning model (eg, a classifier) to perform well over a range of operating contexts …

Sewage sludge and polystyrene are considered very challenging for the waste management
authorities in urban centers across the globe. In the current study, catalytic co-pyrolysis of …

The growing research trends in the field of artificial intelligence have largely impacted the
healthcare sector. Thanks to the high predictive power of machine learning approaches …

Geographic datasets are usually accompanied by spatial non-stationarity–a phenomenon
that the relationship between features varies across space. Naturally, nonstationarity can be …

(PTV) margins but depend on simplifying assumptions that limit their applicability to all
disease sites and situations. More complex strategies using deformable dose accumulation …

Geographic data are information associated with a location on the surface of the Earth. They
comprise spatial attributes (latitude, longitude, and altitude) and non-spatial attributes (facts …

Many evaluation methods exist, each for a particular prediction task, and there are a number
of prediction tasks commonly performed including classification and regression. In binarised …

Ordinal classification or ordinal regression is the supervised learning problem of predicting
categories that have an ordered arrangement. Performance metrics are usually understood …