Quantifying the uncertainty of supervised learning models plays an important role in making
more reliable predictions. Epistemic uncertainty, which usually is due to insufficient …

Hyperspectral image classification (HSIC) has become a hot research topic. Hyperspectral
imaging (HSI) has been widely used in a wide range of real-world application areas due to …

Apart from being relied upon for feeding the entire world, the agricultural sector is also
responsible for a third of the global Gross-Domestic-Product (GDP). Additionally, a majority …

Deep learning-based methods, especially deep convolutional neural network (CNN), have
proven their powerfulness in hyperspectral image (HSI) classification. On the other hand …

In recent years, deep learning models have been widely used in the field of hyperspectral
imaging. However, the training of deep learning models requires not only a large number of …

Due to the rapid development of human–computer interaction, affective computing has
attracted more and more attention in recent years. In emotion recognition …

Electroencephalography (EEG) records the electrical activity of the brain, which is an
important tool for the automatic detection of epileptic seizures. It is certainly a very heavy …

The deep forest (DF) model is built using a multilayer ensemble of forest units through
decision tree aggregation. DF presents characteristics of an easy-to-understand structure, is …

In this article, the graphics processing unit (GPU)-accelerated CatBoost (GPU-CatBoost)
algorithm for hyperspectral image classification is first introduced and comparatively studied …

Hyperspectral image (HSI) analysis generally suffers from issues such as high
dimensionality, imbalanced sample sets for different classes, and the choice of classifiers for …