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 …

Recently, deep neural networks (DNNs) have been applied to emotion recognition tasks
based on electroencephalography (EEG), and have achieved better performance than …

Recently, few-shot learning (FSL) has been introduced for hyperspectral image (HSI)
classification with few labeled samples. However, existing FSL-based HSI classification …

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

The rapid development of remote sensing sensors has made it possible to collect airborne
hyperspectral data with high spectral and spatial resolution. Such data can provide valuable …

Variety identification of seeds is essential to ensure the purity and yield of the variety. A
model based on hyperspectral imaging technology and one‐dimensional convolutional …

Deep learning (DL) has recently been a core ingredient in modern computer vision tasks,
triggering a wave of revolutions in various fields. The hyperspectral image (HSI) …

In the last few years, human age estimation from face images attracted the attention of many
researchers in computer vision and machine learning fields. This is due to its numerous …

Deep random forest (DRF), which combines deep learning and random forest, exhibits
comparable accuracy, interpretability, low memory and computational overhead to deep …