Deep neural networks (DNN) have remarkably progressed in applications involving large
and complex datasets but have been criticized as a black-box. This downside has recently …

This paper presents a review of the central theories involved in hybrid models based on
fuzzy systems and artificial neural networks, mainly focused on supervised methods for …

As one of the three pillars in computational intelligence, fuzzy systems are a powerful
mathematical tool widely used for modelling nonlinear problems with uncertainties. Fuzzy …

Information fusion is an essential part of numerous engineering systems and biological
functions, eg, human cognition. Fusion occurs at many levels, ranging from the low-level …

Deep learning methods have recently found widespread adoption for remote sensing tasks,
particularly in image or pixel classification. Their flexibility and versatility has enabled …

Aerial scene classification is an active and challenging problem in high-resolution remote
sensing imagery understanding. Deep learning models, especially convolutional neural …

An evolving intelligent system (EIS) is able to self-update its system structure and meta-
parameters from streaming data. However, since the majority of EISs are implemented on a …

Biometric technology knows a large attention in the recent years. In the biometric security
systems, the personal identity recognition depends on their behavioral, biological or …

This paper introduces a novel self-training hierarchical prototype-based approach for semi-
supervised classification. The proposed approach firstly identifies meaningful prototypes …

Adaptive boosting (AdaBoost) is a widely used technique to construct a stronger ensemble
classifier by combining a set of weaker ones. Zero-order fuzzy inference systems (FISs) are …