This survey samples from the ever-growing family of adaptive resonance theory (ART)
neural network models used to perform the three primary machine learning modalities …

Validation is one of the most important aspects of clustering, particularly when the user is
designing a trustworthy or explainable system. However, most clustering validation …

This paper presents a novel adaptive resonance theory (ART)-based modular architecture
for unsupervised learning, namely the distributed dual vigilance fuzzy ART (DDVFA) …

The clustering structures formed by Adaptive Resonance Theory (ART) and many other
algorithms are dependent on input presentation/permutation order. In this work, we exploit …

Abstract" The problem of clustering is one of the most widely studied area in data mining and
machine learning. Adaptive resonance theory (ART), an unsupervised learning clustering …

Drilling boreholes through hydrogen sulfide (H2S) bearing formations such as Umm Er
Radhuma and Tayarat Formations poses a critical challenge for the oil and gas industry in …

Hierarchical clustering is an important and powerful but computationally extensive
operation. Its complexity motivates the exploration of highly parallel approaches such as …

A hardware implementation design of parallelized fuzzy Adaptive Resonance Theory neural
network is described and simulated. Parallel category choice and resonance are …

The deep convolutional neural network performs well in current computer vision tasks.
However, most of these models are trained on an aforehand complete dataset. New …

This editorial summarizes selected key contributions of Prof. Stephen Grossberg and
describes the papers in this 80th birthday special issue in his honor. His productivity …