The use of rigorous ethological observation via machine learning techniques to understand
brain function (computational neuroethology) is a rapidly growing approach that is poised to …

Clustering refers to the task of identifying groups or clusters in a data set. In density‐based
clustering, a cluster is a set of data objects spread in the data space over a contiguous …

A new density-based clustering algorithm, RNN-DBSCAN, is presented which uses reverse
nearest neighbor counts as an estimate of observation density. Clustering is performed …

Clustering validation and identifying the optimal number of clusters are crucial in expert and
intelligent systems. However, the commonly used cluster validity indices (CVI) are not …

Ensembles for unsupervised outlier detection is an emerging topic that has been neglected
for a surprisingly long time (although there are reasons why this is more difficult than …

This paper analyses the application of Simplified Silhouette to the evaluation of k-means
clustering validity and compares it with the k-means Cost Function and the original …

Developing robust bridge health monitoring (BHM) frameworks based on the vehicle-
mounted sensing, or so-called indirect structural health monitoring (SHM) or Drive-by Bridge …

The paper presents new annotated corpora for performing stance detection on Spanish
Twitter data, most notably Health-related tweets. The objectives of this research are …

The recent geopolitical conflicts in Europe have underscored the vulnerability of the current
energy system to the volatility of energy carrier prices. In the prospect of defining robust …

Deep Neural Networks (DNNs) have been extensively used in many areas including image
processing, medical diagnostics and autonomous driving. However, DNNs can exhibit …