Abstract Currently, the Internet of Things is spreading in all areas that apply computing
resources. An important ally of the IoT is fog computing. It extends cloud computing and …

Multichannel information processing from a diverse range of channel information is highly
time-and space-complex owing to the variety and enormity of underlying data. Most of the …

An intrusion detection system has become a vital mechanism to detect a wide variety of
malicious activities in the cyber domain. However, this system still faces an important …

Current anomaly detection systems (ADSs) apply statistical and machine learning
algorithms to discover zero-day attacks, but such algorithms are vulnerable to advanced …

In this paper, we develop a novel variational Bayesian learning method for the Dirichlet
process (DP) mixture of the inverted Dirichlet distributions, which has been shown to be very …

Urban scenes are fundamental to assessing urban landscapes and analyzing the spatial
arrangements of functional zonings. Thus, it is important to obtain the information on urban …

In this paper, we focus on the variational learning of finite Dirichlet mixture models.
Compared to other algorithms that are commonly used for mixture models (such as …

We consider the problem of determining the structure of high-dimensional data without prior
knowledge of the number of clusters. Data are represented by a finite mixture model based …

This paper presents an unsupervised approach for feature selection and extraction in
mixtures of generalized Dirichlet (GD) distributions. Our method defines a new mixture …

Abstract Machine learning (ML) models have become a key component in modern world
services. In decision-making domains where human expertise is crucial, for example, for …