Plants are constantly challenged by changing environmental conditions that include abiotic
stresses. These are limiting their development and productivity and are subsequently …

This paper proposes a novel integrated model based on multi-criteria decision-making
(MCDM) method to assess and rank the feeder sections to optimally locate fault indicators in …

In this paper, we consider the problem of distributed unsupervised clustering, where training
data is partitioned over a set of agents, whose interaction happens over a sparse, but …

Robustly determining the optimal number of clusters in a data set is an essential factor in a
wide range of applications. Cluster enumeration becomes challenging when the true …

In the alloy design and development process, a wealth of atomically resolved structural high-
resolution transmission electron microscopy (HRTEM) images are produced. Identifying the …

Brain–computer interfaces (BCIs) provide the measurement of the activities of central
nervous systems, and they convert the activities into artificial outputs. Currently, one of the …

Estimating the number of clusters in an observed data set poses a major challenge in cluster
analysis. In the literature, the original Bayesian Information Criterion (BIC) is used as a …

Distributed signal processing for wireless sensor networks enables that different devices
cooperate to solve different signal processing tasks. A crucial first step is to answer the …

The Bayesian information criterion is generic in the sense that it does not include information
about the specific model selection problem at hand. Nevertheless, it has been widely used …

Community detection refers to finding densely connected groups of nodes in graphs. In
important applications, such as cluster analysis and network modelling, the graph is sparse …