Wireless sensor networks (WSNs) have many applications in the field of disaster
management, military, healthcare and environmental monitoring. Capability of WSNs is
further enhanced by the efficient localization algorithms. Localization is the process by which
a sensor node determines its own location after deployment. Neural approaches are gaining
popularity in evolving new localization algorithms that are capable of optimizing various
parameters of WSNs. In this paper, we analyse two backpropagation algorithms based on …

This study aims to present a comparative analysis of the Bayesian regularization
backpropagation and Levenberg–Marquardt training algorithms in neural networks for the
metrics prediction of damaged archaeological artifacts, of which the state of conservation is
often fragmented due to different reasons, such as ritual, use wear, or post-depositional
processes. The archaeological artifacts, specifically laminar blanks (so-called blades), come
from different sites located in the Southern Levant that belong to the Pre-Pottery B Neolithic …