Context: Machine learning is increasingly used in a wide set of applications ranging from
recommendation engines to autonomous systems through business intelligence and smart …

Inspired by convolutional neural networks, graph convolutional networks (GCNs) have been
proposed for processing non-Euclidean graph data and successfully been applied in …

In this paper, we propose an efficient convolutional neural network (CNN) accelerator
design, entitled RNSiM, based on the Residue Number System (RNS) as an alternative for …

The technology development greatly increases the amount of digital visual information.
Existing devices cannot efficiently process such huge amounts of data. The technical …

Medical imaging using different modalities has many problems. The main ones are low
informativeness, various distortion noises, and a large amount of information. Fusion …

A multi-criteria-guided deep learning (ML) method is proposed to reconstruct 3D porous
microstructures of cementitious materials from a single 2D image in a statistical manner …

Quasi‐chaotic generators are used for producing a pseudorandom behaviour that can be
used for encryption/decryption and secure communications, introducing an implementation …

The article proposes a neural network classification system for pigmented skin neoplasms
with a preliminary processing stage to remove hair from the images. The main difference of …

Abstract Convolutional Neural Networks (CNN) show high accuracy in pattern recognition
solving problem but have high computational complexity, which leads to slow data …

Recursive least mean p-power extreme learning machine (RLMP-ELM) is a newly proposed
online machine learning algorithm and is able to provide a robust online prediction of the …