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Abstract The increasing number of Internet of Things (IoT) devices demands a rigid and adaptive routing methodologies to ensure the reliable communication especially for the low-power and lossy networks. The Routing Prot...

Abstract The Error Diffusion Learning Algorithm (EDLA) is a learning scheme that performs synaptically local weight updates driven by a single, globally defined error signal. Although originally proposed as an alternativ...

Federated learning (FL) in mobile edge computing (MEC) operates over wireless links with time-varying bandwidth, latency, and packet loss, which makes conventional full-model synchronization inefficient and vulnerable to...

Insider threats pose a significant and growing risk to organizational cybersecurity, with recent studies indicating a 47% increase in insider incidents from 2018 to 2022. This paper presents a comparative analysis of uns...

A high efficiency hardware integration of neural networks benefits from realizing nonlinearity, network connectivity and learning fully in a physical substrate. Multiple systems have recently implemented some or all of t...

The rapid growth and increasing complexity of Internet of Things (IoT) networks require efficient real-time monitoring and anomaly detection mechanisms. Traditional machine learning approaches often struggle to handle th...

For the structural health monitoring of composite materials, data analysis technology must be very sophisticated, capable of detecting fault patterns that are multi-level and complicated. A comprehensive deep learning pa...

The paper presents an exploratory study of the intra-organizational learning network in the context of a Spanish high-tech company. It expands the generalization of the network perspective to intra-organizational learnin...

ABSTRACT This paper presents HealthNet, a novel framework for the dynamic optimisation of healthcare transportation networks using multi‐agent reinforcement learning. HealthNet leverages a spatiotemporal dependency mod...

At the present time, the reliance on computers is increasing in all aspects of life, so it is necessary to protect computer networks and computing resources from complex attacks against the network. This is done by build...

The widespread use of social media has led to the rapid generation of opinion-rich short texts, posing increasingly complex and high-variance challenges for public discourse analysis. These texts are typically informal, ...

Abstract The increasing adoption of Industrial Internet of Things (IIoT) devices introduces significant cybersecurity and privacy challenges, particularly anomaly detection and secure data sharing. This study presents Se...

Recently, a three-terminal interface dipole modulation field-effect transistor (IDM FET) memory device has been proposed that leverages electric-field-induced dipole modulation at oxide/oxide interfaces. This device has ...

Abstract The rapid adoption of encrypted communication protocols has raised privacy levels but has also dealt a significant blow to traditional intrusion detection systems that rely on payload inspection for anomaly dete...

Background: The development of accurate artificial intelligence (AI) models for liver cancer diagnosis using contrast-enhanced computed tomography (CT) is often hindered by patient privacy regulations and considerable da...

With the rapid advancement of online education, students’ sentiment feedback serves as a pivotal factor in enhancing course quality and refining pedagogical strategies. However, conventional sentiment analysis approach...

Abstract Alkaline water electrolysis represents a promising pathway for green hydrogen production, yet comprehensive multi-physics simulation remains computationally prohibitive for practical design optimization. This st...

"A Bayesian network is a directed acyclic graph in which each node represents a variable and each arc a probabilistic dependency; they are used to provide: a compact form to represent the knowledge and exible methods of ...

Ensembles of artificial neural networks (ANN) have been used in the last years as classification/regression machines, showing improved generalization capabilities that outperform those of single networks. We propose here...

Learning algorithms for neural networks involve CPU intensive processing and consequently great effort has been done to develop parallel implemetations intended for a reduction of learning time. This work briefly descri...

Fast response, storage efficiency, fault tolerance and graceful degradation in face of scarce or spurious inputs make neural networks appropiate tools for Intelligent Computer Systems. But on the other hand, learning alg...

Laboratory practice is a fundamental aspect of computer network learning. Experiments tend to be very specific, frequently demanding changes in the local network topology and privileged access to the operating system con...

Deep learning offers a novel opportunity to achieve both high-quality and high-speed computer-generated holography (CGH). Current data-driven deep learning algorithms face the challenge that the labeled training datasets...

The rapid proliferation of Learning Management Systems (LMS) in K–12 education has generated a substantial body of research, yet how its core themes emerge, converge, and transform over time remains insufficiently unde...