Prince Sultan University

A Blockchain-Enabled Security Framework for Cloud-Based Sensor Systems with Deep Learning-Driven Attack Classification

Cloud-integrated sensor and Internet of Things (IoT) systems enable scalable data storage, processing, and intelligent monitoring, but their distributed nature exposes network-flow and host-level data to unauthorized access, tampering, and cyberattacks. This study proposes a Weig...

A spatiotemporal attentive recurrent neural network for robust RSS based indoor localization

Indoor localization remains challenging in GPS-denied environments due to weak signal reception and non-line-of-sight conditions. Among various techniques, WLAN Fingerprinting using Received Signal Strength (RSS) has emerged as a cost-effective solution that leverages the widespr...

How perceived virtual-idol advertising appeals shape impulsive hotel booking intention: identity-activated persuasion in Saudi hospitality

Virtual idols – digitally created influencers with programmable personas and symbolic identities – are increasingly used in hospitality marketing, yet the processes through which their perceived advertising appeals shape booking-related responses remain insufficiently understood....

Adaptive modulation in 6G terahertz systems via online reinforcement learning–PSO meta-optimization

The rapid evolution of sixth-generation (6G) wireless systems has intensified the need for adaptive modulation strategies capable of operating reliably and efficiently in highly dynamic terahertz (THz) communication environments. Conventional threshold-based and static optimizati...

Machine Learning-Based Prediction of Six-Minute Walk Distance in Children with Obesity

Background: The six-minute walk test (6MWT) assesses functional exercise capacity, but existing reference equations for children with obesity rely on traditional linear regression, potentially overlooking complex, non-linear relationships between anthropometric characteristics an...

A multiplicative additive bias variational framework for accurate and interpretable brain MRI segmentation in cloud-based medical imaging systems

Abstract Accurate brain magnetic resonance imaging (MRI) segmentation remains challenging due to intensity inhomogeneity, acquisition-related bias fields, and ambiguous tissue boundaries. To address these challenges, a Multiplicative–Additive Bias Single-Function Dual-Level-Set (...