Amir Masoud Rahmani

RPGCN: A relation-preserving graph convolutional network for enhanced node feature prediction in recommender systems

This work introduces RPGCN (Relation-Preserving Graph Convolutional Network), a novel hybrid framework that unifies early and intermediate fusion strategies with multi-branch Graph Attention Networks (GATs) for enhanced relation modeling.

An autonomous system for resource management and improved intrusion detection in fog computing and the internet of things

A Q-learning-based mobility-aware tree routing protocol in wireless body area networks

This study proposes a Q-learning-based mobility-aware tree routing protocol (QMTR) for WBANs integrated with vehicular ad hoc networks (VANETs) to support reliable transmission of passengers’ health data to remote medical centers.

AxisGeo: proactive axis-guided 3D geographic routing with local refinement and void avoidance in flying Ad hoc networks

The performance of AxisGeo was evaluated against A-Geo, UF-GPSR, and GPSR under three simulation scenarios involving variations in UAV density, UAV speed, and network traffic and indicates that AxisGeo provides improved routing performance across the evaluated FANET scenarios.

ARFOR: adaptive random forest and owl optimization for energy-efficient routing in WSN-IoT

ASGRR: Adaptive Swarm-Guided Graph Policy Routing for energy-efficient WSN-IoT networks

This paper proposes ASGRR (Adaptive Swarm-Guided Graph Policy Routing), a novel hybrid routing framework that integrates Message Passing Neural Networks, Policy Gradient Reinforcement Learning (PGRL), and the Artificial Bee Colony algorithm in a self-adaptive hybrid form.

TLQ-Geo: a two-level q-learning-based geographic routing protocol for flying ad hoc networks

A two-level Q-learning-based geographic routing protocol called TLQ-Geo for FANETs, which significantly reduces convergence time and computational overhead and integrates hierarchical decision-making with adaptive reinforcement learning.