Parisa Khoshvaght

Causes of death across the life course in the Association of Southeast Asian Nations (ASEAN): a systematic analysis for the Global Burden of Disease Study 2023

Summary Background The drivers of mortality and their associated risk factors evolve throughout the course of life. Understanding these transitions is essential for effective, equitable, and age-appropriate health interventions. Methods As part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 20...

A smart and fast fuzzy based-trust management scheme in underwater acoustic sensor networks (UASNs)

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

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.

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.