22 citations · 60 across the 8 of their papers we have counts for
8 papers
Poisoning Behavioral-based Worker Selection in Mobile Crowdsensing using Generative Adversarial Networks
Ruba Nasser, Ahmed Alagha, Shakti Singh +3
With the widespread adoption of Artificial intelligence (AI), AI-based tools and components are becoming omnipresent in today's solutions. However, these components and tools are p…
Comprehensive Review of Reinforcement Learning for Medical Ultrasound Imaging
Hanae Elmekki, Saidul Islam, Ahmed Alagha +12
Medical Ultrasound (US) imaging has seen increasing demands over the past years, becoming one of the most preferred imaging modalities in clinical practice due to its affordability…
CACTUS: An Open Dataset and Framework for Automated Cardiac Assessment and Classification of Ultrasound Images Using Deep Transfer Learning
Hanae Elmekki, Ahmed Alagha, Hani Sami +11
Cardiac ultrasound (US) scanning is a commonly used techniques in cardiology to diagnose the health of the heart and its proper functioning. Therefore, it is necessary to consider…
UAV-assisted Internet of Vehicles: A Framework Empowered by Reinforcement Learning and Blockchain
Ahmed Alagha, Maha Kadadha, Rabeb Mizouni +3
This paper addresses the challenges of selecting relay nodes and coordinating among them in UAV-assisted Internet-of-Vehicles (IoV). The selection of UAV relay nodes in IoV employs…
Blockchain-based Crowdsourced Deep Reinforcement Learning as a Service
Ahmed Alagha, Hadi Otrok, Shakti Singh +2
Deep Reinforcement Learning (DRL) has emerged as a powerful paradigm for solving complex problems. However, its full potential remains inaccessible to a broader audience due to its…
Influence- and Interest-based Worker Recruitment in Crowdsourcing using Online Social Networks
Ahmed Alagha, Shakti Singh, Hadi Otrok +1
Workers recruitment remains a significant issue in Mobile Crowdsourcing (MCS), where the aim is to recruit a group of workers that maximizes the expected Quality of Service (QoS).…
Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning
Ahmed Alagha, Jamal Bentahar, Hadi Otrok +2
Multi-Agent Deep Reinforcement Learning (MDRL) is a promising research area in which agents learn complex behaviors in cooperative or competitive environments. However, MDRL comes…
Adaptive Target Localization under Uncertainty using Multi-Agent Deep Reinforcement Learning with Knowledge Transfer
Ahmed Alagha, Rabeb Mizouni, Shakti Singh +2
Target localization is a critical task in sensitive applications, where multiple sensing agents communicate and collaborate to identify the target location based on sensor readings…