141 citations · 308 across the 23 of their papers we have counts for
24 papers
AtlasPatch: Scalable Foundation Model-based Tissue Detection and Patch Extraction for Computational Pathology
Ahmed Alagha, Christopher Leclerc, Yousef Kotp +12
Whole-slide image (WSI) preprocessing, including tissue detection and patch extraction, is critical computational pathology, yet remains a major bottleneck for large-scale workflow…
End-to-End Framework Integrating Generative AI and Deep Reinforcement Learning for Autonomous Ultrasound Scanning
Hanae Elmekki, Amanda Spilkin, Ehsan Zakeri +11
Cardiac ultrasound (US) is among the most widely used diagnostic tools in cardiology for assessing heart health, but its effectiveness is limited by operator dependence, time const…
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…
A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions
Gordon Owusu Boateng, Hani Sami, Ahmed Alagha +10
The rapid evolution of communication networks in recent decades has intensified the need for advanced Network and Service Management (NSM) strategies to address the growing demands…
On-Demand Model and Client Deployment in Federated Learning with Deep Reinforcement Learning
Mario Chahoud, Hani Sami, Azzam Mourad +3
In Federated Learning (FL), the limited accessibility of data from diverse locations and user types poses a significant challenge due to restricted user participation. Expanding cl…
CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication
Mohamad Wazzeh, Mohamad Arafeh, Hani Sami +4
In the ever-changing world of technology, continuous authentication and comprehensive access management are essential during user interactions with a device. Split Learning (SL) an…
Federated Learning and Evolutionary Game Model for Fog Federation Formation
Zyad Yasser, Ahmad Hammoud, Azzam Mourad +3
In this paper, we tackle the network delays in the Internet of Things (IoT) for an enhanced QoS through a stable and optimized federated fog computing infrastructure. Network delay…
Trust Driven On-Demand Scheme for Client Deployment in Federated Learning
Mario Chahoud, Azzam Mourad, Hadi Otrok +2
Containerization technology plays a crucial role in Federated Learning (FL) setups, expanding the pool of potential clients and ensuring the availability of specific subsets for ea…
Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities
Osama Wehbi, Sarhad Arisdakessian, Mohsen Guizani +5
Federated learning is a promising collaborative and privacy-preserving machine learning approach in data-rich smart cities. Nevertheless, the inherent heterogeneity of these urban…
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
Hani Sami, Ahmad Hammoud, Mouhamad Arafeh +14
The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual…
A Survey on Explainable Artificial Intelligence for Cybersecurity
Gaith Rjoub, Jamal Bentahar, Omar Abdel Wahab +5
The black-box nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) i…
ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme
Mario Chahoud, Hani Sami, Azzam Mourad +4
In this paper, we increase the availability and integration of devices in the learning process to enhance the convergence of federated learning (FL) models. To address the issue of…
ModularFed: Leveraging Modularity in Federated Learning Frameworks
Mohamad Arafeh, Hadi Otrok, Hakima Ould-Slimane +3
Numerous research recently proposed integrating Federated Learning (FL) to address the privacy concerns of using machine learning in privacy-sensitive firms. However, the standards…
FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices
Osama Wehbi, Sarhad Arisdakessian, Omar Abdel Wahab +4
Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several participants (e.g., Internet of Things devices) f…
Reward Shaping Using Convolutional Neural Network
Hani Sami, Hadi Otrok, Jamal Bentahar +2
In this paper, we propose Value Iteration Network for Reward Shaping (VIN-RS), a potential-based reward shaping mechanism using Convolutional Neural Network (CNN). The proposed VIN…
Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing
Anahita Mazloomi, Hani Sami, Jamal Bentahar +2
Cloud computing is a reliable solution to provide distributed computation power. However, real-time response is still challenging regarding the enormous amount of data generated by…
A two-level solution to fight against dishonest opinions in recommendation-based trust systems
Omar Abdel Wahab, Jamal Bentahar, Robin Cohen +2
In this paper, we propose a mechanism to deal with dishonest opinions in recommendation-based trust models, at both the collection and processing levels. We consider a scenario in…