15 citations · 33 across the 14 of their papers we have counts for
16 papers
ReasonCLIP-58M: Visually Grounded Commonsense Reasoning Supervision for CLIP
Sicheng Zhang, Muzammal Naseer, Binzhu Xie +5
CLIP and its variants are widely adopted visual backbones in multimodal systems, but their pretraining remains dominated by descriptive image-text alignment. As downstream applicat…
FL-PBM: Pre-Training Backdoor Mitigation for Federated Learning
Osama Wehbi, Sarhad Arisdakessian, Omar Abdel Wahab +3
Backdoor attacks pose a significant threat to the integrity and reliability of Artificial Intelligence (AI) models, enabling adversaries to manipulate model behavior by injecting p…
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…
A Hybrid Swarm Intelligence Approach for Optimizing Multimodal Large Language Models Deployment in Edge-Cloud-based Federated Learning Environments
Gaith Rjouba, Hanae Elmekki, Saidul Islam +2
The combination of Federated Learning (FL), Multimodal Large Language Models (MLLMs), and edge-cloud computing enables distributed and real-time data processing while preserving pr…
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…
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…
A Multi-Modal Unsupervised Machine Learning Approach for Biomedical Signal Processing in CPR
Saidul Islam, Jamal Bentahar, Robin Cohen +1
Cardiopulmonary resuscitation (CPR) is a critical, life-saving intervention aimed at restoring blood circulation and breathing in individuals experiencing cardiac arrest or respira…
Machine Learning Innovations in CPR: A Comprehensive Survey on Enhanced Resuscitation Techniques
Saidul Islam, Gaith Rjoub, Hanae Elmekki +3
This survey paper explores the transformative role of Machine Learning (ML) and Artificial Intelligence (AI) in Cardiopulmonary Resuscitation (CPR). It examines the evolution from…
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…
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…