5 citations · 5 across the 2 of their papers we have counts for
9 papers
Toward Trustworthy Large Language Model Agents in Healthcare
Hadi Hasan, Safaa Salman, Adam Tai Abou Dargham +2
Healthcare appointment scheduling remains a persistent operational bottleneck, driven by manual coordination, fragmented legacy systems, and high administrative overhead. These ine…
Detecting Quishing Attacks with Machine Learning Techniques Through QR Code Analysis
Fouad Trad, Ali Chehab
The rise of QR code-based phishing ("Quishing") poses a growing cybersecurity threat, as attackers increasingly exploit QR codes to bypass traditional phishing defenses. Existing d…
On the Effectiveness of Membership Inference in Targeted Data Extraction from Large Language Models
Ali Al Sahili, Ali Chehab, Razane Tajeddine
Large Language Models (LLMs) are prone to memorizing training data, which poses serious privacy risks. Two of the most prominent concerns are training data extraction and Membershi…
Chained Prompting for Better Systematic Review Search Strategies
Fatima Nasser, Fouad Trad, Ammar Mohanna +2
Systematic reviews require the use of rigorously designed search strategies to ensure both comprehensive retrieval and minimization of bias. Conventional manual approaches, althoug…
Retrieval-Augmented Few-Shot Prompting Versus Fine-Tuning for Code Vulnerability Detection
Fouad Trad, Ali Chehab
Few-shot prompting has emerged as a practical alternative to fine-tuning for leveraging the capabilities of large language models (LLMs) in specialized tasks. However, its effectiv…
CLASP: Cost-Optimized LLM-based Agentic System for Phishing Detection
Fouad Trad, Ali Chehab
Phishing websites remain a significant cybersecurity threat, necessitating accurate and cost-effective detection mechanisms. In this paper, we present CLASP, a novel system that ef…