activity
20162025
most citedASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion Modeling

2 citations · 2 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CL2025

Scaling Arabic Medical Chatbots Using Synthetic Data: Enhancing Generative AI with Synthetic Patient Records

Abdulrahman Allam, Seif Ahmed, Ali Hamdi +1

The development of medical chatbots in Arabic is significantly constrained by the scarcity of large-scale, high-quality annotated datasets. While prior efforts compiled a dataset o…

cs.CL2025

MSLEF: Multi-Segment LLM Ensemble Finetuning in Recruitment

Omar Walid, Mohamed T. Younes, Khaled Shaban +2

This paper presents MSLEF, a multi-segment ensemble framework that employs LLM fine-tuning to enhance resume parsing in recruitment automation. It integrates fine-tuned Large Langu…

cs.CL2025

Augmented Fine-Tuned LLMs for Enhanced Recruitment Automation

Mohamed T. Younes, Omar Walid, Khaled Shaban +2

This paper presents a novel approach to recruitment automation. Large Language Models (LLMs) were fine-tuned to improve accuracy and efficiency. Building upon our previous work on…

cs.CL2025

Two-Stage Quranic QA via Ensemble Retrieval and Instruction-Tuned Answer Extraction

Mohamed Basem, Islam Oshallah, Ali Hamdi +2

Quranic Question Answering presents unique challenges due to the linguistic complexity of Classical Arabic and the semantic richness of religious texts. In this paper, we propose a…

cs.CL2025

An Ensemble Classification Approach in A Multi-Layered Large Language Model Framework for Disease Prediction

Ali Hamdi, Malak Mohamed, Rokaia Emad +1

Social telehealth has made remarkable progress in healthcare by allowing patients to post symptoms and participate in medical consultations remotely. Users frequently post symptoms…

cs.CR2025

MultiFuzz: A Dense Retrieval-based Multi-Agent System for Network Protocol Fuzzing

Youssef Maklad, Fares Wael, Ali Hamdi +2

Traditional protocol fuzzing techniques, such as those employed by AFL-based systems, often lack effectiveness due to a limited semantic understanding of complex protocol grammars…