2 citations · 2 across the 12 of their papers we have counts for
12 papers
Attention-Guided Autoencoder Fusion for Insulator Defect Detection Using UAV Transmission-Line Imaging
Malak Allam, Khaled Shaban, Ali Hamdi
Automated defect detection in high-voltage transmission-line insulators remains challenging due to severe class imbalance, large scale variation, and the small spatial extent of de…
CAMO: A Class-Aware Minority-Optimized Ensemble for Robust Language Model Evaluation on Imbalanced Data
Mohamed Ehab, Ali Hamdi, Khaled Shaban
Real-world categorization is severely hampered by class imbalance because traditional ensembles favor majority classes, which lowers minority performance and overall F1-score. We p…
CMHL: Contrastive Multi-Head Learning for Emotionally Consistent Text Classification
Menna Elgabry, Ali Hamdi, Khaled Shaban
Textual Emotion Classification (TEC) is one of the most difficult NLP tasks. State of the art approaches rely on Large language models (LLMs) and multi-model ensembles. In this stu…
Enhancing Mental Health Classification with Layer-Attentive Residuals and Contrastive Feature Learning
Menna Elgabry, Ali Hamdi, Khaled Shaban
The classification of mental health is challenging for a variety of reasons. For one, there is overlap between the mental health issues. In addition, the signs of mental health iss…
Weather-Aware Transformer for Real-Time Route Optimization in Drone-as-a-Service Operations
Kamal Mohamed, Lillian Wassim, Ali Hamdi +1
This paper presents a novel framework to accelerate route prediction in Drone-as-a-Service operations through weather-aware deep learning models. While classical path-planning algo…
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…
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…
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…
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…
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…
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…
ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion Modeling
Omama Hamad, Ali Hamdi, Khaled Shaban
Effective feature representations play a critical role in enhancing the performance of text generation models that rely on deep neural networks. However, current approaches suffer…