most citedExplainable Ensemble-Based Machine Learning Models for Detecting the Presence of Cirrhosis in Hepatitis C Patients

26 citations · 26 across the 1 of their papers we have counts for

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6 papers

cs.AI202626 cited

Explainable Ensemble-Based Machine Learning Models for Detecting the Presence of Cirrhosis in Hepatitis C Patients

Abrar Alotaibi, Lujain Alnajrani, Nawal Alsheikh +5

Hepatitis C is a liver infection caused by a virus, which results in mild to severe inflammation of the liver. Over many years, hepatitis C gradually damages the liver, often leadi…

cs.CL2026

Large Language Models Hallucination: A Comprehensive Survey

Aisha Alansari, Hamzah Luqman

Large language models (LLMs) have transformed natural language processing, achieving remarkable performance across diverse tasks. However, their impressive fluency often comes at t…

cs.CL2025

AraReasoner: Evaluating Reasoning-Based LLMs for Arabic NLP

Ahmed Hasanaath, Aisha Alansari, Ahmed Ashraf +3

Large language models (LLMs) have shown remarkable progress in reasoning abilities and general natural language processing (NLP) tasks, yet their performance on Arabic data, charac…

cs.CL2025

Pearl: A Multimodal Culturally-Aware Arabic Instruction Dataset

Fakhraddin Alwajih, Samar M. Magdy, Abdellah El Mekki +34

Mainstream large vision-language models (LVLMs) inherently encode cultural biases, highlighting the need for diverse multimodal datasets. To address this gap, we introduce PEARL, a…

cs.CL2025

AraHalluEval: A Fine-grained Hallucination Evaluation Framework for Arabic LLMs

Aisha Alansari, Hamzah Luqman

Recently, extensive research on the hallucination of the large language models (LLMs) has mainly focused on the English language. Despite the growing number of multilingual and Ara…

cs.CL2025

Multi-task Learning with Active Learning for Arabic Offensive Speech Detection

Aisha Alansari, Hamzah Luqman

The rapid growth of social media has amplified the spread of offensive, violent, and vulgar speech, which poses serious societal and cybersecurity concerns. Detecting such content…