141 citations · 157 across the 5 of their papers we have counts for
6 papers
TCAR-Gen: Temporal Graph Retrieval with Evidence Fusion for Knowledge-Grounded Generation
Sidra Nasir, Muhammad Noman Zahid, Rizwan Ahmed Khan
Retrieval-augmented generation systems struggle with temporal reasoning and evidence fusion when answering complex questions over historical criminal case narratives. Existing appr…
ArrhythmiaVision: Resource-Conscious Deep Learning Models with Visual Explanations for ECG Arrhythmia Classification
Zuraiz Baig, Sidra Nasir, Rizwan Ahmed Khan +1
Cardiac arrhythmias are a leading cause of life-threatening cardiac events, highlighting the urgent need for accurate and timely detection. Electrocardiography (ECG) remains the cl…
A Comprehensive Framework for Reliable Legal AI: Combining Specialized Expert Systems and Adaptive Refinement
Sidra Nasir, Qamar Abbas, Samita Bai +1
This article discusses the evolving role of artificial intelligence (AI) in the legal profession, focusing on its potential to streamline tasks such as document review, research, a…
Breast Cancer Diagnosis: A Comprehensive Exploration of Explainable Artificial Intelligence (XAI) Techniques
Samita Bai, Sidra Nasir, Rizwan Ahmed Khan +2
Breast cancer (BC) stands as one of the most common malignancies affecting women worldwide, necessitating advancements in diagnostic methodologies for better clinical outcomes. Thi…
Enhancing Breast Cancer Diagnosis in Mammography: Evaluation and Integration of Convolutional Neural Networks and Explainable AI
Maryam Ahmed, Tooba Bibi, Rizwan Ahmed Khan +1
The Deep learning (DL) models for diagnosing breast cancer from mammographic images often operate as "black boxes", making it difficult for healthcare professionals to trust and un…
Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond
Sidra Nasir, Rizwan Ahmed Khan, Samita Bai
In the past decade, the deployment of deep learning (Artificial Intelligence (AI)) methods has become pervasive across a spectrum of real-world applications, often in safety-critic…