Mohammed Hasanuzzaman

ECG-SurvHF: Adversarial Transformer Modeling for Heart Failure Risk Prediction

Tadiyos Hailemichael Mamo, Mohammed Hasanuzzaman, Hao Wang, Pietro Lio

gb (code pays fourni par la source)

DST-GAN: Dempster-Shafer Based Fusion for Multimodal Oversampling of Healthcare Data

Kevin; id_orcid 0009-0001-5062-4247 Fee, Mohammed Hasanuzzaman, Suneil Jain, Ross G. Murphy et autres

gb (code pays fourni par la source)

Semantic-Enhanced Representation Architecture for Automatic ICD Coding via Hyperbolic Graph Neural Networks

Pritish Patel, Mohammed Hasanuzzaman

ie (code pays fourni par la source)

DST-GAN: Dempster-Shafer based fusion for multimodal oversampling of healthcare data

Kevin; id_orcid 0009-0001-5062-4247 Fee, Mohammed Hasanuzzaman, Suneil Jain, Ross Murphy et autres

Dynamic Model Switching to Mitigate Outdated Knowledge in Large Language Models

Ramakrishna Pinninti, Sabyasachi Kamila, Ayan Mazumder, Mohammed Hasanuzzaman

The Influence of Iconicity in Transfer Learning for Sign Language Recognition

Keren Artiaga, C. Lynch, Haithem Afli, Mohammed Hasanuzzaman

Most sign language recognition research relies on Transfer Learning (TL) from vision-based datasets such as ImageNet. Some extend this to alternatively available language datasets, often focusing on signs with cross-linguistic similarities. This body of work examines the necessity of these likenesses on …

Integrating probabilistic trees and causal networks for clinical and epidemiological data

Sheresh Zahoor, Pietro Lio, Gaël Dias, Mohammed Hasanuzzaman

Healthcare decision-making requires not only accurate predictions but also insights into how factors influence patient outcomes. While traditional machine learning (ML) models excel at predicting outcomes, such as identifying high-risk patients, they are limited in addressing "what if" questions about interventions. This …

ie, ca, gb, fr, bd (code pays fourni par la source)

CAuSE: Decoding Multimodal Classifiers using Faithful Natural Language Explanation

Dibyanayan Bandyopadhyay, Soham Bhattacharjee, Mohammed Hasanuzzaman, Asif Ekbal

Abstract Multimodal classifiers function as opaque black box models. While several techniques exist to interpret their predictions, very few of them are as intuitive and accessible as natural language explanations (NLEs). To build trust, such explanations must faithfully capture the classifier’s internal …

in, gb (code pays fourni par la source)

Improving Authorship Classification with Personalized Linguistic Style in Spoken Language

Soumaya Sabri, Gaël Dias, Mohamad Ghassany, Faten Chaeib-Chakchouk et autres

National audience

DST-GAN: introducing a Generative Adversarial Network (GAN) based on Dempster-Shafer Theory (DST)

Kevin; id_orcid 0009-0001-5062-4247 Fee, Anna Jurek-Loughrey, Mohammed Hasanuzzaman, Suneil Jain et autres

Causal insights into gestational diabetes mellitus

Sheresh Zahoor, Anthony Costa Constantinou, Fiona O’Halloran, Louise O’Mahony et autres

Introduction: Gestational diabetes mellitus (GDM), defined by the onset of hyperglycaemia during pregnancy, remains the most prevalent metabolic complication in pregnancy. It is associated with increased risks of adverse maternal, neonatal, and long-term metabolic outcomes. This study aimed to identify potential causal …

ie, gb (code pays fourni par la source)

Investigating the validity of structure learning algorithms in identifying risk factors for intervention in patients with diabetes

Sheresh Zahoor, Anthony Costa Constantinou, Tim M. Curtis, Mohammed Hasanuzzaman

Diabetes, a pervasive and enduring health challenge, imposes significant global implications on health, financial healthcare systems, and societal well-being. This study undertakes a comprehensive exploration of various structural learning algorithms to discern causal pathways amongst potential risk factors influencing diabetes progression. This …

ie, gb (code pays fourni par la source)