1 citations · 1 across the 2 of their papers we have counts for
6 papers
Interpretable Heart Disease Prediction via a Weighted Ensemble Model: A Large-Scale Study with SHAP and Surrogate Decision Trees
Md Abrar Hasnat, Md Jobayer, Md. Mehedi Hasan Shawon +1
Cardiovascular disease (CVD) remains a critical global health concern, demanding reliable and interpretable predictive models for early risk assessment. This study presents a large…
SS-DPPN: A self-supervised dual-path foundation model for the generalizable cardiac audio representation
Ummy Maria Muna, Md Mehedi Hasan Shawon, Md Jobayer +3
The automated analysis of phonocardiograms is vital for the early diagnosis of cardiovascular disease, yet supervised deep learning is often constrained by the scarcity of expert-a…
FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis
Md. Sajeebul Islam Sk., Md Jobayer, Md Mehedi Hasan Shawon +1
Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide, underscoring the importance of accurate and scalable diagnostic systems. Electrocardiogram (ECG) analy…
SSTAF: Spatial-Spectral-Temporal Attention Fusion Transformer for Motor Imagery Classification
Ummay Maria Muna, Md. Mehedi Hasan Shawon, Md Jobayer +2
Brain-computer interfaces (BCI) in electroencephalography (EEG)-based motor imagery classification offer promising solutions in neurorehabilitation and assistive technologies by en…
CSTRL: Context-Driven Sequential Transfer Learning for Abstractive Radiology Report Summarization
Mst. Fahmida Sultana Naznin, Adnan Ibney Faruq, Mostafa Rifat Tazwar +3
A radiology report comprises several sections, including the Findings and Impression of the diagnosis. Automatically generating the Impression from the Findings is crucial for redu…
Attentive Dilated Convolution for Automatic Sleep Staging using Force-directed Layout
Md Jobayer, Md Mehedi Hasan Shawon, Tasfin Mahmud +2
Sleep stages play an important role in identifying sleep patterns and diagnosing sleep disorders. In this study, we present an automated sleep stage classifier called the Attentive…