M. Hossen

Explainable Graph Convolutional Network Framework for Robust ECG Arrhythmia Classification and Patient-Level Risk Stratification

Accurate and interpretable detection of arrhythmias from electrocardiogram (ECG) signals plays a critical role in the early cardiac risk assessment and patient management. This paper presents a novel, explainable framework that leverages a dynamic Graph Convolutional Network (GCN) to model ECG beat sequences as graphs,...

Temporal and Relational Graph Neural Networks for Fraud Detection in Transaction Networks

Financial fraud in credit card and bank transactions remains a significant challenge, as traditional detection systems often struggle to keep pace with evolving fraudulent strategies. This paper addresses the problem by formulating fraud detection as a supervised link prediction task in transaction networks, with the c...

A Multi-Input Neural Network for Early Detection of Neurological Disordersfrom Vocal and Sleep Data

A multi-input deep neural network that integrates vocal biomarkers and clinical sleep-related features to improve diagnostic accuracy and demonstrates consistent improvements over early and late fusion strategies, demonstrating the benefit of modality-specific representation learning.

Automated fault detection in solar panels using customized EfficientNetB0 with explainable AI solution for real-time monitoring

A deep learning-based fault detection framework utilizing a customized EfficientNetB0 architecture for the classification of common panel defects that demonstrates consistent performance under different lighting and weather conditions, providing a robust solution for real-time solar panel condition monitoring and maint...

REDNet: An Explainable Deep Learning Framework for Mango Leaf Disease Classification

A hierarchical prototype-graph with optimal-transport matching for few-shot rice disease recognition.

A domain-specific prototype-based few-shot framework that avoids pretrained visual backbones and treats rice disease recognition as structured matching over a pathogen-aware class graph is formulated, making it well-suited for real-world agricultural deployment under limited supervision.