Author

Ekramul Haque Tusher

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Open access Aug 2026

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,...

Abu Monsur Mohammad Fahim, Md. Eftekhar Alam, Md. Saiful Islam et al. · Informatica · 0 citations

Open access 2026

From Pneumonia to Multi-Disease: Interpretable and Uncertainty-Aware Semi-Supervised Learning Strategies for Chest X-Ray Classification

The proposed framework shows that lightweight deep learning models combined with semi-supervised learning, uncertainty estimation, and explainability can provide accurate, efficient, and clinically reliable solutions for automated chest X-ray diagnosis.

S. Mahin, Tahmina Hasan, Sara Karim et al. · IEEE Access · 0 citations

Review Open access Jul 2026

A Comprehensive Review of Long Short-term Memory Network for Email Spam Detection

This paper aims to explore the state of the art in LSTM networks for email spam detection and present a systematic approach to their use and combine them with other deep learning methods, for instance, Convolutional Neural Networks (CNNs), to enhance their ability to extract more durable features from email.

Ekramul Haque Tusher, Mohd Arfian Ismail, Nurfadhilah Idris et al. · JOIV: International Journal... · 0 citations