M. Hossen

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

: Early detection of neurological disorders is critical for effective treatment planning and improved quality of life. This study proposes a multi-input deep neural network that integrates vocal biomarkers and clinical sleep-related features to improve diagnostic accuracy. The model processes each modality through sepa...

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

Solar energy offers a sustainable solution for power generation, reducing dependence on fossil fuels. However, surface-level anomalies such as dust, snow, bird droppings, and structural damage significantly impact the operational efficiency of solar panels. This study presents a deep learning-based fault detection fram...

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.

Accurate identification of rice diseases from field images is critical for crop health monitoring and sustainable agriculture, particularly in low-resource environments. However, most deep learning approaches depend on large-scale labeled datasets and pretrained backbones, limiting their applicability to rare or emergi...