Jakir Hossen

Abstract Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide, motivating reproducible benchmark studies on accurate classification methods with calibrated probability estimates. In this paper, we propose a deep learning framework based on a Variational Recurrent Autoencoder (VRAE) with uncertainty estimation for CVD classification from static tabular clinical records represented as synthetic noise-augmented pseudo-sequences.

Abstract Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide, motivating reproducible benchmark studies on accurate classification methods with calibrated probability estimates. In this paper, we propose a deep learning framework based on a Variational Recurrent Autoencoder (VRAE) with uncertainty estimation for CVD classification from static tabular clinical records represented as synthetic noise-augmented pseudo-sequences.

Abstract 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 emerging diseases.