Kranthi Kumar Lella

Abstract Automated medical image analysis plays a vital role in rare disease detection, yet existing deep learning models often struggle with severe class imbalance, limited labeled data, and subtle morphological variations. To address these challenges, this paper proposes Swin-CapsuleNet, a hybrid architecture that integrates a hierarchical Swin Transformer with capsule-based representations, tailored for rare disease classification.

Abstract Gestational diabetes mellitus, often known as GDM, is a major health issue that causes complications for mothers and requires patient data prediction models that are complex and variable. The research in question makes use of graph-based learning in order to investigate the ways in which genetic, biochemical, and demographic elements interact in a variety of different contexts.

Abstract The rapid growth of Internet scale computing has exposed critical limitations in existing edge cloud coordination mechanisms, particularly in terms of resilience, energy efficiency, and adaptability under heterogeneous and dynamic environments. Current optimization and learning based approaches often suffer from slow convergence, limited exploration capability, and poor robustness when managing distributed edge cloud resources.