Nirupma Singh

This study presents the Cellular Deepfake Engine, a novel generative AI framework designed to produce high-fidelity, biologically plausible microscopy images across three domains: blood cells, nuclei, and cancerous cells.

This study develops an AI-based Random Forest framework that integrates genetic variants and demographic data to accurately predict speech outcomes after cochlear implantation, supporting personalized prognosis and rehabilitation strategies.

This study leverages machine learning algorithms to analyze "lub-dub" heart sounds from digital stethoscopes, achieving 98% accuracy in detecting abnormal heartbeat-related heart diseases, offering a breakthrough in non-invasive cardiac diagnostics.

This study employs a multidisciplinary approach, integrating machine learning techniques, gene expression analysis, clinical assessments, and lifestyle data to uncover potential biomarkers and therapeutic targets for Alzheimer's disease