A privacy-preserving system that leverages Gramian Angular Field (GAF) transformations, Federated Learning, and wearable sensor data to detect Freezing of Gait (FoG) in individuals with Parkinson’s Disease
Proposed a wrapper based framework for a National Clinical Data Warehouse (NCDW) designed to address the unique challenges faced by healthcare systems in developing countries
Introduced MetaBoost, a novel hybrid framework that integrates weighted averaging and iterative weight tuning to optimize synthetic data generation and improve model robustness
By integrating BERT-based word embeddings with domain-specific knowledge (i.e., MET values), FUSE-MET optimizes label merging, reducing label complexity and improving classification accuracy.
An innovative self-supervised learning framework developed for real-time detection of Freezing of Gait (FoG) in Parkinson's Disease (PD) patients, using a single triaxial accelerometer
Our patient-independent model achieved an overall accuracy of 78% in detecting FoG events using both medication ‘On’ and ‘Off’ state data.
Lung cancer and bladder cancer can be causally linked, so distinguishing between lung and bladder cancer tissues is critical for accurate diagnosis. The goal of this study was to determine the best method for classifying these tissues based on gene expression analysis data.