7 papers
RealAC: A Domain-Agnostic Framework for Realistic and Actionable Counterfactual Explanations
Asiful Arefeen, Shovito Barua Soumma, Hassan Ghasemzadeh
Counterfactual explanations provide human-understandable reasoning for AI-made decisions by describing minimal changes to input features that would alter a model's prediction. To b…
SenseCF: LLM-Prompted Counterfactuals for Intervention and Sensor Data Augmentation
Shovito Barua Soumma, Asiful Arefeen, Stephanie M. Carpenter +2
Counterfactual explanations (CFs) offer human-centric insights into machine learning predictions by highlighting minimal changes required to alter an outcome. Therefore, CFs can be…
Enhancing Metabolic Syndrome Prediction with Hybrid Data Balancing and Counterfactuals
Sanyam Paresh Shah, Abdullah Mamun, Shovito Barua Soumma +1
Metabolic Syndrome (MetS) is a cluster of interrelated risk factors that significantly increases the risk of cardiovascular diseases and type 2 diabetes. Despite its global prevale…
Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable Sensors
Shovito Barua Soumma, S M Raihanul Alam, Rudmila Rahman +4
Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease that impairs mobility and safety by increasing the risk of falls. An effective FOG detection system must be…
Hybrid Attention Model Using Feature Decomposition and Knowledge Distillation for Glucose Forecasting
Ebrahim Farahmand, Shovito Barua Soumma, Nooshin Taheri Chatrudi +1
The availability of continuous glucose monitors as over-the-counter commodities have created a unique opportunity to monitor a person's blood glucose levels, forecast blood glucose…
Wearable-Based Real-time Freezing of Gait Detection in Parkinson's Disease Using Self-Supervised Learning
Shovito Barua Soumma, Kartik Mangipudi, Daniel Peterson +2
LIFT-PD is 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 triaxia…