N. Tariq

FSED: A Feature-Space Ensemble Defense for Detecting Adversarial Examples

This paper introduces a Feature-Space Ensemble Defense (FSED) framework, which involves adversarial training, joint confidence calibration, and class-conditional Mahalanobis feature-space anomaly scoring to facilitate powerful adversarial detection. The proposed method considers the final-layer uncertainty. It also mod...

A Hybrid Ensemble Framework for Reliable and Interpretable Parkinson’s Disease Prediction Using Clinical Tabular Data

Parkinson’s disease (PD) prediction in clinical tabular data is challenging due to feature interactions and data imbalance. The current methods put emphasis on either predictive performance or interpretability in a unified way. This paper presents a comparative hybrid learning framework that evaluates conventional mach...

Post-Quantum DAG-Based Blockchain for Securing Vehicular Sensor Networks

A Blockchain-Enabled Federated Neuro-Symbolic Framework for Secure Wearable Biosensor-Based Health Monitoring

The proposed system integrates the semantic analysis of clinical text using Bio-ClinicalBERT with temporal numerical data using an LSTM-based model, creating a unified neuro-symbolic artificial intelligence (AI) pipeline, creating a unified neuro-symbolic artificial intelligence (AI) pipeline for smart healthcare appli...