DSTAN-Med is a novel dual-channel attention framework that significantly improves False Data Injection (FDI) attack detection in IoMT medical devices by explicitly separating spatial and temporal depe…

This study compares multiple post-hoc explainable AI methods (e.g., DeepSHAP, GradCAM) to interpret how deep learning models use EEG data to detect Major Depressive Disorder, finding that while method…

The paper proposes an ensemble learning framework combined with SHAP-based Explainable AI (XAI) to achieve robust and interpretable anomaly detection for network traffic in embedded systems.

The paper proposes ExAI5G, a logic-based explainable AI framework that integrates a Transformer-based IDS with XAI techniques to provide highly accurate and transparent intrusion detection for 5G netw…

The paper proposes Family-Grouped Hierarchical Federated Learning (Family-FL) combined with a highly optimized Tiny CNN-LSTM model to enable privacy-preserving ECG monitoring on ultra-resource-constra…

VitalAgent is a novel tool-augmented agentic framework that significantly improves physiological monitoring from wearable health data by enabling both reactive question answering and proactive, long-t…

The paper proposes MaskDiff-AD, a forward-only masked diffusion model trained on nominal data to achieve state-of-the-art anomaly detection across various categorical, mixed-type, and text datasets.

This paper proposes a unified framework for inference-time augmentation to improve the robustness of physiological signal classification in real-world deployments.

This paper proposes a unified framework for inference-time augmentation to improve the robustness of physiological signal classification in real-world deployments.

The paper introduces ArrythML, a highly efficient autoencoder-based TinyML model that enables accurate, low-power arrhythmia detection directly on resource-constrained embedded wearable devices.

This paper proposes a novel, on-device, interpretable Tsetlin Machine (TM)-based Intrusion Detection System (IDS) for IoMT environments, achieving high classification accuracy while providing transpar…

This paper proposes an explainable threat attribution system for IoT networks that uses SHAP and flow behavior modeling to accurately classify and explain over 30 distinct attack variants into 8 meani…

The paper proposes a Class-Aware Adaptive Differential Privacy (CA-ADP) framework integrated with a 3D CNN-BiLSTM architecture to significantly improve privacy-preserving fall detection performance co…

The paper introduces VisAnomReasoner, a parameter-efficient Vision-Language Model (VLM), trained on a new benchmark (VisAnomBench) to accurately and interpretably detect anomalies in time-series data.

Adaptive data selection significantly improves wearable prediction performance, particularly for individuals with poor baseline health metrics, suggesting that selective data sampling should be tailor…

The paper proposes 'Think Fast, Talk Smart,' a pipeline that separates deterministic data analysis from LLM generation, showing that offloading recurring, structured tasks to code significantly improv…

The paper proposes reframing mechanistic anomaly detection (MAD) as a functional attribution problem, using influence functions to measure how much a model's output depends on specific input samples,…

The paper introduces Score-Guided Classification (SGC), a novel framework that uses an unsupervised anomaly score as a 'Pathological Prior' to guide EEG-based depression detection, overcoming the limi…

This paper develops an explainable and deployable machine learning system for highly accurate phishing detection across diverse, heterogeneous datasets, achieving up to 99.78% accuracy using transform…

This paper develops and analyzes various ensemble models, culminating in an XGBoost-based system, to reliably detect UAV intrusions using XAI and advanced statistical methods to pinpoint the root caus…