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Vishal Krishna Singh

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Open access 2026

An EEG-Based Human Emotion Recognition Model Using Attention Hierarchical Variational EEGNet

The physiological factors and decisions we make daily are greatly affected by a person's emotional state. An appropriate subfield of study and research is how brain–computer interface devices can be used to identify people's emotional states. The electroencephalogram (EEG) is used to understand why there are changes ov...

Vinay Kumar Singh, Bharati Rathore, Raja Saeed Iqbal et al. · 0 citations
Open access Sep 2026

Generative Self-Attention for Adaptive Real-Time Traffic Event Detection in Next-Generation Intelligent Transportation Systems

Experimental analysis on benchmark event -detection dataset demonstrates the effectiveness of the proposed methodology, attaining peak performance values of 0.80 in Normalized mutual information (NMI), 0.73 IN Adjusted Mutual Information (AMI), and 0.72 in Adjusted Rand Index (ARI) across the incremental block of heter...

Sakshi Verma, Sushil Tiwari, Vishal Krishna Singh et al. · 0 citations
#edge computing Open access Sep 2026

Real‐Time Anomaly Detection Using Federated Learning in Edge Devices

This work shows that production‐grade, privacy‐preserving intrusion detection is feasible in IoT networks at the edge, and addresses the concerns of data privacy and non‐IID data distribution inherent to distributed IoT networks.

Vaibhav Joshi, Abishi Chowdhury, Amrit Pal et al. · 0 citations
2026

Confidence-Aware Federated Learning for Smart Load Characterization in Cyber-Physical DER Systems

The integration of smart meters into residential environments has allowed smooth collection of electricity consumption data, which is critical for demand response and coordinated operation in cyber-physical distributed energy resource systems. However, existing centralized methods of consumer characteristic identificat...

Vishal Krishna Singh, Vins Patel, Neeraj Jain et al. · 0 citations
Open access 2026

Tiny-IDS: A Pruned Ensemble Distillation Pipeline for Lightweight and Explainable IoT Intrusion Detection

The results demonstrate the effectiveness of the proposed Tiny-IDS in accurately identifying Mirai botnet attacks on IoT devices along with a minimal memory footprint and low inference time, while also emphasizing the need for IoT-specific evaluation frameworks to support the development of robust and lightweight IDS.

Shyam Bahadur, Sudhanshu Kumar Jha, Rajkumar Singh Rathore et al. · 0 citations

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