Low-latency decision-making is a critical requirement for autonomous navigation in dynamic and resource-constrained environments. Conventional deep learning-based navigation systems often suffer from high computational overhead and energy consumption, limiting their deployment in real-time robotic applications. This paper presents a neuromorphic navigation framework based on spiking neural networks (SNNs) that leverages event-driven computation for efficient perception and control. The proposed system integrates biologically inspired neuron models with latency-aware learning mechanisms to enable rapid sensory processing and decision-making. By exploiting temporal information encoded in spike trains, the framework achieves faster response times while maintaining robust navigation performance. Experimental evaluations demonstrate that the neuromorphic SNN-based approach significantly reduces inference latency and energy consumption compared to conventional neural network baselines, making it suitable for real-time autonomous navigation tasks. The results highlight the potential of neuromorphic computing as a scalable and energy-efficient solution for next-generation autonomous systems.
Arvind Kumar Singh, Lakshmi Narayanan· International Journal of Int...· 0 citations
Autonomous robotic surface inspection combines intelligent robotics with computer vision to enable accurate, real-time, and contactless detection of surface defects such as cracks, corrosion, scratches, dents, and coating degradation. Unlike manual inspection, it improves consistency, enhances safety, reduces inspection time, and lowers operational costs. By integrating AI, deep learning, edge computing, IIoT, and Digital Twin technologies, autonomous robots can navigate complex industrial environments, capture high-resolution images, and perform automated defect analysis with minimal human intervention. The proposed framework integrates robotic navigation, visual sensing, image processing, defect classification, and maintenance decision support to achieve reliable inspection across diverse industrial sectors. This approach supports predictive maintenance, improves quality assurance, and advances smart manufacturing in Industry 4.0.
Arvind Kumar Singh, Lakshmi Narayanan· International Journal of Int...· 0 citations
Synthetic identity fraud represents a growing threat in credit risk management, where attackers create fictitious identities by combining real and fabricated information to bypass traditional detection systems. Machine learning models have demonstrated effectiveness in detecting such fraudulent activities; however, they remain vulnerable to adversarial attacks that can manipulate input data to evade detection. This paper investigates the adversarial robustness of various machine learning models applied to synthetic identity fraud detection in credit risk settings. We simulate diverse adversarial attack strategies on benchmark datasets and evaluate the impact on model performance, highlighting critical vulnerabilities. Furthermore, we propose and assess defense mechanisms, including adversarial training and robust feature engineering, to enhance model resilience. Our results reveal significant trade-offs between accuracy and robustness, underscoring the need for balanced solutions tailored to financial fraud contexts. The study provides actionable insights for practitioners aiming to deploy more secure and trustworthy fraud detection systems, contributing to improved credit risk management in an increasingly adversarial environment.
Arvind Kumar Singh, Lakshmi Narayanan· International Journal of Com...· 0 citations