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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

Lakshmi Narayanan

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Open access 2024

Intelligent Model Calibration for Improved Prediction Accuracy

This study proposes an intelligent model calibration framework that integrates adaptive calibration techniques, including Platt Scaling, Isotonic Regression, Bayesian Calibration, Ensemble Calibration, Temperature Scaling, and Meta-Learning-based Calibration, and highlights intelligent calibration as a key mechanism fo...

Arvind Kumar Singh, Lakshmi Narayanan · 0 citations
Review Open access 2020

Adaptive Learning Rate Strategies for Efficient Machine Learning Training

It is concluded that adaptive learning rate strategies are essential for modern machine learning applications such as computer vision, NLP, robotics, healthcare analytics, and intelligent automation, while future research should focus on hybrid and meta-learning-based optimization approaches.

Arvind Kumar Singh, Lakshmi Narayanan · 0 citations
Open access 2018

Neuromorphic Spiking Neural Networks for Low-Latency Autonomous Navigation

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.

Arvind Kumar Singh, Lakshmi Narayanan · 0 citations
Open access 2022

Autonomous Robotic Surface Inspection Using Computer Vision

The proposed framework integrates robotic navigation, visual sensing, image processing, defect classification, and maintenance decision support to achieve reliable inspection across diverse industrial sectors and supports predictive maintenance, improves quality assurance, and advances smart manufacturing in Industry 4...

Arvind Kumar Singh, Lakshmi Narayanan · 0 citations
Open access 2018

Adversarial Robustness in ML Models for Detecting Synthetic Identity Fraud in Credit Risk

This paper investigates the adversarial robustness of various machine learning models applied to synthetic identity fraud detection in credit risk settings, and proposes and assess defense mechanisms, including adversarial training and robust feature engineering, to enhance model resilience.

Arvind Kumar Singh, Lakshmi Narayanan · 0 citations

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