Robust Autonomous Vehicle Computer-Vision-Based Localization in Challenging Environmental Conditions

S Chuprov, P Belyaev, R Gataullin, L Reznik… - Applied Sciences, 2023 - mdpi.com
In this paper, we present a novel autonomous vehicle (AV) localization design and its
implementation, which we recommend to employ in challenging navigation conditions with a …

Are industrial ml image classifiers robust to withstand adversarial attacks on videos?

S Chuprov, S Mahajan, R Zatsarenko… - 2023 IEEE Western …, 2023 - ieeexplore.ieee.org
We investigate the impact of adversarial attacks against videos on the object detection and
classification performance of industrial Machine Learning (ML) application. Specifically, we …

Are industrial ml image classifiers robust to data affected by network qos degradation?

R Zatsarenko, CA Marathe, S Chuprov… - 2023 IEEE Western …, 2023 - ieeexplore.ieee.org
In industrial applications, Machine Learning (ML) services are often deployed on cloud
infrastructure and require a transfer of the input data over a network, which is susceptible to …

Federated learning with trust evaluation for industrial applications

S Chuprov, M Memon, L Reznik - 2023 IEEE Conference on …, 2023 - ieeexplore.ieee.org
We propose and investigate a novel Reputation and Trust-based technique incorporation for
Federated Learning (FL) industrial applications to address possible anomalous local data …

ArcDef is Loss Function for Cracks Classification

NA Andriyanov, VE Dementyiev… - 2024 Systems of Signal …, 2024 - ieeexplore.ieee.org
This paper proposes the use of a new loss function to train a defect classifier on images of
metal structures. The developed function allows to vary different classes in the hyperplane …

Robust Machine Learning Under Vulnerable Cyberinfrastructure and Varying Data Quality

S Chuprov - 2024 - search.proquest.com
In our study, we investigate Machine Learning (ML) application robustness in ML Integrated
with Network (MLIN) systems. We consider MLIN as an integration of three major …