Fixed points of $\rho$-nonexpansive mappings using MP iterative process

Panwar, Anju; Reena, .; Kumar, Santosh

doi:10.31197/atnaa.980093

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…The confidentiality, integrity, and availability of data must therefore be protected at all costs. Sensitive data can be protected from unauthorised access and manipulation by using encryption, access controls, and effective data monitoring systems [28].…”

Section: Introductionmentioning

confidence: 99%

Securing Machine Learning Ecosystems: Strategies for Building Resilient Systems

Dhabliya,

Rizvi,

Dhablia

et al. 2024

E3S Web Conf.

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In today's data-driven environment, protecting machine learning ecosystems has taken on critical importance. Organisations are relying more and more on AI and ML models to guide important decisions and operations, which have led to an increase in system vulnerabilities. The critical need for techniques to create resilient machine learning (ML) systems that can withstand changing threats is discussed in this study.Data protection is an important component of securing ML environments. Every part of the process, from data preprocessing through model deployment, needs to be secured. In order to reduce potential vulnerabilities, this incorporates code review procedures, safe DevOps practises, and container security.System resilience is vitally dependent on on-going monitoring and anomaly detection. Organisations can respond quickly to security problems by detecting deviations from normal behaviour early on and adjusting their defences as necessary.A strong incident response plan is essential. To protecting machine learning ecosystems necessitates a comprehensive strategy that includes monitoring, incident response, model security, pipeline security, and data protection. By implementing these tactics, businesses may create robust machine learning (ML) systems that can endure the changing threat landscape, protect their data, and guarantee the validity of their AI-driven decision-making processes.

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Section: Introductionmentioning

confidence: 99%

Securing Machine Learning Ecosystems: Strategies for Building Resilient Systems

Dhabliya,

Rizvi,

Dhablia

et al. 2024

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…Many researchers introduced different iterative schemes to find out the solutions of VIPs (6)(7)(8)(9)(10)(11) . Lamba and Panwar (12) and Panwar et al (13) introduced iterative schemes for nonexpansive mappings. Sahu et al (14) and Tuyen (15) determined the solutions of VIPs by finding the common fixed point of a sequence of nearly nonexpansive mappings.…”

Section: Introductionmentioning

confidence: 99%

Iterative Approach for Solving Variational Inequality Problems using Fixed Point Concept

Panwar¹,

Kumar²

2023

IJST

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Objectives:The objective of the paper is to find the solutions of variational inequality problems via the concept of common fixed point of a sequence of nearly nonexpansive mappings. Methods: The present work uses three step iterative algorithm to get the solutions of variational inequality problems. Findings: By applying three step iterative algorithm, solutions of variational inequality problem has been obtained. Novelty: In the present work, a specific three step iterative algorithm has been deployed to get solution. Furthermore, Matlab programming has been utilised to eastablish the accuracy of the results.

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Variational Inequality Problems with Applications

Panwar,

Kumar

2023

2023 2nd International Conference on Computational Modelling, Simulation and Optimization (ICCMSO)

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Fixed points of $\rho$-nonexpansive mappings using MP iterative process

Cited by 3 publications

References 21 publications

Securing Machine Learning Ecosystems: Strategies for Building Resilient Systems

Securing Machine Learning Ecosystems: Strategies for Building Resilient Systems

Iterative Approach for Solving Variational Inequality Problems using Fixed Point Concept

Variational Inequality Problems with Applications

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