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Articles by Dipkumar Mehta
Voice-Activated AI Agent with Amazon Connect Customer and Amazon Bedrock
A voice-activated AI agent can make contact center management significantly faster when the AI agents handling your calls need urgent changes. Supervisors running these AI agents on Amazon Connect Customer face a persistent operational challenge. When critical systems go down or AI agent behavior needs immediate adjustment, the path from decision to action involves multiple console logins, manual configuration changes, and time-consuming verification steps.
Tailored support at scale: Turning a unified Salesforce KB into LOB-focused AI agents | Amazon Web Services
AWS Contact Center In today’s hyper-connected world, customer support teams must juggle an ever-growing portfolio of products and services all within a single CRM like Salesforce. Agents need instant access to the right information, yet they too often find themselves wading through a monolithic, consolidated knowledge base that spans every line of business (LOB) from telecom billing to insurance claims to retail returns.
[2505.08837] Adaptive Security Policy Management in Cloud Environments Using Reinforcement Learning
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Privacy Preserved and Practical Implementation for Distributed Machine Learning in Radiology
You are already at the latest version Imaging is one of the strongest and fastest growing sectors of diagnostic medicine; nevertheless, the incorporation of large-scale medical image datasets across different hospitals is still a challenge due to regulatory, privacy, and infrastructure issues. In this paper, we propose a framework for the application of federated machine learning in radiology and explain how it can be implemented in practice using the Chest X-Ray14 dataset from NIH.
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