Cognizant (United States)

Annotating Pre-Reliance Handling for AI-Generated Claims: A Preliminary Protocol and Feasibility Pilot

Large language model outputs can mix stable factual information, time-sensitive claims, interpretations, recommendations, and high-stakes advice within a single response. This working paper introduces pre-reliance handling as a claim-level annotation target: what form of checking...

Secure and scalable IoT device management with blockchain, K-anonymity, and deep reinforcement learning

The vast expansion of Internet of Things (IoT) ecosystems has posed significant challenges regarding security, scalability, latency, and user privacy, which are not properly handled by the conventional architectures. To solve these issues, the authors of this paper suggest an int...

Screen time is differentially related to visual selective attention and visual feature processing across middle childhood

Abstract Increased screen time has been linked to attention difficulties in early childhood, raising concerns among parents and professionals. However, it remains unclear how screen use influences the development of visual attention and visual processing across childhood. In this...

A Cryptographically Secure and Explainable AI Framework for Automated Health Insurance Claim Processing Using the Insurefusionnet

Automated health insurance claim processing has become increasingly important as insurers rely on intelligent systems to handle growing claim volumes and automated decision support systems for claim transparency. However, existing models often suffer from limited interpretability...

Control-as-Load Condition v0.2: A State-Dependent Hypothesis on Additional Control Activity and Subsequent Recoverability

This technical note presents Control-as-Load Condition v0.2 as a standalone and empirically testable hypothesis. The central proposal is that the recovery-related effect of additional Control activity varies with within-person state, while task performance is treated as a separat...

Control-as-Load Condition v0.2: A State-Dependent Hypothesis on Additional Control Activity and Subsequent Recoverability

This technical note presents Control-as-Load Condition v0.2 as a standalone and empirically testable hypothesis. The central proposal is that the recovery-related effect of additional Control activity varies with within-person state, while task performance is treated as a separat...

FRAUD-X: An explainable AI-based deep learning model for real-time cloud-driven financial fraud and risk analysis

FRAUD-X is an explainable, a cloud-native explainable deep learning framework designed to improve real-time financial fraud detection while ensuring interpretability and regulatory compliance. The primary objective is to develop a scalable Temporal Convolutional Network (TCN)-bas...