Cognitive Fairness-Aware Techniques for Human-Machine Interface : Chapman & Hall/CRC Internet of Things - S. Indu Vadhani

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Cognitive Fairness-Aware Techniques for Human-Machine Interface

Name: Cognitive Fairness-Aware Techniques for Human-Machine Interface
Brand: Taylor & Francis Ltd
Price: 496.99 AUD
Availability: PreOrder

By: S. Indu Vadhani (Editor), Vithya Ganesan (Editor), Vishnu S. Pendyala (Editor), Subrata Chowdhury (Editor), Souvik Pal (Editor)

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Hardcover | 26 December 2025 | Edition Number 1

At a Glance

Hardcover
382 Pages

Dimensions(cm)
23.4 x 15.6 x 2.24

Hardcover

$496.99

or 4 interest-free payments of $124.25 with

Available: 26th December 2025

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This book explores the critical issue of fairness in human-machine interfaces. It delves into the integration of technology and cognitive science to develop AI systems that are unbiased, reliable, and user-friendly. The book also sheds light on emotional data processing in AI accelerators and federated learning modules. Additionally, it covers machine learning, knowledge representation, and the application of knowledge graphs to understand and optimize the behaviour of AI assistance devices.

Features:

Explains complex issues of Cognitive Fairness Aware Contextual Proactive Federated Protocol collects data and identifies individual emotional issues and resolves them by contextual solitary proactive communication
Discusses emotional data processing challenges through AI accelerator with federated learning module to generate periodical counselling messages
Addresses data analysis anomalies in Graph Database Modelling by anom-aly prediction and anomaly detection
Describes anomaly detection techniques in the form of abnormal data records, messages, events, groups, and/or other unexpected observations in graph database modelling
Explains how outlier detection for data analysis deals with the detection of patterns in Graph Database

This book is for researchers, academics, students, AI practitioners and developers, ethics experts in AI technology and machine-learning practitioners interested in fairness in human-machine interfaces.

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