Networked artificial intelligence : (Record no. 93178)

MARC details
000 -LEADER
fixed length control field 02528nam a22002177a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20251006124218.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250815b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781032813691
040 ## - CATALOGING SOURCE
Transcribing agency HS LRC
050 ## - LIBRARY OF CONGRESS CALL NUMBER
Classification number TES TA 347.A78
Item number .R888 2025
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Radhika Ranjan Roy
9 (RLIN) 129673
245 ## - TITLE STATEMENT
Title Networked artificial intelligence :
Remainder of title AI-enabled 5G Networking /
Statement of responsibility, etc. Radhika Ranjan Roy.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc. Oxon :
Name of publisher, distributor, etc. CRC Press,
Date of publication, distribution, etc. (c) 2025.
300 ## - PHYSICAL DESCRIPTION
Extent xviii, 201p. :
Dimensions 25cm.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc includes bibliography and index.
520 ## - SUMMARY, ETC.
Summary, etc. The integration of fifth generation (5G) wireless technologies with distributed artificial intelligence (AI) is transforming network operations. AI is increasingly embedded in all network elements, from cloud and edge to terminal devices, enabling AI to function as a networking system. This convergence facilitates AI-based applications across the global network, with notable successes in various domains such as computer vision, natural language processing, and healthcare. Networked Artificial Intelligence: AI-Enabled 5G Networking a comprehensive framework for the deep integration of computing and communications, optimizing networks and applications as a unified system using AI.<br/><br/>The book covers topics ranging from networked AI fundamentals to AI-enabled 5G networks, including agent modeling, machine learning (ML) algorithms, and network protocol architectures. It discusses how network service providers can leverage AI and ML techniques to customize network baselines, reduce noise, and accurately identify issues. It also looks at AI-driven networks that enable self-correction for maximum uptime and prescriptive actions for issue resolution, as well as troubleshooting by capturing and storing data before network events.<br/><br/>The book presents a comprehensive approach to AI-enabled networking that offers unprecedented opportunities for efficiency, reliability, and innovation in telecommunications. It works through the approach’s five steps of connection, communication, collaboration, curation, and community. These steps enhance network effects, empowering operators with insights for trusted automation, cost reduction, and optimal user experiences. The book also discusses AI and ML capabilities that enable networks to continuously learn, self-optimize, and predict and rectify service degradations proactively, even with full automation.
546 ## - LANGUAGE NOTE
Language note in English.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer
General subdivision Artificial intelligence.
9 (RLIN) 129919
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Library of Congress Classification
Koha item type TESDA Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Source of acquisition Inventory number Total Checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
    Library of Congress Classification     TESDA DLSU-D HS Learning Resource Center DLSU-D HS Learning Resource Center TESDA 06/24/2025 HS LRC SY 2025-2026 TES-001743   TES TA 347.A78 .R888 2025 3HS00000001743 08/15/2025 001743 08/15/2025 TESDA Books