عنوان

Deep learning and its applications for vehicle networks

Qualification

(ebook)

Qualification

(hardback)

Qualification

(paperback)

(Number (ISBN

9781003190691

Erroneous ISBN

9781032041377

Erroneous ISBN

9781032041384

Number

7275

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

Deep learning and its applications for vehicle networks

General Material Designation

[electronic resources]

First Statement of Responsibility

edited by Fei Hu and Iftikhar Rasheed.

Edition Statement

First edition.

Place of Publication, Distribution, etc.

Boca Raton

Name of Publisher, Distributor, etc.

CRC Press

Date of Publication, Distribution, etc.

2023.

Specific Material Designation and Extent of Item

xiii, 342 pages

Other Physical Details

ill., tables.

Text of Note

Includes bibliographical references.

Text of Note

"Deep Learning (DL) will be an effective approach for AI-based vehicular networks and can deliver a powerful set of tools for such vehicular network dynamics. In various domains of vehicular networks, DL can be used for learning-based channel estimation, traffic flow prediction, vehicle trajectory prediction, location-prediction-based scheduling and routing, intelligent network congestion control mechanism, smart load balancing and vertical handoff control, intelligent network security strategies, virtual smart & efficient resource allocation and intelligent distributed resource allocation methods. This book is based on the work from world-famous experts on the application of DL for vehicle networks. It consists of the following five parts: (1) DL for vehicle safety and security: In this part, we have a few chapters to cover the use of DL algorithms for vehicle safety or security. (2) DL for effective vehicle communications: Vehicle networks consist of vehicle-to-vehicle and vehicle-to-roadside communications. Intelligent vehicle networks require the flexible selection of the best path across all vehicles, the adaptive sending rate control based on bandwidth availability, timely data downloading from roadside base-station, etc. (3) DL for vehicle control: For each individual vehicle, many operations require intelligent control: the emission is controlled based on the road traffic situation; the charging pile load is predicted through DL; the vehicle speed is adjusted based on the camera-captured image analysis. (4) DL for information management: This part covers some intelligent information collection and understanding. We can use DL for energy-saving vehicle trajectory control based on the road traffic situation and given destination information; we can also natural language processing based on DL algorithm for automatic internet of things (IoT) search during driving. (5) Other applications. This part introduces the use of DL models for other vehicle controls. Autonomous vehicles are becoming more and more popular in the society. The DL and its variants will play more and more important roles in cognitive vehicle communications and control. Other machine learning models such as deep reinforcement learning will also facilitate the intelligent vehicle behavior understanding and adjustment. We expect that this book will become a valuable reference to your understanding of this critical field"--

Place of Publication

Boca Raton : CRC Press, [2023]

Edition Statement

First edition.

Title

Deep learning and its applications for vehicle networks

International Standard Book Number

9781032041377

Bibliographic Record Identifier

(DLC) 2022054949

Entry Element

Vehicular ad hoc networks (Computer networks)

Entry Element

Deep learning (Machine learning)

Subject Term

Machine learning.

Entry Element

Hu, Fei,

Entry Element

Rasheed, Iftikhar,

Dates

1972-

Relator Code

Editor

Relator Code

Editor

Country

ایران

Agency

Central Library of the Kurdistan University

Date of Transaction

20250518172802.0

Cataloguing Rules (Descriptive Conventions))

rda

e

BL

280328

a

Y