OPTIMIZING VEHICULAR NETWORK MANAGEMENT USING CONVOLUTIONAL NEURAL NETWORKS

ICTACT Journal on Communication Technology ( Volume: 14 , Issue: 2 )

Abstract

vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff6fcd32000000f060130001000800
CNN have been utilized in many domains and have revolutionized the field of computer vision, natural language processing and vehicular network management. CNNs are loaded with a number of advantages over the current methods of controlling vehicular networks. For instance, they can effectively handle the dynamic behavior of vehicular network due to their ability to learn recognition patterns. Additionally, CNNs are equipped with the capability to perform feature extraction along with its learning and integrating abilities, which can be highly advantageous for vehicular network management. Furthermore, they enable for parametric optimization thus increasing the speed of convergence with low-cost computational resources. Thus, CNNs are a promising approach for highly reliable communication and control of vehicular networks.

Authors

Mahesh Maurya1, T. Varun2, K. Sree latha3, Atul Kumar4
K. C. College of Engineering, India1, Anna University, India2, St Peter's Engineering College, India3, Dr. D. Y. Patil B-School, Pune, India4

Keywords

Neural, Networks, Dynamic, Vehicular Networks, Optimization

Published By
ICTACT
Published In
ICTACT Journal on Communication Technology
( Volume: 14 , Issue: 2 )
Date of Publication
June 2023
Pages
2913 - 2918

ICT Academy is an initiative of the Government of India in collaboration with the state Governments and Industries. ICT Academy is a not-for-profit society, the first of its kind pioneer venture under the Public-Private-Partnership (PPP) model

Contact Us

ICT Academy
Module No E6 -03, 6th floor Block - E
IIT Madras Research Park
Kanagam Road, Taramani,
Chennai 600 113,
Tamil Nadu, India

For Journal Subscription: journalsales@ictacademy.in

For further Queries and Assistance, write to us at: ictacademy.journal@ictacademy.in