OPTIMIZING VEHICULAR NETWORK MANAGEMENT USING CONVOLUTIONAL NEURAL NETWORKS
Abstract
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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
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012010000000
Published By :
ICTACT
Published In :
ICTACT Journal on Communication Technology
( Volume: 14 , Issue: 2 , Pages: 2913 - 2918 )
Date of Publication :
June 2023
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555
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12

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