ICTACT Journal on Communication Technology

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Optimizing Vehicular Network Management Using Convolutional Neural Networks


Affiliations
1 Department of Computer Science and Engineering, K. C. College of Engineering, India
2 Department of Management Studies, Anna University, India
3 Department of Electrical and Electronics Engineering, St Peter's Engineering College, India
4 Department of Management, Dr. D. Y. Patil B-School, Pune, India
     

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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.

Keywords

Neural, Networks, Dynamic, Vehicular Networks, Optimization.
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  • Optimizing Vehicular Network Management Using Convolutional Neural Networks

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Authors

Mahesh Maurya
Department of Computer Science and Engineering, K. C. College of Engineering, India
T. Varun
Department of Management Studies, Anna University, India
K. Sree latha
Department of Electrical and Electronics Engineering, St Peter's Engineering College, India
Atul Kumar
Department of Management, Dr. D. Y. Patil B-School, Pune, India

Abstract


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.

Keywords


Neural, Networks, Dynamic, Vehicular Networks, Optimization.

References