Journal of Electronics, Computer Networking and Applied Mathematics (JECNAM) ISSN : 2799-1156 https://journal.hmjournals.com/index.php/JECNAM <p>The<strong> Journal of Electronics, Computer Networking and Applied Mathematics (JECNAM) having ISSN: 2799-1156</strong> is a double-blind, peer-reviewed, open access journal that provides publication of articles in all areas of Electronics, Computer Networking and Applied Mathematics. The objective of this journal is to provide a veritable platform for scientists and researchers all over the world to promote, share, and discuss a variety of innovative ideas and developments in all aspects of <strong>Computer Networking and Applied Mathematics.</strong></p> en-US editor.jecnam@gmail.com (Editor in Chief) editor.jecnam@gmail.com (Tech Support) Sat, 01 Jun 2024 05:14:43 +0000 OJS 3.3.0.7 http://blogs.law.harvard.edu/tech/rss 60 Revolutionizing Enterprise Network Management: The Role of Ai-Driven Solutions in Modern Computer Networking https://journal.hmjournals.com/index.php/JECNAM/article/view/4412 <p>In the rapidly evolving landscape of enterprise network management, artificial intelligence (AI) is emerging as a transformative force. This paper, titled "Revolutionizing Enterprise Network Management: The Role of AI-Driven Solutions in Modern Computer Networking," delves into the significant impact of AI technologies on the efficiency, security, and scalability of enterprise networks. By integrating AI-driven solutions, organizations can achieve unprecedented levels of automation, predictive maintenance, and real-time anomaly detection, thus enhancing overall network performance.</p> <p>This study provides a comprehensive analysis of the latest AI techniques employed in network management, including machine learning algorithms, neural networks, and advanced data analytics. Through case studies and empirical data, we demonstrate how AI enhances network security, reduces downtime, and optimizes resource allocation. Our findings suggest that the adoption of AI in network management not only improves operational efficiency but also offers a competitive advantage in the digital economy. Keywords: AI-driven network management, enterprise network security, machine learning in networking, predictive maintenance, network automation, real-time anomaly detection, computer networking, digital transformation.</p> Ayush Kumar Ojha Copyright (c) 2024 Authors https://creativecommons.org/licenses/by-nc/4.0/ https://journal.hmjournals.com/index.php/JECNAM/article/view/4412 Thu, 27 Jun 2024 00:00:00 +0000