IJSMT Journal

International Journal of Science, Strategic Management and Technology

An International, Peer-Reviewed, Open Access Scholarly Journal Indexed in recognized academic databases · DOI via Crossref The journal adheres to established scholarly publishing, peer-review, and research ethics guidelines set by the UGC

ISSN: 3108-1762 (Online)
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NETWORK TRAFFIC OPTIMIZER USING AI

AUTHORS:
B. MANIRATHNAM
Mentor
Dr. V. MANIMEKALAI
Affiliation
Department of Computer Technology,Dr. N.G.P. Arts and Science College, Kalapatti, Coimbatore, Tamil Nadu, India.
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract

Network performance plays a crucial role in ensuring smooth communication, fast data transfer, and reliable connectivity in modern digital systems. However, many networks face issues such as slow speed, congestion, and inefficient bandwidth usage due to increasing data traffic and limited optimization techniques. Traditional network management methods often fail to dynamically adapt to changing network conditions. This study focuses on the development of a Network Optimizer using Artificial Intelligence (AI) to monitor, analyze, and improve network performance. The system collects network data such as speed, latency, and bandwidth usage, and applies AI techniques to identify performance issues and optimize network parameters automatically. Artificial Intelligence helps predict network congestion, recommend optimal configurations, and enhance overall network efficiency. By integrating AI-based analysis with network monitoring tools, the system improves reliability, speed, and resource utilization in network environments. The proposed solution supports efficient network management, reduces downtime, and ensures better connectivity for users

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MANIRATHNAM, B. (2026). Network Traffic Optimizer using AI. International Journal of Science, Strategic Management and Technology, 02(03). https://doi.org/10.55041/ijsmt.v2i3.154

MANIRATHNAM, B.. "Network Traffic Optimizer using AI." International Journal of Science, Strategic Management and Technology, vol. 02, no. 03, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i3.154.

MANIRATHNAM, B.. "Network Traffic Optimizer using AI." International Journal of Science, Strategic Management and Technology 02, no. 03 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i3.154.

References
[1] Mahmoud Daneshmand et al. (2015). Internet of Things and Network Traffic Analysis. IEEE Communications Magazine.

[2] Ian Goodfellow, Yoshua Bengio & Aaron Courville (2016). Deep Learning. MIT Press.

[3] Andrew S. Tanenbaum & David J. Wetherall (2011). Computer Networks. Pearson Education.

[4] Behrouz A. Forouzan (2017). Data Communications and Networking. McGraw-Hill Education.

[5] Christopher M. Bishop (2006). Pattern Recognitionand Machine Learning. Springer.

[6] Tom Mitchell (1997). Machine Learning. McGraw-Hill Education.

[7] Jeffrey Dean & Sanjay Ghemawat (2008). MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM.

[8] International Telecommunication Union (2022). Measuring Digital Development: Facts and Figures.

[9] IEEE (2020). Research Trends in Network Traffic Analysis and Optimization.

[10]Yann LeCun, Yoshua Bengio & Geoffrey Hinton (2015). Deep Learning. Nature Journal.
Ethics and Compliance
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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