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Stream/Area:
-
Subject:
-
Paper ID:
IJSREMWP602
Volume:
9 |
5 |
May 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
The need for effective network traffic prediction has never been higher due to the explosive growth of digital communication and the introduction of 5G networks. Proactive decision-making, optimal resource allocation, latency reduction, and network congestion prevention are all made possible by network traffic prediction. The dynamic nature of contemporary networks is too much for traditional techniques like rule-based systems and statistical forecasting models, which frequently result in inefficient bandwidth usage and increased packet loss. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as powerful tools for solving complex network management challenges. Of these, Long Short-Term Memory (LSTM) neural networks have shown exceptional efficacy in predicting future network conditions and analysing sequential data. Furthermore, anomaly detection tools like Isolation Forest offer crucial information for spotting odd trends that might
KEYWORDS
Publication Date
11/4/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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