Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering
Nurzal Effiyana Ghazali
University Technology Malaysia
Computer Science & IT Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Smoothing-aided long-short term memory neural network-based LTE network traffic forecasting Mohamed Khalafalla Hassan; Sharifah Hafizah Sayed Ariffin; Sharifah Kamilah Syed-Yusof; Nurzal Effiyana Ghazali; Mohammed Eltayeb Ahmed Kanona; Mohamed Rava
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6859-6868

Abstract

There is substantial demand for high network traffic due to the emergence of new highly demanding services and applications such as the internet of things (IoT), big data, blockchains, and next-generation networks like 5G and beyond. Therefore, network resource planning and forecasting play a vital role in better resource optimization. Accordingly, forecasting accuracy has become essential for network operation and planning to maintain the minimum quality of service (QoS) for real-time applications. In this paper, a hybrid network- bandwidth slice forecasting model that combines long-short term memory (LSTM) neural network and various local smoothing techniques to enhance the network forecasting model's accuracy was proposed and analyzed. The results show that the proposed hybrid forecasting model can effectively improve the forecasting accuracy with minimal data loss.
Co-Authors Mohamed Khalafalla Hassan Mohamed Rava Mohammed Eltayeb Ahmed Kanona Sharifah Hafizah Sayed Ariffin Sharifah Kamilah Syed-Yusof