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All Journal International Journal of Electrical and Computer Engineering
Salem, Mofreh Mohamed
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Computer Science & IT Electrical & Electronics Engineering

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Enhanced transformer long short-term memory framework for datastream prediction Dief, Nada Adel; Salem, Mofreh Mohamed; Rabie, Asmaa Hamdy; El-Desouky, Ali Ibrahim
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp830-840

Abstract

In machine learning, datastream prediction is a challenging issue, particularly when dealing with enormous amounts of continuous data. The dynamic nature of data makes it difficult for traditional models to handle and sustain real-time prediction accuracy. This research uses a multi-processor long short-term memory (MPLSTM) architecture to present a unique framework for datastream regression. By employing several central processing units (CPUs) to divide the datastream into multiple parallel chunks, the MPLSTM framework illustrates the intrinsic parallelism of long short-term memory (LSTM) networks. The MPLSTM framework ensures accurate predictions by skillfully learning and adapting to changing data distributions. Extensive experimental assessments on real-world datasets have demonstrated the clear superiority of the MPLSTM architecture over previous methods. This study uses the transformer, the most recent deep learning breakthrough technology, to demonstrate how well it can handle challenging tasks and emphasizes its critical role as a cutting-edge approach to raising the bar for machine learning.
Co-Authors Dief, Nada Adel El-Desouky, Ali Ibrahim Rabie, Asmaa Hamdy