Evaluating the Performance and Behavior of CNN, LSTM, and GRU for Classification and Prediction Tasks

Authors

DOI:

https://doi.org/10.24996/ijs.2024.65.3.43

Keywords:

Deep learning (DL), Recurrent Neural Network (RNN), Convolution Neural Network (CNN), Classification and Prediction

Abstract

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning models for a variety of tasks under the control of a unified architecture for each proposed model.

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Published

2024-03-29

Issue

Section

Computer Science

How to Cite

Evaluating the Performance and Behavior of CNN, LSTM, and GRU for Classification and Prediction Tasks. (2024). Iraqi Journal of Science, 65(3), 1741-1751. https://doi.org/10.24996/ijs.2024.65.3.43

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