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All Journal The Indonesian Journal of Computer Science
Hussein, Nashwan
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : The Indonesian Journal of Computer Science

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Bitcoin Price Prediction Using Hybrid LSTM-GRU Models Hussein, Nashwan; Abdulazeez, Adnan Mohsin
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3725

Abstract

Cryptocurrency price prediction is a challenging task due to the inherent volatility and complexity of the market. In this research, we propose a hybrid Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network model for predicting Bitcoin prices. The model is implemented using the TensorFlow and Keras libraries and is evaluated on historical Bitcoin price data obtained from Yahoo Finance. Our approach aims to leverage the strengths of both LSTM and GRU architectures to enhance the accuracy of price predictions. The results suggest that the proposed hybrid LSTM-GRU model holds promise for effectively capturing the complex dynamics of cryptocurrency markets, addressing the challenges associated with traditional time-series analysis techniques.
Performance Evaluation of Extra Trees Classifier by using CPU Parallel and Non-Parallel Processing Hussein, Nashwan; R. M. Zeebaree, Subhi
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3802

Abstract

This research delves into assessing the performance of the Extra Trees Classifier, specifically examining the influence of CPU parallel processing on classification accuracy and computational efficiency. Fashion MNIST, a collection of grayscale images representing clothing items, serves as the foundational dataset for this study. Two variations of the Extra Trees Classifier are implemented: one configured without CPU parallel processing and another utilizing maximum CPU cores for parallel execution. The primary evaluation metrics include accuracy measurement and computational time taken for both training and prediction tasks. The findings reveal notable insights, showcasing that while the Extra Trees Classifier demonstrates commendable accuracy in classifying Fashion MNIST images, the implementation of CPU parallel processing significantly reduces computational time without compromising accuracy levels. This observation underscores the pivotal role of optimizing computational resources for efficient model training and deployment in machine learning applications. The results of this study are very helpful for understanding how to use parallel processing to make machine learning tasks more accurate and more efficient. It also shows how important it is to optimize resources for scalable and effective model development.
Co-Authors Abdulazeez, Adnan Mohsin R. M. Zeebaree, Subhi