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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Suhad A. Yousif
Al-Nahrain University
Computer Science & IT Engineering

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High performance time series models using auto autoregressive integrated moving average Redha Ali Al-Qazzaz; Suhad A. Yousif
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp422-430

Abstract

Forecasting techniques have received considerable interest from both researchers and academics because of the unique characteristics of businesses and their influence on several areas of the economy. Most academics utilize the autoregressive integrated mov ing average (ARIMA) approach to forecasting the future. However, researchers face challenges, such as analyzing the data and selecting the appropriate ARIMA parameters, especially with large datasets. This study investigates the use of the automatic ARIMA (Auto ARIMA) function for forecasting Brent oil prices. It demonstrates the benefits of using Auto ARIMA over ARIMA for determining the appropriate ARIMA parameters based on measures such as root mean square error ( RMSE ) , mean absolute error ( MAE ) , and aka ike information criterion ( AIC ) without requiring the attention of an expert data scientist as it bypasses several steps needed for manual ARIMA. Auto ARIMA produced an RMSE of 12.5539 and an AIC of 1877.224, which are comparable to the values resulting fr om the manual ARIMA with the help of expert data scientists; thus, it saves analysis time and offers the best model result.
An automated machine learning model for diagnosing COVID-19 infection Noor Maher; Suhad A. Yousif
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1360-1369

Abstract

The coronavirus disease 2019 (COVID-19) epidemic still impacts every facet of life and necessitates a fast and accurate diagnosis. The need for an effective, rapid, and precise way to reduce radiologists' workload in diagnosing suspected cases has emerged. This study used the tree-based pipeline optimization tool (TPOT) and many machine learning (ML) algorithms. TPOT is an open-source genetic programming-based AutoML system that optimizes a set of feature preprocessors and ML models to maximize classification accuracy on a supervised classification problem. A series of trials and comparisons with the results of ML and earlier studies discovered that most of the AutoML beat traditional ML in terms of accuracy. A blood test dataset that has 111 variables and 5644 cases were used. In TPOT, 450 pipelines were used, and the best pipeline selected consisted of radial basis function (RBF) Sampler preprocessing and Gradient boosting classifier as the best algorithm with a 99% accuracy rate.
Co-Authors Noor Maher Redha Ali Al-Qazzaz