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All Journal Perfecting a Video Game with Game Metrics Indonesian Journal of Electrical Engineering and Computer Science
Ziad A. Alqadi
Al-Balqa Applied University
Computer Science & IT

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Analysis of color image features extraction using texture methods Aws AlQaisi; Mokhled AlTarawneh; Ziad A. Alqadi; Ahmad A. Sharadqah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 3: June 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i3.9922

Abstract

A digital color images are the most important types of data currently being traded; they are used in many vital and important applications. Hence, the need for a small data representation of the image is an important issue. This paper will focus on analyzing different methods used to extract texture features for a color image. These features can be used as a primary key to identify and recognize the image. The proposed discrete wave equation DWE method of generating color image key will be presented, implemented and tested. This method showed that the percentage of reduction in the key size is 85% compared with other methods.
Autoregressive prediction analysis using machine deep learning Mohammad S. Khrisat; Anwar Alabadi; Saleh Khawatreh; Majed Omar Al-Dwairi; Ziad A. Alqadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i3.pp1509-1516

Abstract

Regression analysis, in statistic a modelling, is a set of statical processes that can be used to estimate the relationship between a dependent variable, commonly known as the outcome or response, and more independent variables generally called predictors of covariant. On the other hand, autoregression, which is based on regression equations, is a sequential model that uses time to predict the next step data from the previous step. Given the importance of accurate modelling and reliable predictions. in this paper we have analyzed the most popular methods used for data prediction. Nonlinear autoregressive methods were introduced, and then the machine deep learning approach was used to apply prediction based on a selected input data set. The mean square error was calculated for various artificial neural networks architecture to reach the optimal architecture, which minimized the error. Different artificial neural network (ANN) architectures were trained, tested, and validated using various regressive models, a recommendation was raised according to the obtained and analyzed experimental results. It was shown that using the concepts of machine deep learning will enhance the response of the prediction model.
Co-Authors Ahmad A. Sharadqah Anwar Alabadi Aws AlQaisi Majed Omar Al-Dwairi Mohammad S. Khrisat Mokhled AlTarawneh Saleh Khawatreh