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Haddad, Firas
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Computer Science & IT Electrical & Electronics Engineering

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Cardiovascular disease risk factors prediction using deep learning convolutional neural networks Almatari, Mohammad; Abuhaija, Belal; Alloubani, Aladeen; Haddad, Firas; M. Jaradat, Ghaith; Qawqzeh, Yousef; Alsmadi, Mutasem Khalil; Ali Alghamdi, Fahad; Saad Alqurni, Jehad; Alodat, Lena; Dong, Linyinxue
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4471-4487

Abstract

Heart disease remains a leading cause of mortality worldwide, prompting healthcare researchers to leverage analytical tools for comprehensive data analysis. This study focuses on exploring crucial parameters and employing deep learning (DL) techniques to enhance understanding and prediction of cardiovascular disease (CVD) risk factors. Utilizing SPSS and Weka tools, a cross-sectional and correlational design was employed to analyze extensive medical datasets. Binomial regression analysis revealed significant associations between age (???? = 0.004) and body mass index (???? = 0.002) with CVD development, highlighting their importance as risk factors. Leveraging Weka's DL algorithms, a predictive model was constructed to classify CVD causes. Particularly, convolutional neural networks (CNN) showcased remarkable accuracy, reaching 98.64%. The findings underscore the elevated risk of CVD among university students and employees in Saudi Arabia, emphasizing the need for heightened awareness and preventive measures, including dietary improvements and increased physical activity. This study underscores the importance of further research to enhance CVD risk perception among students and individuals in similar settings.
4-total edge product cordial for some star related graphs Azaizeh, Almothana; Hasni, Roslan; Haddad, Firas; Alsmadi, Mutasem; Alkhasawneh, Raed; Hamad, Asma
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4007-4020

Abstract

Let G = (V (G), E(G)) be a graph, define an edge labeling function ψ from E(G) to {0, 1, . . . , k − 1} where k is an integer, 2 ≤ k ≤ |E(G)|, induces a vertex labeling function ψ∗ from V (G) to {0, 1, . . . , k − 1} such that ψ∗(v) = ψ(e1) × ψ(e2) × . . . × ψ(en) mod k where e1, e2, . . . , en are all edge incident to v. This function ψ is called a k-total edge product cordial (or simply k-TEPC) labeling of G if the absolute difference between number of vertices and edges labeling with i and number of vertices and edges labeling with j no more than 1 for all i, j ∈ {0, 1, . . . , k − 1}. In this paper, 4-total edge product cordial labeling for some star related graphs are determined.
Modified hotelling’s T2 control charts using modified mahalanobis distance Haddad, Firas
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i1.pp284-292

Abstract

This paper proposed new adjusted Hotelling’s T^2 control chart for individual observations. For this objective, bootstrap method for producing the individual observations were employed. To do so, both arithmetic mean vector and the covariance matrix in the traditional Hotelling’s T^2 chart were substituted by the trimmed mean vector and the covariance matrix of the robust scale estimators〖 Q〗_n, respectively which, in turn, its performance is carried out by simulated. In fact, the calculation of false alarms and the probability of detection outlier is used for determining the validity of this modified chart. The findings revealed a considerable significance in its performance.
Co-Authors Abuhaija, Belal Ali Alghamdi, Fahad Alkhasawneh, Raed Alloubani, Aladeen Almatari, Mohammad Alodat, Lena Alsmadi, Mutasem Alsmadi, Mutasem Khalil Azaizeh, Almothana Dong, Linyinxue Hamad, Asma M. Jaradat, Ghaith Qawqzeh, Yousef Roslan Hasni, Roslan Saad Alqurni, Jehad