Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Suhaib Abduljabbar Altamir
University of Mosul
Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Classification improvement of gene expression for bipolar disorder using weighted sparse logistic regression Abdulnasir Younus Ahmed; Mohammed Abdulrazaq Kahya; Suhaib Abduljabbar Altamir
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3594

Abstract

The computer-aided diagnosis system plays an important role in the classification of diseases and genes such as psychological or other diseases. Bipolar disorder (BD) is a commond psychological disease nowdys. Genes that describe this type of disease may include irrelative values to bipolar disorder disease. These values may adversely impact the classification performance. Logistic regression (LR) and recently sparse logistic regression (SLR) were used as a common technique to solve such binary classification problems. Gene selection has been applied to be a successful technique to get better classification output by excluding the irrelative values of genes. In this work we go further in improving the classification accuracy by restoring to incorporating the weight of these genes utilizing integrating the standardization of T-test with the sparse logistic regression, aiming to accomplish high classification accuracy. A bipolar dataset of gene expressions measured for 22283 genes using Affymetrix technology was used. Two performance indicators; classification accuracy, and geometric-mean of specificity and sensitivity are considered in evaluating the proposed method. Experimental results show an improvement over the two competitor methods; SLR-smoothly clipped absolute deviation (SCAD) and SLR-lasso in three indicators: classification accuracy, geo-means, and area under the curve. Therefore, our technique is beneficial to predict and classify BD psychopaths.
Shooting swarm algorithm for solving two-point boundary value problems Suhaib Abduljabbar Altamir; Mohammed Abdulrazaq Kahya; Azzam Salahuddin Younus Aladool
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp553-561

Abstract

Boundary value problems (BVPs) are solved using the more detailed swarm algorithm (SA) based on particle swarm optimization (PSO) and firefly algorithm (FA). In the field of optimization techniques, both PSO and FA have good features to solve many problems in applied mathematics. Due to the sensitivity of the use of the controversial shooting method for solving BVPs, which can not able to reach the exact solution oftentimes. A shooting Swarm algorithm (SSA) is proposed based on PSO and FA. Several BVPs including stiff BVPs were principally used to investigate the SSA. The numerical experiments and analyses revealed that the algorithm was able to overcome the shooing method drawbacks. On another hand, the proposed method that is based on FA significantly reduces the number of iterations required for solving BVPs, because of its flexible properties in the exploration and exploitation phases, and it is in good agreement with the exact solution of BVPs. The SSA was investigated to solve stiff BVPs and Its efficacy has been proven with the accurate solutions.
Co-Authors Abdulnasir Younus Ahmed Azzam Salahuddin Younus Aladool Mohammed Abdulrazaq Kahya Mohammed Abdulrazaq Kahya