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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Mohammed Abdulrazaq Kahya
Mosul University, Mosul, Iraq

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Journal : Indonesian Journal of Electrical Engineering and Computer Science

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Classification enhancement of breast cancer histopathological image using penalized logistic regression Mohammed Abdulrazaq Kahya
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 1: January 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i1.pp405-410

Abstract

Classification of breast cancer histopathological images plays a significant role in computer-aided diagnosis system. Features matrix was extracted in order to classify those images and they may contain outlier values adversely that affect the classification performance. Smoothing of features matrix has been proved to be an effective way to improve the classification result via eliminating of outlier values. In this paper, an adaptive penalized logistic regression is proposed, with the aim of smoothing features and provides high classification accuracy of histopathological images, by combining the penalized logistic regression with the smoothed features matrix. Experimental results based on a publicly recent breast cancer histopathological image datasets show that the proposed method significantly outperforms penalized logistic regression in terms of classification accuracy and area under the curve. Thus, the proposed method can be useful for histopathological images classification and other classification of diseases types using DNA gene expression data in the real clinical practice.
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 Azzam Salahuddin Younus Aladool Suhaib Abduljabbar Altamir