Z
Zuwairie Ibrahim
Researcher at Universiti Malaysia Pahang
Publications - 275
Citations - 2040
Zuwairie Ibrahim is an academic researcher from Universiti Malaysia Pahang. The author has contributed to research in topics: DNA computing & Particle swarm optimization. The author has an hindex of 21, co-authored 272 publications receiving 1813 citations. Previous affiliations of Zuwairie Ibrahim include Universiti Teknologi MARA & Meiji University.
Papers
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Proceedings ArticleDOI
Printed circuit board defect detection using mathematical morphology and MATLAB image processing tools
TL;DR: This project proposes a PCB defect detection and classification system using a morphological image segmentation algorithm and simple the image processing theories to detect and classify the defects on bare single layer PCBs by introducing a hybrid algorithm.
A Kalman filter approach for solving unimodal optimization problems
Zuwairie Ibrahim,Nor Hidayati Abdul Aziz,Nor Hidayati Abdul Aziz,Nor Azlina Ab Aziz,Saifudin Razali,Mohd Ibrahim Shapiai,Sophan Wahyudi Nawawi,Mohd Saberi Mohamad +7 more
TL;DR: The experimental results show that the proposed SKF algorithm is a promising approach in solving unimodal optimization problems and has a comparable performance to some well-known metaheuristic algorithms.
Journal ArticleDOI
Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects
TL;DR: Two models of evolutionary fuzzy ARTMAP (FAM) neural networks are introduced to deal with the imbalanced data set problems in a semiconductor manufacturing operations and the outcomes positively indicate the effectiveness of the proposed networks in handling classification problems with im balanced data sets.
Journal ArticleDOI
An enhancement of binary particle swarm optimization for gene selection in classifying cancer classes
Mohd Saberi Mohamad,Sigeru Omatu,Safaai Deris,Michifumi Yoshioka,Afnizanfaizal Abdullah,Zuwairie Ibrahim +5 more
TL;DR: An enhanced binary particle swarm optimization to perform the selection of small subsets of informative genes which is significant for cancer classification is proposed and proved to be superior to other previous related works in terms of classification accuracy and the number of selected genes.
Journal ArticleDOI
Feature selection using angle modulated simulated Kalman filter for peak classification of EEG signals.
TL;DR: The experimental results have shown that the proposed AMSKF feature selector is able to find the best combination of features and performs at par with the existing related studies of epileptic EEG events classification.