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  1. 1

    Some metaheuristic algorithms for solving multiple cross-functional team selection problems by Ngo, S.T., Jaafar, J., Izzatdin, A.A., Tong, G.T., Bui, A.N.

    Published 2022
    “…We compared the developed algorithms with the MIQP-CPLEX solver on 500 programming contestants with 37 skills and several randomized distribution datasets. …”
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    Article
  2. 2

    Feature selection with integrated Gaussian seahorse optimization data mining for cross-border business cooperation between the Malaysian medical industry and tourism industry by Ma, Yuaner, Jabar, Juhaini, Abdul Aziz, Nor Azah

    Published 2023
    “…The cross-border collaboration between the medical industry and the tourism industry has gained significant attention as a promising avenue for economic growth and development. …”
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  3. 3

    Development of a syncope classification algorithm from physiological signals acquired in tilt-table test by Gan, Ming Hong

    Published 2023
    “…There are 8 set of feature selection model has built and a total of 24 set of classifiers with 3 different type of classification techniques were developed. …”
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    Final Year Project / Dissertation / Thesis
  4. 4

    AI recommendation penetration testing tool for cross-site scripting: support vector machine algorithm by Salim, Nur Saadah, Saad, Shahadan

    Published 2025
    “…This research introduces a new approach to enhancing cybersecurity by integrating Support Vector Machine (SVM) algorithms with penetration testing to develop a recommendation system focused on Cross-Site Scripting (XSS) attack detection. …”
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    Article
  5. 5

    A Study On Gene Selection And Classification Algorithms For Classification Of Microarray Gene Expression Data by Yeo, Lee Chin, Deris, Safaai

    Published 2005
    “…In This Paper, A Study On Numerous Combinations Of Gene Selection Techniques And Classification Algorithms For Classification Of Microarray Gene Expression Data Is Presented. …”
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    Article
  6. 6

    VLSI floor planning optimization using genetic algorithm and cross entropy method / Angeline Teoh Szu Fern by Angeline Teoh, Szu Fern

    Published 2012
    “…Two methods of optimization are used for CBLL. They are Cross Entropy and also Genetic Algorithm. CE is a new algorithm that was recently developed using probability. …”
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    Thesis
  7. 7

    Academic leadership bio-inspired classification model using negative selection algorithm by Jantan, Hamidah, Sa’dan, Siti ‘Aisyah, Che Azemi, Nur Hamizah Syafiqah

    Published 2015
    “…In the experimental phase, academic leadership competency data were collected from a selected higher learning institution as training data-set based on 10-fold cross validation. …”
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    Conference or Workshop Item
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    Development of effluent removal prediction model efficiency in septic sludge treatment plant through clonal selection algorithm by Sie Chun, Ting, Ismail , Amelia Ritahani, A. Malek, Marlinda

    Published 2013
    “…This study aims at developing a novel effluent removal management tool for septic sludge treatment plants (SSTP) using a clonal selection algorithm (CSA). …”
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    Article
  12. 12

    Tracking using normalized cross correlation and color space by P., Sebastian, V.V., Yap

    Published 2007
    “…The aim of this paper is to describe the implementation a face tracking algorithm for video conferencing environment using the normalized cross correlation method. …”
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    Conference or Workshop Item
  13. 13

    Resource allocation in coordinated multipoint long term evolution-advanced networks by Katiran, Norshidah

    Published 2015
    “…The resource allocation algorithm is developed through three phases, namely Low-Complexity Resource Allocation (LRA), Optimized Resource Allocation (ORA) and Cross-Layer Design of ORA (CLD-ORA). …”
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    Thesis
  14. 14

    A genetic algorithm based student attendance management system with biometric identification / Mohammad Hanif Rashid by Rashid, Mohammad Hanif

    Published 2014
    “…We have incorporated Generic Algorithm (GA) in one stage of the system. The importance of using the Genetic Algorithm is to find the best fingerprints after going through all processes of selection, cross-over and mutation. …”
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    Thesis
  15. 15

    Abnormalities and fraud electric meter detection using hybrid support vector machine & genetic algorithm by Yap K.S., Abidin I.Z., Ahmad A.R., Hussien Z.F., Pok H.L., Ismail F.I., Mohamad A.M.

    Published 2023
    “…SVM is a classification technique developed by Vapnik [1] but a practical difficulty of using SVM is the selection of parameters such as C and kernel parameter, � in Gaussian RBF kernel. …”
    Conference Paper
  16. 16

    Effective gene selection techniques for classification of gene expression data by Yeo, Lee Chin

    Published 2005
    “…When classifying tissue samples, gene selection plays an important role. In this research, some existing gene selection techniques are studied and better gene selection techniques are proposed and developed. …”
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    Thesis
  17. 17

    PV fault classification: Impact on accuracy performance using feature extraction in random-forest cross validation algorithm by Muhamad Zahim, Sujod, Siti Nor Azlina, Mohd Ghazali, Mohd Fadzil, Abdul Kadir, Al-Shetwi, Ali Qasem

    Published 2024
    “…This paper introduces a Solar PV Smart Fault Diagnosis and Classification (SFDC) model that harnesses the Random Forest (RF) algorithm in conjunction with Cross-Validation (CV) and an optimized feature extraction (FE) set. …”
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    Article
  18. 18

    Development of effluent removal prediction model efficiency in septic sludge treatment plant through clonal selection algorithm by Ting S.C., Ismail A.R., Malek M.A.

    Published 2023
    “…This study aims at developing a novel effluent removal management tool for septic sludge treatment plants (SSTP) using a clonal selection algorithm (CSA). …”
    Article
  19. 19

    A comparative analysis of machine learning algorithms for diabetes prediction by Alansari, Waseem Abdulmahdi, Masnizah Mohd

    Published 2024
    “…The study contributes insights into the importance of pre-processing and feature selection in improving algorithm performance. The findings have implications for developing accurate predictive models and improving diabetes detection.…”
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    Article
  20. 20

    Development of an explainable machine learning model for predicting depression in adults with type 2 diabetes mellitus: a cross-sectional SHAP-based analysis of NHANES 2009-2023 by Tang, Yan, Jia, Lei, Zhou, Junjun, Dou, Jin, Qian, Jingjuan, Yi, Xin, Soh, Kim Lam

    Published 2026
    “…Five machine learning algorithms - random forest, extreme gradient boosting (XGBoost), multilayer perceptron, logistic regression, and support vector machine - were trained and evaluated using 5-fold cross-validation. …”
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    Article