Search Results - (( evolution optimization bat algorithm ) OR ( variable validation learning algorithm ))

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  1. 1

    Multi-Swarm bat algorithm by Taha A.M., Chen S.-D., Mustapha A.

    Published 2023
    “…In this study a new Bat Algorithm (BA) based on multi-swarm technique called the Multi-Swarm Bat Algorithm (MSBA) is proposed to address the problem of premature convergence phenomenon. …”
    Article
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    Quality of service and energy efficient aware (QEEA) scheduling algorithm for long term evolution (LTE) network / Nurulanis Mohd Yusoff by Mohd Yusoff, Nurulanis

    Published 2017
    “…Basically, the QEEA is based on the Time Domain (TD) and Frequency Domain (FD) scheduling where it is dependent on the QoS requirements to allocate resources. The proposed algorithm is compared against other scheduling algorithms, namely, the Channel and QoS Aware (CQA), Priority Set Scheduler (PSS), Proportional Fair (PF), Maximum Throughput (MT) and Blind Average Throughput (BAT). …”
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    Utilizing machine learning to predict hospital admissions for pediatric COVID-19 patients (PrepCOVID-Machine) by Chuin-Hen Liew, Song-Quan Ong, David Chun-Ern Ng

    Published 2024
    “…Grid Search was used to optimize the hyperparameters of each algorithm. The study analyzed 1988 children and 30 study variables after data were processed. …”
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    Article
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    Predicting dengue transmission rates by comparing different machine learning models with vector indices and meteorological data by Ong, Song Quan, Pradeep Isawasan, Ahmad Mohiddin Mohd Ngesom, Hanipah Shahar, As’malia Md Lasim, Gomesh Nair

    Published 2023
    “…Previous work has focused only on specific weather variables and algorithms, and there is still a need for a model that uses more variables and algorithms that have higher performance. …”
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    Article
  7. 7

    Green building valuation based on machine learning algorithms / Thuraiya Mohd ... [et al.] by Mohd, Thuraiya, Jamil, Syafiqah, Masrom, Suraya, Ab Rahim, Norbaya

    Published 2021
    “…This experiment used five common machine learning algorithms namely 1) Linear Regressor, 2) Decision Tree Regressor, 3) Random Forest Regressor, 4) Ridge Regressor and 5) Lasso Regressor tested on a real estate data-set of covering Kuala Lumpur District, Malaysia. 3 set of experiments was conducted based on the different feature selections and purposes The results show that the implementation of 16 variables based on Experiment 2 has given a promising effect on the model compare the other experiment, and the Random Forest Regressor by using the Split approach for training and validating data-set outperformed other algorithms compared to Cross-Validation approach. …”
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    Conference or Workshop Item
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    Machine Learning Classifications of Multiple Organ Failures in a Malaysian Intensive Care Unit by Shah N.N.H., Razak N.N.A., Razak A.A., Abu-Samah A., Suhaimi F.M., Jamaluddin U.

    Published 2025
    “…This study demonstrates the performances of different machine learning algorithms in the classification of multiple organ failures. …”
    Article
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    The Determinant Factors for the Issuance of Central Bank Digital Currency (CBDC) in Malaysia using Machine Learning Framework by Normi Sham Awang, Abu Bakar, Norzariyah, Yahya, Norbik Bashah, Idris, Engku Rabiah Adawiah, Engku Ali, Jasni, Mohamad Zain, Erni Eliana, Khairuddin, Ahmad Firdaus, Zainal Abidin, Murtaj, Sheikh Mohammad Tahsin, Siti Sarah, Maidin

    Published 2024
    “…The overall CentralBank Digital Currency Project Index (CBDCPI) was selected as a target variable,while two machine learning algorithms, Random Forest and XGBoost were utilized to identify the determining variables. …”
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    Article
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    Predicting mortality of Malaysian patients with acute coronary syndrome (ACS) subtypes using machine learning and deep learning approaches / Muhammad Firdaus Aziz by Muhammad Firdaus , Aziz

    Published 2022
    “…The purpose of this study is to use machine learning (ML) and deep learning (DL) algorithms to predict and identify variables linked to short and long-term mortality in Asian STEMI and NSTEMI/UA patients and to compare these results to a conventional risk score. …”
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    Thesis
  14. 14

    Effective use of artificial intelligence by Malaysian manufacturing firms to enable sustainability 4.0 by Chong, Agnes Wen Lin

    Published 2023
    “…In this project, Fornell-Larker parameters are used to test the measurement algorithm for the research's discriminant validity. 380 valid questionnaires returnedback and conducted the calculation of the algorithm's assessment to constructs' validity and realibility assessment, discriminant validity using HTMTand Fornell-Larker approaches. …”
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    Final Year Project / Dissertation / Thesis
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    Regression study for thyroid disease prediction Comparison of crossing-over approaches and multivariate analysis by Ong, Song Quan, Pradeep Isawasan, Khairulliza Ahmad Salleh

    Published 2022
    “…Future studies could explore the effects of cross-validation and multivariate analysis on other machine learning algorithms.…”
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    Conference or Workshop Item
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    A New Quadratic Binary Harris Hawk Optimization For Feature Selection by Abdullah, Abdul Rahim, Too, Jing Wei, Mohd Saad, Norhashimah

    Published 2019
    “…In this study, twenty-two datasets collected from the UCI machine learning repository are used to validate the performance of proposed algorithms. …”
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    Article
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    Development of a syncope classification algorithm from physiological signals acquired in tilt-table test by Gan, Ming Hong

    Published 2023
    “…Syncope also known as transient loss of consciousness which caused problem to human daily life. Since machine learning is much more advanced, classification of syncope can be done with machine learning. …”
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    Final Year Project / Dissertation / Thesis
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    Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm by Reza M.S., Hannan M.A., Mansor M., Ker P.J., Rahman S.A., Jang G., Mahlia T.M.I.

    Published 2025
    “…To address these problems, this paper introduces a novel hybrid approach for RUL prediction, combining a Lightning Search Algorithm (LSA) with a Long-Short Term Memory (LSTM) deep learning model. …”
    Article