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  1. 1
    Parallel distributed genetic algorithm development based on microcontrollers framework

    Parallel distributed genetic algorithm development based on microcontrollers framework by Krishnan P.S., Kiong T.S., Koh J.

    Published 2023
    Conference paper
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  2. 2
    A new variant of black hole algorithm based on multi population and levy flight for clustering problem

    A new variant of black hole algorithm based on multi population and levy flight for clustering problem by Haneen Abdul Wahab, Abdul Raheem

    Published 2020
    “…Black Hole (BH) optimization algorithm has been underlined as a solution for data clustering problems. …”
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  3. 3
    Investigation of evolutionary multi-objective algorithms in solving view selection problem / Seyed Hamid Talebian

    Investigation of evolutionary multi-objective algorithms in solving view selection problem / Seyed Hamid Talebian by Talebian, Seyed Hamid

    Published 2013
    “…In addition to the normal metrics, the computational time for executing each algorithm was also measured and compared. …”
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  4. 4
    An improved recommender system based on normalization of matrix factorization and collaborative filtering algorithms

    An improved recommender system based on normalization of matrix factorization and collaborative filtering algorithms by Zahid, Aafaq

    Published 2015
    “…Another problem called cold start gives wrong recommendations amongst new users as data of new users is not enough for recommendation. …”
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  5. 5
    Smart energy meter with adaptive communication data transfer algorithm for electrical energy monitoring

    Smart energy meter with adaptive communication data transfer algorithm for electrical energy monitoring by Ab Halim, Haizum Hanim

    Published 2021
    “…Thus, in this thesis, a new Smart Energy Meter (SEM) with adaptive data transfer algorithm is designed to accommodate the problem. …”
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  6. 6
    The normalized random map of gradient for generating multifocus image fusion

    The normalized random map of gradient for generating multifocus image fusion by Ismail, ., Kamarul Hawari, Ghazali

    Published 2020
    “…In order to handle this problem, the proposed method a concise algorithm which is able to generate an accurate fused image without using a complicated mathematical equation and tough algorithm. …”
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  7. 7
    Normalized Relational Storage for Extensible Markup Language (XML) Schema

    Normalized Relational Storage for Extensible Markup Language (XML) Schema by Kamsuriah, Ahmad, Reduan, Samad

    Published 2011
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  8. 8
    Mitigating Unbalanced And Overlapped Problems Of Large Network Intrusion Data Using Multiplelevel Detection Techniques

    Mitigating Unbalanced And Overlapped Problems Of Large Network Intrusion Data Using Multiplelevel Detection Techniques by Ho, Yan Bing

    Published 2022
    “…These problems have caused a low detection rate for intrusions that are the minority in data sets because learning algorithms favour the majority class (normal traffic). …”
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    Final Year Project / Dissertation / Thesis
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  9. 9
    Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly

    Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly by Zulkifly, Ahmad Zuladzlan

    Published 2019
    “…Even a normal people using clustering to grouping their data. …”
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  10. 10
    Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares

    Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares by Uraibi, Hassan Sami

    Published 2009
    “…To rectify this problem, we propose a Dynamic Robust Bootstrap-LTS based (DRBLTS) algorithm where the percentage of outliers in each bootstrap sample is detected. …”
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  11. 11
    Improving Channel Assignment for Vehicular Ad-hoc Sensor Network in Disaster Management System

    Improving Channel Assignment for Vehicular Ad-hoc Sensor Network in Disaster Management System by Chiu Shoon, Sia

    Published 2024
    “…Moreover, an average of 22.91% for LTE-AIC with respect to normal LTE-A and 38.63% compared to LTE for high data rate. …”
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  12. 12
    Modifying maximum likelihood test for solving singularity and outlier problems in high dimensional cases

    Modifying maximum likelihood test for solving singularity and outlier problems in high dimensional cases by Hafeez, Ahmad

    Published 2021
    “…However, the performance of the classical location and scatter estimators is usually flawed by singularity and outliers’ problems in high dimensional data sets. The study aimed to modify the existing ML test by incorporating the Cholesky banded regularization methods and thresholding sample covariance matrix to resolve singularity problems and incorporating the L₁-median with Weiszfeld’s algorithm (MLw) covariance matrix to solve outliers’ problem in high dimensional data sets. …”
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  13. 13
    Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm

    Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm by Dalatu, Paul Inuwa

    Published 2018
    “…We introduced two new approaches to normalization techniques to enhance the K-Means algorithms. …”
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  14. 14
    Using predictive analytics to solve a newsvendor problem / S. Sarifah Radiah Shariff and Hady Hud

    Using predictive analytics to solve a newsvendor problem / S. Sarifah Radiah Shariff and Hady Hud by Shariff, S. Sarifah Radiah, Hud, Hady

    Published 2023
    “…The best algorithm will not be the same for all the data sets. …”
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    Book Section
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  15. 15
    A Method for Mapping XML DTD to Relational Schemas In The Presence Of Functional Dependencies

    A Method for Mapping XML DTD to Relational Schemas In The Presence Of Functional Dependencies by Ahmad, Kamsuriah

    Published 2008
    “…This concept is used to specify the constraints that may exist in the relations and guide the design while removing semantic data redundancies. This approach leads to a good normalized relational schema without data redundancy. …”
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  16. 16
    Statistical approach on grading: mixture modeling

    Statistical approach on grading: mixture modeling by Md. Desa, Zairul Nor Deana

    Published 2006
    “…A solution to this problem is using the Markov Chain Monte Carlo method namely Gibbs sampler algorithm. …”
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  17. 17
    Integrating genetic algorithms and fuzzy c-means for anomaly detection

    Integrating genetic algorithms and fuzzy c-means for anomaly detection by Chimphlee, Witcha, Abdullah, Abdul Hanan, Sap, Noor Md., Chimphlee, Siriporn, Srinoy, Surat

    Published 2005
    “…Traditional anomaly detection algorithms require a set of purely normal data from which they train their model. …”
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    Conference or Workshop Item
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  18. 18
    Deforestation detection in Kinabalu Area, Sabah, Malaysia by using multi-sensor remote sensing approach

    Deforestation detection in Kinabalu Area, Sabah, Malaysia by using multi-sensor remote sensing approach by Phua, Mui How, Tsuyuki, Satoshi

    Published 2004
    “…It can also provide more satellite data for the application and thus lessen data acquisition problem due to cloud cover which is a consistent problem for the tropics.…”
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  19. 19
    An Integrated Principal Component Analysis And Weighted Apriori-T Algorithm For Imbalanced Data Root Cause Analysis

    An Integrated Principal Component Analysis And Weighted Apriori-T Algorithm For Imbalanced Data Root Cause Analysis by Ong, Phaik Ling

    Published 2016
    “…A semiconductor manufacturing case study with Work In Progress data and true alarm data is used to proof the proposed algorithm. …”
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  20. 20
    Abnormal data detection model based on autoencoder and random forest algorithm: camera sensor data in autonomous driving systems

    Abnormal data detection model based on autoencoder and random forest algorithm: camera sensor data in autonomous driving systems by Shengwen, Geng, Osman, Mohd Hafeez

    Published 2025
    “…Based on the above problems, this study addresses this challenge by proposing a hybrid anomaly detection model (called CAE-RF) that combines convolutional autoencoders and random forest algorithms to achieve efficient and accurate identification of abnormal data patterns to improve the safety of autonomous driving systems. …”
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