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  1. 1
    Determination of the Best Single Imputation Algorithm for Missing Rainfall Data Treatment

    Determination of the Best Single Imputation Algorithm for Missing Rainfall Data Treatment by Saeed, Gamil Abdulraqeb Abdullah, Chuan, Zun Liang, Roslinazairimah, Zakaria, Wan Nur Syahidah, Wan Yusoff

    Published 2016
    “…Consequently, a competent imputation algorithm for missing data treatment algorithm is very much needed. …”
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  2. 2
    Missing tags detection algorithm for radio frequency identification (RFID) data stream

    Missing tags detection algorithm for radio frequency identification (RFID) data stream by Zainudin, Nur 'Aifaa

    Published 2019
    “…Thus in this research, an AC complement algorithm with hashing algorithm and Detect False Negative Read algorithm (DFR) is used to developed the Missing Tags Detection Algorithm (MTDA). …”
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  3. 3
    Determination of the best single imputation algorithm for missing rainfall data treatment

    Determination of the best single imputation algorithm for missing rainfall data treatment by Gamil Abdulraqeb Abdullah Saeed, Chuan, Zun Liang, Roslinazairimah Zakaria, Wan Nur Syahidah Wan Yusoff, Mohd Zuki Salleh

    Published 2016
    “…Consequently, a competent imputation algorithm for missing data treatment algorithm is very much needed. …”
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  4. 4
    Auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset

    Auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset by Amirruddin, A., Aziz, I.A., Hasan, M.H.

    Published 2020
    “…This problem inspires the idea to develop a prediction algorithm to predict the missing values in the dataset, where Support vector regression (SVR) has been proposed as a prediction method to predict missing values in several academic types of researches. …”
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  5. 5
    Missing Value Imputation for PM10 Concentration in Sabah using Nearest Neighbour Method(NNM) and Expectation-Maximization (EM) Algorithm

    Missing Value Imputation for PM10 Concentration in Sabah using Nearest Neighbour Method(NNM) and Expectation-Maximization (EM) Algorithm by Muhammad Izzuddin Rumaling, Chee, Fuei Pien, Jedol Dayou, Chang, Jackson Hian Wui, Steven Soon Kai Kong, Justin Sentian

    Published 2020
    “…The missing data is imputed by using both NNM and EM algorithm. …”
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  6. 6
    Systematic review on missing data imputation techniques with machine learning algorithms for healthcare

    Systematic review on missing data imputation techniques with machine learning algorithms for healthcare by Ismail, Amelia Ritahani, Zainal Abidin, Nadzurah, Maen, Mohd Khaled

    Published 2022
    “…Many machine learning algorithms have been applied to impute missing data with plausible values. …”
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  7. 7
    An Evaluation of Machine Learning Algorithms for Missing Values Imputation

    An Evaluation of Machine Learning Algorithms for Missing Values Imputation by Kohbalan, Moorthy, Ali, Mohammed Hasan, Mohd Arfian, Ismail, Chan, Weng Howe, Mohd Saberi, Mohamad, Safaai, Deris

    Published 2019
    “…It represents the research and imputation of missing values in gene expression data. By using the local or global correlation of the data we focus mostly on the contrast of the algorithms. …”
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  8. 8
    Identifying the Ideal Number Q-Components of the Bayesian Principal Component Analysis Model for Missing Daily Precipitation Data Treatment

    Identifying the Ideal Number Q-Components of the Bayesian Principal Component Analysis Model for Missing Daily Precipitation Data Treatment by Chuan, Zun Liang, Azlyna, Senawi, Wan Nur Syahidah, Wan Yusoff, Noriszura, Ismail, Tan, Lit Ken, Mu, Wen Chuan

    Published 2018
    “…Contrarily, the single imputation algorithm is superior in missing daily precipitation data treatment for an inland region time series rather than the BPCAQ-VB algorithm.…”
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  9. 9
    Performance of missing transverse momentum reconstruction in proton-proton collisions at root s=13 TeV using the CMS detector

    Performance of missing transverse momentum reconstruction in proton-proton collisions at root s=13 TeV using the CMS detector by Sirunyan, A. M., Tumasyan, A. R., Adam, Wolfgang, Ambrogi, Federico, Asilar, Ece, Md. Ali, Mohd. Adli

    Published 2019
    “…The performance of missing transverse momentum (p® miss T ) reconstruction algorithms for the CMS experiment is presented, using proton-proton collisions at a center-of-mass energy of 13 TeV, collected at the CERN LHC in 2016. …”
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  10. 10
    Detection Of Misplaced And Missing Regions In Image Using Neural Network

    Detection Of Misplaced And Missing Regions In Image Using Neural Network by Tan , Jin Siang

    Published 2017
    “…Based on research, most of the algorithms are not intelligent enough and only able to detect the missing component. …”
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  11. 11
    Missing-values imputation algorithms for microarray gene expression data

    Missing-values imputation algorithms for microarray gene expression data by Moorthy, Kohbalan, Jaber, Aws Naser, Mohd Arfian, Ismail, Ernawan, Ferda, Mohd Saberi, Mohamad, Safaai, Deris

    Published 2019
    “…By using local and global correlation of the data, we were able to focus mostly on the differences between the algorithms. …”
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  12. 12
    MISSING DAILY RAINFALL PREDICTION USING GREY WOLF OPTIMIZER-BASED NEURAL NETWORK

    MISSING DAILY RAINFALL PREDICTION USING GREY WOLF OPTIMIZER-BASED NEURAL NETWORK by Lai, Wai Yan, Kuok, King Kuok, Chiu, Po Chan, Md. Rezaur, Rahman, Muhammad Khusairy, Bakri

    Published 2024
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  13. 13
    Enhanced mechanism to handle missing data of Hadith classifier

    Enhanced mechanism to handle missing data of Hadith classifier by Aldhlan, Kawther A., Zeki, Ahmed M., Zeki, Akram M.

    Published 2011
    “…Decision tree algorithms have the ability to deal with missing values or wrong data. …”
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  14. 14
    An improved K-nearest neighbour with grasshopper optimization algorithm for imputation of missing data

    An improved K-nearest neighbour with grasshopper optimization algorithm for imputation of missing data by Zainal Abidin, Nadzurah, Ismail, Amelia Ritahani

    Published 2021
    “…K-nearest neighbors (KNN) has been extensively used as imputation algorithm to substitute missing data with plausible values. …”
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  15. 15
    Data-driven continuous-time Hammerstein modeling with missing data using improved Archimedes optimization algorithm

    Data-driven continuous-time Hammerstein modeling with missing data using improved Archimedes optimization algorithm by Islam, Muhammad Shafiqul, Mohd Ashraf, Ahmad

    Published 2024
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  16. 16
    Imputation Analysis of Time-Series Data Using a Random Forest Algorithm

    Imputation Analysis of Time-Series Data Using a Random Forest Algorithm by Nur Najmiyah, Jaafar, Muhammad Nur Ajmal, Rosdi, Khairur Rijal, Jamaludin, Faizir, Ramlie, Habibah, Abdul Talib

    Published 2024
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  17. 17
    SINE COSINE ALGORITHM BASED NEURAL NETWORK FOR RAINFALL DATA IMPUTATION

    SINE COSINE ALGORITHM BASED NEURAL NETWORK FOR RAINFALL DATA IMPUTATION by Chiu, Po Chan, Ali, Selamat, Kuok, King Kuok

    Published 2024
    “…This chapter presents the ability of the sine cosine algorithm-based neural network (SCANN) to predict and optimize missing rainfall at different percentages of missing rates. …”
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  18. 18
    DISSERTATION TITLE: AN OPTIMIZED SUPPORT VECTOR REGRESSION USING EVOLVED BAT ALGORITHM FOR HANDLING MISSING VALUES IN DATASETS

    DISSERTATION TITLE: AN OPTIMIZED SUPPORT VECTOR REGRESSION USING EVOLVED BAT ALGORITHM FOR HANDLING MISSING VALUES IN DATASETS

    Published 2022
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  19. 19
    Improving knowledge extraction of Hadith classifier using decision tree algorithm

    Improving knowledge extraction of Hadith classifier using decision tree algorithm by Aldhaln, Kawther, Zeki, Akram M., Zeki, Ahmed M., Alreshidi, Hamad

    Published 2012
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  20. 20
    New Learning Models for Generating Classification Rules Based on Rough Set Approach

    New Learning Models for Generating Classification Rules Based on Rough Set Approach by Al Shalabi, Luai Abdel Lateef

    Published 2000
    “…Missing values lead to the difficulty of extracting useful information from that data set. …”
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