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  1. 1
    Direct approach for mining association rules from structured XML data

    Direct approach for mining association rules from structured XML data by Abazeed, Ashraf Riad

    Published 2012
    “…The thesis also provides a two different implementation of the modified FLEX algorithm using a java based parsers and XQuery implementation. …”
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  2. 2
    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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  3. 3
    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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  4. 4
    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
    “…Numerous bioinformatics examination tools are used for cancer prediction, including the data set matrix (Bailey et al., Cell 173(2):371–385, 2018); thus, it is necessary to resolve the problem of missing-values imputation. …”
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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
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  6. 6
    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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  7. 7
    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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  8. 8
    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
    “…The neural network uses the RGB value from the image processing phase and analyzes the regions to check whether there is misplaced or missing jigsaw puzzle. …”
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  9. 9
    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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  10. 10
    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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  11. 11
    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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  12. 12
    ExtraImpute: a novel machine learning method for missing data imputation

    ExtraImpute: a novel machine learning method for missing data imputation by Alabadla, Mustafa, Sidi, Fatimah, Ishak, Iskandar, Ibrahim, Hamidah, Affendey, Lilly Suriani, Hamdan, Hazlina

    Published 2022
    “…This approach imputes each missing value that exists in features by predicting its value using other observed values in the dataset. …”
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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
    Missing values imputation tool using imputex algorithm

    Missing values imputation tool using imputex algorithm by Sidi, Fatimah, Abdullah, Lili Nurliyana, Alabadla, Mustafa, Ishak, Iskandar

    Published 2024
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  15. 15
    OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT

    OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT by Murad S.S., Badeel R., Alsandi N.S.A., Alshaaya R.F., Ahmed R.A., Muhammed A., Derahman M.

    Published 2023
    “…To achieve this, we propose a new noble mechanism called Optimized Min-Min (OMin-Min) algorithm, inspired by the Min-Min algorithm. The objectives of this work are: i) to provide a comprehensive review of the cloud and scheduling process; ii) to classify the scheduling strategies and scientific workflows; iii) to implement our proposed algorithm with various scheduling algorithms (i.e., Min-Min, Round-Robin, Max-Min, and Modified Max-Min) for performance comparison, within different cloudlet sizes (i.e., small, medium, large, and heavy) in three scientific workflows (i.e., Montage, Epigenomics, and SIPHT); and iv) to investigate the performance of the implemented algorithms by using CloudSim. …”
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  16. 16
    Missing value estimation methods for data in linear functional relationship model

    Missing value estimation methods for data in linear functional relationship model by Adilah Abdul Ghapor, Yong Zulina Zubairi, A.H.M. Rahmatullah Imon

    Published 2017
    “…Missing value problem is common when analysing quantitative data. …”
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  17. 17
    Evaluating A New Adaptive Group Lasso Imputation Technique For Handling Missing Values In Compositional Data

    Evaluating A New Adaptive Group Lasso Imputation Technique For Handling Missing Values In Compositional Data by Tian, Ying

    Published 2024
    “…The complexity of compositional data with missing values renders traditional estimation methods inadequate. …”
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  18. 18
    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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  19. 19
    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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  20. 20
    Data prediction and recalculation of missing data in soft set / Muhammad Sadiq Khan

    Data prediction and recalculation of missing data in soft set / Muhammad Sadiq Khan by Muhammad Sadiq , Khan

    Published 2018
    “…Soft sets with incomplete data cannot be used in applications. Few researchers have worked on data filling and recalculating incomplete soft sets, and the current research focuses on predicting missing values and decision values from non-missing data or aggregates. …”
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