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  1. 1
    Study and Implementation of Data Mining in Urban Gardening

    Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

    Published 2019
    “…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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  2. 2
    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
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  3. 3
    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection

    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection by Saputra , Dhany, Rambli, Dayang R.A., Foong, Oi Mean

    Published 2008
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  4. 4
    Determination of tree height based on tree crown using algorithm derived from UAV imagery / Suzanah Abdullah ... [et al.]

    Determination of tree height based on tree crown using algorithm derived from UAV imagery / Suzanah Abdullah ... [et al.] by Abdullah, Suzanah, Tahar, Khairul Nizam, Abdul Rashid, Mohd Fadzil, Osoman, Muhammad Ariffin

    Published 2021
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  5. 5
    Framework for mining XML format business process log data

    Framework for mining XML format business process log data by Ang, Jin Sheng

    Published 2024
    “…Besides, statistical analysis and data mining techniques are difficult to be applied in eXtensible Markup Language (XML) format documents. This study introduces an alternative approach for mining XML format documents which can be modelled into tree-structured format. …”
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  6. 6
    Mining Sequential Patterns Using I-PrefixSpan

    Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

    Published 2007
    “…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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  7. 7
    A web-based implementation of k-means algorithms

    A web-based implementation of k-means algorithms by Lee, Quan

    Published 2022
    “…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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  8. 8
    Mining Sequential Patterns using I-PrefixSpan

    Mining Sequential Patterns using I-PrefixSpan by Saputra , Dhany, Dayang R.A. Rambli, Foong, Oi Mean

    Published 2008
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    Citation Index Journal
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  9. 9
    Analysis of daytime and nighttime ground level ozone concentrations using boosted regression tree technique

    Analysis of daytime and nighttime ground level ozone concentrations using boosted regression tree technique by Yahaya, Noor Zaitun, Ghazali, Nurul Adyani, Ahmad, Sabri, Mohammad Asri, Mohammad Akmal, Ibrahim, Zul Fahdli, Ramli, Nor Azman

    Published 2017
    “…This paper investigated the use of boosted regression trees (BRTs) to draw an inference about daytime and nighttime ozone formation in a coastal environment. …”
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  10. 10
    Improving performance of automated coronary arterial tree center-line extraction, stent localization and tracking

    Improving performance of automated coronary arterial tree center-line extraction, stent localization and tracking by Boroujeni, Farsad Zamani

    Published 2012
    “…Over the last decade, many algorithms have been developed to address this problem. …”
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  11. 11
    Features selection for intrusion detection system using hybridize PSO-SVM

    Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

    Published 2016
    “…The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics.…”
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  12. 12
    Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction

    Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction by Jumin E., Zaini N., Ahmed A.N., Abdullah S., Ismail M., Sherif M., Sefelnasr A., El-Shafie A.

    Published 2023
    “…The results revealed that wind speed, humidity, Nitrogen Oxide, Carbon Monoxide and Nitrogen Dioxide have significant influence on ozone formation. Boosted Decision Tree outperformed Linear regression and Neural Network algorithms for all stations. …”
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  13. 13
    An Advance Ozone Concentration Prediction By Implementing Machine Learning Algorithms In Selangor And Kuala Lumpur Malaysia

    An Advance Ozone Concentration Prediction By Implementing Machine Learning Algorithms In Selangor And Kuala Lumpur Malaysia by Ellysia Jumin

    Published 2023
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  14. 14
    Application of machine learning algorithms to predict removal efficiency in treating produced water via gas hydrate-based desalination

    Application of machine learning algorithms to predict removal efficiency in treating produced water via gas hydrate-based desalination by Nallakukkala, Sirisha, Tackie-Otoo, Bennet Nii, Aliyu, Ruwaida, Lal, Bhajan, Nallakukkala, Jagadish Ram Deepak, Devi, Gayathri

    Published 2025
    “…In this context. ML algorithms provide powerful data driven means to model complex relationship within experimental datasets to improve process optimisation This study systematically evaluated several supervised ML models, including Random Forest (RF) Support Vector Machines (SVM), Ridge Regression, Lasso Regression, Decision Tree, Extra Tree Regression, Gradient Boost, and XGBoost, to predict removal efficiency in GHBD system. …”
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  15. 15
    An artificial intelligence approach to monitor student performance and devise preventive measures

    An artificial intelligence approach to monitor student performance and devise preventive measures by Khan I., Ahmad A.R., Jabeur N., Mahdi M.N.

    Published 2023
    “…Decision tree triumph over other models and thus is further transformed into easily explicable format. …”
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  16. 16
    Enhancing a cluster-based TDMA MAC protocol for vehicle-to-vehicle communications / Abubakar Bello Tambawal

    Enhancing a cluster-based TDMA MAC protocol for vehicle-to-vehicle communications / Abubakar Bello Tambawal by Abubakar Bello , Tambawal

    Published 2020
    “…A dynamic time slot allocation strategy using a binary tree algorithm has been proposed to prevent vehicles from the adjacent clusters to reserve the same time slot. …”
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  17. 17
    An enhanced feature selection technique for classification of group-based holy quran verses

    An enhanced feature selection technique for classification of group-based holy quran verses by Oyekunle, Adeleke Abdullahi

    Published 2018
    “…In the experiment, six feature selection algorithms were applied: In.formation Gain (JG), Chi-square (CH), Pearson Correlation Coefficient (PCC), RelieJF, Correlation-based (CFS), and the proposed JG-CFS algorithms. …”
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  18. 18
    A Systematic Approach to Transform Machine Learning Students� Performance Prediction Model into Preventive Procedures

    A Systematic Approach to Transform Machine Learning Students� Performance Prediction Model into Preventive Procedures by Khan I., Ahmad A.R., Jabeur N., Mahdi M.N.

    Published 2023
    “…J48 decision tree prevails other models by achieving accuracy and F-measure of nearly 0.90. …”
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  19. 19
    Comprehensive comparison of various machine learning algorithms for short-term ozone concentration prediction

    Comprehensive comparison of various machine learning algorithms for short-term ozone concentration prediction by Yafouz A., AlDahoul N., Birima A.H., Ahmed A.N., Sherif M., Sefelnasr A., Allawi M.F., Elshafie A.

    Published 2023
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  20. 20
    A quality of experience approach in smartphone video selection framework for energy efficiency

    A quality of experience approach in smartphone video selection framework for energy efficiency by Jofri, Muhamad Hanif

    Published 2021
    “…The VCS outcomes are essential to help users and developer deciding which suitable video streaming format that can satisfy energy consumption and user QoE.…”
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