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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
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  2. 2
    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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  3. 3
    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
    “…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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  4. 4
    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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    Conference or Workshop Item
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  5. 5
    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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    Final Year Project / Dissertation / Thesis
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  6. 6
    Development of a mathematical model for the prediction of chip formation instability and its verification by fuzzy logic with genetic algorithm

    Development of a mathematical model for the prediction of chip formation instability and its verification by fuzzy logic with genetic algorithm by Ullah Patwari, Mohammed Anayet, Amin, A. K. M. Nurul, Istihyaq , M.H., Faris, Waleed Fekry

    Published 2010
    “…In this paper, a new analytical technique is proposed to predict the frequency of chip formation instability as a function of cutting parameters. …”
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  7. 7
    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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  8. 8
    PREDICTION OF THE THERMODYNAMIC HYDRATE FORMATION CONDITIONS FOR METHANE GAS

    PREDICTION OF THE THERMODYNAMIC HYDRATE FORMATION CONDITIONS FOR METHANE GAS by Abbasi, A., Soomro, A.A., Hashim, F.M.

    Published 2022
    “…Thus, the developed algorithm was applied to the experimental data of gas pipeline to validate the results. …”
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  9. 9
    Development of a hydrate formation prediction model for sub-sea pipeline

    Development of a hydrate formation prediction model for sub-sea pipeline by Abbasi, A., Hashim, F.M.

    Published 2017
    “…In this research work, a novel model is developed to the hydrate formation pressure and hydrate formation temperature for a single component of methane (CH4) gas. …”
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  10. 10
    Virtual manufacturing for prediction of Martensite Formation and Hardness Value induced by Laser Welding Process using Subroutine Algorithm in MSC Marc/Mentat / Omar Yahya...[et al.]

    Virtual manufacturing for prediction of Martensite Formation and Hardness Value induced by Laser Welding Process using Subroutine Algorithm in MSC Marc/Mentat / Omar Yahya...[et al... by Yahya,, Omar, HP Manurung, Yupiter, Sulaiman,, Mohd Shahar, Prajadhiana,, Keval, Omar, Abdul Rahman, Kuntjoro, Wahyu

    Published 2018
    “…The core purpose of the simulation is to predict Martensite formation and hardness value after laser welding process. …”
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  11. 11
    A guided hybrid k-means and genetic algorithm models for children handwriting legibility performance assessment / Norzehan Sakamat

    A guided hybrid k-means and genetic algorithm models for children handwriting legibility performance assessment / Norzehan Sakamat by Sakamat, Norzehan

    Published 2021
    “…Recognition software achieved 87.14%, EPD algorithm achieved 73.57% and HMT algorithm achieved 74.30%) prediction accuracy with OTs. …”
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  12. 12
    Empirical modeling of hydrate formation prediction in deepwater pipelines

    Empirical modeling of hydrate formation prediction in deepwater pipelines by Hashim, F.M., Abbasi, A.

    Published 2016
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  13. 13
    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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  14. 14
    Improvement and application of particle swarm optimization algorithm

    Improvement and application of particle swarm optimization algorithm by Deevi, Durga Praveen, Kodadi, Sharadha, Allur, Naga Sushma, Dondapati, Koteswararao, Chetlapalli, Himabindu, Perumal, Thinagaran

    Published 2025
    “…When developed for a group of Wheeled Mobile Robots (WMR), a Fault Tolerant Formation Control (FTFC) technique is designed to protect against serious actuator defects. …”
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  15. 15
    Development and validation of promoter prediction system for recombinant lipase expression in Pichia pastoris

    Development and validation of promoter prediction system for recombinant lipase expression in Pichia pastoris by Gogo, Mayaki Fatima

    Published 2017
    “…This study aims to develop a promoter prediction system for recombinant lipase expression in commonly used yeast expression system, P. pastoris using BLAST heuristic algorithm. …”
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  16. 16
    Jaya algorithm hybridized with extreme gradient boosting to predict the corrosion-induced mass loss of agro-waste based monolithic and Ni-reinforced porous alumina

    Jaya algorithm hybridized with extreme gradient boosting to predict the corrosion-induced mass loss of agro-waste based monolithic and Ni-reinforced porous alumina by Dele-Afolabi, T.T., Jung, D.W., Ahmadipour, Masoud, Azmah Hanim, M.A., Adeleke, A.O., Kandasamy, M., Gunnasegaran, Prem

    Published 2024
    “…The Jaya-XGBoost model developed in this study effectively predicted the mass loss in NaOH (R2 = 0.9984; MAE = 0.0168) and mass loss in H2SO4 (R2 = 0.9824; MAE = 0.0217) of the monolithic and nickel-reinforced porous alumina. …”
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  17. 17
    The prediction of diesel engine NOx emissions using artificial neural network / Mohd. Mahadzir Mohammud and Khairil Faizi Mustafa

    The prediction of diesel engine NOx emissions using artificial neural network / Mohd. Mahadzir Mohammud and Khairil Faizi Mustafa by Mohammud, Mohd. Mahadzir, Mustafa, Khairil Faizi

    Published 2003
    “…In order to reduce or to control diesel engine polluting emissions, the formation mechanism of NOx can be predicted. A neural network model is developed to obtain the NOx emission concentration under various operating condition. …”
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  18. 18
    The prediction of diesel engine NOx emissions using artificial neural network / Mohd Mahadzir Mohammud and Khairil Faizi Mustafa

    The prediction of diesel engine NOx emissions using artificial neural network / Mohd Mahadzir Mohammud and Khairil Faizi Mustafa by Mohammud, Mohd Mahadzir, Mustafa, Khairil Faizi

    Published 2003
    “…In order to reduce or to control diesel engine polluting emissions, the formation mechanism of NOx can be predicted. A neural network model is developed to obtain the NOx emission concentration under various operating condition. …”
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  19. 19
    Prediction of PVT properties in crude oil systems using support vector machines

    Prediction of PVT properties in crude oil systems using support vector machines by Nagi J., Kiong T.S., Ahmed S.K., Nagi F.

    Published 2023
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  20. 20
    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
    “…However, the stochastic nature of hydrate formation, is influenced by gas composition, temperature, pressure, and ion concentration, makes it difficult to predict accurately removal efficiency. …”
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