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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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  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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  6. 6
    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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  7. 7
    Abnormalities and fraud electric meter detection using hybrid support vector machine & genetic algorithm

    Abnormalities and fraud electric meter detection using hybrid support vector machine & genetic algorithm by Yap K.S., Abidin I.Z., Ahmad A.R., Hussien Z.F., Pok H.L., Ismail F.I., Mohamad A.M.

    Published 2023
    “…Genetic Algorithm (GA) is used to search for the best parameter of SVM classification by using combination of random and pre-populated genomes from Pre-Populated Database (PPD). …”
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  8. 8
    Hospital site suitability assessment using three machine learning approaches: evidence from the Gaza strip in Palestine

    Hospital site suitability assessment using three machine learning approaches: evidence from the Gaza strip in Palestine by Almansi, Khaled Yousef, Mohamed Shariff, Abdul Rashid, Abdullah, Ahmad Fikri, Syed Ismail, Sharifah Norkhadijah

    Published 2021
    “…The results of the predicted sites were validated using CFS cross-validation and the receiver operating characteristic (ROC) curve metrics. …”
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  9. 9
    Performance evaluation of hospital site suitability using multilayer perceptron MLP and analytical hierarchy process AHP models in Malacca, Malaysia

    Performance evaluation of hospital site suitability using multilayer perceptron MLP and analytical hierarchy process AHP models in Malacca, Malaysia by Almansi, Khaled Yousef, Mohamed Shariff, Abdul Rashid, Kalantar, Bahareh, Abdullah, Ahmad Fikri, Syed Ismail, Sharifah Norkhadijah, Ueda, Naonori

    Published 2022
    “…The outcome of the predicted site models was validated utilizing CFS 10-fold cross-validation, as well as ROC curve (receiver operating characteristic curve). …”
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  10. 10
    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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  11. 11
    Optimized techniques for landslide detection and characteristics using LiDAR data

    Optimized techniques for landslide detection and characteristics using LiDAR data by Mezaal, Mustafa Ridha

    Published 2018
    “…In this task, two neural network algorithms, Recurrent Neural Networks (RNN) and Multi-Layer Perceptron Neural Networks (MLP-NN) were used and the hyper-parameters of the network architecture was optimized based on a systematic grid search. …”
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  12. 12
    Modeling flood occurences using soft computing technique in southern strip of Caspian Sea Watershed

    Modeling flood occurences using soft computing technique in southern strip of Caspian Sea Watershed by Borujeni, Sattar Chavoshi

    Published 2012
    “…This approach was based on fuzzy expert system (FES) using Fuzzy Toolbox of MATLAB software. Genetic algorithm (GA) was employed to adjust parameters of FES and optimize the system. …”
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  13. 13
    Differential evolution for neural networks learning enhancement

    Differential evolution for neural networks learning enhancement by Ismail Wdaa, Abdul Sttar

    Published 2008
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  14. 14
    Blogs Search Engine Adopting RSS Syndication Using Fuzzy Logic

    Blogs Search Engine Adopting RSS Syndication Using Fuzzy Logic by Mohammed, Athraa Jasim

    Published 2012
    “…The rapid development of Internet increases the writers of blog sites. Sometimes these blog sites focused on solving some important problems. …”
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  15. 15
    Introducing a design procedure for Archimedes Screw Turbine based on optimization algorithm

    Introducing a design procedure for Archimedes Screw Turbine based on optimization algorithm by Kazem, Shahverdi, Gholamhassan, Najafi, Rizalman, Mamat, Mohd Fairusham, Ghazali, A.S., EI-Shafy, Mohamed, Mousa

    Published 2023
    “…One aspect that hasn't been considered yet is optimizing those parameters that are considered assumptions in the prediction models. …”
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  16. 16
    Construction Noise Prediction Using Stochastic Deep Learning

    Construction Noise Prediction Using Stochastic Deep Learning by Ooi, Wei Chien

    Published 2022
    “…The deep learning model was trained with stochastic data to predict the noise levels emitted from the construction site. The programming algorithm of stochastic modelling was executed in MATLAB, whereas the deep learning model was established by using Python 3.6 programming language in Spyder. …”
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  17. 17
    Enhancing river health monitoring: Developing a reliable predictive model and mitigation plan

    Enhancing river health monitoring: Developing a reliable predictive model and mitigation plan by Azha S.F., Sidek L.M., Ahmad Z., Zhang J., Basri H., Zawawi M.H., Noh N.M., Ahmed A.N.

    Published 2024
    “…The dynamic and non-linear characteristics of water quality parameters pose significant challenges for conventional machine learning algorithms like multi-linear regression, as they struggle to capture these complexities. …”
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  18. 18
    The assessment of ambient carbon monoxide concentration levels / Hasmawati Mat Hassan

    The assessment of ambient carbon monoxide concentration levels / Hasmawati Mat Hassan by Mat Hassan, Hasmawati

    Published 2020
    “…The regression plot of ANN model for training, validation and testing stage for all sites reveal that the feedforward neural network able to predict the CO with a close relationship between the measured and predicted values. …”
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  19. 19
    Price prediction model of green building based on machine learning algorithms / Nur Syafiqah Jamil

    Price prediction model of green building based on machine learning algorithms / Nur Syafiqah Jamil by Jamil, Nur Syafiqah

    Published 2021
    “…Meanwhile, experiments using five common algorithms, Random Forest Regressor Model outperforms four (4) other algorithms in predicting the price of green building condominium, by training and validating the data-set using Split approach. …”
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  20. 20
    Grid-based remotely sensed hydrodynamic surface runoff model using emissivity coefficient / Jurina Jaafar

    Grid-based remotely sensed hydrodynamic surface runoff model using emissivity coefficient / Jurina Jaafar by Jaafar, Jurina

    Published 2015
    “…The development of the model strongly depends on the physical based parameters, examples of physical parameters that include roughness Manning’s n, hydraulic conductivity, soil depth, river geometry and the surface land cover. …”
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