Search Results - (( data distribution function algorithm ) OR ( data selection methods algorithm ))

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  1. 1
    Network reconfiguration and control for loss reduction using genetic algorithm

    Network reconfiguration and control for loss reduction using genetic algorithm by Jawad, Mohamed Hassan Izzaldeen

    Published 2010
    “…Note that the 18-bus system is originally without any capacitor. Two selection methods that are used in Genetic Algorithm are the roulette wheel and tournament selections. …”
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  2. 2
    Parallel algorithms for numerical simulations of EHD ion-drag micropump on distributed parallel computing systems

    Parallel algorithms for numerical simulations of EHD ion-drag micropump on distributed parallel computing systems by Shakeel Ahmed, Kamboh

    Published 2014
    “…In order to evaluate the scalability at specific data size the appropriate regression models are fitted through the measured data as functions of number of workers. …”
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  3. 3
    Transient analysis for leak signature identification based on Hilbert Huang transform and integrated kurtosis algorithm for z-notch filter technique

    Transient analysis for leak signature identification based on Hilbert Huang transform and integrated kurtosis algorithm for z-notch filter technique by Muhammad Hanafi, Yusop

    Published 2018
    “…However, this method has the difficulty in selecting the suitable IMF for the next data post-processing method, which is Hilbert Transform (HT). …”
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  4. 4
    Efficient genetic partitioning-around-medoid algorithm for clustering

    Efficient genetic partitioning-around-medoid algorithm for clustering by Garib, Sarmad Makki Mohammed

    Published 2019
    “…This study utilizes genetic algorithms based upon the medoid rather than the mean as a centroid-selection schema to improve the clustering efficiency. …”
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  5. 5
    Not seeing the forest for the trees: Generalised linear model out-performs random forest in species distribution modelling for Southeast Asian felids

    Not seeing the forest for the trees: Generalised linear model out-performs random forest in species distribution modelling for Southeast Asian felids by Chiaverini, Luca, Macdonald, David W., Hearn, Andrew J., Kaszta, Zaneta, Ash, Eric, Bothwell, Helen M., Can, Ozgun Emre, Channa, Phan, Clements, Gopalasamy Reuben *, Haidir, Iding Achmad, Kyaw, Pyae Phyoe, Moore, Jonathan H., Rasphone, Akchousanh, Tan, Cedric Kai Wei, Cushman, Samuel A.

    Published 2023
    “…Species Distribution Models (SDMs) are a powerful tool to derive habitat suitability predictions relating species occurrence data with habitat features. …”
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  6. 6
    A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem

    A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem by Mohd Pozi, Muhammad Syafiq

    Published 2016
    “…Algorithmic level based methods however are based on introducing new optimization task to improve the minority class classification rate, without changing the data characteristics. …”
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  7. 7
    Space allocation for examination scheduling using Genetic Algorithm / Alya Kauthar Azman

    Space allocation for examination scheduling using Genetic Algorithm / Alya Kauthar Azman by Azman, Alya Kauthar

    Published 2025
    “…Data for the study was collected from university records, and algorithm performance was tested against predefined scheduling criteria. …”
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  8. 8
    Enhancing high-dimensional streaming data analysis: optimizing Online Feature Selection for handling drift using optimization technique and ensemble learning

    Enhancing high-dimensional streaming data analysis: optimizing Online Feature Selection for handling drift using optimization technique and ensemble learning by Kamaru-Zaman, Ezzatul Akmal

    Published 2024
    “…In the era of data-driven decision-making, managing dynamic data streams characterized by evolving data distributions and high dimensionality presents a formidable challenge for online feature selection. …”
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  9. 9
    Short Text Classification Using An Enhanced Term Weighting Scheme And Filter-Wrapper Feature Selection

    Short Text Classification Using An Enhanced Term Weighting Scheme And Filter-Wrapper Feature Selection by Alsmadi, Issa Mohammad Ibrahim

    Published 2018
    “…In the second stage, grey wolf optimization (GWO) algorithm, a new heuristic search algorithm, uses the SVM accuracy as a fitness function to find the optimal subset feature.…”
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  10. 10
    Vibration-based structural damage detection and system identification using wavelet multiresolution analysis / Seyed Alireza Ravanfar

    Vibration-based structural damage detection and system identification using wavelet multiresolution analysis / Seyed Alireza Ravanfar by Seyed Alireza, Ravanfar

    Published 2017
    “…However, most of existing damage detection methods requires reference data which are not always available. …”
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  11. 11
    Development of Intelligent Leak Detection System Based on Artificial Pressure Transient Signal Using Integrated Kurtosis-Based Algorithm for Z-Filter Technique (I-Kaz) / Hanafi.M.Yusop...[et al.]

    Development of Intelligent Leak Detection System Based on Artificial Pressure Transient Signal Using Integrated Kurtosis-Based Algorithm for Z-Filter Technique (I-Kaz) / Hanafi.M.Y... by M.Yusop, Hanafi, Ghazali, M.F., M.Yusof, M.F., W.Hamat, W.S.

    Published 2017
    “…However, this method has the difficulty in selecting the suitable IMF for the next data post-processing method which is Hilbert Transform (HT). …”
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  12. 12
    Development of intelligent leak detection system based on artificial pressure transient signal using integrated Kurtosis-based Algorithm for Z-filter Technique (I-Kaz)

    Development of intelligent leak detection system based on artificial pressure transient signal using integrated Kurtosis-based Algorithm for Z-filter Technique (I-Kaz) by Hanafi, M. Yusop, M. F., Ghazali, Mohd Fadhlan, Mohd Yusof, Wan Sofian, Wan Hamat

    Published 2017
    “…However, this method has the difficulty in selecting the suitable IMF for the next data post-processing method which is Hilbert Transform (HT). …”
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  13. 13
    Controller placement problem in the optimization of 5G based SDN and NFV architecture

    Controller placement problem in the optimization of 5G based SDN and NFV architecture by Ibrahim, Abeer Abdalla Zakaria

    Published 2021
    “…A heuristic called dynamic mapping and multi-stage CPP algorithm (DMMCPP) was developed to solve CPP as resource allocation in a distributed 5G-SDN-NFV-based network. …”
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  14. 14
    Parameter estimation of Kumaraswamy Burr type X models based on cure models with or without covariates

    Parameter estimation of Kumaraswamy Burr type X models based on cure models with or without covariates by Yusuf, Madaki Umar

    Published 2017
    “…Asa consequence of this, it can be used suitably for censored data. Based on the problem stated, we develop a new model using the method of confounding the existing parametric models by adopting the BX model as the baseline distribution. …”
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  15. 15
    Power prediction using the wind turbine power curve and data-driven approaches / Ehsan Taslimi Renani

    Power prediction using the wind turbine power curve and data-driven approaches / Ehsan Taslimi Renani by Ehsan Taslimi , Renani

    Published 2018
    “…To set up direct wind power prediction, six data-driven approaches are employed and the same procedure as in indirect approach is applied to select the best method for longer horizon predictions, up to 60-min. …”
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  16. 16
    Predictive Framework for Imbalance Dataset

    Predictive Framework for Imbalance Dataset by Megat Norulazmi, Megat Mohamed Noor

    Published 2012
    “…Properties of the proposed framework include; developing an approach to correlate materials defects, developing an approach to represent data attributes features, analyzing various ratio and types of data re-sampling, analyzing the impact of data dimension reduction for various data size, and partitioning data size and algorithmic schemes against the prediction performance. …”
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  17. 17
    Fault identification in pipeline system using normalized hilbert huang transform and automatic selection of intrinsic mode function

    Fault identification in pipeline system using normalized hilbert huang transform and automatic selection of intrinsic mode function by Hanafi, M. Yusop, M. F., Ghazali, M. F. M., Yusof, M. A., Pi Remli

    Published 2018
    “…In this research the application of Normalised Hilbert Huang Transform (NHHT) as the method to analyse the pressure transient signal. However, this method has the difficulty in selecting the suitable IMF for the further data analysing method which is Normalised Hilbert Transform (NHT). …”
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  18. 18
    Reservoir Inflow Forecasting Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Techniques

    Reservoir Inflow Forecasting Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Techniques by Googhari, Shahram Karimi

    Published 2007
    “…The selected ANFIS model was trained with normalized data with 6 Gaussian membership functions for each of 9 inputs and 6 rules. …”
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  19. 19
    Pipe Leak Diagnostic Using High Frequency Piezoelectric Pressure Sensor And Automatic Selection Of Intrinsic Mode Function

    Pipe Leak Diagnostic Using High Frequency Piezoelectric Pressure Sensor And Automatic Selection Of Intrinsic Mode Function by Hanafi, M. Yusop, M. F., Ghazali, M. F. M., Yusof, Remli, M. A. Pi, M. H., Kamarulzaman

    Published 2017
    “…HHT is a way to decompose a signal into intrinsic mode functions (IMF). However, the advantage of HHT is its difficulty in selecting the suitable IMF for the next data postprocessing method which is Hilbert Transform (HT). …”
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  20. 20
    Garra Rufa‐inspired optimization technique

    Garra Rufa‐inspired optimization technique by Abdulbari, Hayder A., Jaber, Aqeel S., Shalash, Nadheer A., Abdalla, Ahmed N.

    Published 2020
    “…The results show that the proposed method provides good data and greater convergence to the optimal point compared with the classical methods for most of the functions tested.…”
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