Search Results - (( data identification using algorithm ) OR ( variable selection based algorithm ))

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  1. 1
    Deterministic Mutation-Based Algorithm for Model Structure Selection in Discrete-Time System Identification

    Deterministic Mutation-Based Algorithm for Model Structure Selection in Discrete-Time System Identification by Abd Samad, Md Fahmi

    Published 2011
    “…Identification studies using NARX (Nonlinear AutoRegressive with eXogenous input) models employing simulated systems and real plant data are used to demonstrate that the algorithm is able to detect significant variables and terms faster and to select a simpler model structure than other well-known EC methods.…”
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  2. 2
    Deterministic Mutation Algorithm As A Winner Over Forward Selection Procedure

    Deterministic Mutation Algorithm As A Winner Over Forward Selection Procedure by Md Fahmi, Abd Samad

    Published 2016
    “…One of the steps in system identification is model structure selection which involves the selection of variables and terms of a model. …”
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  3. 3
    Development of an effective clustering algorithm for older fallers

    Development of an effective clustering algorithm for older fallers by Goh, Choon Hian, Wong, Kam Kang, Tan, Maw Pin *, Ng, Siew Cheok, Chuah, Yea Dat, Kwan, Ban Hoe

    Published 2022
    “…A total of 1279 subjects and 9 variables were selected for clustering after the data pre-possessing stage. …”
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  4. 4
    Development of fault detection, diagnosis and control system identification using multivariate statistical process control (MSPC)

    Development of fault detection, diagnosis and control system identification using multivariate statistical process control (MSPC) by Ibrahim, Kamarul 'Asri, Ahmad, Arshad, Ali, Mohamad Wijayanuddin, Mak, Weng Yee

    Published 2006
    “…In this research work, an FDD algorithm is developed using MSPC and correlation coefficients between process variables. …”
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    Monograph
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  5. 5
    CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING

    CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING by ISLAM, BADAR UL ISLAM

    Published 2017
    “…The correlation analysis is used for the identification and selection of the most influential input variable vector (IVV). …”
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    Thesis
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  6. 6
    Parameter Magnitude-Based Information Criterion For Optimum Model Structure Selection In System Identification

    Parameter Magnitude-Based Information Criterion For Optimum Model Structure Selection In System Identification by Mohd Nasir, Abdul Rahman

    Published 2020
    “…For simulated data, PMIC2 was compared to AIC and BIC using enumerative approach and genetic algorithm. …”
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  7. 7
    Nonlinear identification for dengue fever / Herlina Abdul Rahim.

    Nonlinear identification for dengue fever / Herlina Abdul Rahim. by Abdul Rahim, Herlina

    Published 2009
    “…The best parameters' settings for the NARMAX model can be found using the Lipschitz number criterion for the model order selection with artificial neural network structure of 5-2-1 trained using the Levenberg Marquardt algorithm.…”
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  8. 8
    Determination of tree stem volume : A case study of Cinnamomum

    Determination of tree stem volume : A case study of Cinnamomum by Noraini Abdullah

    Published 2013
    “…Illustrations and algorithms are incorporated into the procedures. Non-normal and nonlinear data variables are addressed, hence data characterization is presented. …”
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  9. 9
    Modelling the yield loss of oil palm due to Ganoderma Basal Stem Rot disease

    Modelling the yield loss of oil palm due to Ganoderma Basal Stem Rot disease by Assis Kamu

    Published 2016
    “…The identification of the main sources of multicollinearity was also performed based on correlation-based test and also variance-based test. …”
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  10. 10
    Modelling the yield loss of oil palm due to ganoderma basal stem rot disease

    Modelling the yield loss of oil palm due to ganoderma basal stem rot disease by Assis bin Kamu

    Published 2016
    “…The identification of the main sources of multicollinearity was also performed based on correlation-based test and also variance-based test. …”
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  11. 11
    Feature Ranking Techniques For 3D ATS Drug Molecular Structure Identification

    Feature Ranking Techniques For 3D ATS Drug Molecular Structure Identification by Saw, Yee Ching

    Published 2018
    “…The proposed feature selection approach has a simple algorithmic framework and makes use of the existing feature selection techniques to cater different variety of data issues, namely Ensemble Filter-Embedded Feature Ranking Approach (FEFR). …”
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  12. 12
    Zero distortion-based steganography for handwritten signature

    Zero distortion-based steganography for handwritten signature by Iranmanesh, Vahab

    Published 2018
    “…Thus, developing a steganographic algorithm to use cover media (c) without raising attention is the most challenging task in data hiding. …”
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  13. 13
    A machine learning approach of predicting high potential archers by means of physical fitness indicators

    A machine learning approach of predicting high potential archers by means of physical fitness indicators by Muazu Musa, Rabiu, Abdul Majeed, Anwar P.P., Taha, Zahari, Chang, Siow Wee, Ab. Nasir, Ahmad Fakhri, Abdullah, Mohamad Razali

    Published 2019
    “…Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the significant variables identified. k-NN model variations, i.e., fine, medium, coarse, cosine, cubic and weighted functions as well as logistic regression, were trained based on the significant performance variables. …”
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  14. 14
    A machine learning approach of predicting high potential archers by means of physical fitness indicators

    A machine learning approach of predicting high potential archers by means of physical fitness indicators by Musa, Rabiu Muazu, Anwar, P. P. Abdul Majeed, Zahari, Taha

    Published 2019
    “…Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the significant variables identified. k-NN model variations, i.e., fine, medium, coarse, cosine, cubic and weighted functions as well as logistic regression, were trained based on the significant performance variables. …”
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  15. 15
    Neural network based adaptive pid controller for shell-and-tube heat exchanger

    Neural network based adaptive pid controller for shell-and-tube heat exchanger by Othman, Mohamad Hakimi

    Published 2019
    “…Dynamic time series neural network model was used together with Levenberg-Marquardt algorithm as the training method. …”
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  16. 16
    Neural network based adaptive pid controller for shell-and-tube heat exchanger: article

    Neural network based adaptive pid controller for shell-and-tube heat exchanger: article by Othman, Mohamad Hakimi

    Published 2019
    “…Dynamic time series neural network model was used together with Levenberg-Marquardt algorithm as the training method. …”
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  17. 17
    An optimized ensemble for predicting reservoir rock properties in petroleum industry

    An optimized ensemble for predicting reservoir rock properties in petroleum industry by Kenari, Seyed Ali Jafari

    Published 2013
    “…A total of 3695 data points from the 5 wells having conventional well log data and core data were used. …”
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  18. 18
    Tree species and aboveground biomass estimation using machine learning, hyperspectral and LiDAR data / Nik Ahmad Faris Nik Effendi

    Tree species and aboveground biomass estimation using machine learning, hyperspectral and LiDAR data / Nik Ahmad Faris Nik Effendi by Nik Effendi, Nik Ahmad Faris

    Published 2022
    “…Besides, Artificial Neural Network (ANN) and Random Forest (RF) algorithm was used to predicted the AGB using different combination of variables. …”
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  19. 19
    Identification of debris flow initiation zones using topographic model and airborne laser scanning data

    Identification of debris flow initiation zones using topographic model and airborne laser scanning data by Lay, Usman Salihu, Pradhan, Biswajeet

    Published 2017
    “…To achieve this, the data set was divided into two, 70% (840) for the training dataset and 30% (360) for validation. The best model was selected based on the model performance using the generalized cross validation (GCV) and the receiver operating characteristic (ROC) curve/area under curve (AUC) values. …”
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  20. 20
    Development of an artificial neural network algorithm for predicting the cutting force in end milling of Inconel 718 alloy

    Development of an artificial neural network algorithm for predicting the cutting force in end milling of Inconel 718 alloy by Hossain, Ishtiaq, Amin, A. K. M. Nurul, Patwari, Muhammed Anayet Ullah

    Published 2011
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