Search Results - (( variable integration selection algorithm ) OR ( java application customization algorithm ))

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  1. 1
    A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models

    A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models by ul Islam, B., Baharudin, Z.

    Published 2017
    “…The significance of the selected input variable vectors is studied to analyze their effects on the prediction process. …”
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  2. 2
    Integrated ACOR/IACOMV-R-SVM Algorithm

    Integrated ACOR/IACOMV-R-SVM Algorithm by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2017
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  3. 3
    SURE-Autometrics algorithm for model selection in multiple equations

    SURE-Autometrics algorithm for model selection in multiple equations by Norhayati, Yusof

    Published 2016
    “…This automatic model selection algorithm is better than non-algorithm procedure which requires knowledge and extra time. …”
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  4. 4
    Feature selection and model selection algorithm using incremental mixed variable ant colony optimization for support vector machine classifier

    Feature selection and model selection algorithm using incremental mixed variable ant colony optimization for support vector machine classifier by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2013
    “…Support Vector Machine (SVM) is a present day classification approach originated from statistical approaches.Two main problems that influence the performance of SVM are selecting feature subset and SVM model selection. In order to enhance SVM performance, these problems must be solved simultaneously because error produced from the feature subset selection phase will affect the values of the SVM parameters and resulted in low classification accuracy.Most approaches related with solving SVM model selection problem will discretize the continuous value of SVM parameters which will influence its performance.Incremental Mixed Variable Ant Colony Optimization (IACOMV) has the ability to solve SVM model selection problem without discretising the continuous values and simultaneously solve the two problems.This paper presents an algorithm that integrates IACOMV and SVM.Ten datasets from UCI were used to evaluate the performance of the proposed algorithm.Results showed that the proposed algorithm can enhance the classification accuracy with small number of features.…”
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  5. 5
    Sure (EM)-Autometrics: An Automated Model Selection Procedure with Expectation Maximization Algorithm Estimation Method (S/O 14925)

    Sure (EM)-Autometrics: An Automated Model Selection Procedure with Expectation Maximization Algorithm Estimation Method (S/O 14925) by Kamarudin, Nur Azulia

    Published 2021
    “…Hence, this study concentrates on an automated model selection procedure for the SURE model by integrating the expectation-maximization (EM) algorithm estimation method, named SURE(EM)-Autometrics. …”
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  6. 6
    Formulating new enhanced pattern classification algorithms based on ACO-SVM

    Formulating new enhanced pattern classification algorithms based on ACO-SVM by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2013
    “…This paper presents two algorithms that integrate new Ant Colony Optimization (ACO) variants which are Incremental Continuous Ant Colony Optimization (IACOR) and Incremental Mixed Variable Ant Colony Optimization (IACOMV) with Support Vector Machine (SVM) to enhance the performance of SVM.The first algorithm aims to solve SVM model selection problem. …”
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  7. 7
    Mixed variable ant colony optimization technique for feature subset selection and model selection

    Mixed variable ant colony optimization technique for feature subset selection and model selection by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2013
    “…This paper presents the integration of Mixed Variable Ant Colony Optimization and Support Vector Machine (SVM) to enhance the performance of SVM through simultaneously tuning its parameters and selecting a small number of features.The process of selecting a suitable feature subset and optimizing SVM parameters must occur simultaneously,because these processes affect each ot her which in turn will affect the SVM performance.Thus producing unacceptable classification accuracy.Five datasets from UCI were used to evaluate the proposed algorithm.Results showed that the proposed algorithm can enhance the classification accuracy with the small size of features subset.…”
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  8. 8
    Enhanced Adaptive Confidence-Based Q Routing Algorithms For Network Traffic

    Enhanced Adaptive Confidence-Based Q Routing Algorithms For Network Traffic by Yap, Soon Teck

    Published 2004
    “…ECQ Routing Algorithm is integrates the Variable of Decay Constant and Update All Q Value approaches for updating the C values of non-selected Q values. …”
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  9. 9
    Variable order step size algorithm for solving second order ODEs

    Variable order step size algorithm for solving second order ODEs by Rasedee, Ahmad Fadly Nurullah, Abdul Sathar, Mohamad Hassan, Wong, Tze Ji, Koo, Lee Feng

    Published 2019
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  10. 10
    Artificial immune system based on real valued negative selection algorithms for anomaly detection

    Artificial immune system based on real valued negative selection algorithms for anomaly detection by Khairi, Rihab Salah

    Published 2015
    “…Classifier algorithms, namely the Support Vector Machine and K-Nearest Neighbours were used for benchmarking the performance of the Real-Valued Negative Selection Algorithms. …”
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  11. 11
    Cloudlet deployment and task offloading in mobile edge computing using variable-length whale and differential evolution optimization and analytical hierarchical process for decision-making

    Cloudlet deployment and task offloading in mobile edge computing using variable-length whale and differential evolution optimization and analytical hierarchical process for decisio... by Daba, Layth Muwafaq Abdulhussein

    Published 2023
    “…Unlike the existing optimization algorithm, VL-WIDE features the capability of searching different lengths of solutions to cover the variable number of cloudlets for deployment. …”
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  12. 12
    Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting

    Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting by Afan H.A., Allawi M.F., El-Shafie A., Yaseen Z.M., Ahmed A.N., Malek M.A., Koting S.B., Salih S.Q., Mohtar W.H.M.W., Lai S.H., Sefelnasr A., Sherif M., El-Shafie A.

    Published 2023
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  13. 13
    A hybridisation of adaptive variable neighbourhood search and large neighbourhood search: Application to the vehicle routing problem

    A hybridisation of adaptive variable neighbourhood search and large neighbourhood search: Application to the vehicle routing problem by Sze, Jeeu Fong, Salhi, S., Wassan, N.

    Published 2016
    “…The adaptive aspect is integrated in the local search where a set of highly successful local searches is selected based on the intelligent selection mechanism. …”
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  14. 14
    Extended multiple models selection algorithms based on iterative feasible generalized least squares (IFGLS) and expectation-maximization (EM) algorithm

    Extended multiple models selection algorithms based on iterative feasible generalized least squares (IFGLS) and expectation-maximization (EM) algorithm by Nur Azulia, Kamarudin

    Published 2019
    “…In conclusion, SURE(IFGLS)-Autometrics and SURE(EM)-Autometrics can be used as models selection algorithms. Additionally, both algorithms are suitable in improving performance of automated models selection procedures. …”
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  15. 15
    Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli

    Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli by Seyed Hamidreza , Aghay Kaboli

    Published 2018
    “…In the developed feature selection approach, multi-objective binary-valued backtracking search algorithm (MOBBSA) is used as an efficient evolutionary search algorithm to search within different combinations of input variables and selects the non-dominated feature subsets, which minimize simultaneously both the estimation error and the number of features. …”
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  16. 16
    Application of Hybrid Evolutionary Algorithm and thematic map for rule set generation and visualization of chlorophyta abundance at Putrajaya lake / Lau Chia Fong

    Application of Hybrid Evolutionary Algorithm and thematic map for rule set generation and visualization of chlorophyta abundance at Putrajaya lake / Lau Chia Fong by Lau, Chia Fong

    Published 2013
    “…This study describes application of a hybrid combination of hybrid evolutionary algorithm (HEA) and thematic map visualization technique in modeling, predicting and visualization of selected algae division growth, Chlorophyta for tropical Putrajaya Lakes and Wetlands (Malaysia). …”
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  17. 17
    DESIGN AND IMPLEMENTATION OF INTELLIGENT MONITORING SYSTEMS FOR THERMAL POWER PLANT BOILER TRIPS

    DESIGN AND IMPLEMENTATION OF INTELLIGENT MONITORING SYSTEMS FOR THERMAL POWER PLANT BOILER TRIPS by FIRAS BASIM, ISMAIL ALNAIMI

    Published 2011
    “…Also, the optimal selection of the most influencing variables was performed successfully by the hybrid intelligent system. …”
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  18. 18
    Enhancing obfuscation technique for protecting source code against software reverse engineering

    Enhancing obfuscation technique for protecting source code against software reverse engineering by Mahfoudh, Asma

    Published 2019
    “…The proposed technique can be enhanced in the future to protect games applications and mobile applications that are developed by java; it can improve the software development industry. …”
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  19. 19
    An improved negative selection algorithm based on the hybridization of cuckoo search and differential evolution for anomaly detection

    An improved negative selection algorithm based on the hybridization of cuckoo search and differential evolution for anomaly detection by Ayodele Nojeem, Lasisi

    Published 2018
    “…Real-Valued Negative Selection Algorithm with Variable-Sized Detectors (V-Detectors) is an offspring of AIS and demonstrated its potentials in the field of anomaly detection. …”
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  20. 20
    Workload utilization dissemination on grid resources for simulation environment

    Workload utilization dissemination on grid resources for simulation environment by Yahaya, Bakri, Latip, Rohaya, Abdullah, Azizol, Othman, Mohamed

    Published 2013
    “…The selection of resource variables and the number of RI's involved will result in different definitions of the workload. …”
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