Search Results - (( code classification issues algorithm ) OR ( variables classification _ algorithm ))

Refine Results
  1. 1

    Improving hand written digit recognition using hybrid feature selection algorithm by Wong, Khye Mun

    Published 2022
    “…While mRMR was capable of identifying a subset of features that were highly relevant to the targeted classification variable, it still carry the weakness of capturing redundant features along with the algorithm. …”
    Get full text
    Get full text
    Final Year Project / Dissertation / Thesis
  2. 2
  3. 3

    Classification for large number of variables with two imbalanced groups by Ahmad Hakiim, Jamaluddin

    Published 2020
    “…This study proposed two algorithms of classification namely Algorithm 1 and Algorithm 2 which combine resampling, variable extraction, and classification procedure. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis
  4. 4

    Crow Search Freeman Chain Code (CS-FCC) feature extraction algorithm for handwritten character recognition by Muhammad Arif, Mohamad, Zalili, Musa, Amelia Ritahani, Ismail

    Published 2023
    “…With so many algorithms developed to improve classification accuracy, interest in feature extraction in Handwritten Character Recognition (HCR) has increased. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
  5. 5
  6. 6
  7. 7

    Automatic multilevel medical image annotation and retrieval by Mueen, A., Zainuddin, R., Baba, M.S.

    Published 2008
    “…To address third issue, automatic multilevel code generation is proposed for image classification and multilevel image annotation. …”
    Get full text
    Get full text
    Article
  8. 8
  9. 9

    Hybrid ACO and SVM algorithm for pattern classification by Alwan, Hiba Basim

    Published 2013
    “…The average classification accuracies for the ACOR-SVM, IACOR-SVM, ACOMV-R and IACOMV-R algorithms are 94.73%, 95.86%, 97.37% and 98.1% respectively. …”
    Get full text
    Get full text
    Get full text
    Thesis
  10. 10

    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. …”
    Get full text
    Get full text
    Get full text
    Article
  11. 11
  12. 12

    Classification of tropical rainforest using different classification algorithm based on remote sensing imagery: A study of Gunung Basor by Intan Noradybah Md Rodi

    Published 2019
    “…Remote sensing technologies are used globally to derive some of crucial spatial variable parameter such as vegetation cover. Three different classification algorithm, minimum distance classifier, Mahalanobis distance classifier and maximum likelihood algorithm was applied to classify the forest area in Gunung Basor. …”
    Get full text
    Get full text
    Undergraduate Final Project Report
  13. 13

    Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2017
    “…Experimental results obtained from the proposed algorithm are better compared with other approaches in terms of classification accuracy and feature subset selection.…”
    Get full text
    Get full text
    Article
  14. 14
  15. 15

    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
    “…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.…”
    Get full text
    Get full text
    Get full text
    Article
  16. 16

    Intelligent classification algorithms in enhancing the performance of support vector machine by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2019
    “…The average classification accuracies for the proposed ACOMV–SVM and IACOMV-SVM algorithms are 97.28 and 97.91 respectively. …”
    Get full text
    Get full text
    Get full text
    Article
  17. 17

    Study on numerical solution of a variable order fractional differential equation based on symmetric algorithm by Liu, Jingrui, Pan, Dongyang

    Published 2019
    “…A fully symmetric classification of the boundary value problem for a class of fractional differential equations with variable sequences is determined by using a fully symmetric differential sequence sorting algorithm. …”
    Get full text
    Get full text
    Get full text
    Article
  18. 18

    A multilayered convolutional sparse coding framework for modeling of pooling operation of convolution neural networks by , Abdul Wahid, Khan, Adnan Umar, , Mukhtarullah, Khan, Sheroz, Shah, Jawad

    Published 2019
    “…The multilayered version of CSC(ML-CSC) is shown to be connected to forward pass of CNNs and dictionary learning and sparse coding algorithms of this model are analyzed for solving classification and inverse problems in image processing. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Proceeding Paper
  19. 19
  20. 20

    Gas Identi cation by Using a Cluster-k-Nearest-Neighbor by Brahim Belhaouari, samir

    Published 2009
    “…We find 98.7% of accuracy in the classification of 6 different types of Gas by using K-means cluster algorithm and we find almost the same by using the new clustering algorithm.…”
    Get full text
    Get full text
    Article