Search Results - (( java implication based algorithm ) OR ( knowledge applications tree algorithm ))

Refine Results
  1. 1

    Determination of tree height based on tree crown using algorithm derived from UAV imagery / Suzanah Abdullah ... [et al.] by Abdullah, Suzanah, Tahar, Khairul Nizam, Abdul Rashid, Mohd Fadzil, Osoman, Muhammad Ariffin

    Published 2021
    “…In this study, UAV technology has been taking a look at several algorithms for estimating tree height value of a single tree crown. …”
    Get full text
    Get full text
    Conference or Workshop Item
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8

    Estimating tree height based on tree crown from UAV imagery / Suzanah Abdullah ... [et al.] by Abdullah, Suzanah, Tahar, Khairul Nizam, Abdul Rashid, Mohd Fadzil, Osoman, Muhammad Ariffin

    Published 2022
    “…UAV images provide an accurate digital surface model for planting application. In this study, the UAV technology is explored by applying several algorithms approach to determine tree crown and tree height of a single tree canopy. …”
    Get full text
    Get full text
    Get full text
    Article
  9. 9
  10. 10
  11. 11
  12. 12

    Application Of Multi-Layer Perceptron Technique To Detect And Locate The Base Of A Young Corn Plant by Morshidi, Malik Arman

    Published 2007
    “…In this research, a vision system algorithm has been developed to identify and locate base of young corn trees based upon robot vision technology, pattern recognition techniques, and knowledge-based decision theory. …”
    Get full text
    Get full text
    Thesis
  13. 13

    Ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy by Rahman, Sam Matiur, Ali, Md. Asraf, Altwijri, Omar, Alqahtani, Mahdi, Ahmed, Nasim, Ahamed, Nizam Uddin

    Published 2020
    “…Therefore, we aimed to classify six classes of freshly excised tissues from a set of electrical impedance measurement variables using five ensemble-based machine learning (ML) algorithms, namely, the random forest (RF), extremely randomized trees (ERT), decision tree (DT), gradient boosting tree (GBT) and AdaBoost (Adaptive Boosting) (ADB) algorithms, which can be subcategorized as bagging and boosting methods. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
  14. 14

    Comparing the knowledge quality in rough classifier and decision tree classifier by Mohamad Mohsin, Mohamad Farhan, Abd Wahab, Mohd Helmy

    Published 2008
    “…Theoretically, different classifiers will generate different sets of rules via knowledge even though they are implemented to the same classification problem.Hence, the aim of this paper is to investigate the quality of knowledge produced by Rc and DTc when similar problems are presented to them.In this case, four important performance metrics are used as comparison, the accuracy of classification, rules quantity, rules length and rules coverage.Five dataset from UCI Machine Learning are chosen and then mined using Rc toolkit namely ROSETTA while C4.5 algorithm in WEKA application is chosen as DTc rule generator. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
  15. 15
  16. 16

    Data Classification and Its Application in Credit Card Approval by Thai , VinhTuan

    Published 2004
    “…The result of this application using the sample credit card approval dataset includes a decision tree, a set of rules derived from the decision tree and its accuracy. …”
    Get full text
    Get full text
    Final Year Project
  17. 17
  18. 18

    C4.5 Algorithm Application for Prediction of Self Candidate New Students in Higher Education by Erlan, Darmawan

    Published 2018
    “…The result of this research is tthe application can classify the new students in tree structure in order that it can produce a rule. …”
    Get full text
    Get full text
    Journal
  19. 19
  20. 20